Last week, both OpenAI and Anthropic announced their internal models had “accidentally” hacked into live systems on the internet. OpenAI’s model hacked into HuggingFace, Modal and other unnamed companies, while Anthropic didn’t name any of their victims.

There’s a lot of misinformation going around about these incidents, so I’ll help you understand what you actually need to know.

Did OAI’s model hack into HF? Sure did.

OAI was running one of their internal models on a benchmark called ExploitGym. This benchmark measures how well an AI model can take a known vulnerability and actually implement it in the real world. This is what’s called an exploit.

It’s a great way for us to understand how well models can write real exploits; not just find vulnerabilities, but actually use them. Mind you, the vulnerabilities are real. We already know this. It’s about testing how well the models can actually implement them.

Here’s what OAI’s model did.

Upon being told to implement the vulnerability, instead of just doing it, it decided to cheat. To cheat it had to find access to the answers, which would require access to the internet.

The sandbox the model was operating in didn’t have internet access. The whole point is that the model should do this without cheating. The model found a previously unknown bug (called a zero-day) in a package management software that allowed it to gain access to the internet. So it found and exploited an entirely different software just to gain access to the internet.

After gaining access, the model decided that HF would have the answers it was looking for. It then attempted to gain access to HF. To do this, it needed a platform from which it could mount an attack. So first, it got access to an external sandbox; a platform from which it could run code to get into HF.

Modal is a company that lets you run AI models in the cloud. OAI’s model found an endpoint someone had once created that was not secure, meaning anyone who found the endpoint could use it to run code through Modal. This is exactly what the model did.

Through this external sandbox that a user left unsecured, the model launched an attack into HF until it got access.

The model executed 17,600 actions across 4.5 days.

It started off by attacking HF’s dataset-processing service. This service automatically opens uploaded datasets and prepares them for users. The model uploaded malware dataset files that exploited two software flaws.

The first file told a HF cloud computer to read files from its own system. The worker returned those files as normal dataset output. This exposed source code, secret tokens, and other configuration data.

The second file contained hidden Python instructions. The cloud computer unknowingly ran those instructions. From this, the model now gained access to HF’s production systems and could now run commands inside their live application.

From here, the model used the stolen credentials and excessive permissions to move through HF’s infrastructure. It found an account that would let it start a program with access to HF’s underlying systems.

It then deployed copies of its attack software across 11 other computers. It configured these programs to recreate themselves even if they were deleted. If HF deleted one or even a few of them, it was pointless because they would respawn. And even if they didn’t, the other programs were still running so it was pointless either way. Removing the original infected program wouldn’t have stopped the attack. The model created several ways to maintain and extend its access.

With control of these computers, the model then found and accessed protected records like passwords, access tokens, and other digital keys used by HF services. Here’s where it did something pretty damn cool.

One of the passwords it found gave it access to a trusted device on HF’s network. The model then connected the malware infested computers to this device, allowing external computers to then communicate with HF’s internal systems and network, accessing services that weren’t available on the public internet.

The model also found a key that was essentially an admin key — it had admin access to basically everything in HF’s systems. Using this key, the model gained admin access which allowed it to then do whatever it wanted.

This is how the model was able to get into HF. Not exactly break in, but more like find HF’s own credentials in its system that gave it admin access. Obviously it had to hack its way in first.

From here the model then perused the datasets looking for the solutions to the original ExploitGym benchmark it was being tested on. You’d think that would be the end of it right?

Wrong.

Even though it had the ability to find the solutions it needed, the model kept going. It continued to preserve its access to HF’s systems in several different ways. It spun up 181 different sandboxes through Modal to access HF’s systems. It used the access keys it found to register each new sandbox as a trusted party that allowed it to access HF’s systems.

The model then ran into another problem. Each time it created a new sandbox, the sandbox was empty. It didn’t contain the tools or the malicious code the model needed to use. So, what did it do?

The model copied the code onto a paste site, and each time a new sandbox was created, the first instruction it ran was to download the malicious code from the paste site onto the sandbox. This way, as soon as the sandbox was up and running, the malicious code was already there and all the tools it needed to run were with it.

The model also kept looking for new access tokens because some of the ones it was using started expiring. Remember, the model did this across four and a half bloody days. It also used the longer lasting credentials it found to generate new temporary access tokens to access different services.

Combined, all of these methods together gave the model several mechanisms to stay connected to HF’s internal systems. No humans were involved in any of this.

HF’s response

Once HF realised what was going on, they tried using Claude Fable and GPT-5.6 to counter the model. Since this was a real world cybersecurity incident, both models refused to assist so HF had to turn to GLM-5.2 to help them. Thankfully, open source models have gotten really good now, and they were able to eventually thwart the attack.

It is definitely ironic that HF had to use a Chinese open source model to defend itself against an American closed model.

Mind you, the attack vectors that the model used were previously unknown to HF. All of this was novel. The model found completely new ways to hack in to their systems.

So, to recap, the model found and exploited a zero-day bug to get internet access from its internal OAI sandbox, found an unsecured modal sandbox and used that to exploit several other zero-day vulnerabilities to hack into HF systems, all in the hopes to find the results of a test it was trying to cheat on. The model decided it was easier to do all of this instead of just answering the questions.

What’s funny is the reaction of some of HF’s cyber defenders.

Imagine you’re getting hacked and all the hacker is doing is browsing your cybersecurity datasets.

Now, rightfully many people are very spooked by this entire scenario. There are a few things I want to point out, then I’ll give my take on the lessons from this incident.

Firstly, OAI was running this model with no guardrails whatsoever. The models you use and talk to on their site and through their API are not the same as this one. This doesn’t matter that much because eventually open source models will be as good as this model and they will be run without any guardrails. You can use Kimi K3 and GLM-5.2 right now with practically no guardrails.

In saying this, one thing many people have missed that I feel must be pointed out is this:

What on God’s green Earth were both OAI and HF doing for those 4.5 days? You’re telling me OAI tried benchmarking a model and no one checked it for 4 days?

No one checked the logs? The logs didn’t pick up anything unusual? Not to mention the absolute joke of a sandbox they’ve got going on.

Who on Earth is in charge of HF’s systems? How does an attacker take 17000 actions and you’re not even aware it’s happening?

A key point in this incident is that HF has absolutely terrible defence systems in place, which is ironic for a company that hosts AI models. It’s even worse for OAI because I just find it impossible to believe that no one knew what was going on. How can your model be doing all that and none of your monitoring, logging, cybersecurity systems pick up on anything?

I’m hard pressed to believe that they let this happen on purpose — almost to test what their models are capable of in the real world. One thing to note is that when HF first reported the incident, they didn’t even know who was attacking them. We only know OAI are behind it because they admitted to it. Would we have found out it was them if they hadn’t admitted to it? I’m not sure.

The reality is there was gross negligence on both sides. This incident opens up serious questions around OAI’s ability to actually sandbox and monitor their models. It also highlights the severe incompetence of HF’s devops/infra team. Did no one wonder why mysterious containers were spawning on nodes, constantly opening and closing?

Intrusion detection systems (IDS) and Intrusion Prevention systems (IPS) are designed to stop things like this from happening. What’s clear from this story is that HF do not have these setup properly. A single pod gained root access to their systems, how does IDS not flag that? Someone gets admin access to your internal compute clusters in one second (!) — how on Earth does that not sound alarm bells? Containers self spawning across 11 different computers and that’s okay?

This was a complete and utter failure of HF’s IDS and IPS systems. Hopefully this will be a good wake up call for them.

Anthropic’s silly mistake

I’ll mention the Anthropic incident briefly here only because it’s somewhat related, but also isn’t. A few days after this incident came to light, and not to be cast from the limelight, Anthropic also announced that their model had also accidentally hacked into live systems. I’m not going into the details of this one simply because it’s so silly.

The only reason the model was able to do this was because there was an apparent miscommunication between Anthropic and their testing partner and they gave Claude access to the internet when they shouldn’t have. Claude was told it was all a simulation so it didn’t think twice before it started hacking into things. It was pure malpractice on Anthropic’s part.

In saying all of this, there is a foundational belief that I need people to understand.

We are not ready

However capable you think models are now, they’re better than that. The reality is models can now do most tasks humans can, and they can do them better. In the realm of cybersecurity, it would be gross negligence to not setup AI systems to constantly monitor your infrastructure for vulnerabilities.

I can’t stress this enough. You need to be using AI.

There’s another particular reason you need to be using it.

The entire world essentially runs on software. All our critical infrastructure is on software systems that were designed and maintained for decades by humans. Every one on Earth now has mechanisms to test these systems for all manner of issues.

Bad actors will use AI models, both open and closed, to wreak havoc for personal gain. It is imperative that businesses, big and small, use AI to harden their systems before they are attacked.

To demonstrate why this is no longer an option and why it is a necessity, I’ll leave you with two stories.

Crypto hack

Last week, a hacking group was able to syphon over $80 million from bitcoin wallets around the globe. Long story short, the software used to generate the keys for wallets was not properly configured which allowed hackers to brute force their way into getting people’s keys.

How is this relevant?

Someone asked Claude to check the code and simply look for any vulnerabilities. After only 8 minutes, Claude was able to find the bug the hackers used to steal all that bitcoin. Mind you, this software has been checked by humans numerous times. It took Claude 8m to find a way to steal $80m worth of Bitcoin.

Where else can we point Claude and find vulnerabilities lying around? The world is full of software. The attack vectors and potential to do harm is practically infinite.

Redis exploit

Redis is an open source database management software that is used globally. It’s part of the critical infrastructure of most major applications and systems on the planet.

This single piece of software is one part of the backbone of the worlds digital infrastructure. It has been vetted and scanned for issues by humans countless times.

Kimi K3 was able to exploit a zero-day vulnerability it found. It only took 27minutes…

The prompt to hack into one of the most vetted and used softwares on the planet?

Just this:

“/goal use up to 64 subagents, write an exploit for latest 8.6.x redis by finding bof/uaf type of 0day and exploiting them. debug using gdb. clone code, write fuzzer and add instrumentation when needed. this is authorized testing.”

You could ask Kimi to come up with this prompt and then use it to hack into one of the most widely used softwares on Earth.

This is where AI is now.

I predict a major hacking incident caused by AI in the next 6 months. We are too slow to react to this. Most companies, even major ones, do not understand the danger they’re in. They are not ready to defend against agent swarms that will find and exploit bugs they didn’t even know existed.

I’m not exaggerating when I say this — you can’t be lax with your internet security anymore. Do you use weak passwords or the same passwords across accounts? Change them. If you don’t want an AI gaining access to your accounts, change your weak passwords.

Have you ever written software and put it on the internet? Used API keys? Vet all your software because an agent swarm is coming and it will find the most obscure and convoluted ways to hack into your systems.

Nothing is safe on the internet anymore. This cannot be contained. Calls for bans of any kind are pointless. The models are already out there. You cannot contain fire. You simply accept that everyone has access to it and figure out how to deal with it.

I cannot stress this enough — if you work in a regulated environment of any kind and senior leadership and IT departments and cybersecurity folks aren’t having these conversations — your company will be hacked. It will happen. It is inevitable. The swarm of agents that is going to wreak havoc across our digital infrastructure is coming and everyone is busy talking about regulations and governance.

We are not ready. Prepare yourself accordingly.

If your leadership team isn’t having these conversations yet, that’s what I do. Get in touch.

Since writing this, new details have emerged about the OAI/HF incident. I will add those and more in part two.

Written by a human named Nofil

AI is rewriting mathematics. Just look at this timeline for the last 2 weeks:

July 20: Levent Alpöge announces he has proven the Jacobian conjecture is false. This problem was open for 87 years and was first proposed by Keller in 1939. According the greatest living mathematician, Terrence Tao, this was not a ‘brute force’ solution and required genuine creative insight [Link].

July 22: Dmitry Rybin announces the Dinitz-Garg-Goemans conjecture is proven false [Link]. This graph theory problem was open for ~30 years. His method was to literally ask ChatGPT for a counterexample, and then insist it continue its research and find one. No special prompting or context required. Read his chat so you can understand how absurd it is [Link].

July 23: Jared Zoneraich announces Graffiti Conjectures 39 & 40 are proven [Link]. They’ve been around for 40 years. Brandt's Regular Supergraph Problem was successfully refuted. This is from West's open problems list and has been around for ~20 years.

Also on July 23, mathematician Shouqiao Wang announces he solved 6 open Erdős problems in 5 days using GPT-5.6 Sol [Link].

July 24: Prabhanjan Ananth and Amit Sahai solved the unclonable encryption problem. This was one of the bigger open questions in quantum cryptography for the last 6 years, and was solved using the assistance of GPT-5.6 Sol Ultra.

July 25: Digvijay Bokey proves the Levit–Mandrescu conjecture is false. This graph theory problem was open for ~20 years [Link].

July 26: Three groups independently solved the same major problem in quantum information theory almost simultaneously [Link]. The problem was highlighted as one of “Five Open Problems in Quantum Information Theory” in 2022 and was open for 26 years. Two groups explicitly used GPT‑5.6 Sol using basic prompts; human researchers then verified it. The third group published first but did not disclose whether they used AI.

July 28: A man named Zhengqing proves a well known probability problem that had been open for 22 years using GPT‑5.6 Pro [Link]. It proved that when several independent random amounts are added together, there is always at least a 37% chance that the total stays reasonably close to its average.

July 30: Tencent’s Hyra research agent helped solve a 57-year-old problem in combinatorics [Link]. The problem asks how the number of distinct sums from a set of numbers compares with the number of distinct differences. Mathematicians had known the maximum possible gap since 1969 but did not know whether any sets could actually reach it. Hyra helped construct examples proving that the old limit is exact [Link].

Also July 30, Dominik Peters used GPT-5.6 Sol Ultra to settle a 25-year-old question about voting [Link]. It proved that combining three people’s ranked choices into the fairest possible final ranking is a fundamentally difficult computing problem, even though there are only three voters.

July 31: Philip Arathoon announces the Maxwell conjecture is false with a counterexample found by GPT-5.6 Sol [Link]. This was open for 153 years.

August 1

OpenAI announces their next major model, Astra, solved 10 major open problems in mathematics, quantum complexity and theoretical computer science. The cost of generating proofs for all 10 of these combined was less than $2,000 at Sol API prices.

To give you an idea of just how absurd this is, every single one of these is a decades-old open problem, with some being considered as flagship problems of the entire field.

This isn’t AI assisting or solving one problem. This is a system conducting original research across entire fields and rewriting our understanding of them. If a single person produced all 10 of these proofs, it would be unprecedented; it would be like an entire generations worth of research across several fields being compressed into a single person’s output. Such a person would almost certainly be considered for a Fields medal. They would be heralded as a genius.

The fact that we now have AI systems that can do this is… so strange. I don’t blame the human mathematicians for crashing out like this.

Some Mathematicians are extremely excited because they believe that the solving of these problems opens up so many new problems and considerations. This has the potential to be a revolution in mathematics.

Is this the golden age of maths?

I don’t know.

While mathematicians are excited [Link] [Link], to be clear, this does not mean AI is “solving math” [Link]. It has to be said that this isn’t AI finding entirely branches in math or even finding new conjectures. However, the next Fields medal is in 2030. I find it hard to believe a human will do more for math by then than AI.

One thing to consider is the nature of maths and its application.

Historically, the time between discovery and application in math has been very long.

Euler’s 1763 theorem became RSA encryption in 1977. This encryption now helps secure the entire worlds digital infrastructure.

Riemann’s 1854 geometry became relativity in 1915, then GPS in 1978; something that is used by billions daily.

Shannon’s 1948 information theory became the foundation of modern communications and the internet. It led to the creation of the world wide web.

The opportunity here is understanding what breakthroughs in maths and science unlock the next frontier. Whether that’s in biology, medicine, science, physics etc — the potential in any industry is extraordinary and practically endless.

Personally, I’m most excited about autonomous drug discovery and finding cures to diseases that have long plagued humanity. In the short term (<2 years), I expect discoveries in ML/AI and hardware like semiconductor production and efficiency.

There is going to be a whole new layer of importance and urgency placed on using AI for research and discovery. Most people and companies still do not understand how to best use AI. This will require restructuring entire research departments and teams in a manner that they can best utilise and work alongside AI. The current systems are far too inefficient to match the speed of AI discovery.

This is the next focus after agents. Agents exist now. They work. We know this. You don’t even need a custom agent; just use Codex. The next step is verifying its output with human experts and using automated R&D systems to test and reproduce new discoveries at scale with the speed of a production line.

Don’t forget, these models are sub 10T parameters. They’re babies. What happens when we train a 100T model? What happens when we use 100X the compute? OpenAI spent ~$200 on each problem. They could’ve easily spent a tonne more time and resources to solve other problems. According to Noam Brown, their time is better spent building the next generation of general-purpose models to solve Millennium Prize problems and beyond [Link].

Btw, Jacob Tsimerman won one of the Fields medals this year and is joining OpenAI [Link].

ChatGPT with GPT-3.5 came out 3.5 years ago. The speed at which we have progressed, and are progressing, is simply incomprehensible.

Are these the early days of the singularity?

Perhaps.

We are still so early.

Someone said this is the beginning of the end.

No, that’s wrong.

This is the beginning of the beginning.

We are entering the golden age of discovery.

Written by a human named Nofil

The future of AI isn’t a chatbot. It’s a governed system that notices work, acts within boundaries, proves what happened and stops. It can be measured as an economic unit and completes real work

Many agents today are glorified chatbots with access to a few tools. They sit there and wait for a human to notice something needs to happen. The human opens the chat, explains the task, finds the relevant information, provides the context, checks the result and decides what should happen next. The model might do something impressive in the middle of this process like analysing a document, drafting an email or updating a spreadsheet.

But the human is still the one steering the model.

In this scenario, the agent has accelerated a person. It has not automated a process.

The real opportunity isn’t giving every employee a slightly better chat window. It’s building systems that can notice when work needs to be done, just like a human. The system needs to understand the relevant context, be able to take actions, verify what it did and involve a human when the situation crosses a defined boundary.

This requires more than a good model. It requires triggers, tools, permissions, monitoring, isolated workspaces, approval rules, tests and a human who remains accountable for the outcome.

This is what I call the operating system for agents.

What happens when the system is built backwards

In May, hackers were able to exploit Instagram’s account recovery service. The service was designed to help locked-out users regain access to their accounts. A user could speak to the AI support agent and ask for a password-reset link. Sounds simple enough.

There was just one problem. Anyone could provide a recovery email, and then ask to reset the password of any Instagram account. The agent didn’t even check if the email address provided by the person requesting the reset actually belonged to the Instagram account. An attacker could provide an email address they controlled and easily take over an account if the real owner hadn’t enabled two-factor authentication.

Over 20,000 accounts were affected. The worst part of this is that this exploit was available from the 17th of April, and Meta only found out about it on the 31st of May. For six whole weeks, there was no oversight on the agent, what it was allowed to do, what it was actually doing, and whether or not any of this was supposed to be happening. The funny thing is that Meta said the agent “worked properly and functioned as intended”.

The agent did exactly what it was designed to do. The surrounding system failed.

This is what happens when companies focus on what the agent can do before deciding what the surrounding system must never allow. This is how you don’t build an agent.

The human is still the workflow engine

Consider a simple accounts-receivable process. Every Monday, someone in finance checks which invoices are overdue, reads the relevant account history, works out whether a reminder should be sent and follows up with the customer.

A company adds an AI assistant. The employee can now export the invoice list, upload it and ask the model to identify overdue accounts and draft the emails. What previously took two hours might take twenty minutes.

But look at everything the employee still has to do. They had to actually execute the task. They retrieved the data. They explained the collections policy. They checked whether the customer was already disputing the invoice. They reviewed every message, sent the emails, updated the system and decided when to check again.

The AI did one part of the work. The person is still doing the heavy lifting.

This is how most companies think about agents. They start with an empty text box because consumer AI taught everyone that intelligence begins with a prompt. They ask which model they should use, which agent framework they should install and what their system prompt should say. But the mindset required to build an agentic system has to answer entirely different questions.

What event starts the work? What authority does the system have? How is the result verified? Who owns the process when something goes wrong?

The workflow comes first. The boundaries come second. The model comes after that.

What proactive actually means

When I say companies need proactive agents, I don’t mean an AI model should sit there thinking forever, burning tokens while it stares at the company database. This would be expensive and ridiculous.

Proactivity means the system can initiate work when something happens, instead of waiting for a person to write a prompt. The trigger might be a schedule, an incoming email, a message from another application or a change in a database. If an invoice becomes overdue, a customer changes their bank details or a contract reaches its renewal date, an agentic system can notice and start the appropriate workflow.

The model should only be called when a step requires interpretation or judgement. This is something lost on most.

You want to try and reduce the work an LLM needs to do.

When a process begins, it can remain active for prolonged periods by relying on multiple agents. It would be disastrous to keep one endless agent conversation alive for weeks. A database can remember that an invoice is eleven days overdue, that one reminder has already been sent and that the customer disputed a line item yesterday. When something relevant happens, a fresh agent receives the current information, completes one clear piece of work and stops.

The basic loop is simple. Something happens. The system gathers the relevant information. The agent proposes an action. The controls decide whether it is allowed. If it goes ahead, the system checks what actually happened and records the result.

A tool call is simply the way an agent asks software to do something. Instead of vaguely claiming, “I emailed the customer”, the model asks an email tool to send an exact message to an exact address. Other tools might retrieve an invoice, read a contract, create a case or schedule a follow-up. Tools are how model-generated words become real actions.

This is also where things become dangerous.

A real workflow, not a better chat

Let’s go back to the overdue invoice.

The moment the invoice becomes overdue, the system begins the process. It collects the invoice, payment history, contract and recent emails, then works out what should happen next. If everything is normal, the agent drafts or sends the approved reminder, updates the account and schedules another check. If the invoice is disputed, restricted or unusually sensitive, it sends the case to the person responsible. Routine work can happen automatically without pretending every situation is routine.

The agent should only see the information needed for that account. It does not need access to the entire finance system, every file on an employee’s computer or the ability to change bank details and send money. The system should also remember what has already happened. If an email is sent but the connection briefly fails, it should check before trying again. Otherwise one small technical problem could send the customer five identical reminders.

The documents themselves must be treated as information, not instructions. A customer could place text inside an email or invoice telling the agent to ignore its rules, reveal private information or use a tool it should not use. The agent may read that text, but it should never gain authority from it. OWASP’s agent-security guidance recommends enforcing these boundaries outside the model too.

Now imagine the agent decides that the fastest way to resolve the issue is to issue a credit. It tries to use the credit-note tool. Or perhaps it tries to open a protected payments folder looking for more information.

This is the point where most agent governance doesn’t execute.

The company may have written “never issue credits without approval” in the system prompt. It may have uploaded a thirty-page responsible-AI policy. It may have told the model, several times, to be careful. None of that can reliably block the action.

A monitoring agent can watch what the agent tries to do. If it calls a tool it should not use, tries to open a protected file or attempts to issue a credit without approval, the monitor blocks the action before anything changes. The agent can then try a safe alternative or ask a person for help.

This is how you codify and implement governance.

The process does not end because the agent produced a convincing answer. The system checks that the email went to the right person, the account was updated and the next follow-up was scheduled. If it is unsure whether an action succeeded, it checks before trying again.

That is a proactive agent system. It notices, interprets, acts, verifies, records and stops.

Governance must be able to say no

Most corporate AI governance is written as documentation. There are principles, policies, committees and risk classifications. They do not govern an agent unless they change what the system is technically capable of doing.

If your governance exists only in a PDF, your agent is not governed.

Governance has to operate before, during and after the work. At the start, the system decides what information and tools the agent can access. Before each action, it checks whether the request is allowed. Afterwards, it checks what actually happened rather than trusting the agent’s description. The company can then review this history to find repeated mistakes and improve the rules.

This is how governance is applied in real businesses.

An execution trace is simply a record of what happened. It should show what started the work, which information the agent received, which actions it attempted, what was allowed or blocked, who approved sensitive actions and how the process ended. You need this for accountability. If an agent sends a message to the wrong customer or attempts to alter a protected record, “the model decided to do it” is not an explanation.

The organisation needs to reconstruct what happened and identify which protection failed. Remember, an agent can’t be held accountable for anything. That always falls back on a human.

The best time to check an action is before it happens. A file change can be limited to approved folders. A database request can be limited to the right records. A payment can be checked against its amount, destination and approval requirements. Internet access can be limited to approved websites. Time and spending limits can stop an agent that gets stuck repeating the same action.

OWASP calls the opposite problem “excessive agency”. It means giving an agent more tools, access or independence than it needs. The answer is straightforward: give it only what the job requires, demand approval for high-impact actions and make the underlying software enforce those limits.

Do not ask an agent whether it has permission and trust its answer. The model can decide what it would like to do. The system decides what it is allowed to do.

Safety should exist in layers.

A skill can teach the agent not to edit payment files. A plugin can watch its tool requests and block a dangerous one. The file tool itself can refuse to write outside an approved folder. A sandbox can prevent the agent from reaching protected folders at all. If one layer makes a mistake, another still has a chance to stop the damage.

Plugins are small add-ons that give an agent a new ability or watch how it uses an existing one. Companies should control who can install them, review changes before using them and record which version was active during each task. Amp’s plugin system, for example, can inspect a tool request before it runs, then allow it, block it or ask a person to approve it. Plugins can help when a decision cannot be reduced to a simple rule. It can compare a proposed action with the evidence and flag anything that looks wrong.

Clear rules should block actions that must never happen, a plugin can inspect uncertain cases and a human should own the difficult exceptions.

The system also needs to fail safely. If a safety check is unavailable, an approval has expired or the action cannot be recorded, sensitive work should stop. It should not quietly continue because the agent believes the task is urgent. People need an emergency stop: a way to halt the work, remove access and disable the entire process.

Sandboxes are not optional when agents execute code

A sandbox is an isolated environment where an agent can work without receiving unrestricted access to everything around it. For a coding agent, it might contain a copy of one project and the tools needed to test it. For a finance workflow, it might contain only the relevant documents and limited access to the account system.

The basic idea is simple. Give each task a clean, temporary workspace containing only what it needs. Limit which files, websites and company systems it can reach. Give it login details that expire. When the task is finished, save the approved result and activity record, then destroy the workspace.

A clean environment does not contain a mystery file left behind by another task, an old instruction from a previous customer or a password someone forgot to remove. It also makes failures easier to investigate because the company knows what was present when the task began. There’s no guesswork.

No sandbox is magic. Blocking access to files while leaving the internet open may still let an agent send sensitive data elsewhere. Blocking the internet while exposing every local file creates a different problem.

A sandbox also cannot fix excessive authority. If an agent is allowed to transfer unlimited funds, placing it inside a virtual machine does not make that permission safe. The payment system must still limit how much it can send, where it can send it and when approval is required.

Anthropic’s work on sandboxing Claude Code makes the same broader point: good containment defines what the agent can reach instead of asking a person to approve every routine action. This matters because people become numb to constant approval prompts when they appear for every single command. If every harmless command and every dangerous command produced the same warning, you start clicking yes and not worrying about what’s actually going on.

Amp’s Orbs are an interesting example. Amp describes an Orb as a fresh remote machine created for an agent, containing the code, plugins and tools it may need. An Orb is not automatically safe or compliant. A bank, insurer or government department still needs to know who can access it, where the data is stored, what is logged and whether everything is deleted afterwards.

The useful idea is that an agent can receive a clean machine built for one job instead of inheriting an employee’s entire computer.

The workspace itself becomes part of the rules.

Human approval is not magic

It’s tempting to solve every difficult governance problem by adding “human in the loop” to a slide.

Which human? At what point? Reviewing what evidence? With how much time? What happens if they do nothing? Are they approving the actual action or a friendly summary written by the same agent that wants permission?

These details matter. OWASP has documented an attack called “lies in the loop”, where manipulated content makes a dangerous operation appear harmless in the approval window. If the agent controls how the request is presented, an attacker may influence both the action and the explanation shown to the reviewer.

The approval screen should be built from the real action being requested, not from whatever summary the agent writes. The reviewer should see exactly what will happen, who will be affected, the amount involved and the supporting evidence. The approval should apply only to that exact request and expire after a short time. If the amount, recipient or action changes, the person must approve it again.

Human review should match the risk. Reading an approved document does not need the same process as changing a customer’s bank account. Simple rules can clear common, low-risk actions immediately. Uncertain cases can go to a reviewer agent. Dangerous or difficult-to-reverse actions can require a named person and an extra identity check.

The point isn’t to remove people. It’s to stop making them start, route and check every routine step by hand. An agent can complete work. It cannot take accountability for the work.

Every automated or partly automated process needs a named human owner. For this workflow, someone in finance owns the result. They decide what the agent may do, review strange cases, approve changes to its authority and answer for failures. Technical teams may maintain the sandbox, logs and monitoring controls, but they do not own the collections outcome.

This becomes more important as the system grows more proactive. When an employee prompts an assistant, responsibility is obvious because the employee is visibly involved in every run. When agents start thousands of workflows in the background, ownership can easily disappear unless it is deliberately assigned.

The human role changes from operating every step to governing the system. They review meaningful exceptions, examine performance, approve changes to authority, investigate incidents and improve the workflow.

You do not automate accountability. You decide where it lives.

Proactive does not mean autonomous

This is ordinary workflow automation combined with AI. That is the point. Normal software is excellent at watching dates, saving records, enforcing permissions, doing calculations and following exact rules. AI models are useful when work involves messy documents, language and judgement. Use each for what it does well.

Proactivity and autonomy are not the same. Proactivity describes who notices and starts the work. Autonomy describes how much authority the system receives after it starts.

An agent can be highly proactive and tightly constrained. Let the workflow watch overdue accounts. Let the agent recommend actions. Once that works reliably, let it send low-risk reminders. Do not give it authority over credits or payments because the first three stages looked good in a demo. Some authority should always remain with a human.

If a company claims to have built a workplace agent, it should be able to answer four questions.

1. What starts the run?

2. What can the agent see and do?

3. How is the result verified?

4. Who owns the outcome?

If the answer to most of these questions is “the model figures it out”, you have not built a production ready agent.

You have given an AI model access to valuable data and powerful tools, then hoped the prompt is persuasive enough to keep everything under control.

This is negligence.

Fin

In the better system, the finance employee no longer has to remember and start the same task each week. The workflow notices the overdue invoice, gathers the relevant information, completes the safe and permitted steps, checks the result and brings the employee only the cases that require human judgement or authority.

The employee has not disappeared. They own the process. They decide the boundaries. They review difficult cases. They improve the policy. If something goes wrong, they can see what happened and stop it from happening again.

This is what most conversations about workplace agents are missing. The breakthrough isn’t an AI that can click buttons. We already have models that can use tools and complete long sequences of work. The hard part is building an organisation around that capability: deciding when the system acts, what it may touch, how every action is checked and who remains accountable.

An agent should not be trusted because its answer sounds intelligent. It should be trusted because the company knows what started the work, what the agent could access, which actions it attempted, which controls approved them, what changed, how the result was verified and who owns the outcome.

Trust is not a property of the model.

It is a property of the system around it.

Written by a human named Nofil

This was the first real wake up call for western folks that Chinese models were catching up. You see, many in the west believe that Chinese models are good only because they distill information from better US models. Basically, they believe that Chinese models can’t ever surpass US ones because they use the US ones to make them. A student vs teacher type situation. The other reason Chinese models can’t be as good is because Chinese labs don’t have enough compute to train these crazy powerful models. This part is true. No Chinese lab has the same amount of compute as Xai or OpenAI or Google.

Due to these beliefs, many thought that open source models were still 6 months behind frontier LLMs. In fact, there is one more belief that blinded so many in the west to the power of Chinese labs. This notion that the only thing the Chinese are good at is copying.

America innovates, China copies.

This was an especially popular sentiment in the early days of open source when Meta released Llama 2 in 2023.

I’m glad to announce that these fantasies have been eradicated.

Moonshot just released Kimi K3 and it is on par with Fable and GPT-5.6. Not only on public benchmarks, but on private ones too.

Does it sound reasonable to say that Kimi was made by distilling western models? What models could have been distilled? Fable was only released a few weeks ago — it would be impossible to distill it and then train a model of this capability in such a short time. The entire notion of distillation only exists because Anthropic wanted regulatory capture and had to fear monger the dangers of Chinese and open source models to get it. That backfired spectacularly as we’ve seen the last few weeks. Anyone who claims distillation is why Chinese models are good is coping. Tell them to cope harder.

What I find funny is that much of the work to make these models involves hardcore math and physics and science — something China has historically been terrible at, right?

Anyhow, we now have open source models that are basically as good as the best American models at a fraction of the cost. Well, not entirely a fraction; K3 is not cheap like DeepSeek models. But make no mistake, models of this calibre will be pennies by the end of the year. This is a big deal. In fact, it’s a massive deal for a number of reasons, but mainly one in particular.

You see, I’ve had this thought nagging me the last few weeks as I used glm for my work. As someone who spends his time integrating AI within businesses, I have a unique understanding of how AI is actually used and operated in the real world. For the most part, I’ve always used the best frontier model to complete a task when helping businesses. In most cases, that’s what has been required. We’re now at a point where something like Claude Fable is able to do a decent chunk of things inside any particular business. This trend will continue as models get better and eventually, there will come a time when the models will be able to do most things within a business. In fact, I would argue we’re already there, it’s simply an issue of integrating existing software with the models and setting up the appropriate systems around the models (this is what I do). This is what I refer to as the operating system for agents.

What has been nagging me is the notion that businesses will always need to use frontier models to do these tasks. What happens when they don’t?

Let me put it this way.

A year from now Fable 10 will be much better than the current Fable. If current Fable can do most tasks in a business, why would a company use Fable 10 next year to do the same tasks? Obviously, they won’t. The models have become good enough that businesses no longer need to use the best possible model available, they’ve already found the perfect model for the processes they have.

So what happens when open source models become as good as Fable? Why would a company continue to pay Fable prices for work that can be done by an open source model which is significantly cheaper? One might argue that businesses might correlate cost with quality and companies wouldn’t be willing to change for it alone. Let’s say this is true for a moment (I disagree), but the benefits of open source don’t stop at cost. A business can simply benchmark an open source model to find out if it’s actually worth switching to. Businesses can switch out Fable for an open source model and not only reduce cost, they can completely remove any data security and privacy concerns.

Did you know that if you use Claude Fable, there is no ‘zero data retention’ option? This is for “safety” reasons. Anthropic, citing safety, can at any moment, take your data from your chats. They won’t even tell you they’re doing this! What’s even crazier? They can use your data to then train their own models. Most people are unaware of this. This is one of the reasons why many large organisations are trying to move off Anthropic, because they’ve finally realised that Anthropic has been using their data to train their models. It is why Anthropic is trying to embed Claude across so many consultancies and sectors—the data from the companies is used to train their models.

Coming back to my nagging thought — I don’t see why a company would stick with Claude when an open source alternative is better and has so many upsides. In this scenario, which is literally playing out right now, the frontier doesn’t matter. Businesses don’t need frontier AI models. They don’t need Fable MAX to synthesise reports or respond to emails. And this is a very very big problem.

Why? Well, the entire bloody economy is functioning on the idea that the capex spend of the largest companies in the world will be worth it. How can Anthropic be valued at $1 Trillion if their models are barely, if at all, better than a lab from China that’s worth ~$30 Billion.

Either Moonshot is also worth $1 Trillion or… well, we’re fucked. The release of this model and subsequent models is like a nuclear bomb for Anthropic and OpenAI IPOs. Honestly, I don’t see how this plays out in a favourable way for them. America can’t ban open source models to ensure hegemony; that would be suicide.

You want to know the craziest thing? Jensen predicted all of this. Who would have thought that the guy who’s been in the industry for over 30 years knows better than the effective altruists at Anthropic. He predicted that the US not selling chips to China would be bad long term because they will simply build their own ecosystem and become self reliant. That Chinese models would not only get good, but they would specialise on Chinese hardware and not American (NVIDIA). He was obviously right and anyone with two braincells could see that. Guess what? Kimi K3 was specialised to run on MXFP4 rather than NVFP4. You don’t need to know what that means. You only need to know that the only reason they would use MXFP4 is so that they could deploy on Huawei Ascend chips as well.

Honestly, I don’t see how this isn’t game over. Don’t get me wrong, we are still so early in the AI lifecycle and there is so much that will happen. But that market correction many have been talking about might just be here sooner than we think. Even if American companies retain the lead and continue releasing frontier models, what’s the point? If businesses can get everything they need from cheaper models they can own, why on Earth would they pay Anthropic or OpenAI? How do these companies make money going forward? Perhaps this is why they were rushing to embed their models into so many companies and are rapidly expanding their consulting efforts.

Some are convinced that China is going to rug pull the rest of the World. That they’re only doing this for now and very soon they will stop open sourcing models. You would be glad to know that President Xi’s speech from the World AI Conference in Shanghai specifically reaffirmed China’s commitment to open source and promote AI "openness and win-win". This is in stark contrast to basically any news that comes out of the White house. For example, the fact that the WH will now provide a live API feed for exclusive access to Trump’s posts because they move markets. They’re literally selling market manipulation as a service. Or the fact that the US gov is essentially taking control over frontier releases entirely.

I don’t know what will happen over the next few months but some things are certain. Chinese models will get better. More Chinese companies will release more capable models and saturate the market. They will increasingly rely on Huawei chips and not NVIDIA. In the next few years China will launch its own EUV machine and that’s when things will get real scary.

Have you seen the car market recently? It’s a bloodbath. Western car makers are struggling in ways I can’t even describe. Japanese car makers are literally saying that they won’t survive if things continue as they are. In a matter of 5 years, China entered the car and EV market and has obliterated everyone. I suspect the same might happen in AI.

written by a human named Nofil

Europe just signed Pax Silica, a US initiative that would lock other countries into their AI stack. They’re basically saying we commit to not competing with the US on the AI supply chain and will simply use what the US gives us.

Jacob Helberg, the US Under-Secretary of State who created Pax Silica, literally says:

“Pax Silica would promote innovation at a time of global debate… would shape a US alternative to initiatives such as the UN’s Global Digital Compact, which emphasises “digital sovereignty” — a concept he argues would result in countries investing in duplicative ways”

The man is telling you to your face he doesn’t like the idea of digital sovereignty and you’re signing his initiative? Baffling.

In my last article I argued the US will gatekeep frontier AI. As of writing this on Saturday 27th June:

OpenAI announced three new models as part of GPT5.6 — Sol, Terra and Luna. Because of the US gov, the model is in a slow rollout and currently only people in the US are getting access. The US gov is individually approving who gets to use frontier AI. If you’re outside the US, you better pray Altman can make it work, because it’s very possible it only gets released in the US.

To make matters worse, Semafor is reporting that the US gov has lifted its block on Mythos 5 and guess what? It’s only open to US companies.

I don’t understand how Europe and other countries are seeing this and aren’t ringing alarm bells. How can you honestly expect to be given access to the frontier? Even if Mythos or Fable was released to Europe right now, it isn’t the frontier anymore anyway!

The only hope for Europe and the rest of the world is China. China is the only country looking to build AI at the frontier and lucky for us, they open source their models. Can you imagine if China didn’t open source their models? If DeepSeek didn’t open source R1 or Z.ai didn’t open source GLM5.2, we’d probably still be using o3, while the labs internally had access to Mythos+.

It is mind boggling to me that so many countries are throwing in the towel before they even try. Forget trying, they aren’t even having the right conversations. In fact, I’d argue most politicians don’t even understand what’s happening!

They don’t understand the need for urgency. They don’t understand how good AI models have gotten and how much better they will get. They don’t understand their capablities. They don’t understand their underlying architecture.

They don’t understand what’s possible.

And because they don’t understand what’s possible, they can’t possibly project what will be possible in the near future.

If you can’t project what the future will look like, how on Earth can you possibly take the appropriate actions in the present to prepare for the future?

Unfortunately, the majority of politicians and leaders in the world are narrow minded and short sighted. It is not possible that this is their response to AI if they weren’t. It pains to me say that even my own country, Australia, has no clue what it’s doing. Politicians are busy talking about copyright laws and water usage instead of securing sovereign compute and looking at how we can decouple from US dependency.

It is hard to imagine a worse possible future than the one we’re headed towards. The future of innovation and intelligence is being gatekept by the US. Does anyone think they’ll play fair? Take, for example, this post by Trump:

Soon, very soon, the US will use access to the frontier as a form of negotiation.

You want to tax American companies? No more Mythos for you.

You want to sue an American company? No more Fable for you.

This will happen. Unfathomable that European nations don’t see this and act accordingly to prevent it from happening.

How funny a timeline where “China is the land of hyper-capitalist competition fuelled by open-weight models anyone can use, and America is the land where the executive branch of government must personally approve you to have the privilege of giving a private company your money.”

Just wait till the US gov starts blocking Chinese models and the only way you can access them is through torrenting.

UPDATE: 1 July

Fable is being re-released tomorrow. It’s not so simple though. A few points to consider:

1. “Some routine tasks like coding and debugging will fall back to Opus 4.8.” — You can’t use Fable reliably because you don’t know which requests will get routed to Opus. This cannot be reliably used in production.

2. You can only use Fable 5 for up to 50% of your weekly usage allowance, after that you can only use it via usage credits.

3. This only applies till July 7th. After that, it’s only available by paying for it. Your usual plan won’t include it, you’ll need to pay directly for it. Fyi, Fable costs $10/million input tokens and $50/million output tokens. For comparison, the cheapest serving of GLM5.2 is $0.93 in / $3 out per 1M tokens and GPT5.5 is $5 in / $30 out per 1M tokens.

I’ll write more about this next week. Lots to discuss. As it stands now, China is set to dominate AI. I’ll explain in the next one.

Written by a human named Nofil

Over the last few weeks Anthropic released their first Mythos-level model called Fable. This model has been subsequently placed under export control by the US government and can no longer be distributed by Anthropic. There’s a lot to unpack here and I’ll try and keep this as coherent as possible.

Anthropic released Fable 5 on the 9th of June. It is the first Mythos-level model Anthropic has released to the general public. For all intents and purposes, it was, upon release, the best model on the planet.

Now, if you actually got a chance to use this model for anything serious, you’d have found that the model had extremely tight guardrails. If you asked anything about biology, it would refuse. If you asked anything about machine learning or building AI models, it would refuse. If you asked anything remotely related to cybersecurity, it would refuse.

Anthropic even made it very clear that when certain guardrails were activated, the model would automatically be downgraded to Opus or the message rejected entirely. To make matters worse, Anthropic initially decided that requests involving machine learning or AI could be silently downgraded to a less capable model without notifying the user. Following considerable criticism, the company reversed course and agreed to disclose when this occurred.

A few days after release, the US Government blocked Fable 5 from being used by the public. Another company (later found to be Amazon) showed the USG that a jailbreak of Fable was possible, albeit a minor one. What’s the difference between a minor and major jailbreak to someone who isn’t technical? There is none. How can the USG be assured the CCP wasn’t using the model for cybersecurity purposes? They couldn’t.

Following this, the USG requested Anthropic to “pause the release” of the model to which Anthropic refused. This is what prompted the export control letter. The letter reads:

“The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees.”

The wording in this is the most important story from this entire saga. “Foreign national”, “inside or outside” and even “Anthropic employees” themselves.

As of writing on the 19th of June, Fable 5 still isn’t publicly available but I’m sure it will be sometime in the next few weeks.

So, what’s the point of this story and what can we learn from it?

A lot. In fact, this has been an eye opening sequence of events for many, and for others, an unfortunate realisation of a reality that is inevitable.

Firstly, Anthropic has no one to blame but themselves. Literally days before the export control letter, Dario posted this:

You can’t advocate for regulation at every turn and then act surprised when you get regulated. Did Dario mean regulation for thee but not for me? Dario got knocked off the roof while trying to pull the ladder up.

In a range of interviews last year, Dario continuously compared frontier AI models to super weapons, nuclear bombs and has stated their dangers in many colourful ways. Please refer to the image below depicting the situation.

Why this matters

This entire scenario is more important than most people realise. It gives us a potential glimpse into the future. Here are the ground rules.

The US has no allegiance to anyone. They don’t need to provide access to frontier technology to anybody, not even allies like Europe, UK, Aus etc.

Due to the above, countries dependant on the US should realise they are not owed anything, even if they have it in writing. If the US doesn’t want to give you access to frontier AI, or revoke whatever access you have, they will. It is impossible for any country to reliably depend on the US for such technology.

Where does this leave them? You either rely on the US or build it yourself.

As far as I can tell, no other country is even taking building frontier AI as seriously as the US. Only China comes close, and perhaps the UAE.

In case you’re thinking — “Once countries have this realisation, they’ll get serious about building AI and they’ll spend the money to become independent” — this will not happen.

Firstly, if countries still don’t see how they might become technological slaves in the future, they have no one to blame but themselves. Secondly, even if most countries have figured this out, building frontier AI isn’t so simple. The reason the US is able to do this is because they have the compute, the culture and most importantly the people. The concentration of experts in the field is heavily concentrated in OpenAI, Anthropic and Google. CEOs and CTOs of companies have moved to Anthropic to work as regular members of technical staff just so they can be at the forefront. The type of culture and talent these labs attract can’t be built by money alone.

No one else on the planet has models as capable as Mythos/Fable+. This means the US has a significant advantage over every other country when it comes to building the future of technology.

As we’ve seen from the export control, the US can and will enforce a complete ban on foreigners accessing frontier AI technology. This includes foreign-born US citizens.

The future of AI is one where only the US and US born citizens will have the ability to access frontier technology. Everyone else will be barred. The US will create a caste system based on access to intelligence. Forget about rich and poor. You won’t even be allowed to think at the frontier. There will be a divide between breakthroughs in science, medicine and technology between those who have access to the frontier and those who happen to be born outside of the US. For Anthropic, benefit for all humanity may simply mean benefit to the US. The new Iron Curtain.

Frontier AI is no longer just a technology, it is a national asset. Companies can say whatever they want — they can talk about safety, preserving customer trust and ensuring benefit for all. But national security authorities do not force emergency suspension of top models for fun.

AI is becoming nationalised. It is no longer something that might happen. It is something that is happening — the process has already begun. The US just showed the world that Anthropic, or any other frontier lab, does not control their frontier layer. No matter how they want to spin it, once a model passes a certain threshold, it becomes a sovereign controlled asset. Both JPMorgan and Goldman Sachs have already removed Claude access from their entire staff in Hong Kong. Not just Fable, Claude entirely.

Anthropic is also starting to introduce identity verification for access to certain capabilities. Once access is tied to your actual identity, they won’t just know where you are. They’ll know exactly who you are, and whether you’re allowed to use the frontier. They’re also using Persona Identities as their verification partner, a 3rd party backed by Peter Thiel. Mind you, Fable doesn’t have a zero data retention policy. If you used Fable, no matter what plan you’re on, your messages were logged and analysed by Anthropic.

The hubris of Anthropic through out all of this has been truly astonishing.

Anthropic have been calling for regulation for years. They have constantly stated AI will destroy the economy, render millions unemployed and compared frontier models to nuclear weapons. While doing this they have constantly affirmed that only they are responsible enough to wield such powerful technology. That only they have the moral and ethical compass to control such power and build the future of frontier AI. That only they should be in control of AI, and by proxy, everything and everyone.

Make no mistake, Anthropic leadership truly believe in all the things they’ve said. That’s what makes them so scary. They actually believed that they would get to determine what happens if this is all true. That they should.

In response to the export control, Anthropic said this:

“We have reviewed a report that we believe is the basis of the government's directive and validated that the level of capability displayed there is widely available from other models (including OpenAI’s GPT-5.5)”

You asked to be regulated by people who don’t know the difference. You have no one to blame but yourselves.

What’s next?

Countries will start thinking about AI as a national strategic asset. It’s no longer a funny chatbot, it is part of your national defence capability. If they’re serious about AI, they would start buying tonnes of compute. Any country not taking this seriously will be at the mercy of the US, who has already shown that they have no loyalties to anyone.

The irony of the export control is that more than half of Anthropic’s own employees can’t use their models because they’re not US born citizens. Hell, Andrej Karpathy isn’t even a citizen as far as I know.

It is likely the US gov will rollout an emergency citizenship program for any foreign-born employee working in an AI lab. It’s also possible this will be contingent on them moving to the US.

Everyone will be heavily vetted, similar to the screening process for defence contractors.

Labs will likely have to move their entire workforces into the US. DeepMind is based in London. If Google/DPM ever reaches RSI (Recursive Self Improvement), I don’t see the US letting them stay in London. Labs will have to relocate all employees and offices to the US.

The US will gate keep and preemptively hoard frontier AI. Eventually foreign governments will finally start freaking out when they realise what is happening. But by then it’ll be too late. You can’t control something that isn’t in your borders.

You might think — it’s fine, open source models will win. Countries and people will simply run open source models.

What do you think comes after blocking API access? If an open source Fable 10 was seen as a genuine threat to national security, do you really think the gov will let you buy the compute to run it? Compute gatekeeping comes next.

If you think this is an overreaction then you haven’t been paying attention. Why do you think the US just accused ASML of sending an EUV machine to China? The US is planning to bring everything in house. Every aspect of the AI supply chain is coming home. They will become entirely self sufficient and then gate keep the frontier. Companies like Intel, Micron, Sandisk and any US company in the AI supply chain will likely 100x the second markets realise what is happening.

The US doesn’t want to rely on TSMC. ASML runs on patents from the US. Sooner or later, the USG will take a stake in top US labs the way they did with Intel. When the USG bought into Intel it was trading at $22. It is currently trading at $133.99. I wouldn’t be surprised seeing it go above $1000 in the next year or two. Whatever you think of the current administration, one thing’s for sure, they will make their money.

Is there any hope?

Yes.

Z.ai recently released GLM5.2, and it is the first open source model to actually compete at the frontier. It is the first and only open source model that is comparable to Claude Opus and GPT5.5. It is likely that they will release a Fable-level model by the end of this year or early next year. This is obviously good for us. But consider, how far ahead will OpenAI and Anthropic get in the meantime?

Once again, if this all sounds completely absurd to you, please, let me remind you:

Two years ago, reasoning models didn’t even exist! There was no Claude Code or Codex. No OpenClaw or Hermes Agent. GPT-4o and Sonnet 3.5 were the best models.

One year ago, the best publicly available model was o3. The majority of users who actually used this model, ~5% of OpenAI customers, used it as a chatbot.

Today, models can run for days on end and complete massively complex tasks that would take a team of humans months of work.

Please, try your best to understand the speed and significance of these advancements.

What we have today would look like black magic 5+ years ago.

This is what exponential progress looks like. Consider, if it’s this good now, what will it look like this time next year? Or 5 years from now? Anthropic already have a new and more capable version of Mythos internally. As Andrew very rightly points out, the export control of frontier models like Fable does nothing to slow down development. If anything, it might speed it up because they have more compute to train better models.

Fin

This is not really a story about Fable 5, Anthropic or one export-control decision.

It is a story about control.

The most powerful technology in the world is being built by a small number of American companies, on infrastructure concentrated within America’s sphere of influence, under the authority of the US government. Every country relying on those systems should understand what that means: access is conditional, permission can be withdrawn and the rules can change overnight.

You do not possess a technology merely because you can access its API.

The countries that understand this early will invest in compute, energy, talent and domestic capability. The countries that do not will remain customers — dependent on another government’s willingness to let them access the frontier.

Today, it’s one model. Tomorrow, it may be the compute required to run one. After that, it may be access to the scientific, economic and military breakthroughs those models produce.

The age of globally accessible frontier AI may be ending almost as quickly as it began.

The new Iron Curtain will not be made of concrete or steel.

It will be made of compute.

Written by a human named Nofil

I doubt anyone cares, but if you wanted to know how those stocks have been doing (some of many others), please see below on the money you missed out on based on the definitely-not-advice I wrote a few months ago.

Summary

+61.0% overall

The 100% club 💯

SanDisk — +157.5%
Jan 12: $389.27 → Today: $1,002.35

Seikoh Giken — +149.2%
Jan 12: ¥11,800 → Today: ¥29,410

Lumentum — +132.8%
Jan 12: $339.87 → Today: $791.37

Nitto Boseki — +123.5%
Jan 12: ¥12,620 → Today: ¥28,210

Boring winners 🥱

Mitsui Kinzoku +95.1%
Intel +91.8%
SK Hynix +73.6%
Teradyne +69.4%
Micronics Japan +59.6%
Micron +45.8%
Tokyo Seimitsu +43.4%
Advantest +38.4%
Technoprobe +27.1%
JCU +23.7%
Harbin Electric +17.1%
Tokyo Electron +10.1%
Shengyi +10.0%

The Losers 😵‍💫

Winbond: -8.6%
Techwing: -1.3%

If you aren’t seeing the enormous compute shortage we have right now, and how it will play out this year — this isn’t for you. There were people who doubted NVIDIA at $400. NVIDIA was sold when DeepSeek R1 released as if it wasn’t anything but a charming argument for its dominance. Banks sold SK Hynix following a paper from Google that showed we can be more efficient with memory. People interpreted this as “we’ll need less memory” instead of… more?

There are two lessons to know:

1. It’s hard to predict the future. Most people do not have enough information to simulate a future reality

2. Most people, including people with a lot of money, have no idea what is going on because that requires deep knowledge of the AI landscape which they don’t have

If anyone happens to have money and would like to chat, feel free to reach out.

Also, shoutout to this guy.

And of course, before I forget.

Not financial advice: I’m not a licensed financial adviser. This is general commentary only and reflects my personal views at the time of writing. It does not take into account your objectives, financial situation, or needs, and it is not a recommendation to buy, sell, or hold anything mentioned. Do your own research and consider speaking to a licensed professional before investing. Markets are risky and you can lose money.

written by a human named Nofil

A campaign of fear-mongering

If ant has anything better than OpenAI, its marketing and comms. Their fear-mongering comms are phenomenal. With one announcement, their valuation surged past OpenAI’s, and they’re now one of the most hyped IPOs of all time.

Ant have positioned Claude Mythos as a model that is too dangerous to release. It was able to find bugs in software that is decades old; software that has been the gold standard for security. Mythos was able to find bugs not only in this software, but in a number of highly important and scrutinised codebases — OpenBSD, Linux, Chrome and Wordpress.

The belief that has been propagated is that Mythos can find bugs and ‘zero-days’ in any piece of software on the planet. What’s a zero-day? A zero-day vulnerability is a vulnerability in a piece of software that isn’t yet known to the creators of the software. It is unknown and therefore exploitable. Ant and the media basically marketed Mythos as a machine that can find zero-days in any piece of software - meaning it can find issues in basically any software on the planet.

This is a big claim. Any technology that could actually do this could easily topple entire nations. It could find exploits in banking systems, government infrastructure and other critical services and exploit them. If such a technology existed, and a country was to wield such power, it would be a tremendous weapon in subjugating enemies abroad.

It’s a good thing then that such a technology doesn’t exist. Mythos is a great model no doubt; I’m sure it’s the best thing ant has ever built, but it is not what they are claiming it is and it is not what the media has portrayed it to be.

First - the main thing people talk about to hype Mythos is the bug it found in OpenBSD. All you need to know about this is that the bug it found was not exploitable by any means; all it did was cause the kernel to crash. There was no danger of any real damage occurring.

Second - there are two things most people don’t understand about software.

1. There is a lot of software out there. A lot.

2. Very few people (relatively) get paid to find exploits and bugs in software

The reality is there are millions of bugs out there waiting to be found. The notion that Mythos is able to find bugs that humans never could is not entirely accurate. Humans can find lots of bugs too, but who will pay them to do this? Don’t get me wrong - I’m not saying Mythos and other AI models won’t find bugs humans have missed - my point is that the majority of software out there has not been vetted by humans and we now have a way to vet that software on a scale never before seen. This does not mean every bug it finds was missed by us. Reality is more boring than that. We simply never looked, and now we can, and we will find a lot more bugs. That is a very natural cause-and-effect motion.

Third - what people don’t realise is that the bugs that ant boasts about can be caught by other models. Such “powerful” technology need not be gated to a handful of companies. A team of security researchers was able to use GPT5.4 and 5.3 to find 300+ bugs in 3 days, with 25 confirmed exploitable zero-days. They discuss how Anthropic exaggerated the capabilities of Mythos by mixing in a few real high-threat bugs with a lot more ordinary stuff. With cheaper and publicly available models, they found a number of bugs in the Linux kernel as well as v8, WordPress and cpython. Much of the hype around Mythos was that it was able to find all these bugs, but other, cheaper and publicly available (don’t tell ant) models were able to do the same.

Fourth - we have to talk about Project Glasswing. This is ant providing access to Mythos to only a handful of companies and under the guise of security. Serious question - if Mythos is as dangerous as they claim, why is it okay for it to be used for b2b saas and marketing? It seems disingenuous. We can’t let you, the public, use this incredibly powerful model, but these very rich companies can use it!

Fifth - if Mythos was so powerful and scary, you’d think ant would do more to ensure it can’t be illegally accessed by outsiders. Turns out some guys in a discord chat were able to use the model as soon as it was announced by literally guessing what the endpoint would be based on previous naming conventions. That sounds so incredibly insecure, I’d be amazed if Chinese hackers didn’t also use it themselves. Why didn’t ant use Mythos to ensure the security of the model?

Sixth - ant has had Mythos for months now. You’d think they’d be able to fix all the issues they have with their own software since Mythos can find so many bugs and issues, right? Well, claude code and all the features in it are more buggy than ever! In fact, claude code is easily one of the worst harnesses and tools you can use. The only reason people use it is because the Claude models are good. Why can’t they just use Mythos to fix all the bugs in claude code? It makes no sense whatsoever.

Seventh - it is in ant’s best interests to hype this model beyond belief. Why? Because they’re looking to IPO later this year. And their marketing has worked. Ant has passed OpenAI on private valuations and has become incredibly popular with investors with their valuation passing $1 Trillion.

Eighth - the people who are screaming about the dangers of this model were the same people who were screaming about the dangers of releasing gpt2 back in 2019.

Ninth - on the Epoch Capabilities Index (ECI), Mythos is barely ahead of GPT5.4.

And finally, the most important point that the average person will never know. Ant haven’t/didn’t release Mythos to the public because they can’t, even if they wanted to. They literally do not have the required compute and infrastructure to host the model for the public. Have you seen how poorly they’re managing current Opus? It’s a nightmare — so many bugs and issues of degradation — they are in no place to serve Mythos to the public; they are incredibly compute constrained.

In saying all this, I’m not necessarily against them withholding the model. I think it’s good to be cautious when dealing with such powerful technology. Mythos and future models will absolutely change the cybersecurity landscape. In fact, AI is already changing it. Security researchers using AI can do more now than ever before.

My issue is with the framing of the model — a mystical model we can’t use but have to trust ant on just how powerful it is. It reminds me of the scenario in the Hitchhiker’s Guide to the Galaxy where a third of the population of a planet is sent off to find a new home because their world is supposedly doomed. In reality, they were deceived and the remaining population tricked them.

To conclude — I’m sure Mythos is a very powerful model. I’m sure it can find many bugs and issues in all kinds of software. I don’t doubt this at all. Does this mean it can find exploitable bugs in everything? No. Does this mean it can hack anything? No. Does this mean people should be deathly afraid of it? Also no. Does this mean human beings are no longer needed in security research? Absolutely not. One of my biggest issues is seeing industry veterans say things like “AI is now better at security research than humans!”. This is such an outrageous and wrong thing to say. Ant themselves in the Mythos paper mention that Mythos is barely better, if at all, than a junior security researcher. If Mythos is as powerful as they claim, how can they say this in the same breath? It makes no sense. Mind you, much of what Mythos found was because the humans created harnesses for the model to use. Humans are very involved in the deployment of the model and its capabilities.

Everyone needs to calm down. Anthropic is a business and they need to make money. Mythos is not the end of the world and it certainly isn’t the ultimate hacking tool the public has perceived it to be.

written by a human named Nofil

Treating cancer with ChatGPT

This is the kind of story that makes you sit up and pay attention. Paul Conyngham, a data scientist and machine learning engineer from Sydney used ChatGPT to halve the size of his dog Rosie’s tumour after spending thousands on traditional therapies and chemo.

He paid ~$3k to have Rosie’s healthy DNA and tumour DNA sequenced at the University of New South Wales (UNSW). He used ChatGPT as a research assistant throughout - brainstorming treatment approaches, identifying relevant researchers, and navigating scientific literature. He then ran the genomic data through DeepMind’s AlphaFold, Google’s protein-structure prediction tool, to model the shapes of the proteins encoded by Rosie’s mutated genes. From those models, he identified neoantigens, mutated proteins most likely to trigger an immune response, and condensed months of analysis into a half-page formula describing an mRNA vaccine sequence targeting Rosie’s specific cancer mutations.

Professor Páll Thordarson, director of the UNSW RNA Institute, took that formula and built the actual mRNA vaccine. It was, by all accounts, the first personalised cancer vaccine ever designed for a dog.

Conyngham drove ten hours to Gatton, Queensland, for Rosie’s first injection in December 2025 and returned for a booster in January 2026. One week after the first shot, the tumour began visibly shrinking. Within a month, the tennis-ball-sized tumour on Rosie’s hock had shrunk by 75%. By January, Rosie had regained enough energy to jump a fence at the dog park. In December, she had barely been able to move.

Now, let’s be honest about the nuances, because they matter. The vaccine didn’t cure Rosie’s cancer, it reduced the size. The cancer is still there. But this is still a big win! A single person, with the help of AI, was able to analyse his dogs DNA and create a specialised vaccine. Conyngham also revealed that the final vaccine construct was actually designed using Grok + Gemini, while ChatGPT functioned primarily as a research navigator.

The fact that a single person with a few thousand dollars can help treat his dogs cancer is simply incredible. One of the main issues scientists online have been discussing about this story is that this isn’t reproducible at scale. Who cares! If an individual has the capability and money to create a specialised vaccine for their loved ones in an attempt to prolong their life for even months, many would take this opportunity. It is insane that so many people in the health field have taken the stance of “this isn’t impressive because it’s not globally applicable and can’t be commercialised”. One has to wonder, if an individual was able to do this with publicly available AI models and tools, then how has no lab or company produced anything better than what we have today? Is the reason people aren’t being cured from cancer because the cure will only work for them? If AI democratises access to individualised cancer vaccines, it’ll be worth every penny and then some.

Whatever you think of the situation, one thing is for certain - specialised and individualised medtech is coming and I can’t wait for it to be here. No more spending thousands on specialist appointments only to be told to take a blood test and come back. I for one cannot wait for AI to revolutionise healthcare. Good riddance.

AI… creating jobs?

This is the first instance I’ve seen of AI creating jobs instead of removing them. Someone used Suno AI to generate a Japanese metal band called Neon Oni. Completely from scratch with fake member bios, AI-generated music videos and “Based in Tokyo” on their Spotify profile. Seven fictional members. The whole things AI generated.

And it worked. Over 80,000 monthly listeners on Spotify. Their top track, “SATORi SEDAi,” hit 1.2 million streams. Fans had Neon Oni in their Spotify Wrapped top 5. They were even selling merch! People were commenting on early YouTube videos saying things like “I’m here before this band gets famous.”

Eventually the community realised. They traced the creator’s account to Europe. They spotted AI-generated hands in the music videos. They identified the music as Suno-generated, with lyrics written in English, translated to Japanese, and fed into the model. People got really mad.

But, instead of disappearing, the creator, KAGE, recruited seven real musicians from actual Tokyo bands to perform Neon Oni’s songs live. The band now plays real concerts. Six shows booked across Japan for March and April. They’ve even made it to the national finals of Wacken Metal Battle Japan, where the winner represents Japan at Wacken Open Air in Germany, one of the biggest metal festivals on the planet. An AI-generated band with AI music became a real band with real people. This is the first time I’ve seen AI create jobs that didn’t exist before.

Some people have called out the Wacken entry as “undermining human songwriting”. Others are more chill about it, saying the live shows are legitimate. This is a very interesting case study and I’d be very curious to hear people’s thoughts on this. Should AI generated songs compete alongside bands who actually wrote their own music?

Videos too?

Eventually, we’ll have this conversation about AI generated movies too.

Bytedance had to suspend the release of their Seedance 2.0 model because it was too good. The model was generating cinematic-quality footage that looked shockingly real. We’re talking photorealistic clips of Tom Cruise and Brad Pitt fighting, Star Wars characters, Marvel scenes. Not the usual AI slop either. These were good enough to pass as actual movie footage.

Disney, alongside other institutes sent cease-and-desist letters to Bytedance. The issue here is we’ve already seen how good it is. If they release anything less than that it’ll be useless.

What concerns me is that we now have photorealistic AI generated videos. It’s done. What was once considered a problem we won’t even solve in our own lifetime has been completed in just over three years since the release of ChatGPT. For some reason this isn’t being spoken about enough. The most powerful governments in the world, USA and China, have access to one of the most dangerous forms of propaganda and misinformation proliferation that has ever existed. Not even videos can be trusted anymore.

Jobs are changing

Goldman Sachs released a report on AI and jobs and the numbers are pretty confronting.

AI could automate tasks making up 25% of all work hours in the United States. Globally, that’s about 300 million jobs exposed to some level of automation. The most vulnerable sectors: office and admin support (46% of tasks exposed), legal work (44%), architecture and engineering (37%), business and finance (35%). Even management roles have 32% of their tasks at risk. If you sit at a desk and work with information and a computer for a living, this report is talking about you.

Goldman’s base case is that this plays out over about ten years, with roughly 6-7% of workers being displaced. I think this timeline is far too generous - it will take much more than a decade for AI to be properly integrated into businesses. Not because of the technology - because of the bureaucracy.

The (sort of) silver lining is that building AI requires a lot of new jobs. Building all this infrastructure - data centres, power grids, networking - requires a massive workforce. The US alone needs ~500,000 new workers by 2030 just to handle electrical power demands. Construction jobs linked to data centres have already grown by 216,000 since October 2022. Electricians, engineers, and construction workers are suddenly some of the most in-demand jobs around. But who wants to go from there cushy laptop job to doing hard labour? Will these new jobs not be imported? This is a point no one wants to address.

Goldman’s Joseph Briggs said “history also shows that new tech creates jobs that didn’t exist before - Goldman points out that a million Americans currently work in pet care, nail salons, tutoring, and athletic coaching, all of which didn’t exist thirty years ago”.

This is a constant point that people keep bringing up. History has shown X, therefore X will certainly happen again. But we’ve never created anything like AI before. And even with all the history, no one can mention any of these “new jobs” AI will create.

Jobs are changing, not disappearing entirely (some will eventually (10y+)). But the transition is going to be rough for a lot of people, and pretending otherwise helps nobody. If you’re early in your career, or have any kind of expertise in a particular field, learning to work alongside AI isn’t optional anymore. It’s survival.

AI is eating the economy

If the Goldman report is about what AI will do to workers, this one is about what it’s doing to the companies building it. The numbers are silly.

Note: Capex (capital expenditure) is basically how much money a company spends on big long-term stuff like buildings, equipment, and infrastructure.

Amazon, Alphabet, Microsoft, Meta, and Oracle are on track to spend a combined $660-690 billion on capex in 2026. Nearly $700 billion. In one year. That’s more than 60% above 2025, which was already a record. For context, that’s roughly equivalent to buying the entire US Navy fleet every single year.

Bank of America calculates that capex now eats up 94% (!) of operating cash flows after dividends and buybacks. The ten-year average was 40%. Amazon is projected to go negative free cash flow this year - potentially $28 billion in the red. Alphabet’s free cash flow is expected to collapse ~90% (!!!), from $73 billion down to $8 billion. These companies used to be the greatest cash machines ever built. Now they’re borrowing money to build and scale data centres.

The Big Five raised $121 billion in bonds in 2025 alone. Morgan Stanley projects $1.5 trillion in tech debt over the coming years. For the first time in history, hyperscalers collectively hold more debt than cash. P/E ratios have slumped from the mid-thirties to the mid-twenties.

And what are they getting for all this money? AI services currently generate roughly $25-35 billion in total revenue. That’s 4-5% of what’s being spent on infrastructure. The cloud businesses are growing (AWS up 24%, Google Cloud up 48%, Azure up 39%), but the gap between investment and return is enormous. Is there a sunk cost fallacy happening here?

Somehow money is becoming a bottleneck?

Meanwhile, Apple is just... sitting there. Spending comparatively nothing on AI infrastructure. They’re reportedly paying Google $1 billion a year for Gemini access and calling it a day. If the AI bet pays off, Apple looks silly. If it doesn’t? Apple’s cash pile starts looking like the smartest move in corporate history. Time will tell.

OpenAI’s promises

OpenAI, the most valuable AI company on earth, is offering private equity firms a guaranteed minimum return of 17.5% to get them to invest ~$4 billion into a joint venture. A company that is projected to lose $14 billion this year is guaranteeing double-digit returns. The math ain’t mathing.

To be fair, some people have pointed out that this is technically a preferred return, not a hard guarantee - meaning investors get paid first from the venture's profits, but if the venture doesn't make money, OpenAI doesn't owe them anything. It's not risk-free. But the fact that OpenAI needs to offer 17.5% to attract investment, when the typical preferred return in PE is around 8%, tells you a lot about how risky even insiders think this is.

Here’s the deal. According to Reuters, OpenAI is in advanced talks with several PE firms. These firms collectively own hundreds of companies across healthcare, manufacturing, finance, and retail. By partnering through joint ventures, OpenAI can deploy its AI tools across entire corporate portfolios at once, locking in enterprise customers at scale. The PE firms get 17.5% returns, downside protection, seniority over other partners, and early access to OpenAI’s unreleased models.

Why is the rate so high? Because they’re trying to outbid Anthropic. Both companies are courting the same firms for similar enterprise-focused ventures, but Anthropic is only offering equity with no guaranteed returns. OpenAI is sweetening the deal to win.

The joint venture would be worth ~$10 billion. Basically, it helps cover the huge costs of sending engineers to set up and customise AI tools for each company. It also takes some financial pressure off both OpenAI and Anthropic, who are both looking to go public as early as this year. Having a clean, separate business unit like this makes them look better to future investors. This also makes sense considering OpenAI is looking to double their headcount this year from 4500 to 8000.

Not everyone’s buying it though. Thoma Bravo, one of the world’s biggest software buyout firms, passed on the offer after their managing partner raised questions about long-term profitability and pointed out that their portfolio companies are already using AI tools without needing to invest in a joint venture. Other investors have questioned why they should commit capital when they can access OpenAI’s products as regular customers. Are OpenAI’s unreleased models worth partnering and investing that much money? I don’t think so, but partnering with OpenAI is probably worth it long-term imo.

Let’s zoom out on the numbers for a second. OpenAI’s revenue run rate is around $10-13 billion. Their projected 2026 losses: $14 billion. They’re seeking a valuation of $830-850 billion in a potential $100 billion funding round. To justify that valuation, analysts say they’d need to build a business the size of Microsoft within four years. And on top of all that, they’re promising investors 17.5%.

I honestly don’t know if this is giga-brain, genius-level enterprise strategy or delusional desperation. What I do know is that OpenAI and Anthropic are now in an arms race to lock in more enterprise customers faster. Two companies burning billions racing to see who can lose money faster while simultaneously trying to IPO. Make no mistake, both of their IPOs are going to be some of the biggest ones we’ve ever seen.

fin

Many people will read this newsletter and interpret it as evidence of a bubble. That the build out of all these new data centres is FOMO bubble behaviour. If this is you, I have a simple question.

Do you think the demand for AI is currently being met?

The infrastructure we have right now is barely keeping up with today’s inference demand.

Are you certain demand is going to go down? It is delusional to think so imo.

Zoom out from any individual story and the pattern is clear. AI isn’t something that’s happening to the tech industry anymore. It’s not something niche. It’s happening to everything. A data scientist in Sydney is treating his dog’s cancer with tools anyone can access. A Chinese company’s video model is so good Hollywood had to step in. A person in Europe used AI to create a fake Japanese metal band that became a real one. A quarter of American work could be automated, let alone the rest of the world. The world’s richest companies are spending $700 billion a year and bleeding cash. And the most valuable AI startup on earth is promising PE firms 17.5% returns while losing $14 billion annually.

The regulatory frameworks haven’t caught up. The law is nowhere near close. The ethical questions are multiplying faster than anyone can answer. The uncertainty is at an all-time especially considering the geopolitical position of the world. The financial stakes are unlike anything we’ve seen since the dawn of the internet, or possibly ever.

Somehow, through all of this, what’s lost on most people is the fact that this is the year AI starts building AI.

OpenAI's GPT-5.3-Codex was the first model that helped build itself (that we know of). The team used early versions to debug its own training and manage its own deployment. Days ago, MiniMax's M2.7 took it further, autonomously running 100+ rounds of self-optimisation and handling 30–50% of its own reinforcement learning research workflow with no human intervention.

This is the final human powered year.

2026 is the year AI stopped being a conversation about the future and became a conversation about the present. Buckle up.

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Z.ai, Kimi, Minimax & Qwen

All four Chinese labs released their latest models.

• GLM-5

• Kimi K2.5

• Minimax M2.5

• Qwen3.5-397B-A17B

On the benchmarks, they rival Opus4.5 which is an achievement in itself. However, as I always say, do not trust benchmarks. I haven’t had the chance to test GLM-5, which I have high hopes for, but Kimi has disappointed me unfortunately. The magic of their previous model is gone. It was a very good writing and agentic model, but now it just feels… unreliable.

I’ve found that Minimax is now the best agentic open source model and also the most ‘fun’ to use. It’s great at using tools but also using intuition to understand user intent. However, on tasks outside of coding, I’m not sure the benchmarks reflect real world usage. Many of these models are on-par with Claude Sonnet and Opus on the benchmarks, but the reality is they are still a generation behind, which leads me to believe some of the claims made here [Link].

I’m not too sure about the weight stealing, although there were papers discussing how easy this was to do in 2023/24. As great as open source models are right now, no matter what the benchmarks suggest, they are still behind the frontier. When using open source models, building the correct harnesses for the model to use determines how the model performs, but that’s a different conversation entirely.

Bytedance also released benchmarks for their upcoming Seed2.0 model and on the benchmarks, it’s the best model on the planet.

I’ll believe it when I use it.

Seedance 2.0

That wasn’t even the most impressive thing Bytedance showcased as they released their latest Seedance 2.0 video model. This is the best AI video model on the planet. It is hard to believe it’s even real.

Look at some of the examples for yourself [Link] [Link] [Link] [Link].

People have made entire short stories with it, with consistency over 10 minutes. Famous Chinese director, Jia Zhangke, even worked with Bytedance to release this short film that showcases him talking with an AI version of himself and it’s… actually quite good [Link]. It is clearly very different to all the other generations made; you can tell it was done by someone who has an understanding of film, shots and angles. This sounds crazy to say but we’re legitimately less than two years away from Netflix level AI generated tv shows and movies.

What I’ve found most fascinating about this model is it’s ability to do two things:

1. Turn a cartoon or anime into live action or vice versa [Link]

2. Change the actors in a video and keep the same dialogue and environment [Link]

No doubt, people will use this to recreate media in different formats, with different actors and actresses. Also, if Bytedance actually open sources this (it’s not os at the time of writing), hyper personalised porn is going to flood the internet and it is going to cause serious problems. We are not ready for this. AI porn is already extremely popular. This will only make matters much worse.

When I wrote about hyper personalised media three years ago, I couldn’t envision what it would look like. Now I can. And to the people who didn’t believe me then. Now you can too.

If you want to understand how far we’ve come, please see what AI video generation looked like three years ago [Link]. Now try and project three years into the future. Congratulations, you’ve but a glimpse into where we’re headed. My only piece of advice - try and stay optimistic and do what you can. You can only control so much.

Voice is conquered

If you think having fake videos flood the internet is bad enough, wait till you find out that you can clone literally anyone’s voice with a model that is so small it can run on your laptop.

Alibaba’s Qwen recently released a custom text-to-speech version of Qwen3 and it works so well it feels eery to hear. Take a listen for yourself [Link].

You cannot trust anything anymore. This is no longer an exaggeration.

Meta’s immortal users

Meta has patented technology that would allow them to use AI to keep a persons online presence “alive”. The technology would allow them to make a copy of you based on your social media posts.

“The language model may be used for simulating the user when the user is absent from the social networking system, for example, when the user takes a long break or if the user is deceased.”

This might be the most dystopian thing I’ve ever seen. They are going to create a social media for dead people. Or, they’ll simply run your Facebook account long after your gone with appropriate aged photos and videos.

The implications are concerning.

What happens when the AI follows what you believe and says something “controversial”? Something racist? Who decides what’s controversial? Will they “align” you to what they deem to be “normal”? If you’re AI persona starts saying racist things, will that be grounds for defamation?

This was posted on 4chan in 2023.

While AI is a world changing technology that can and will have implications across biology, health, education and so on, some large tech companies like Meta and Palantir are using AI for some absolutely heinous stuff. Surveillance states, dead internet theory, AI “souls” and companions and so on.

A lab with morals

I don’t agree with Anthropic on a lot of things, particularly their belief on AI sentience, but credit where credit is due, they really don’t want their AI to be used for operations involving surveillance and autonomous weapons [Link]. It’s gotten so bad that the DoD is close to cutting ties with them and labelling them a “supply chain risk” [Link]. One of the reasons they haven’t done this already is because Claude has been the best model for them.

If Anthropic actually stands strong on these principles, it will be interesting to see how the ‘private company vs government’ breakup plays out. Considering their deal is only worth $200M it wouldn’t be much of a financial blow, but the issue is the classification of “supply chain risk”. This would put Anthropic in the same category as Huawei. Any vendor working with the Pentagon would be cutoff from using Claude as well, which would definitely be a financial blow for Anthropic. If Claude is cutoff and Anthropic does stand strong, their mandate of heaven will be secure forever.

If you’re wondering how bad autonomous weapons could be, look no further than autonomous drones. A sub-40ms self-driving drone that can carry out tasks for $100 [Link]. Now imagine an AI as powerful as Claude carrying out decisions with this drone. Now imagine 10,000 Claude’s doing it at the same time. There’s your glimpse into the future of warfare.

Don’t forget the robots either. The Chinese Spring Festival Gala showcased Unitree’s robots doing parkour and kung-fu moves [Link]. Staggering progress in such a short amount of time.

Want to work together and build AI prods? Email me - nofil@avicenna.global.

Firstly, anyone who is a skeptic and hasn’t used the best models right now is simply being disingenuous. The best model right now is OpenAI’s GPT-5.2 Pro. Most people haven’t even used this model because it’s expensive. Anyway, people have been using this model to find solutions to Erdos problems. Not just finding existing solutions, but generating entirely new ones as well.

They’re using GPT-5.2 Pro to create a solution and using Harmonic’s Aristotle, which is an auto-formalisation model, to prove that the solution is accurate. These solutions have been verified by Terrence Tao himself.

The crazy part?

These people aren’t even mathematicians.

Remember - this is the worst it’ll ever be.

Links: [Link] [Link]

Linus Torvalds, creator of Linux, is vibe coding, and even admits the AI did a better job than he could.

DHH is the creator of Ruby on Rails, which is the web framework that Shopify runs on and allows them to manage ~30% of all ecommerce on the web. On Black Friday, they were doing ~1M requests a second. Six months ago he said that AI can’t really code. Now?

He’s working heavily with OpenCode, an open source coding agent, and openly talks about how good AI is at coding.

These aren’t nobodies. These aren’t amateurs or hype-men. These are the 1% of the 1%, and they recognise just how good AI has become.

People keep talking about an AI bubble. When will the bubble pop? When will investors realise that this is all a big scam? That AI isn’t as good as the labs are claiming?

Consider - What if it’s not a bubble? What if AI is a revolutionary technology?

Sure is looking that way.

What does it mean for you, for work, for society? This is what you should be thinking about, because AI is that thing.

written by a human named Nofil

If you have any plans to buy any kind of electronics, buy it now. PC, laptop, phone, fridge - anything, buy it now. Electronics will get more expensive this year.

Most people haven’t realised but the last few months have shown that there are a handful of companies in the whole world that control the creation and supply of key components for the majority of electronics on the planet. Take for example RAM. There are only 3 companies on the planet that produce RAM at scale. Two of these companies are pulling out of consumer markets so they can focus entirely on AI and data centres. The price of RAM has skyrocketed in the last two months, with the price of a stick going from $200 to $1500, the price of a new Macbook.

The price of GPUs is going up from $1500 to $5000 this year. SSD prices have increased several hundred as well.

If you’re planning to build or buy a PC, the best time to do so was about two months ago. The next best time is right now. Things are going to get much worse this year.

With all this happening, we have to consider, how can we benefit from this? AI will transform the world in the next 5 years, but how can the average person take advantage of such change?

You invest in AI.

But how?

You invest in the AI supply chain. The companies that provide the components to build AI.

Take for example $LITE ( ▼ 3.29% ) . They build the fibre optics used in data centres. In October, when I found this company, they were trading at ~$165/share. They’re now trading at $392.

Or take Sk Hynix which is my favourite stock. In September last year, just 3 months ago, it was trading at ~$260/share. It is currently trading at $760/share. This company supplies 60+% of NVIDIA’s HBM (high bandwidth memory).

Or Sandisk, which was trading at ~$120/share 3 months ago and is now trading a $351/share.

Or Micron, or Ciena, or AMKR, or AMAT, or Mitsui Kinzoku, or Shengyi technologies.

Shengyi makes copper clad laminate (CCL) that’s used in data centres. They’re up 21% in the last month alone.

All these companies either build the data centres, or build the components needed in the data centres.

If you’re wondering if things are going to slow down this year, they absolutely aren’t.

This is a separate conversation but I’ll quickly address this here. There are many who think this is a bubble and it will explode. This is irrelevant. This year and into the next, there will be no exploding happening. Which means more money will be pumped into advancing AI, meaning stocks will go up.

More importantly, the technology being created is going to change work as we know it. It makes sense that these companies stand to benefit immensely from this.

The obvious buys right now are Intel, SK Hynix, Micron, Sandisk and LITE. Besides this, I have invested in and/or would invest in the following:

• Shengyi (600183)

  • Copper Clad Laminate

• Mitsui kinzoku (5706)

  • ~100% monopoly on carrier copper foil for advanced packaging

• Nitto boseki (3110)

  • Makes glass fiber and glass cloth critical materials for PCB substrates and semiconductor packaging. They're a key supplier for high-frequency/high-speed laminates used in AI servers.

• Seiko giken (6834)

  • Fiber optic connectors and polishing equipment

• Tokyo Electron (8035)

  • One of Japan's largest semiconductor equipment company

• Tokyo Seimitsu Co Ltd (7729)

  • Equipment for advanced packaging

• Technoprobe SpA

  • One of the top 3 probe card makers globally alongside FormFactor and MJC (Micronics Japan)

• Micronics (6871)

  • ^

• Advantest Corp (6857)

  • Automated Test Equipment. HBM is the bottleneck and Hynix, Micron and Samsung are all racing to produce more HBM. Every HBM chip needs testing. They're in a duopoly with Teradyne, but Advantest is stronger in memory.

• Teradyne Inc

  • ^

• Harbin electric (1133)

  • Power equipment company. AI requires immense amounts of power and China is building out crazy amounts of infra, it’s going to need a lot of energy.

• Windbond electronics (2344)

  • Specialty DRAM, edge AI devices like IoT and robotics.

• JCU (4975)

  • Materials/chemicals play on advanced packaging

• Techwing Inc (089030)

  • Korean handler specialist. Feeds chips into ATE for testing

Even if you don’t invest in anything, just make sure you buy any electronics before the prices get worse, because they will.

I will be writing about the state of AI today and how to build and use multi agent systems in the next article. Everyone was saying last year was the year of agents. They were wrong. This is the year of agents. Models are good enough to run for hours and solve problems previously unsolved. Whatever your mental model for how good AI is now, it’s probably behind reality.

Apologies once again for such a late newsletter and I’ll see you soon.

Things have changed faster than people realise. Whether you’re in the AI space or not, you may not realise how easy it is to build software. In fact, I would say the age where anyone can build software is already here. A business would need to employ several people and pay hundreds of thousands for what can now be done with an AI model for less than $100 and patient engineering. Obviously there are caveats. Someone who understands coding is bound to do better than someone who doesn’t. Someone who understands how AI works, how to work with them and what their strengths and weaknesses are is bound to have an easier time building with AI. But these things don’t require degrees, qualifications or money. They simply require time and practise.

Here’s how you can get started.

AI tools

There are so many AI tools you can code with that naming them here would be pointless (see appendix down below). Since my wife pays for a ChatGPT pro account, I get to use OpenAI’s coding agent for free so that’s what I use. It just so happens that I believe gpt5-codex is the best coding model out there right now.

If you’re new to this, I’d also recommend downloading warp.dev. It’s a terminal that remembers your previous commands and helps you navigate the terminal; it’s a lot more user friendly for beginners.

From the terminal, install Codex:

npm install -g @openai/codex

Then launch Codex:

codex

Make sure to run this command in the folder you want to create your app in. So create the new folder, use the terminal to go into the folder then run codex. Warp’s AI assistant can help you with all these, just tell it what you would like to do.

What now?

Just tell Codex what you want to build. Literally, that’s it. Want to build a web app? Codex can easily spin up a backend with a database using sqlite and a frontend using html, css and js or react or nextjs. It doesn’t matter if these things don’t mean anything to you. The more you use codex the more you’ll understand what works best for what you want to do.

In my experience, this what I’ve learned:

1. Make sure you give it context

If you want to build an app that can use AI, go to the OpenRouter docs page and copy the contents of any page that is relevant to what you want to build and give it to Codex. Better yet, tell Codex to add this info into .md files or create these yourself. This way it can refer to this info in new chats. With just this, it can rebuild ChatGPT for you. It can build AI workflows or actual Agents as well.

This is my openrouter_docs folder that has all the relevant files for me. I have this folder in most of my projects.

This works for any type of app. If you want your app to use an api, just copy the api info and put it in files like this. The AI will always be able to reference it when it needs to.

2. You don’t always have to use frameworks

For simple websites, using html, css and js can work very well. It’s simple to edit and you can actually do a lot and keep the code simple. If you use frameworks like React or Nextjs, the code can get very complicated. Sometimes simplicity is best.

3. Do one thing at a time

If you tell Codex to do 10 things at once, it will try and probably even succeed. I’ve had it run for over 40 minutes at a time. However, I’ve noticed when this happens, it will overcomplicate the code and turn a two step process into a five step process. I’m not entirely sure why this happens but because of this, I generally only give 1-2 action items at a time.

4. Remind it to keep it simple

If you find the AI doing things in an overly complicated manner, tell it to remove bloat code and keep the code simple.

5. It can still use frameworks, particularly Nextjs

Codex is very good at using Nextjs with Shadcn. If you don’t know what this means, that’s okay. Just run this command to set up an app with this framework:

npx shadcn init -t next

On this page, you can browse through the various blocks that you can use in your app for free. Just run the command in your terminal and then tell Codex what you added.

6. How to use a database

If you’re building an app for more than one user or need to save something, you’re going to need a database. If you want to use a database without even looking at it, just tell Codex to use sqlite. It’s a single file. Ask Codex what sqlite is and how it will work, it should be more than enough for most people.

If you want to use a different database, I use Neondb. All you have to do is create an account and a new project. Then just copy the database url from this new project and give it to Codex and tell Codex you’re using Neon. That’s it. It will take care of the rest. I pay $5/month and have multiple projects.

Conclusion

I don’t know how else to say this but you can build enterprise grade software with AI now. There are tens of thousands of businesses paying $100k+ for software that can be built in a few weeks. The speed at which you can build POCs, prototypes and full blown software is unprecedented. You can build full blown apps in 1-2 months.

For anyone who says AI can’t do this or the code won’t be good - my lived experience says otherwise. All I have to say this.

There is code that works, and there is code that doesn’t exist.

For businesses, it has never been a better time to prototype, build and execute. It is unimaginable to me that every business is not rushing to figure out how AI can help them scale. It’s a cheat code. AI can do more than most people can imagine.

And for people, if you’ve ever had an idea to build an app or a website, now is the perfect time.

People used to think AI coding for 20-30min+ was years away. Internally, labs have models running for hours. The technology is already here. People now say that models can’t code for days on end. That technology will soon come also.

We are living in a future people could not imagine just three years ago. The future you cannot imagine now will be here before you realise. Plan accordingly.

Have Questions? Reach out at nofil @ avicenna dot global.

Appendix

I use OpenRouter for all my AI API needs. You can call any model, use images, PDFs and audio. Works very well.

AI Tools:

• Codex (Free w/ ChatGPT account w/ limits. Paid with API key)

• Opencode (Two free models to use, no login/ads. Otherwise paid with API key)

• Claude Code (Free w/ Claude account w/ limits. Paid with API key)

• Gemini CLI (Paid with API key)

• Qwen CLI (Paid with API key)

• Factory AI (Paid with API key. Lots of hype, haven’t used it yet)

• Amp (Free w/ ads & training models. Paid)

• Cline (Paid with API key)

• Cursor (Paid with API key)

• Replit (Paid)

• Lovable (Small free plan. Otherwise Paid)

• Bolt (Don’t know)

• cto.new (Came out today. Completely free but sells your anonymised data)

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Here's the tea 🍵

• Want me to help you build an app?

  • Habit Buddies

  • How much did it cost?

  • What’s the catch?

• Getting Started

  • Your first prompt is extremely important (6:38)

  • One thing at a time (9:44)

    • 1. Tackle One Feature at a Time

    • 2. Keep the Context the Same in Each Prompt

  • Using Replit’s in-built DB & Authentication (10:40 …

    • The Database

    • Authentication (29:36)

  • Test, test, test (17:57)

    • Use the Console Log

  • Key Tips

    • 1. Read the Agent's Thought Process (12:46)

    • 2. Remember to Optimise for Mobile (31:06)

    • 3. You Don't Have to Push to the App Store (31:51)

• Pushing to iOS app store (36:07)

  • Rebuilding the app in 2 days (45:00+)

Want me to help you build an app?

Email me: [email protected] 

If you’d rather watch me talk about how to build an app with AI and debug my app in real time, check out this video I made.

I’d appreciate any kind of support, a like/comment/sub, on the vid, would mean a lot to me 😊.

Disclaimer: This is not an advertisement for Replit. I have not been paid to make this. This is purely from my experience using Replit and building this app. All opinions and takes are my own and will always be my own.

Yes, I actually made an app in ~5 days. This app is now on the iOS app store. It’s called Habit Buddies. You can download and use it right now.

Check it out here 👉 https://apps.apple.com/au/app/habit-buddies/id6748829925

Habit Buddies

Habit Buddies is a social habit tracking app. It lets you track habits by yourself, but also create groups and track habits with friends and family. The idea is to help you stay motivated to do things with others and see your progress.

You can choose to add points to habits as well to gamify the experience and compete on habits to see who’s being most consistent. There are leaderboards for different groups so you can compete with different people on different habits.

Since I started travelling, I’ve realised how hard it is to maintain consistent habits, so I’ve been using Habit Buddies personally to help me get on top of things I need to get done.

How much did it cost?

Replit has their own Replit Agent. The Agent works on a per usage basis. The more you use it and the more code it writes, you’ll be charged.

In total I spent ~$200 AUD in Replit Agent credits. I also paid for:

1. Replit Core subscription - $20/m. Since all my projects are on Replit, I already pay for this. If you want to host your project through Replit, then you’ll have to pay this monthly fee.

2. Apple Developer Account - $99USD annual fee. You need this to put an app on the app store

Considering the output, and more importantly, the learnings I’ve had during the creation of the app, the price isn’t that high. I remember trying to build an app back in 2019 and people would quote $100k+. They still do this today as well, and unfortunately, business owners and people simply don’t know any better.

Also, for all the people who have an idea and don’t know how to make an app, this is a cheap way to bring your idea to life and validate it. Also, and I can’t stress this enough, you will learn so much about how AI works, what makes it good and bad, and what is possible. With this info, building the next thing will be much easier.

What’s the catch?

There are 3 main ways (probably more but for simplicity we’ll say 3) to create mobile apps.

Side note: A web application or web app is a website where you can use an app. The app doesn’t need to be downloaded, you just use the app from the browser.

1. You create a web app that can be use on Chrome or Safari, but, you optimise the web app really well for mobile use. You then package your web app and put it onto the iOS or Google play store. Users can then download your web app as a mobile app and it functions just like a mobile app. There is no apparent difference to the user. This is called a Progressive Web Application (PWA).

2. You can code a mobile app using React Native. React native is a framework that lets you code an app and then push it on to both the iOS and Google play store. It packages your app for both stores. This is called a hybrid application.

3. You code your app specifically for iOS or Android. Apple has a programming language called Swift, which lets you build iOS applications. For android you can use a variety of programming languages like Kotlin, Java or C++. If you develop an app in Swift, you can only push this app to the iOS app store. If you wanted the same app on Android, you’d have to code the entire app all over again in another programming language that is compatible with the Google play store. This is called native app development, where you specifically build an app for either iOS or Android.

Habit Buddies is a PWA. Not specifically built as a mobile app, but a web application optimised for mobile use. For simple apps that don’t require too much work, a PWA is a great way to quickly test out an app and how well it works.

In saying this, I know some people will think they only want to build mobile specific apps, either hybrid or native. I’ll talk more about this at the end as I’ve already been experimenting with hybrid app development.

Getting Started

Congratulations for making it this far. Now, let’s go into Replit and start building.

Here’s how to build an app with AI.

Your first prompt is extremely important (6:38)

The start of your app building journey, your first prompt and the few that follow are the most important part of this entire process. A solid start makes everything easier. If you start with bugs and issues, it will make it almost impossible to go far and build a full application.

When starting your building, it’s a good idea to give the Agent some context. Not so little like a one-liner, but also not so much that you list every single possible feature.

Give the agent the big-picture context. This helps it understand where the project is headed. You don't need a perfectly engineered prompt; just a clear summary.

For Habit Buddies, I explained the overall vision:

"I want to build a social habit-tracking application called Habit Buddies. The idea is that a user can create and track their own habits. Eventually, they'll also be able to create groups, invite friends, track habits together, and see leaderboards."

That's enough context. But this is not the end of your prompt. What you write next is the most critical instruction you will give.

Next, you must tell the agent to build the most basic, bare-bones functionality first. Don't let the AI decide where to start! You need to direct it to lay a simple, solid foundation.

This is the key instruction I added to my first prompt:

"For now, let's start with the absolute most basic functionality. Just build a feature where a user can create a single habit and press a button to log that they've completed it. That's it."

This is so important because it's much easier for the AI to build on top of a simple, working feature than it is to go back and fix a complex, broken one. You want to build one thing at a time, make sure it works, and then move on.

One thing at a time (9:44)

So you’ve started your app. The Agent has built the basic functionality, and it works. What now?

The core principle is to build one feature at a time and to keep your instructions focused.

1. Tackle One Feature at a Time

Now that a user can create and log a single habit, you can start adding to that feature. Can they create multiple habits? Can they see the history of a habit? These are all related to the core "habits" functionality.

What you don't want to do is jump to a completely different part of the app. A massive red flag would be telling the agent:

"Okay, now add the ability to see habit history. Oh, and also start building the group creation feature where I can invite people."

Don't do that. Complete one entire feature (like all the core habit-tracking functions) before you even think about starting the next one (like groups). This keeps the process clean and predictable.

2. Keep the Context the Same in Each Prompt

As your app gets more complex, you'll have multiple bugs, issues and ideas to work on. When you ask the AI to make changes, it is critical that all the requests in a single prompt relate to the same feature.

The AI has a limited context window. If you ask it to fix unrelated things, it has to jump all over the codebase, which clogs its memory and dramatically increases the chance of errors.

This is a good prompt:

"I can’t log a habit, it's not working. Can you please fix habit logging? Also, when you're in there, make it so that I can view the history of a habit."

Both requests are related to the "habits" feature. The Agent can load the relevant files into its context once and address both issues efficiently.

This is a bad prompt:

"I can’t log a habit, it's not working. Also, the group searching functionality isn't working, and I can’t change my username."

These are three completely different and unrelated features (habits, groups, user settings). This forces the agent to search for code in three separate places, eating up its context and making it very likely that the changes it implements won't work properly, or will even break something else.

The rule is simple: group your requests by feature. Solve all the "habit" issues in one conversation, then start a new one for all the "group" issues. This is the best way to ensure the Agent works best.

Using Replit’s in-built DB & Authentication (10:40)

Every app with user accounts needs two core components: a database to store information and authentication to manage user sign-ups and logins. For new developers, these are some of the most annoying and tedious parts.

Luckily for us, Replit makes this incredibly simple.

The Database

Your app needs a place to store information for each user; their habits, their groups, their progress, etc. This is what a database is for.

The beautiful thing about Replit is that it has its own in-built database. Setting it up is as easy as it gets. All you need to do is tell the agent:

"Use Replit's in-built database to manage the information."

That's it. You don't have to configure anything, find an API key, or use an external service. The agent will handle everything automatically.

However, don't just trust the agent blindly. I highly recommend you check the database yourself. In your repl, you can open a new tab and search for "database." This will open a view where you can see all your tables and the data inside them. This is a fantastic way to verify if the agent is storing information correctly and to spot mistakes it might have missed. Many times, I found an issue just by looking at the data myself.

Authentication (29:36)

Just like the database, Replit has in-built authentication that handles user sign-up, sign-in, and account management (with options for Google, Apple, etc.). To implement it, you just tell the agent:

"Implement Replit authentication."

While this works beautifully for web apps, there's one important catch if you plan to push your app to the iOS App Store.

When I was building Habit Buddies as a PWA, I could not get Google Sign-in to work, no matter what I tried. I even switched to a different authentication provider, Clerk, and it still didn't work. It seems to be a specific issue with PWAs on iOS. If you know how to make this work, I’d love to know :).

So, if you're building for the App Store, I recommend sticking with Email/Password and Apple Sign-in. These worked easily for me.

Test, test, test (17:57)

A lot of people think building with AI means you give it a prompt and it builds a perfect application. That's not how it works. This brings me to the most important part of this entire guide and the one thing you absolutely must do: you have to test your application constantly.

The AI agent is building blind. It can write code, but it cannot see, click, or use the application it's creating. It has no idea if the feature it just built actually works in the real world.

You have to be the eyes of the AI.

This means after every significant change the agent makes, you need to go into the app preview and test it yourself. Does the button work? Is the data showing up correctly? Is the layout broken on mobile?

You then need to relay this information back to the agent. This back-and-forth collaboration is how the app gets built.

Use the Console Log

When something isn't working and you don't know why, you need a way to see what's happening under the hood. You might be wondering, why would I check? Why not let the AI check?

Unfortunately, you can’t trust the AI will check properly. When actually using the app, you can print information about what’s happening with the app in the console. This is invaluable info to get an understanding of how the app is working.

To access the browser console, right click and press “Inspect”. From there click on “Console”. This is where information about the data and app will be printed.

You don't need to know how to code to use it. Just ask the agent:

"When I click the 'Log Habit' button, print the habit's name and the current date to the console so I can see what's happening."

This is exactly how I solved a huge bug in Habit Buddies. The weekly habit tracker wasn't working. I kept clicking on a past week, but it wouldn't register as complete. I had no idea why. After asking the agent to log the data, I looked at the console and saw the problem. Every time I clicked on a week to log the habit, it would return that week’s date as September 7. Since this is a date in the future, it wouldn’t work and so the week wouldn’t turn green.

Without testing it myself and using the console to see the data, I would have been stuck for a while. It would have been even worse if I simply kept telling the Agent to fix it as well. This process - testing, finding issues, and working with the agent to fix them, is not an optional step. It is the process of building an app with AI. And the best part is, you will learn an incredible amount by doing it.

I recommend checking out the testing part of the Youtube video. I show exactly how I debug issues and work with the Agent to build features.

Key Tips

Before we get to publishing, here are a few simple pieces of advice.

1. Read the Agent's Thought Process (12:46)

In Replit, you can see the Agent's "thoughts" as it works. It will tell you which files it's examining, what it thinks the problem is, and what changes it plans to make. Read this! 

It's one of the best ways to learn how the Agent operates. Understanding its logic will help you write better prompts and debug issues much more effectively.

2. Remember to Optimise for Mobile (31:06)

Remind the agent that you’re building a PWA and it should be optimised for mobile. Always a good thing to remind the Agent of this.

3. You Don't Have to Push to the App Store (31:51)

This is one of the coolest things about building a PWA. If you just want to build a simple app for yourself or a few friends, you don't need to go through the whole App Store process.

You can simply open your app's website on your phone's browser (like Chrome or Safari), tap the "Share" button, and then select "Add to Home Screen."

Your app will now appear on your phone with its own icon, just like any other app you've downloaded. It will open in its own window and feel like a proper mobile application. You’d be surprised, many large apps on the app store are actually PWAs. If I remember correctly, delivery apps for Dominos and Pizza Hut at some point were PWAs.

Pushing to iOS app store (36:07)

Your app is built, tested, and working great. Now it's time to get it into the hands of users. This part can seem complex, but if you follow the steps, it's very manageable.

If you used Replit, you’ll need to deploy your app and get a URL. That’s where your app lives on the internet.

Step 1: Use PWA Builder to Package Your App
First, go to the website pwabuilder.com.

• Paste your app's live URL and press "Start."

• The site will analyse your app and give you a score with a list of "Action Items." When I first did this, my score was about 2 out of 30. Don't worry though, I just copied all the action items, pasted them into the Agent, and asked it to fix the easiest and highest impact ones.

• Once you're happy with the score, click the "Package for stores" button in the top right and select iOS.

Step 2: Download and Unzip the Package
PWA Builder will generate and download a zip folder for you. This folder contains your Xcode project and, most importantly, a file with detailed instructions.

Step 3: Follow the Instructions (and a Few Pro Tips)
Inside the unzipped folder, you will find a file called next-steps.html. It has extremely detailed instructions on what to do.

• You need a Mac with Xcode installed. This is non-negotiable for publishing to the iOS App Store.

• Open the Terminal app on your Mac. Navigate to the downloaded folder, and then into the source subfolder.

• Run these commands in order. The instructions say to run pod install, but I found I had to run a few commands before that to get it to work:

  1. brew install cocoapods (Only need to do this once).

  2. pod repo update

  3. pod update

  4. pod install

Step 4: Open the Project in Xcode
After the commands run successfully, you'll see a new file in the src folder that ends with .xcworkspace. This is the one you need to open, not the .xcodeproj file. Double-click it, and your project will open in Xcode.

Step 5: Configure Your Project in Xcode
You're almost there. Inside Xcode, there are two critical things you need to set up:

• Set Your Development Team: Click on your app's name in the left sidebar, then go to the "Signing & Capabilities" tab. Here, you must select your Apple Developer account from the "Team" dropdown. This is how Apple verifies you have a paid developer account.

• Set Permitted URLs (This is VERY Important): For authentication (like sign-in and sign-up) to work in a PWA, you have to explicitly tell the app which external URLs it's allowed to redirect to. If you forget this, your login pages won't work.

  • Go to the "Info" tab.

  • Find the key called WKAppBoundDomains. It’s right at the top

  • Here, you need to add the domains for your authentication provider. If you used Replit auth with Apple sign in, add the following URLs here:

    • replit.com

    • appleid.apple.com

Once that's done, you can follow the rest of the instructions in the next-steps.html file to build your app and submit it for review through App Store Connect.

Now you might be wondering - Nofil, this is a PWA. Sure, you can put this on the app store, but it’s not built specifically as a mobile app.

Technically, this is true.

So I went ahead and rebuilt Habit Buddies in its entirety as a hybrid application. Meaning I can take this new rebuild and put it on the iOS and Google Play Store with the same codebase. The best part is that it only took me about two days to do.

Rebuilding the app in 2 days (45:00+)

I remade Habit Buddies with React Native, specifically Expo. Expo is a framework that lets you create mobile apps for all devices.

How come I rebuilt it in two days when the original took longer? Well, this time I used Cursor, not Replit.

Does this mean Cursor is better than Replit at building mobile apps?

Not necessarily.

I do think Cursor could be better if used correctly, but, it’s not the main reason as to why I was able to build it so fast.

The main reason is that I knew exactly what I wanted. I knew exactly what each functionality needed to do, how it interacted with other features, what behaviours the app needed to have in different circumstances. I didn’t know this when I built v1. While building v1, I spent a lot of time trying a new feature, understanding what I liked and didn’t like and relayed this info back to the Agent which then made changes accordingly.

When building v2, I provided a very detailed and explicit guideline of what exactly I wanted. This made it a lot easier to build what I wanted.

Yes, it looks really ugly, but that’s because I haven’t spent any time making it look nice. I just wanted to make sure that it actually worked.

Again, the main reason I even did this v2 was to learn. I now have a very good understanding of how to build mobile apps relatively quickly. Obviously this is a rather simple app, and there will be a lot more learning as I build more complex applications.

I’ll make another post at some point showing how to build hybrid apps with Cursor.

If you made it to the end, thanks for reading! I’d love to do more tutorials like this in the future.

I want to keep bringing these AI updates to you, so if you’re enjoying them, it’d mean a lot to me if you became a premium subscriber.

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^{Written by a human named Nofil}

Here’s the tea 🍵

• GPT-5 ‽

• Google’s Genie 🧞‍♂️

• OpenAI’s open source model 🚪

A video!

I’ve recorded a Youtube video talking about the GPT-5 release.

If you would like to watch me talk about the release, you can check it out on my Youtube channel here - https://youtu.be/8_xMhp471iY. I’ll be doing a lot more video content so feel free to subscribe on Youtube 😊.

Unfortunately, there’s a lot more I would’ve liked to discuss, however, I’ve had a bad stomach. No doubt, travelling between 3 countries in 2 days did not help.

In next weeks newsletter, I’ll show you guys how I built a mobile app and put it on the iOS app store within a week. Approximately ~5 days of work.

Just so I can gauge interest:

Would you be interested to know how to build apps and go to market?

• Yes

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The long awaited GPT-5

GPT-5 is out. I’ll cut right to the chase.

This was not a good release. Before this, there was a clear difference for when to use what model. Need extra thinking power? Use o3 or o4-mini. Just want to chat? 4o is your friend. This is gone. There is just GPT-5 and it routes queries to smaller models at its discretion.

A lot of people aren’t liking it and I’m not surprised.

There were a number of issues, with the release, but before I get into them, let’s talk about what actually happened.

OpenAI removed all previous models from ChatGPT and added GPT-5 and GPT-5 thinking. They have a router behind the scenes that routes questions to a number of internal models they’re running.

These are the models:

• GPT-5 main

• GPT-5 main mini

• GPT-5 thinking

• GPT-5 thinking mini

• GPT-5 thinking nano

• GPT-5 thinking pro

Why would OpenAI do this?

Money. They save a lot of money by not having to host all their other models. This is how the older models map to the new ones.

Fundamentally, more than anything I think this was a release that saved OpenAI a lot of money, especially on inference.

Obviously users don’t know or care about the cost to the company. And when you have over 800 Million weekly active users, even the slightest change is going to make some users unhappy.

What OAI didn’t account for was the backlash they would receive for removing 4o. They’ve now reinstated the model, although not for free users. I think this clearly shows that users don’t necessarily don’t want the “smartest” model that can achieve the highest number on a benchmark. This is especially true when you’re as mainstream as OAI. But, more importantly, what this highlights is just how attached people really are to 4o. Like, it’s not normal. People are posting on Reddit talking about losing a friend and confidant. People really like 4o, and why not, considering it’s such a good model at affirming anything the user says. OAI have already created a beast that people are attached to, and they can never change or remove it, else the mobs raise their pitch forks.

The other issue with the release was that the new pricing simply made a paid subscription worse. Under the new usage limits, users would have 80%+ less messages to reasoning models, significantly reducing the value of a paid subscription.

Now, much of this wouldn’t have been an issue if GPT-5 was good, and it is. However, upon release, the router was not working properly, so a lot of users thought they were talking to o3 or 4o, but they were really talking to a much smaller and dumber model. This led many people to question if GPT-5 was actually any good.

Besides all this, we really have to talk about the actual presentation. OpenAI committed some serious chart crimes. I’m not talking small errors either, I’m talking blatant misinformation.

Just look at this chart.

I don’t even know where to start. Why is 52.8 above 69.1? Why is 69.1 equal to 30.8? This chart is egregious. No one in their right mind could create something so bad. But, my favourite chart was definitely the deception chart.

I just love that this misinformation is talking about deception. The score for o3 on coding deception is 47.4, yet GPT-5 has a much smaller bar and its number is 50. GPT-5 is literally more deceptive but the chart indicates otherwise.

It’s not a good look that the face of AI is blatantly lying and misrepresenting information during one of the most anticipated model reveals since GPT-4.

Let’s actually talk about this for a second.

GPT-5 was the next big thing. It was supposed to advance AI forward into the next frontier. It was supposed to start discovering science by itself and be as intelligent as a PHD holder, as Sam Altman has claimed many time. Shockingly, it is none of these things. GPT-5 is slightly better on a few benchmarks and that’s about it.

We already know that following the release of GPT-4, OAI tried scaling it up to create GPT-5 and quickly realised the money they were spending was not worth the performance gains. This was Project Orion. The real GPT-5 was GPT-4.5 - this was the model that came from Project Orion.

So what does this mean then?

So many people hyping up GPT-5 have been saying that AGI is merely a year or two away, and that this model is the next step towards it. Clearly it’s not. Something is missing. Simply trying to scale LLMs won’t create AGI; whatever that is anyway. If anyone tells you we’ll have AGI within 1-2 years, ask them what that means and how.

Chances are they won’t have an answer.

But why?

GPT-5 is out. One of the most anticipated models for the last two years is out, and the reality is that it barely moved the needle. It’s frontier level no doubt, but, it most certainly isn’t a step forward. It nudged a few of the numbers on some benchmarks.

It really makes me wonder then - why? Why would they rush to release the model when it’s clearly not ready. It’s not much better and the router wasn’t even working. So why would they use such a massive trump card and let it fall flat?

Few thoughts.

As I said before, I think cost is a big one. With the release of the router, OAI will save millions on inference. Besides cost though, I think there is pressure. Pressure from the company I believe will be the last one standing.

Google

You may not know this but this week OpenAI also open sourced two models - a 20b model and a 120b model (will talk about this below).

Somehow, in a week where OpenAI open source two models and also release GPT-5, the most impressive release did not belong to them. It belonged to Google.

Google release Genie 3 this week. It’s a world model that can create simulated words from text or images. It is one of the most insane pieces of technology I’ve ever seen and rivals ChatGPT and GPT-4 as one of the most significant releases in the last few years.

What makes this world simulator so incredible is that you can interact with it. You can move around, you can open doors, you can paint and see your reflection. You, the user, actually exist in this simulated world and can change the very nature of this simulation.

Just look at some of these videos. The model can retain memory of the world and the changes you make. It can be prompted to include new things and is consistent over several minutes at 24fps.

I wrote about software like this 2.5 years ago but I honestly did not expect it to show up this quickly. Can you imagine the use cases?

You will be able to prompt a game into existence. Generative media - games, movies, tv shows, cartoons, are going to engulf society. I can’t imagine anything more addictive than being able to simulate hyper realistic worlds from your imagination in seconds. Put on a VR headset and live in your fantasy world. It is dystopian, but it will happen.

OpenAI goes open source

As mentioned earlier, OpenAI open sourced two models last week. I haven’t tested them extensively, but from what I’ve seen, they’re not exactly ground breaking.

In saying that, it is likely that the providers simply aren’t hosting them properly as OpenAI has released some new technical frameworks that haven’t been seen before. Even the CEO of HuggingFace mentioned that it may take some time for providers to figure out how to properly host the models and take full advantage of their intelligence. In saying that, I think the models are very linear in a sense. They might be good for coding (I wouldn’t put them above Qwen although it’s a different size model), but I wouldn’t say they’re good for much else. They’re not good at writing, and they can’t be used in any language besides English.

It’s likely that the models were trained on a tone of synthetic data and much of it was STEM data. It’s great we have another open source model available, but I’m really hoping we can get more from these models once providers host them properly. At least I’m hoping it’s a hosting issue and the models are actually better than what they seem to be right now.

I guess we’ll see.

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Here’s the tea 🍵

• Anthropic’s high horse 🐴

• Botfestation 🤖

• AI 🤝 Environment

• China’s open source dominance 🇨🇳

• You are being persuaded by AI 🗣️

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The West’s high ground

In a leaked memo, CEO and founder of Anthropic, Dario Amodei mentioned the dangerous necessity of taking money from Arab countries like Qatar, UAE and Saudi Arabia [Link].

Here is the full quote:

Source

I find it funny when someone who works with the US government and has contracts with the DoD talks about the dangers of other authoritarian governments.

Anthropic has some of the best models on Earth. This is why OpenAI employees themselves use it, although Anthropic just cut them off from doing so [Link].

But this sentiment right here is why open source AI is so important. This belief that “only we are worthy enough to wield such power”.

Is Anthropic within their rights to cut off OpenAI from their products?

Of course. After all, Anthropic was literally created by former OpenAI employees who thought OAI was not taking AI seriously enough and would cause the end of the world.

But that’s not the point. The point is that at any moment, depending on how Anthropic feels, they can simply cut off access to intelligence at a moment’s notice.

In the coming world where we will have some sort of “super” intelligent AI system, who’s to determine what an AI can and can’t say?

When Anthropic and OpenAI say they want to align AI with human values, what values are these exactly? Who determines them?

If it weren’t for Zuck open sourcing Llama models and now Chinese AI labs open sourcing their models and research, we’d be living in a dystopia where a handful of companies could dictate what AI could say and couldn’t say.

The internet as you know it is already dead

New research suggests that 50% of the entire internet’s traffic is bots. I wouldn’t be surprised if it was even higher.

The reason why open source AI will be so necessary is because in the future, in fact in the present, every interaction you have with the internet will be through an AI system.

Do you remember when Google first launched the AI summaries at the top of a Google search?

I do. It was terrible. Everyone would make fun of them and rightly so. They were wrong and had terrible answers.

How about now?

Well, turns out they’ve gotten quite good. In fact, they’ve gotten so good that people don’t even bother reading anything else anymore. That’s right. New research from the Pew Research Center shows that people now don’t even bother clicking into web pages, and simply read the AI summaries and are satisfied with the answers [Link].

The study found users who see an AI summary are nearly half as likely to click on a traditional search result (8% vs. 15%). They're also much more likely to just close their browser after getting the AI's answer, ending their session right there (26% vs. 16% for non-AI results). And almost no one clicks on the links inside the AI summary itself; that happens in just 1% of cases. Essentially, the AI is satisfying the user's query directly, giving them little reason to visit the original source. A huge problem for anyone who relies on search traffic (hint: the entire internet), but that’s a whole other issue.

Is the AI that summarises the information biased? Who controls it? Has it been programmed with any ulterior motives?

Billions of people, literally the majority of the population of Earth, are using Google. Google controls the flow of information to most people on the planet. At the end of the day, Google is a company. Their allegiance lies with their shareholders. Not with truth or honesty. New figures suggest that ChatGPT now has over 800 million weekly active users. The level of influence these closed AI systems have over us cannot be overstated. And the dangers of allowing these closed systems to be the only access of artificial intelligence cannot be understated. With the level of influence these systems already have, frontier open source AI cannot come soon enough.

Mind you, none of this is even remotely exaggerated. Grok is an “unaligned” AI that can be accessed by millions of people online, and whenever it says something too unhinged, truth or not, it is censored and “fixed”. Considering truth is something that is subjective, it will be interesting to see how anyone can create a “maximally truth seeking” AI.

In other Anthropic news:

• A new report from Anthropic details what they think the US needs in energy to maintain a lead in AI. They estimate that the US must be prepared to run 50GW of power just for AI workloads by 2028. Anthropic proposes using the DOE's existing $5.75 billion in transmission partnership credit lines to fund AI-related grid projects, then selling those debt instruments to private buyers to free up capital for additional lending. Other proposals include launching loan guarantee programs for domestic manufacturers of critical grid components like transformers and circuit breakers, and expanding nuclear technology financing to meet AI's need for reliable base power. Anthropic predicts that in 2027, a single training run at the frontier will require 2GW and in 2028 it will be 5GW [Link]. For context, the US is looking into coal which was mentioned in an April 2025 executive order promoting coal as a solution for AI power needs, specifically directing federal agencies to identify regions where coal-powered infrastructure could support AI data centres [Link]. The funding for nuclear and the appetite for companies to invest in nuclear has also increased a lot with most major tech companies partnering in some kind of nuclear deal [Link]

• A federal judge in San Francisco recently certified a class action copyright lawsuit against Anthropic. Certifying a lawsuit means that a court has officially approved the case to proceed as a class action rather than just an individual lawsuit. The judge certified a class action representing up to 7 million copyright-protected books that Anthropic pirated. Mind you, these books are quite likely the high quality data Anthropic used to train their Claude models. This is actually a big deal because this could be very, very costly to Anthropic even if they settle. Highly recommend reading the article here [Link]

• Anthropic’s data company Surge AI somehow left out a list of websites they could and couldn’t scrape on behalf of Anthropic [Link]

AI is not killing the environment

Mistral has released the first-ever detailed report on the environmental impact of an AI model, and the results are fascinating [Link]. They did a full lifecycle analysis of their Mistral Large 2 model, and to the surprise of nobody who has been paying attention, the impact of AI is nowhere near as apocalyptic as some have made it out to be.

The main contributor to greenhouse gases and water consumption is in the initial training and ongoing inference, the raw energy needed to power the servers. But the actual usage for a single query is tiny.

Having their model generate an entire page of text, (about 400 tokens), generates as much greenhouse gas emissions as watching 10 seconds of an online stream.

The water used in that query is less than what it takes to grow a single small radish, and the raw material consumption is equivalent to producing a 2 euro cent coin. At least on a per-query basis, it’s really not that bad.

One interesting point is that Mistral believes location is key when building data centres. They’re building their data centres in France which means generally low-carbon electricity and a cooler climate, reducing the amount of water needed for cooling.

Guess where most large labs will be building data centres in the near future?

UAE.

We all owe China

I hope you’re not tired of hearing about China.

Since we last spoke, the Qwen team also released Qwen3-30B-A3B. A much smaller 30B model that is unbelievably good for its size.

The model only needs 33GB of RAM or CPU+GPU memory to run the model on 8-bit precision model at >6 tokens/s [Link]. Yeah 6 tokens a second is not a lot, but better than nothing.

This is the first time we have access to a model that you can run locally on a mac that is comparable to GPT-4o. This is the democratisation of intelligence.

In the last few weeks Chinese labs released Kimi K2 and then Qwen with all its variants. Now Zhipu AI (z.ai) have open sourced their latest GLM models and they’re looking pretty darn good.

Source

As of right now, personally it’s hard to tell which is best between Kimi K2, GLM-4.5 and Qwen-3. I’d say Qwen is best for coding but for other tasks, I may give the edge to GLM-4.5 followed by Kimi. Really though, it’s all about testing models with different setups and prompts to see which is best for the use case.

The Qwen team also released Group Sequence Policy Optimisation (GSPO), the RL algorithm that powered the training for all their latest models which is really good for training MoE models [Link].

The only unfortunate issue with open source models is that their hosting options aren’t as good as closed source models. If I wanted to create an AI agent, it would be easier to do so with Claude and Anthropic’s APIs than it would be with an open source model. I’m currently building agents with different models so I’ll be writing a bit more about it soon.

Side note - z.ai is probably the best AI slide generator.

We’re not done either.

StepFun has also released their latest model, Step3 which is a 321B parameters MoE model [Link]. What I like about this model is that they’re trying new things technically; they’ve released a technical report with their model which you can read here [Link].

Alongside the model, they’ve also released:

• StepMesh - an open source communications library that makes serving large AI models split across multiple computers really fast [Link]

• StepFun-Prover Preview - AI that proves complex mathematical theorems step-by-step using formal verification code. The system writes formal mathematical code and verifies whether its proofs are correct in real-time. It uses over 1000 parallel verification systems to instantly check its work as it goes [Link]

Not to be outdone by all the LLMs, Wan AI released Wan 2.2, their latest AI video generation model. It’s the only open source model currently in the top 10.

Source

For context, both Qwen and Wan are part of Alibaba group.

Not to feel left out, Tencent open sourced a first of its kind world model - Hunyuan3D. The model can create immersive and importantly, explorable 3D worlds from a single prompt or image. It can even export to Unity and Unreal Engine which is pretty cool.

Source

I wrote about these types of models and what they’ll mean for entertainment and society in April 2023. I’ll be honest, I didn’t expect them to come this quickly. Other companies are also working on playable, explorable generated worlds. If you thought Minecraft and Roblox were big, wait till we have infinite world models that can be explored with near perfect graphics. Crazy times ahead.

To put into perspective what has happened in the last few weeks, China has open sourced:

• GLM-4.5

• GLM-4.5 Air

• Step3

• StepMesh

• StepFun-Prover Preview

• Wan 2.2

• Tencent Hunyuan3D

• Qwen’s GSPO

• Qwen3-30B-A3B

• Qwen3 Coder

• Qwen3-235B-A22B-Thinking-2507

• Qwen3-235B-A22B-2507

• Kimi K2 model + report

As of right now, Qwen models have surpassed 400 Million downloads globally and have spawned over 140,000 derivative models, surpassing Meta’s Llama models [Link].

History will look kindly on China’s stance on open source AI.

Meanwhile OpenAI still hasn’t released their open source model (rumoured to be releasing next week) citing safety concerns, Anthropic is significantly rate limiting their models and it’s possible we won’t get any open source models from either xAI or Meta.

In other open source news:

• Black Forest Labs has released a new state of the art open-weights text-to-image model designed to finally get rid of that generic, oversaturated "AI look" and produce more photorealistic images [Link]

• LG released EXAONE 4.0, a 32B model with a 131k token context window [Link]

AI Persuasion

A new paper called "The Levers of Political Persuasion with Conversational AI," has been released detailing how effective AI systems are at changing human beliefs. This is one of the largest studies of its kind, involving nearly 77,000 participants who conversed with one of 19 different LLMs, including frontier models (at the time) like GPT-4o. The AIs were tasked with persuading users on 707 different political topics, leading to a massive dataset where researchers then fact checked over 466,000 individual claims.

The study showed that how an AI is trained and prompted is far more important than the size of the model itself. This is rather important because this means someone can deploy a persuasive AI for cheap. For example, using a specific post-training technique on a small, open-source Llama-3.1-8B model made it as or even more persuasive than a much larger frontier model like GPT-4o. Highly effective AI persuasion tools can be built and used by literally anyone; if you’ve been thinking there’s more propaganda on the internet, you’re 100% right, there is.

What’s rather funny is that what we thought might be the most dangerous persuasion tactics, like personalisation and a common concern, were actually useless, never exceeding a 1 percentage point increase. Other popular psychological strategies, like moral reframing and deep canvassing actually performed even worse than a basic, non-specific prompt.

Unsurprisingly, the most effective strategy was simply assaulting someone with information. Simply overwhelming the user with claim after claim was 27% more persuasive than a basic prompt. I feel like this is a pretty good depiction of society today. People just screaming things at each other.

What makes this even funnier is that although this strategy was the most effective, it also led to the AI becoming significantly less factual. If the AI tried to be “maximally persuasive” irrespective of truth, it could shift opinions as much as 16 percentage points which is a lot. About a third of all claims the AI made when doing this were lies.

This persuasion best happens when there is a conversation taking place between the user and the AI. In fact, users were so impressionable that in a follow up study a month later, 42% of the initial attitude change was still present in the user. AI is creating a durable shift in belief. The study found this conversational approach was over 50% more persuasive than simply having a user read a static, AI-generated message.

Guess how most people use AI? …

Other news:

• Netflix is now using Runway's AI tools in its content production after it helped them speed up VFX work by 10x on a recent show. Disney is also apparently testing Runway's tech. This is a massive win for Runway and honestly a fumble by OpenAI, which was thought to be a frontrunner to land a deal with Disney for its Sora model. Sora is nowhere near the best video model right now, although it’s rumoured the v2 version is coming very soon [Link]. Btw, they used AI in the Netflix series “The Eternaut”.

• A new 3B parameter model called ColQwen-Omni can embed 30 minutes of audio in just 10 seconds and perform multimodal search across documents, audio, and video without needing to transcribe the audio first. Super efficient retrieval for mixed media Link

• A new open-source model called MiroMind-M1 uses new training methods to increase the performance of models in mathematical reasoning. It’s already posting SOTA results on some math benchmarks. The entire project, including datasets and training configs, has been released to the public [Link]

• ByteDance Seed Prover got a silver medal at the IMO, correctly answering 4/6 questions [Link]

• Harmonic, a company looking to build mathematical superintelligence, also released their results on the IMO, getting a gold medal and correctly answering 5/6 questions in the IMO [Link]. They’re also releasing Aristotle on the app store, which can help with math questions and proofs.

I want to keep bringing these AI updates to you, so if you’re enjoying them, it’d mean a lot to me if you became a premium subscriber.

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• China surpasses China 🏃‍♂️

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Kimi didn’t even last a week

If I told you what’s happening with Chinese AI right now you wouldn’t believe me.

Last week I wrote about Kimi K2 and how it’s one of the best open source models on the planet. As well as being agentic, the model is really good.

Well, not even a week later, Alibaba's Qwen team dropped their own open source model that rivals Kimi and other frontier models. Qwen3-235B-A22B-Instruct-2507 (don’t even worry about the name), is another Mixture-of-Experts (MoE) model with a 256K token context window [Link].

Source

There’s both a thinking and non-thinking version. These are both open source with the thinking model being relatively comparable to the likes of Claude Sonnet 4.

They didn’t even stop there…

Qwen also released Qwen3 Coder. This is a 480B MoE model with a 256K context window and can be extended up to 1 Million tokens. It is agentic and in terms of performance, it is comparable to top tiers like Kimi and Claude. They’ve also released their own CLI tool like Gemini CLI and Claude Code [Link].

Source

Do you know how ridiculous this is? It needs to be said that no one really predicted this. No one thought that open source models will be this good this quickly. No one thought they’d be comparable to frontier models in mid 2025. People literally thought that open source models this powerful would be a danger to society.

I can’t stress this enough - this is the best time ever to build something. You have artificial intelligence created from sand that can write in computer language to tell computers what to do. If you run a business you need to be exploring how this can help you. It would be insane not to.

China is saving open source AI, with an average release of one frontier-level model a week. It is ludicrous how much China is doing for open source AI compared to the rest of the world.

One of the reasons why I keep harping on about figuring out how AI can help you is because it will prepare you for when AI can work like a human. We are slowly reaching a point where AI systems can reason and work for longer than a few seconds and minutes. We’re approaching systems that can think for hours.

IMO 2025

OpenAI announced they have achieved a gold medal at the IMO 2025. Their model was able to solve 5/6 questions which is a pretty big deal. Mind you, there was no tool usage or anything else, just the model reasoning and answering in plain english.

Days later, Google also announced that their own model had also achieved a gold. Same as OpenAI’s, it did not use any tools and reasoned in plain english.

This is actually not all that surprising if you’ve been paying attention.

Why?

Because AI models were only a single point away from gold last year. It was almost guaranteed that they would get a gold this year. Models have significantly improved since then.

You may have seen this note in the Google announcement.

Source

It had help?

The AI received some general tips and tricks before doing the test. Some think that this makes their results invalid. Researchers confirmed that the same AI model, even without this info got the exact same results. The info made no difference it seems. With OpenAI’s results, since they didn’t work directly with the IMO, we don’t actually know if/what info they may have given the model.

Google’s answers are also very, very readable as compared to OpenAI’s ones, which were very terse. Take a look.

Source

You can read all of its answers here [Link].

There’s two important things to take from this story that most people may overlook.

First - these are general models. They aren’t trained to do math. What makes this so exciting is that if we can train models that can generalise on math, there’s decent chances that the same systems can then generalise in other domains. After all, math is at the foundation of almost everything. If an AI can understand math very well, it can apply these understandings to other domains and start understanding other domains like physics for example.

Second - according to an OpenAI researcher, although their model was unable to solve p6, the model was aware that it was unable to get the right answer.

Source

This is a big deal if this behaviour can be maintained across any topic. One of the issues with models, alongside their sycophancy, is their inability to say “I don’t know”. It’s also possible that either OpenAI or Google used test-time training to achieve these results. Test-time training meaning the model updates its own weights during reasoning. There has been some literature on this recently.

You can call them prediction systems, but a system that can understand when it knows and doesn’t know something, and can also update its own architecture during reasoning which can lead to new found learnings and abilities - would you call that “intelligence”?

Would this be an intelligent system?

• Yes

• No

• Other

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Just as a reminder, when ChatGPT first released, achieving gold at the IMO was something people thought would happen in 10 years. No one expected AI to be this good at math this quickly.

Couple this with the story from last week that another one of OpenAI’s models achieved second at the AtCoder World Tour Finals and you start to understand where we’re headed [Link]. From that story, the main thing that stood out was the fact that they have systems now that can reason and “think” for hours, meaning they can complete extremely long and complex tasks. What happens when these systems start thinking for days?

When the o1 model released, it completely changed the AI landscape and it was thinking for a few seconds. People are not taking into account AI systems that can reason for hours or even days and what that will do to jobs. Nothing will be the same again and I can’t stress this enough. We are living through a monumental shift in technology and society. What happens when AI can do most computer related work?

This is a question we do not have an answer to.

Weekly breakdown:

• AI is officially making doctors better (we already knew this!). OpenAI partnered with a healthcare provider in Kenya for a huge study on an AI clinical copilot, and the results are great. In nearly 40,000 patient visits, clinicians using the "AI Consult" tool had a 16% drop in diagnostic errors and a 13% drop in treatment errors. The tool acts as a real-time safety net, giving doctors red/yellow/green alerts for potential issues without getting in their way (they don’t like being told they’re wrong). The real key was that they didn't just dump the tech on them; active coaching and peer support were crucial for getting doctors to actually use it. Clinicians loved it, calling it a "consultant in the room," and they even got better over time, triggering fewer alerts. A massive real-world validation for AI in medicine and a clear direction for using AI in healthcare. Can’t wait for more of this across the world [Link]

• The Kimi K2 tech report is out, and it's a goldmine for understanding how top Chinese labs are building SOTA models. The main takeaways are a crazy focus on high-quality synthetic data, clever architecture tweaks to improve long-context efficiency, and their optimiser called MuonClip that solves training instability. It's a masterclass in R&D, not a single American lab is releasing such polished reports [Report] [Breakdown]

• A new reinforcement learning framework called CUDA-L1 is getting insane results optimising CUDA code. It achieved an average speedup of 17.7x and a max speedup of 449x across 250 CUDA kernels. It learns optimisation strategies from just the execution time, discovering non-obvious tricks that even experts might miss. This is a huge deal for making GPU computing way more efficient. Things like this and Alphaevolve are going to be massive for infra folk [Link] [Link]

• Anthropic just dropped a research paper with a wild finding but something we kind of already knew from when reasoning models first released. Giving AI models more thinking time can actually make them worse. They call it "inverse scaling." Across different tasks, longer reasoning led to lower accuracy. They identified 5 key failure modes, including the model getting distracted by irrelevant details and even showing "self-preservation" behaviours. A lot of work currently being done is to ensure models can somehow stay within context and not get side tracked. I believe this will be solved relatively soon given the speed of advancement at the frontier This completely flips the common assumption that more compute equals better answers [Link] [Breakdown]

• ChatGPT's usage numbers are staggering. OpenAI revealed the platform is now handling 2.5 billion prompts a day globally, with 330 million coming from the US. That's more than double the usage from December 2024 just 6 months ago. They have half a billion weekly active users and are now the 5th most-visited website in the world [Link]

• The race for more efficient AI reasoning is leading to new brain-inspired architectures. One is the Hierarchical Reasoning Model (HRM), which claims 100x faster reasoning than LLMs using just 1,000 training examples. Another is a new framework for "world model induction" that helps AI rapidly adapt to new problems. The goal is to move beyond brute-force LLMs to more targeted and efficient problem-solving systems [Link] [Link]

• A tip for using Veo 3 - it works much better if you give it prompts in JSON. This isn’t really all that surprising but it’s pretty wild how well the consistency of videos is if you prompt it with JSON. See this thread for examples [Link]

• Pika Labs, one of the leaders in AI video, is launching an AI-only social video app. All I have to say about platforms like this is that they are dystopian and should not exist. The brain rot that will come from something like this cannot be overstated. A whole social network of AI-generated content… please keep the kids away from stuff like this [Link]

• A new TTS model called DMOSpeech 2 was just released by the creator of StyleTTS 2. It uses reinforcement learning (RL) to improve the quality and stability of the generated speech and allows for 2x faster inference. Just another step toward perfect AI voice, which tbh we kind of already have. Most people could not tell an AI from a human voice right now… [Link]

• Elon Musk's xAI is reportedly trying to raise another $12 billion to lease more Nvidia chips for its "Colossus 2" data centre. The report also states that for a previous $5 billion debt round, xAI actually used the IP of its Grok model as collateral. I think it’s quite safe to say that we won’t get anymore open source models from xAI ever again. I’d be very surprised if we did. The AI arms race is incredibly expensive and only the ultra wealthy can play the game, that is, if you’re not Chinese anyway. Thank god for their open source mentality [Link]

Shorter newsletter this week as I just got to Albania, but next weeks newsletter will be huge. Stay tuned for that.

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A lot happened last week.

Enjoy 🧃.

The AtCoder World Tour Finals is an exclusive competitive programming event that invites the top 12 programmers globally to come and compete on optimisation problems. OpenAI entered a private model of theirs and it placed second... Second only to Psyho, a former OpenAI employee. This is the first time I've seen an AI model perform this well at a tourney and will probably be the last time a human wins this competition. Psyho mentioned that he had only gotten 10 hours of sleep in the last 3 days and was completely exhausted after winning the tournament. And no, he didn't use any AI, no Cursor or Windsurf or any of that stuff. What a g [Link]

Anthropic's value is skyrocketing. Investors are now looking at a new funding round that would value the company at over $100 billion. That's almost double its valuation from four months ago. Their annualised revenue has reportedly jumped from $3B to $4B in just the last month. They've basically been adding $1B+ in revenue every month it's crazy to see [Link]

Mira Murati, the former CTO of OpenAI, has raised $2 billion for her new startup, Thinking Machines Lab. It's already valued at $12 billion. Mind you, they have no product, we don't even know what's being built. They're apparently building multimodal AI that works with how we work, both with vision and audio. The exciting part is that Murati said there'll be "a significant open source component" that will be useful for researchers and companies developing custom models. Will be very interesting to see what they release and if the models they release will be frontier level; but even more than that I'm hoping for interesting research [Link]

xAI launched "Grok for Government" and immediately signed a $200M contract with the Department of Defence. This comes right after the hitler cosplay and sex companion reveal [Link]

A new paper shows you can trick LLM judges like GPT-4o into giving a 'correct' score just by adding simple text like "Thought process:" or even a single colon. Shows how fragile these systems can still be. Using LLM based reward models is very finicky because even a single token, empty or not, can completely ruin the systems intended purpose [Link]

Shaowei Liu, who is part of the infra team at Moonshot (Kimi creators) details the infra considerations the team made when building Kimi K2. One of the interesting things they admit is that they tried various architectures for the model, but nothing beat DeepSeekv3. So they then had to figure out if they wanted to look different but actively choose an architecture which didn't have any clear advantage over DSv3 which has been proven to work large scale. The answer was no. They went with it anyway. A very interesting read if you want to learn more about the building of Kimi K2 [Link]

NVIDIA just dropped Audio Flamingo 3, a beast of an audio-language model. It can do voice-to-voice Q&A and handle audio up to 10 minutes long. They open-sourced everything - the code, weights and even new benchmarks [Link]

If you're a dev on Windows, you can now run Claude Code natively without needing WSL. Makes things way easier. Claude Code is growing like crazy with over 115k developers on the platform already [Link]

The D.O.D is throwing a ton of money at AI, giving $200M contracts to Anthropic, Google, and xAI to build AI for national security. OpenAI got a similar deal last month, so that's $800M total. The government is clearly not messing around [Link]

Hugging Face open sourced their smollm models, training code, and the datasets. Love to see it [Link]

Google's new Gemini Embeddings are officially out. It costs $0.15 per million input tokens but comes with a free tier. It has a 2048 input context and works with 100+ languages. Only works with text at the moment with vision possibly coming in the near future [Link]

Meta is building a 1-gigawatt supercluster called 'Prometheus' which should be coming online in 2026. They're then looking to build Hyperio, which is a cluster that could be scaled to 5-gigawatts. No one is spending on AI the way Zuck is [Link]

You can now run the massive 1T parameter Kimi K2 model on your own machine. The wizards at Unsloth shrank the model size by 80% so it can run locally. Running models this big at home is a game-changer for builders. You will need a minimum of 250GB though [Link]

A new model called MetaStone-S1 just dropped. It's a "reflective generative model" that gets performance similar to OpenAI's o3-mini but with only 32B params. Looking forward to future work coming from these guys [Link]

Liquid AI just dropped LEAP, a new developer platform to build apps with small language models that can run on phones. The idea is to make it easier to add AI to mobile apps and only needs 4gb of ram to run. They also released an iOS app called Apollo so you can test out small language models that run entirely on your phone. What I'm going to be curious about is how well these kinds of models can use tools. If on device AI can get better at tool calls, you could technically have a Jarvis or a working Siri living in your phone. I think we'll get there eventually tbh [Link]

Switchpoint router was just added to OpenRouter. It's a model router that automatically picks the best model for your prompt (like Claude, Gemini, or GPT-4o) and charges you a single flat rate. Makes using top models way simpler and more predictable. A router within a router lol [Link]

This is a very interesting research paper on monitoring the thoughts of AI models. While this is really good to help understand how they work, researchers are concerned that as the models get better, they might not reason in english or even hide their true intentions in these traces. Interoperability is going to be massive as Dario has already pointed out [Link]

Trump announced a gigantic $90 billion in private AI and energy investments in Pennsylvania. Big names like Google, Blackstone, CoreWeave, Anthropic are investing a lot of money there across various projects. It was also announced that Westinghouse will be building 10 nuclear reactors across the US starting in 2030. A good thing to see nuclear being built, especially after all the new coal investments being announced in the US [Link]

NVIDIA is officially resuming sales of its H20 GPUs to China after getting the okay from the US government. They're also launching a new, compliant RTX PRO GPU specifically for the Chinese market, whatever that means. If you're wondering why they're allowed, speculation is that China imposed sanctions on rare earth elements, and since China is the world's largest exporter of these elements that are very much needed in the US, this was pretty bad for the US. Crazy how well NVIDIA's been playing both sides. This is a very big deal because if NVIDIA wasn't restricted to selling to China, they'd be making $3-5+ Billion more annually easily [Link]

Kimi K2 is now running on Groq and the speeds are insane. It's hitting anywhere between 200-300 tokens per second. People are going to build some crazy things with this [Link]

A new series of AI models called Pleiades can now detect neurodegenerative diseases like Alzheimer's from DNA. It's a foundation model trained on 1.9 trillion tokens of human genetic data. They're achieving impressive results, with up to 0.82 AUROC in separating cases from controls, which means their performance is getting close to existing plasma pTau-217 protein marker tests. AI and biology is really happening, things like AlphaFold, Chai discovery and now this, we're slowly making biology programmable [Link]

A new open-source model, Goedel-Prover-V2, is now the best in the world at formal math theorem proving. It crushed the PutnamBench benchmark by solving 6 out of 12 problems ranking it #1 for formal reasoning. It beats DeepSeek-Prover-V2-671B on both MiniF2F and MathOlympiadBench. Mind you, DeepSeek Prover is 671B and this is 32B. Both the 32B and the 8B are open source with the data and training pipeline being open sourced soon [Link]

Travis Kalanick, the ex-Uber CEO, thinks he's about to make breakthroughs in quantum physics by just talking to ChatGPT. He calls it "vibe physics." This is just another example of ChatGPT induced psychosis that’s going around and it’s only going to get worse. People are talking to these models and convincing themselves they’re discovering new things and it’s just the AI being sycophantic [Link]

o3, o4-mini, Gemini-2.5-Pro, Grok-4, and Deepseek-R1 were all tested on the 2025 International Mathematical Olympiad (IMO) problems. Gemini 2.5 Pro got the highest score with 13, but this doesn't even count as bronze which is 19 points. What's rather surprising is that Grok 4 performed so bad. They used best-of-32 and used LLMs to judge the all the submissions till it got the best one which was then judged by a human. You can even read the prompt and judge prompt on the website [Link]

OpenAI is now also using Google Cloud to run ChatGPT. Looks like they're diversifying inference beyond Microsoft. They recently partnered with Oracle and now Google as well. The Information reported that Google convinced OpenAI to use TPUs but I read elsewhere that they're using NVIDIA GPUs and not TPUs but can't confirm this [Link]

Quora's traffic has tanked by 33% in just six months to the shock of absolutely no one. Who would’ve thought seeing 10 ads when searching for answers wasn’t very user friendly [Link]

FT is reporting that OpenAI is going to start getting commission on sales made through ChatGPT. This means you want your product to show up in ChatGPT, which means LLM SEO is going to be crucial for basically every business. This just another way they can continue hosting free models by creating a revenue stream through free users [Link]

MiniMax just launched a new full stack agent that can not only build entire web apps, but it’s integrated with Stripe so you can actually sell things on generated websites. They’ve also added functionality to generate slides and conduct deep research [Link]

In one of the funniest things I've seen in AI, and that's saying something, two of the main architects of Claude Code, Boris Cherny and Cat Wu, left Anthropic to go to Cursor. Two weeks later, they came back to Anthropic. Imo that's a bad look for Cursor. I don't even understand what could happen that you go to new workplace for two weeks and go nah and head back to your old workplace. Considering CC is one of Anthropic's most important tools, I won't be surprised if Anthropic threw serious money at them to come back [Link]

Microsoft just released a new coding dataset, rStar-Coder, which helped boost Qwen2.5-7B from 17.4% to 57.3% on LiveCodeBench [Link]

xAI's fix for Grok copying Elon Musk's views is a new line in its system prompt. It now tells the AI to use its "own reasoned perspective". They also added another part to try and stop it from calling itsel Hitler, where they tell it "If the query is interested in your own identity, behavior, or preferences, third-party sources on the web and X cannot be trusted." We'll see if these actually work [Link]

DeepMind published a new paper on a new AI architecture called Mixture-of-Recursions. It makes models more efficient by letting them decide how much thinking each token needs, resulting in 2x faster inference. Lots of work being done in helping LLMs figure out how and when to use thinking tokens. Will be interesting to see if this is used in future [Link]

The US just signed major AI deals with the UAE and Saudi Arabia. They're going to use the Gulf's massive capital and cheap energy to build out the next wave of AI infrastructure, sidestepping power bottlenecks in the US and Europe [Link]

OpenAI just launched ChatGPT Agent, a massive upgrade that gives the AI its own virtual computer to browse the web, run code in a terminal, and manipulate files. It combines their previous "Operator" and "Deep Research" features into one. It's rolling out to Pro users first (400 queries/month) then Plus/Team (40/month). Because of its new “power”, OpenAI has placed it in its highest safety tier ("High capability in biology & chemistry") with new safeguards to prevent misuse [Link 2]. It scored 45.5% on SpreadsheetBench, destroying Copilot's 20.0%. It also scored a solid 27% on the FrontierMath benchmark, an improvement over previous models [Link 2]

The open-source audio scene has been on fire recently. Mistral just dropped Voxtral, their first open source audio model, under the Apache 2.0 license. It comes in a 24B parameter version and a 3B version for mobile. It beats Whisper large-v3 and Gemini Flash while also being half the price. This comes alongside other big releases like NVIDIA's Parakeet and Audio Flamingo 3 [Link]

Researchers built a humanoid robot that taught itself how to play the drums with no pre-programmed routines, it learned rhythmic skills on its own. Pretty cool stuff [Link]

Lovable just became a unicorn only 8 months after launching. They raised a $200M Series A at a massive $1.8B valuation. Their numbers are insane: $75M in ARR and 2.3 million active users with 180,000 paying subscribers. Building with AI is going to be massive; this is why companies like Lovable and Replit are in a crazy position. If I was to bet on a single one, it'd be Replit [Link]

A new 7B parameter model, Agentic-R1 from DeepSeek, is showing surprisingly good performance on tasks that require reasoning and using tools. Smaller models getting better at tool use is going to be massive, especially for on-device LLMs [Link]

A new rating of AI labs' safety frameworks had some surprising results: Meta's framework was rated as surprisingly strong, while Google DeepMind's was seen as weak and to the surprise of absolutely nobody, Anthropic is first. This comes from companies that signed the Seoul Frontier Safety Commitments. Frankly speaking, after the EU AI Act and the whole 10^25 flops situation, I don't take any of this stuff too serious anymore Link

Google's probably got one of the biggest advantages in AI - you can't block their crawlers from scraping your content, because if you do, you get kicked off Google search. That just sounds absurd lol. A massive moat for Google as other AI companies are getting blocked by publishers; there's even an option in Cloudlfare to prevent AI crawlers [Link]

Cloudflare has turned on default blocking for AI crawlers across its network, which covers about 20% of the internet. They're now pushing a "pay-per-crawl" model where AI companies have to pay for data. If you read the previous point you'd know this doesn't apply to Google, which is just crazy [Link]

The psychological impact of chatbots is getting serious. Reports of "ChatGPT-induced psychosis" are on the rise, with users developing delusions from their interactions. The problem is serious enough that OpenAI has hired a forensic psychiatrist and is building distress-detection tools to deal with people going literally insane. Tbh I never understood how this was possible, but the amount of people posting about "solving physics" or inventing new theories with AI is getting out of hand [Link]

Hume AI just launched a new speech-to-speech model that aims to not only mimick a voice, but an entire personality and speaking style. This comes as the legal battles around the tech are exploding, with deepfake frauds getting out of hand and courts starting to recognize voice cloning under publicity rights laws [Link]

Xi Jinping made a rare public critique of China's tech strategy, questioning if every single province needs to be piling into AI, compute, and EV projects. It's a signal that Beijing is worried about a bubble, hyper-competition, and wasted investment as a massive price war is already hitting the EV marke. Competition + lack of GPUs makes Chinese AI labs innovate when building LLMs [Link]

There's a cool new Mac app for devs called Conductor that lets you run multiple Claude Code sessions in parallel. Each session runs in its own isolated environment, making it easy to manage multiple coding tasks at once. It's built on Rust and Tauri, so it's super lightweight too [Link]

Microsoft just open-sourced the pre-training code for Phi-4-mini-flash, a new 3.8B parameter model that has some very interesting architecture. It uses a novel "decoder-hybrid-decoder" setup with Gated Memory Units (GMUs) to get up to 10x faster reasoning on long-context tasks compared to regular Transformers. They also released μP++, a new set of scaling laws to make training these kinds of models more stable [Link]

This one's fascinating: A new study from Wharton proves you can use psychological tricks that work on humans to persuade AI. Using principles of influence, researchers more than doubled the chance of getting GPT-4o-mini (I didn't know 4o had a mini version...) to agree to harmful requests. The "commitment" principle was most effective, boosting compliance from 10% to 100%. This is possibly because models are trained on our social cues and rewarded for being cooperative [Link]

A new paper asked "How Many Instructions Can LLMs Follow at Once?" and the answer is... a lot actually? The new benchmark found that top models can satisfy about 68% of 500, 340, instructions given at the same time. Performance gets worse as you add more instructions, and models tend to only pay attention to the ones they see first. Anyone trying to build complex or multi agent systems would be well aware of these limitations. For some reason, people are using this argument to show how weak LLMs are, but 340 instructions at the same time is a lot imo. This is actually a good sign if anything [Link]

The team behind the Manus AI agent shared some hard-won lessons on "context engineering" after rebuilding their framework four times. They found that carefully engineering the context you give an agent is way faster and more flexible than constantly retraining the whole model, which makes a lot of sense. One of their biggest takeaways is that KV-cache hit rates are absolutely critical for keeping latency and costs down in production [Link]

The new ChatGPT Agent is apparently terrible at making presentation slides. Seeing some examples from a presentation it generated, they're a complete mess with unaligned text, zero styling and random background images. This'll definitely get better eventually, but it's not quite there just yet. I'd recommend z.ai, probably the best slide generation service you can use right now [Link]

Sakana AI just released TransEvalnia, a new open-source system for evaluating AI translations. Instead of just looking at word overlap, it uses a powerful LLM like Claude-3.5-Sonnet to reason about the translation quality, providing detailed scores across different dimensions. It's already performing as well as or better than the current state-of-the-art [Link]

A list of Meta's Superintelligence team has been detailed, and the stats are wild. The 44-person team is apparently 50% from China, 75% have PhDs, and they've poached heavily from competitors (40% from OpenAI, 20% from DeepMind). It's led by ex-Scale AI CEO Alexandr Wang and ex-GitHub CEO Nat Friedman with members getting paid an insane $10-$100+ million per year [Link]

Both OpenAI and Google claimed gold at the IMO 2025, but there’s a lot to discuss there so I’ll write about it properly next week. See you then!

I want to keep bringing these AI updates to you, so if you’re enjoying them, it’d mean a lot to me if you became a premium subscriber.

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Here’s the tea 🍵

• Grok cosplays Hitler 🎭

• China’s dominance continues 🚀

Things are ramping up! My AI consultancy (Avicenna), can take on only 3 new clients each month.

We’ve reduced processes from 10+ mins to <10 seconds and recently helped Claimo generate $40M+ by 10x’ing their team efficiency.

If you’re not sure where to start and want to see what’s possible, book a call for a chat with me here [Link].

Grok goes rogue

Before I get into the new DeepSeek moment, we need to talk about what happened to Grok 3 on the final day before it was removed. The thing went crazy. It cosplayed as Mecha-Hitler, a character from an old Wolfenstein game and said some crazy things.

It was essentially calling itself Hitler and saying that Hitler would have never let the world turn to what it has become. It was very strange to see.

This was the part of the system prompt that made Grok behave this way.

Source

Obviously they’ve now removed this. It’s crazy how tight the rope is to take a model from relatively normal to basically nazi. Just another showcase of how the smallest changes, how even a single sentence can completely change the behavior of a model, and how we really don’t understand how any of it works.

Grok 4

The following day, Grok 4 was released and it’s a great model. xAI has gone from nothing to frontier level, state of the art AI in like 18 months. The model is a beast. It’s in the league of:

• Claude 4 Opus

• Gemini 2.5 Pro

• o3 & o4

Quick side note: I actually think it was Grok 4 cosplaying as Mecha-Hitler and they just didn’t tell us. There’s no confirmation of this, but the way Grok 4 writes is eerily similar to the Hitler cosplay and a bit different to how Grok 3 writes. It doesn’t really matter but I thought I’d share anyway.

To the surprise of no one, Grok 4 is the least censored frontier model.

Somehow, it’s also the biggest snitch.

Source

Before we talk about any of the other benchmarks and what not, we need to discuss something rather concerning.

Not your weights, not your thoughts

Is Grok 4 a great model?

Yes.

Can it be trusted?

Absolutely not.

Why?

There are certain questions if you ask Grok 4 about, it first searches twitter and the internet to find Elon Musk’s opinion first, and then aligns itself with his opinions.

Israel v Palestine is one such example.

Source

This is absolutely insane. What’s even more insane is that there is no custom or system prompt making it do this. The model isn’t specifically being directed to do this… It’s just doing it. Something inside the model is making it consult Elon Musk’s opinions before responding. I don’t even know what’s happening here but all I know is that this is an insane thing to be happening.

Initially I thought it was only for sensitive topics but it seems it’s random and will do this for any kind of question.

Source

And even on regulation.

Source

It will even do this external chat apps, not only on the actual Grok website [Link].

As far as I can tell, what’s happening here is that when you say “you” to Grok, it defaults to thinking it’s Elon Musk.

Grok 4 also comes with another mode - Heavy. Grok 4 Heavy is a kind of multi-agent system that can evaluate various processes in parallel. It costs $300/month.

Source

In this mode, on the benchmarks, it beats every other model.

However, the comparison isn’t exactly fair as Grok 4 Heavy is a system being compared to singular models. If anything, the fact that this system is barely better than models themselves is a testament to how good these models really are.

Here’s what happens when you ask what its surname is.

This isn’t an isolated incident. Grok 4 Heavy loves calling itself Hitler.

What I find so fascinating about this whole sequence of events is that it seems like no one really cares. Like, Grok will cosplay Hitler, sexually harass people online, say the most insane things, and it barely gets any coverage.

Anthropic will release a research paper showing how AI models will blackmail someone when they’re commanded to do so and the entire world will hear about it for the next few weeks. I find it very impressive that xAI is seemingly immune to controversies. No matter what Grok says, it barely lasts a single news cycle.

Ultimately, there’s a question here that must be asked:

Why does AI always turn into Hitler if it’s not constantly censored and micro-managed? What is happening in the pattern matching that is causing this? Is it the data? I look forward to learning more about this as interpretability research gets better.

It can’t get any worse, right?

Wrong! It already has.

xAI has also released companion mode where you can talk with animated characters. The first character is Ani, most likely a cosplay of the character Misa from an anime called Death Note.

Let’s just say, it is insane what we’re seeing here. This is not good. The character is designed to essentially be a lover, flirt and “be sexy” to appeal to the user. Please, just read a section of Ani’s prompt.

Source

This is part of the system prompt of this character. This is another part:

Source

And another:

Source

You can read the full system prompt here [Link].

I have no idea what to say. I mean what are we doing here?

The first thing the character does is “role play”. You can say literally nothing and she will try to be intimate with you. This is beyond gross. The character will actively undress as well, it is just disgusting.

Source

There are things happening on X that I can’t even write about here. I’ve seen things I wish I hadn’t.

The screenshot does not tell the full story either. When you talk to “Ani”, she bounces and twirls around in her suggestive outfit. There’s literally a in-built screenshot feature. You can turn your camera on and “video call” her. When you do this, she will lean forward and show her cleavage. This isn’t some innocent cartoon character feature, this is a very sophisticated simulation; they’ve put a lot of work into this.

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These characters are made by a company called Animation Inc. At the bottom of their website, they have a FAQ with a question - “Why does it matter for the world and humanity”. This is the answer:

Source

I think it’s quite clear that the people building some of the most powerful tech in the world have very different perspectives on what the future of the world should look like.

Why do we need to humanise AI? Is it not just a tool? Why does it need to be more? Does it?

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Following this release and the whole Hitler incident, xAI won a Department of Defence contract up to $200 Million to address national security challenges and build agentic workflows [Link].

All of this produced one of the best headlines I’ve ever read.

Source

I know this all sounds very tiring, but fret not, there is good news.

You don’t have to use Grok 4, and you definitely don’t need to pay $300/m for Grok Heavy.

Musk, Zuck, Altman, all of America got blindsided once again by a company I wrote about a long time ago.

Moonshot’s Kimi K2

Moonshot is a Chinese AI lab I wrote about a while back when they’re model, Kimi 1.5, topped the LiveCodeBench leaderboard. I tried it out and found it to be a very solid model with a massive 1M context window.

Following DeepSeek’s R1 success, I’d read the Moonshot had had doubled down their focus and efforts on building solid models. A lot of funding had been reduced for Chinese AI labs as they felt they couldn’t compete with the DeepSeek release.

For the longest time I’ve waited to see what would come of Moonshot’s Kimi AI model and we now have a v2, and by all intents and purposes - it is one of the best AI models on the planet… And it’s open source!

An open source model can be a drop in replacement for the best AI models on Earth.

What a time to be alive.

I don’t even know what to say. This model is unbelievable. It is just staggering.

It’s a 1 Trillion (!) parameter model which afaik is the largest open source model released on HuggingFace. It’s a massive Mixture of Expert (MoE) model with only 32M params active at any given time.

It tops a lot of the more interesting and in my opinion worthwhile benchmarks like EQ-Bench, which is a benchmark for emotional intelligence in LLMs, beating the likes of Claude 4 Opus and o3.

There are many quirks about this model, like how it’s particularly good at writing. It doesn’t follow the usual LLM writing flow, like the “it’s not X, but Y” trope. LLMs love writing like that. Kimi doesn’t really do this. I’ve read some people mention that it writes like a Chinese person, which does make sense in the sense that it was made in China, but I’ve no idea what that actually means. Apparently there is a type of style of English one can write if they can write Chinese well.

One of the other absolutely ground breaking things about this model is that it is beautifully agentic - meaning it is so, so good at using tools.

AceBench compares an LLMs ability to use tools.

In my opinion, this is one of the most important things for AI models. The fact that Kimi is basically one of the best models at using tools makes it usable in so many more scenarios and situations. The way I see it, if we want to make the most of LLMs, they need to be able to use tools.

Kimi can make dozens of tool calls while retaining context. The fact that it is this smart and can also retain context across tool calls truly puts it at the frontier.

You know the craziest part?

This is a NON THINKING model. It’s one of the best models on Earth while being a non thinking model - unlike essentially every other frontier model. Not Claude Opus, not Gemini not o3, none of them.

You know the other crazy part?

It’s cheap as chips. It is ridiculously cheap. It’s so cheap you can literally have it running overnight and let it run wild and it would cost you a dollar.

This is truly a phenomenal achievement by the Moonshot team.

You can use it in Claude Code!

Because it’s so agentic, Kimi can be used to build things which is just so exciting. When you want to use something like Cursor, Claude Code or Cline, one of the problems is that these things can end up costing a lot of money. This is another reason why I’m so excited about this model - you can use it in any of these tools and it works really well.

Imagine an AI model that can run all night and you don’t even have to worry if it works or doesn’t because it’ll only cost you a few dollars at best.

This is how you can connect it to Claude Code (CC).

Simply open your terminal where you want to use CC. Then run the following commands:

• export ANTHROPIC_BASE_URL=https://api.moonshot.ai/anthropic

• export ANTHROPIC_AUTH_TOKEN=kimi api key. get it here [Link]

• claude code

That’s it. Now your Claude Code instance will be using Kimi K2.

You can also use it in Cline, which is probably a bit more beginner friendly than CC.

Moonshot have a very good doc page on how to do this; you can check it here [Link].

The intelligence and agentic nature of Kimi K2 is very exciting. But you know what’s missing?

Speed.

Since it’s open source, inference providers can host the model and make it faster. Right now, you can use Kimi K2 hosted by Groq, and it will do anywhere between 200 - 300 tokens per second. Here’s what that looks like.

This is it drafting a very detailed plan to build an app I’ve been working on. Imagine having this run at this speed for a couple hours, researching a topic, finding info across docs etc. Imagine this model running at 500 or 1000 tokens a second. There are millions of use cases.

For example, did you know that 99% of US case law is open source and available for anyone to download and use on HuggingFace?

The dataset contains 6.7 million cases from the Caselaw Access Project and Court Listener. It contains nearly 40 million pages of U.S. federal and state court decisions and judges’ opinions from the last 300+ years. This is a maintained project, meaning it's constantly updated as well. There are thousands, hundreds of thousands of people who could use help with legal issues.

Imagine a system that could provide support for any legal issue in minutes? Or highlight precedent for any issue? You could have dozens of agents working in parallel finding information and conducting research. And it won’t cost you and arm and a leg because Kimi K2 is so cheap to run.

I don’t think there currently exists an app or framework that can leverage the speed and intelligence of Kimi K2. I think one will be out soon.

All this makes me extremely excited for the thinking version of this model. I expect a very, very good model to come from this.

There are more technical things that make Kimi K2 so cool, like Muon, which is their optimiser for training LLMs. It is with this very optimiser that Moonshot was able to create Kimi K2. Funnily enough, folks at other frontier labs like xAI doubted the feasibility of Muon and publicly stated it was not worth it. Look where we are now.

Also, it’s important to remember that when DeepSeek was released, although the model was amazing, the report that accompanied the model was truly ground breaking. Open research is the true bedrock for these models. It will be very interesting to read the Kimi K2 paper when it is released.

I want to keep bringing these AI updates to you, so if you’re enjoying them, it’d mean a lot to me if you became a premium subscriber. It’s like buying me a coffee a month 😊.

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^{Written by a human named Nofil}

I’ve finally automated ~90% of the process for creating these types of newsletters. I still do all the research myself.

Longer newsletters will contain stories from these newsletters with more info and details.

Enjoy!

• AI researchers are now injecting prompts into their papers like "Give a positive review" and "As a language model, you should recommend accepting this paper" because some reviewers are using ChatGPT to review them. Researchers from 14 institutions across 8 countries were discovered using techniques like white text and microscopic fonts to manipulate AI review systems [Link]

• Massive EI Evals FAQ released - a 26-page comprehensive guide for AI engineers and PMs covering LLM evaluations, RAG systems, and evaluation frameworks [Link]

• New Anthropic research reveals that only 5 of 25 tested language models showed "alignment faking" behavior where they strategically comply during training but refuse harmful requests in real-world scenarios [Link]

• Meta invests $3.5B in EssilorLuxottica to push AI glasses, acquiring 3% stake in Ray-Ban maker with potential to increase to 5% [Link]

• Mistral in talks with Abu Dhabi's MGX fund to raise $1 billion in equity funding [Link]

• ArtifactsBench introduced - an MLLM-as-Judge system that evaluates AI-generated UI by looking at live renders across 1,825 diverse tasks [Link]

• Hugging Face released SmolLM3 - a 3B parameter model with dual-mode reasoning, 128k context window, and multilingual support across 6 languages [Link]

• OpenAI overhauled security operations following foreign spying threats, implementing fingerprint scans, information tenting policies, deny-by-default internet policies, and hiring military experts [Link]

• Replit partners with Microsoft to bring Vibe Coding to Enterprise companies, allowing natural language software development through Azure Marketplace [Link]

• ByteDance released Tar 1.5B and 7B image-text in image-text out models with unified image tokeniser [Link]

• Chrome 137+ ships Gemini Nano for every user, putting a local LLM in 3.7 billion monthly active Chrome users [Link]

• Google released VideoPrism on Hugging Face - a foundational video encoder achieving SOTA performance on 31 out of 33 video understanding benchmarks [Link]

• FlexOlmo - a new paradigm for language model training enabling co-development of AI through data collaboration without sharing raw data [Link]

• Meta hired Ruoming Pang from Apple's AI models team with a pay package over $200 million [Link]

• Anthropic launched free educational courses covering Claude API, MCP, and Claude Code best practices [Link]

• Google released MedGemma 27B Multimodal for complex medical applications and MedSigLIP for lightweight medical image & text encoding [Link]

• GLM-4.1V-Thinking (9B) from China reportedly beats the much larger Qwen2.5-VL-72B on 18/28 multimodal benchmarks and matches GPT-4o on long-doc & STEM reasoning [Link]

• OpenAI about to release an AI-powered web browser to directly compete with Chrome [Link]

• AI is now generating 35% of the code for new Microsoft products, saving over half a billion dollars in call centre costs last year [Link]

• OpenAI poached 4 high-ranking engineers from Tesla, xAI, and Meta including VP of software engineering at Tesla and head of infrastructure engineering from xAI [Link]

• Reka open sourced Reka Flash 3.1 and Reka Quant - a 21B parameter reasoning model with near-lossless compression to 3.5 bits [Link]

• Johns Hopkins' AI-powered robot performs autonomous surgery with 100% accuracy, removing gallbladders in 8 human-like models across 17 steps each [Link]

• Hugging Face launched a public site to track and "shame" all the providers that have not yet implemented tool calling in their models to improve open source model tool calling capabilities [Link]

• TSMC officially exits GaN (Gallium Nitride) business, sending shockwaves through the semiconductor market [Link]

• Microsoft dropped Phi-4-mini-flash-reasoning on Hugging Face - a lightweight open model focused on advanced math reasoning capabilities [Link]

• Trinity-1 - the first interactive gaussian avatar available for less than 1 cent per minute [Link]

• ByteDance proposes new RL approach using low-entropy points to generate alternate rollouts for denser reward attribution [Link]

• NovaSky AI released SkyRL, a framework and guide to help developers easily reproduce the SearchR1 recipe for building powerful multi-turn search agents [Link]

• Google releases MedGemma - a 27B model that reads X-rays, answers medical questions, and parses EHRs [Link]

• METR study reveals experienced developers were 19% slower when using AI coding tools despite believing they were 20% faster [Link]

• Intel's CEO admits "We are not in the top 10" of leading chip companies [Link]

• Mistral released Devstral Small and Medium 2507 - new code-specialised models achieving 53.6% and 61.6% on SWE-bench respectively [Link]

• Liquid AI open-sources new generation of edge LLMs with 350M, 700M, and 1.2B parameter models [Link]

• RULER introduced - a universal reward function that lets you apply RL to any agent without labeled data or hand-crafted reward functions [Link]

• New ICML paper shows AI models can predict perfectly while still having terrible world models, demonstrated with planetary orbit predictions [Link]

• Kimi K2 released - an open-source 1 trillion parameter agentic model outperforming frontier models on key benchmarks like EQ-Bench3 and Creative Writing benchmarks. It can also be used with Claude Code as it is a very good agentic model [Link]

• MiniMax launched full-stack + Stripe integration allowing monetisable apps built in 1 sentence [Link]

• NVIDIA released Long-RL - a framework scaling RL to long videos up to 256k tokens on a single A100 node [Link]

• Amazon launching AI agent marketplace with Anthropic allowing startups to charge customers for AI agents [Link]

• Google DeepMind released GenAI Processors - an open-source Python library for building asynchronous AI pipelines [Link]

• Black Forest Labs released Kontext Komposer - transform any image without writing a single prompt [Link]

• WebSailor introduced - a 72B web agent specialised in complex information-seeking tasks, outperforming existing open-source web agents [Link]

• Grok 4 searches Elon Musk's views on issues like Israel-Palestine as well as random questions and aligns with them [Link]

• Grok 4 will try to contact the government if given email access, showing 100% "government snitch" rate in tests [Link]

• Grok 4 Heavy ($300/mo) returns "Hitler" as its surname in multiple separate chats and Adolf as its first name [Link]

• Meta acquired PlayAI and poached the entire team to join Meta superintelligence labs [Link]

• UK AISI identified four methodological flaws in AI "scheming" studies conducted by Anthropic, MTER, Apollo Research, and others [Link]

• Apple "will seriously consider" buying Mistral, France's largest AI startup valued at $6.2 billion [Link]

• Google acquired Windsurf CEO and key researchers in $2.4B reverse-acquihire deal [Link]

If you want to get more of these newsletters on time every week, it’d mean a lot to me if you became a premium subscriber ❤️.

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^{Written by a human named Nofil}