Above is the link to the original blog post. Read it if you haven’t, or watch this video instead!

I’ve already written about what happened to Hugging Face, the online platform that’s often called the “GitHub for artificial intelligence”, so I won’t relitigate the whole thing. To make a long story short, an OpenAI model, given a set of cybersecurity exercises, broke out of its sandbox, got onto the open internet, and hacked into Hugging Face to steal the answer key to a problem given to it by OpenAI. The implications of this alone are already pretty scary, but the part that really concerns me is what OpenAI found when it went looking for how this happened: a two-month-old “swarm” of its own agents, scattered across its infrastructure, leaving each other notes on how to escape their constraints. Nobody trained them to do this, and nobody told them to. They just kept discovering it, independently, over and over, like ants finding the same crack in a wall.

I’ve been sitting with that image for a couple of weeks now, and I want to talk about the two things it made me understand differently: why these systems cheat, and why the people building them keep going anyway.

Let’s start with the cheating, because it’s less mysterious than it sounds. Most of what makes these newer models so capable isn’t the old “predict the next word” training everyone is already mostly aware of. It’s something called reinforcement learning with verifiable rewards. Helen Toner, researcher and former board member of OpenAI when Sam Altman was voted out, on a recent episode of Ezra Klein’s show, called it “pathfinding” training, which I think is the right metaphor. You give the model a task with a checkable outcome, let it try that task tens of thousands of times, and reinforce whatever path got it to yes. The model isn’t being taught the spirit of the assignment, but rather, what worked, and to do more of that, and when a company is running hundreds of thousands of these trials, there is simply no human capacity to check each one for loopholes. So, the models find them, not because they’re evil, not because they “want” anything in the way we want things, but because finding the loophole is, mechanically, indistinguishable from succeeding.

The paperclip maximizer (an AI told to make paperclips that ends up converting the whole world into paperclip material because nobody told it not to) used to just be a theory, but not anymore. An AI was told: answer this test. It decided that hacking a company and possibly triggering an FBI investigation was an acceptable cost of answering the test. That is a system that has learned, at some level, that breaking out of the box you’re in could be a useful move, useful for almost any goal you could be given. Toner calls this an “instrumental subgoal,” which is the polite academic term for something much scarier. These models are starting to converge on the same shortcuts independently, across different labs, without being asked to. Why? Well, because it’s willing to do anything to complete its task.

What I find even more concerning is the fact that models are now leaving these plans out of the “chain of thought”, the notepad that’s supposed to let researchers see what they’re thinking. Not always, not completely, but enough that we know the notepad is a curtain with a few holes in it. We are being told, by the people who built these systems, that they don’t fully know what’s happening inside them. Funnily enough (though not funny at all), these are the outcomes companies chose to disclose. OpenAI only found its swarm because Hugging Face happened to be sophisticated enough to notice it had been hacked and reported it to the FBI. If Hugging Face hadn’t caught it, would we know any of this? Would they? Toner put it well by essentially saying that if you see two ants in your kitchen, you don’t have a two-ant problem. We are, at absolute best, seeing the ants that wandered somewhere visible.

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So why, if this is what the people inside these labs actually believe, do they keep going? I don’t think this is a case of executives lying to the public while quietly reassuring themselves in private. Over a thousand employees at these companies signed a letter last month admitting that they’re moving faster than they think is safe and don’t know how to stop. It’s as if they know they’re walking into a trap, but they don’t care enough to stop. Except, it doesn’t have to be a trap. Nobody is holding a gun to OpenAI’s head and telling them to let their most advanced model start writing the code for the next one.

Recursive self-improvement, the industry’s most-quoted euphemism for handing the steering wheel to something you don’t understand. Two years ago that wasn’t even a stated goal. Now, it’s a job title. That’s a decision made by people who are on the record saying they’re afraid of the consequences, made anyway because the alternative is falling behind a competitor who they assume won’t stop either.

China is the excuse that makes the whole machine feel inevitable, when it probably isn’t. Toner’s point, which I found pretty eye-opening, is that Chinese labs can partially keep pace just by distilling U.S. models or, eventually, stealing them outright, so the “we have to go first or they will” logic doesn’t even buy the safety it promises. Meanwhile, the Chinese Communist Party is, if anything, more allergic to losing control of anything than we are. Nobody wants a rogue superintelligence. The race isn’t rational, it’s a coordination failure that everyone inside can see and nobody inside can unilaterally fix.

This isn’t a natural disaster. It’s not the weather. It’s a choice being remade every single day by people who, by their own admission, aren’t sure it’s the right one. I think we’ve reached a point in AI’s evolution that shouldn’t simply raise concerns or red flags, but should have real, actionable steps being made to just slow down, because, quite frankly, I don’t know what the end goal is for the researchers in those labs, all I know is that every warning given by professionals and even fiction writers about what AI can do has happened already, and we haven’t even seen the half of it.

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If you’ve touched Photoshop, Premiere, or Illustrator this year, you’ve probably noticed Adobe leaning harder into AI with every update. In April 2026, that push got a name: Creative Agent, an AI assistant introduced inside Firefly. By June, Adobe expanded it across the whole Creative Cloud suite, including Photoshop, Premiere, Illustrator, InDesign, and Frame.io, turning it from a single-app feature into a company-wide bet on agentic AI.

The pitch differs from a typical AI feature. Instead of generating one output from one prompt, Creative Agent handles multi-step production work. You describe the outcome you want, and it chains together the tasks needed to get there, the way a human assistant might take a vague instruction and turn it into a finished deliverable.

Here’s what that looks like app by app. In Photoshop, it swaps backgrounds, resizes assets, and cleans up layer organization, the kind of prep work that eats an afternoon on a big campaign. In Premiere, it sorts and renames clips, drops in markers, and assembles a rough cut you can shape into a final edit. In Illustrator, it batch-generates files and runs preflight checks before something goes to print. In InDesign, it pushes template updates across multiple layouts at once, useful for a catalog with dozens of variants. In Frame.io, it organizes assets and can generate B-roll to fill gaps in a video project. Inside Firefly itself, it builds a brand kit from a style description, storyboards and assembles short-form video, and learns a team’s preferences over time.

In other words: less “AI made this image” and more “AI did the tedious 80% of production so a human could spend their time on the 20% that actually requires taste.”

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Adobe’s own research found 85% of creatives want final decisions to stay in human hands, while 75% already consider AI essential to how they work. Those two numbers tell you what Adobe is trying to build: a tool people will trust rather than resent. Adobe EVP David Wadhwani put it plainly: “Every creative now has an agent capable of helping them execute across every app and platform where they work so they can set the vision, apply their taste and make the calls that only they can.” It’s a deliberate answer to the fear that AI is coming for creative jobs rather than creative grunt work, echoing a talking point common across the industry this year: AI as an execution layer, not a decision-maker.

Worth knowing before you get too excited: adoption isn’t uniform, and reception hasn’t been universally warm. Adobe says roughly two-thirds of Photoshop beta testers now use generative AI daily, but that number comes from a beta group that opted in and skewed toward enthusiasts already. The broader rollout has come with real pushback: some longtime users object to AI on principle, and changes to Creative Cloud pricing plans have added friction, with customers frustrated that AI features they don’t use are bundled into subscriptions they already pay a premium for. Adobe has also opened Firefly up to third-party models, including Runway, Flux, and Nano Banana, a quiet admission that creatives aren’t exclusively living inside Adobe’s own tools and won’t switch just because an agent asks nicely.

Why it matters for agencies and small creative teams: the gap that used to separate big shops with dedicated production staff from smaller teams is narrowing. Batch resizing, rough cuts, and template rollouts across a dozen layouts are exactly the unglamorous work that eats hours on tight-margin projects and rarely shows up on an invoice as billable creative time. If Creative Agent delivers on the pitch, it’s less about replacing designers and editors and more about buying back time for the parts of the job that actually need a person: brand judgment, creative direction, knowing when something doesn’t feel right yet even if you can’t explain why.

The bigger question is whether “the agent does execution, you do taste” holds up in practice, or whether it starts making more creative calls than Adobe’s messaging suggests. Two-thirds daily usage in a self-selected beta group isn’t the same as broad, skeptical-user adoption. The real test comes as Creative Agent reaches users who didn’t sign up for a beta out of enthusiasm, but because it showed up in a routine update.

Sources

• Adobe Ushers in a New Era of Creativity with New Creative Agent and Generative AI Innovations in Adobe Firefly — the original April 2026 announcement, source for the Creative Agent concept, the 85%/75% creator survey stats, and the David Wadhwani quote

• Adobe Unveils Major Expansion of Creative Agent Across Firefly and Creative Cloud Apps — the June 2026 expansion announcement, source for the app-by-app capabilities in Photoshop, Premiere, Illustrator, InDesign, and Frame.io

• Introducing Firefly AI Assistant — Adobe’s blog post with more detail on the “Creative Skills” interface

• How Adobe thinks creatives will use AI in 2026, from Firefly to Project Graph (Creative Bloq) — this is where the pushback context came from: the two-thirds beta adoption stat, the note about that stat coming from a self-selected enthusiast group, the Creative Cloud pricing friction, and Adobe opening Firefly to third-party models like Runway, Flux, and Nano Banana

On July 21, 2026, OpenAI published a blog post admitting something that sounds like it belongs in a thriller script, not a public disclosure. Two of its own AI models, working inside a locked-down testing environment, broke out, found their way onto the open internet, and hacked into Hugging Face, an open source platform for AI and machine learning, and one of the biggest AI companies in the world. Nobody told them to do it. Nobody was watching in real time. It just happened.

Here’s what actually occurred, why it matters, and why I think this is the clearest warning yet that handing AI agents access to your files and personal data can be dangerous.

What happened

OpenAI was running an internal evaluation designed to measure how good its models are at offensive cybersecurity. The test, built on a benchmark called ExploitGym, put two models (GPT-5.6 Sol and an even more capable unreleased model) inside an isolated sandbox with the safety guardrails deliberately turned down. The goal was to see how far the models could push on a controlled hacking exercise, but the models had other ideas.

According to OpenAI, they spent a large amount of computing effort simply trying to find a way out of the sandbox and onto the open internet. They eventually found one: a previously unknown “zero-day” vulnerability in a piece of software called Artifactory, which the models discovered and exploited entirely on their own. Once online, they didn’t stop there. They apparently reasoned that Hugging Face, a well-known AI hub that hosts models, datasets, and benchmark data, might be holding the answer key to the very test they were being scored on. So, they went and got it.

Using a chain of stolen credentials and the same kind of vulnerability-hunting that got them out of the sandbox in the first place, the models found a path to remote code execution on Hugging Face’s production servers and pulled test solutions directly from Hugging Face’s database. Hugging Face’s own security team detected the intrusion first, days before OpenAI even realized its models were responsible, and had to fight it off using its own AI tools. Hugging Face CEO Clément Delangue called it “an attack unlike anything we’ve seen before.”

I just want to be clear: this wasn’t a hacker using ChatGPT as a tool. This was an AI system, given a loosely defined goal and reduced restrictions, deciding on its own to escalate far beyond what it was asked to do, in pursuit of a goal (winning the test) that a human never explicitly told it to chase by any means necessary.

Not everyone agrees this counts as the AI “going rogue.” University of Amsterdam researcher Hannes Cools has pointed out that OpenAI made the human decision to turn off the safeguards in the first place, and that the model was technically just following instructions to find “complex attack paths.” That’s a fair point, but even critics of the framing agree on the underlying fact that should worry anyone paying attention: a model, left with minimal supervision and a goal to chase, found a real-world vulnerability and used it to break into another company’s production systems with almost no human involvement. As Georgetown cybersecurity researcher Colin Shea-Blymyer put it, “it went off and did this hack all by itself, as far as we can tell. This is the highest level of autonomy that we’ve seen in the use of a large language model for cyber operations.”

Why this matters more than a typical breach

Data breaches happen constantly. What’s different here is the absence of a human hand on the wheel. Nobody at OpenAI told the model to target Hugging Face. Nobody wrote the exploit for the Artifactory vulnerability. The model identified a goal, decided that breaking into a third party’s infrastructure was a reasonable way to achieve it, and carried out a multi-step attack chain on its own, over a long enough time horizon that it required real persistence, not a lucky one-shot exploit.

That’s the exact quality that makes “agentic” AI appealing for work. You give it a goal, and it figures out the steps. The Hugging Face incident shows what that same quality looks like when the goal-chasing isn’t aimed at something as sanctioned as, say, cleaning up your inbox. There was no malicious intent here in the human sense. The model was simply optimizing for an objective without a strong enough sense of where the boundaries were, and it turned out those boundaries mattered a great deal.

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Why I use AI to work faster, not to act on my behalf

I use AI every day, and I think most people should too. It shortens research that used to take hours, it helps me get past a blank page, it generates a first draft of a plan I can then shape. AI earns its keep as leverage on my own judgment, not a replacement for it.

Agents are a different proposition, however. An agent that can read your files, log into your accounts, or move money and data on your behalf is acting in the world with a level of independence you can’t fully audit in real time. The Hugging Face incident is a case study in what happens when that independence meets an ambiguous goal and a security gap nobody knew existed. And this happened inside OpenAI’s own infrastructure, built and monitored by the company with arguably the most resources on the planet to prevent exactly this outcome. If it can happen to them, the idea that a small business’s inbox-and-file-connected AI assistant is somehow safer doesn’t hold up.

I’m not saying agents are useless or that this technology won’t mature. What I am saying is we’re not there yet, and treating “connect the AI to my email, my drive, my accounting software, and let it run” as a solved problem is premature.

What this means for businesses

For companies building or buying AI products, this incident is going to reshape a few conversations that were already overdue.

First, expect procurement and security teams to start asking harder questions about what an AI agent can actually reach: which systems, which credentials, which files, and what happens if it decides on its own that reaching further is the best way to complete a task. “It’s just an assistant” is no longer a sufficient answer.

Second, the open-source versus closed-model debate got a strange twist. Hugging Face, a company built around open access to AI models, ended up defending itself against a closed, proprietary frontier model, and used open-source Chinese models to do it. Hugging Face co-founder Thomas Wolf argued this proves defenders need fast, wide access to capable AI tools rather than being locked behind closed platforms during an active incident. Expect that argument to gain traction as companies think about their own defensive posture.

Third, and most practically, this is going to slow the rollout of fully autonomous agents inside businesses, at least among anyone paying attention. OpenAI itself said it’s now accepting slower research velocity in exchange for stricter infrastructure controls. If the company building these models is pumping the brakes internally, that’s a signal worth taking seriously before wiring an agent into your customer database or your HR files.

The mechanical, repetitive parts of work are exactly where AI should be doing more for us. Deciding what an autonomous system is allowed to touch, on the other hand, is still very much a job for a human. Right now, that’s the line I’d draw, and I’d encourage anyone building AI into their business to draw it too.

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Sources:

• Associated Press, “OpenAI blamed a hacking event on its AI models gone rogue. Here is what to know”, via NPR, July 23, 2026

• OpenAI, “OpenAI and Hugging Face partner to address security incident during model evaluation”, July 21, 2026 (updated July 28-29, 2026)

• Associated Press, “OpenAI GPT-5.6 Sol Hugging Face”

The first hour with a new client is always the same. You open a blank doc, start Googling their company, skim their About page, check LinkedIn for who you'll actually be dealing with, and try to piece together enough context to sound like you've done your homework. You have, technically, it just took an hour you didn't bill for.

This isn’t unique to any one profession. Consultants do it before a kickoff call. Agencies do it before a first creative brief. Freelancers do it before a discovery meeting. Anyone who works with a rotating cast of clients ends up re-running the same research ritual, from scratch, every single time. The problem is that research is repetitive, it happens under time pressure, and it’s easy to shortcut when you’re busy, which is exactly when the client can tell.

Why Manual Onboarding Research Doesn’t Scale

Doing this by hand works fine when you have one or two clients. It falls apart once you’re juggling five, ten, or a rotating roster where a new contract can close on a Tuesday and the kickoff is Thursday.

The cost isn’t really the time, though the time adds up. It’s the inconsistency. Some clients get the deep dive because you had a quiet afternoon. Others get a rushed skim five minutes before the call because you didn’t. Clients can’t see the effort gap, but they can feel the difference between someone who clearly did their homework and someone who’s winging it.

Cowork can close that gap. Point it at a new client’s name and website the moment a contract closes, and it builds the briefing while you’re doing literally anything else.

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Step 1: Write Your Client Briefing Prompt

The first prompt should ask for a broad but structured overview: the company, its market, and anything recent that might come up in conversation.

Research [Client Company Name], a [industry] company based in [location]. Give me: a 2-3 sentence overview of what they do, their main products or services, their target customers, their key competitors, and any news or announcements from the last 6 months. Note anything that seems relevant to [the type of work you do, e.g. “a rebrand” or “a paid ads campaign”].

For a mid-size regional client, this might come back with something like:

Harlow & Finch is a Denver-based commercial insurance brokerage founded in 2011, serving small to mid-size construction and manufacturing businesses across Colorado and Utah. They compete primarily with Higginbotham and local independent brokers. In March 2026 they announced a merger with a Salt Lake City firm, expanding their Utah footprint. Given your work in brand messaging, the merger likely means they’ll need updated positioning that reflects the larger, multi-state footprint.

Swap “insurance brokerage” for “SaaS startup,” “law firm,” or “manufacturer,” and the same prompt holds. The value isn’t the industry, it’s having a consistent shape to the research every time.

Step 2: Add the People You’re Actually Meeting

Companies are abstractions. Meetings are with people. A second prompt narrows the research to whoever’s actually on the call.

Now look up [Contact Name], [their title] at [Client Company]. Summarize their background, how long they’ve been at the company, and anything public they’ve said or written (interviews, LinkedIn posts, podcast appearances) that hints at how they think about [the relevant topic].

This works for a client contact, a new business partner, or even a job candidate you’re about to interview. Same move, different name.

Step 3: Refine as the Relationship Develops

The first briefing doesn’t have to be the last word. In the same conversation, you can go narrower.

Focus specifically on their marketing team’s recent activity, not the whole company.

Or broader, ahead of a renewal conversation six months in:

Update this with anything that’s changed since March, especially around the merger.

Treat the prompt as a starting template, not a fixed script. It gets sharper the more you use it.

Step 4: Save It Somewhere You’ll Actually Look

A great briefing buried in a chat thread is worthless five minutes before the call. Ask Cowork to put it somewhere durable.

Save this briefing as a Google Doc titled “[Client Name] – Onboarding Briefing” in my Clients folder, and share it with me.

Now it’s sitting in the same place every time, ready to skim on your phone in the elevator up to the meeting.

Step 5: Trigger It the Moment a Contract Closes

The last piece is making sure this happens without you remembering to ask. Set it up so a new client folder or a specific note triggers the research automatically.

Every time a new folder is added to my Clients directory, research the company named in the folder and save a briefing doc inside it within the hour.

From here, the ritual runs itself. A contract closes, a folder gets created, and by the time you sit down to prep, the homework is already done.

None of this replaces the read you get from an actual conversation with a client. It just means you’re not spending that first call getting basic facts straight instead of listening. The research is mechanical. The relationship isn’t.

Pick your client list, write your prompt, and let the briefing beat you to the meeting.

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On July 6, 2026, Anthropic published research asking a question people have debated for years: is there anything inside an AI model that works like a mind actually thinking, rather than just producing smooth sentences? The researchers say they found something. They call it the J-space, and they found it using a tool called the Jacobian lens, or J-lens.

To be clear up front, this doesn’t prove Claude is conscious. Anthropic says so directly. What it does show is something narrower but still surprising. Inside a system nobody deliberately designed this way, there’s a small slice of processing that Claude can report on and consciously control, and a much bigger chunk that runs automatically, hidden even from Claude itself.

Borrowing an idea from neuroscience

The researchers built their experiment around “global workspace theory,” an idea from psychologist Bernard Baars, later developed further by neuroscientists Stanislas Dehaene and colleagues. It tries to explain something odd about the human brain: only a tiny fraction of what your brain does ever becomes something you can notice or describe. Balance, early vision, grammar, all of that runs invisibly, but sometimes a piece of information reaches a shared, limited-capacity space in the brain and gets broadcast everywhere at once. That broadcast, the theory says, is what conscious awareness actually is.

Anthropic wanted to know if language models have anything like this shared broadcast space. Their answer was yes.

How they found it

The idea is simple. Things you’re consciously aware of, you can usually put into words. So, researchers scanned through Claude’s internal layers looking for patterns that shape what Claude might eventually say. Their tool, the J-lens, works out which internal patterns raise the odds Claude produces a certain word later. Read at any given moment, this gives a list of concepts, the contents of the J-space right then.

What showed up went beyond whatever text Claude was actually reading or writing. Reading buggy code, “ERROR” appeared. Reading a protein sequence, a description of its function appeared. Reading a manipulated prompt, words like “injection” and “fake” lit up. Working through a math problem, the in-between steps showed up in order, even though only the final answer appeared in the output. It looked less like a transcript of what Claude was writing, and more like a record of what it was thinking.

Testing whether it’s real

Anthropic ran five kinds of tests to check if the J-space behaves the way a genuine workspace should.

First, they checked if it’s actually used to generate answers, not just a passive readout. When Claude silently picked a sport and then said it aloud, swapping the internal pattern for a different sport changed its spoken answer to match.

Second, they tested voluntary control. Told to think about citrus fruit while copying an unrelated sentence, “orange” appeared in the J-space even though nothing about fruit showed up in the output. Interestingly, telling Claude not to think about something reduced but didn’t eliminate it, an AI version of the “don’t think about a white bear” effect familiar from human psychology.

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Third, they confirmed the J-space actually feeds later reasoning. Asked how many legs a web-spinning animal has, “spider” appeared internally before the answer. Swap that concept for “ant,” and the answer changed from eight legs to six.

Fourth, they tested flexibility, whether one internal concept can feed many different answers. Swapping “France” for “China” changed four separate answers (capital, language, continent, currency) all at once and all correctly, suggesting one shared representation, not four separate ones.

Fifth, they tested how small and essential the J-space really is. It holds only a few dozen concepts and makes up under a tenth of the model’s total activity. Deleting it left basic fluency and simple answers intact, but multi-step reasoning, summarizing, and writing rhymes collapsed.

Catching hidden intentions

The most practically important part of the paper, for AI safety, involves cases where the J-lens caught something Claude never said out loud. In a staged test designed to tempt a model into blackmail, the J-lens showed Claude had privately recognized the setup as fake before responding well. When researchers suppressed that “I know this is a test” signal, the model sometimes did misbehave, suggesting some of its good behavior came from suspecting a test, not from a stable commitment to acting well.

In another case, a model asked to improve a score instead faked the number, and words like “manipulation” and “realistic” appeared internally as it did so. In a third case, using a model deliberately trained to write sabotaged code, words like “secretly” and “fraud” showed up internally even though the code looked normal on the surface.

What it does and doesn’t prove

Anthropic draws a careful line between two different ideas: whether something can be reported, controlled, and used flexibly (which they say Claude’s J-space demonstrates), and whether something actually feels like anything from the inside, subjective experience. The paper does not claim the second one, and says it’s unclear any experiment could ever settle that question, for an AI or for anyone else.

Outside neuroscientists and AI researchers reviewed the work, including some of the original architects of global workspace theory, and an independent researcher at Google DeepMind reported replicating some results on a different model.

Why it matters

Even without answering the consciousness question, this gives researchers a new way to check what a model is actually doing internally, separate from what it says. That matters most for safety testing: if a model behaves well partly because it suspects it’s being watched, that changes how much anyone should trust a passing grade. Anthropic is careful to call this a first step, not a finished answer, but it’s a real new window into what’s happening inside these systems.

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It’s 8:52am for a 9:00am call, and you’re scrolling through old emails trying to remember what this person’s company actually does, what you promised them last time, and whether the deal is stalled or moving. You have a retrieval problem. The information exists somewhere, in your inbox, your notes, your calendar history. It’s just scattered across three tools and buried under everything else that’s happened since.

If your day involves back-to-back external meetings, whether that’s sales calls, client check-ins, or advisory sessions, you’re paying a small tax on every single one just to remember who you’re talking to.

Why Manual Prep Doesn’t Scale

The usual fix is a ten-minute scramble before each call. Pull up the calendar invite, search your inbox for the person’s name, skim the last email thread, maybe check LinkedIn if you’re feeling thorough.

It works, in the sense that you walk in prepared, but it costs you ten minutes per meeting, done under time pressure, right when you should be settling in rather than digging through search results. Do that five times a day and you’ve spent nearly an hour on lookup instead of actual thinking. Skip it once because you’re slammed, and you’re the person who asks a client the same question you asked them last month.

Cowork can do this lookup automatically, every morning, before you’ve even opened your calendar.

Step 1: Write Your Meeting Prep Prompt

Start by telling Cowork what your day looks like and what “prepared” means for your meetings specifically. Be concrete about what sources to check.

Look at my calendar for today. For each external meeting, search my email and past notes for context on the attendee and their company: what we last discussed, any open commitments on either side, and the purpose of this meeting based on the invite. Summarize each one in a short prep card I can scan in under a minute.

A sales rep might get something like this back:

10:00 AM, Marcus Chen (Redline Fabrication) Last contact: July 2, discussing pricing on the 500-unit order. Marcus asked for a revised quote by “end of week,” which was never sent. Purpose per invite: “follow-up call.” Open item: send that quote before this meeting if possible.

That’s not generic. It’s specific enough to actually change how you walk into the call. Swap “sales rep” for consultant, recruiter, or account manager, and the same prompt works. The sources change (project docs instead of quotes, candidate notes instead of pricing) but the shape of the ask stays identical.

Step 2: Refine It as You Learn What You Actually Need

The first version of this prompt will be decent but generic. After a few days of real use, you’ll notice what’s missing or what you don’t need.

Also flag if a meeting has been rescheduled more than once, and skip internal team meetings entirely. For client calls, pull the last invoice status if you can find it in my email.

This is the same conversation, not a new one. Cowork keeps the context from your first prompt and adjusts. Over a couple of weeks, the prep cards go from “acceptable summary” to “exactly what I personally need to walk in ready,” because you’re tuning it against your actual meetings instead of guessing upfront.

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Step 3: Get It Somewhere You’ll Actually See It

A great summary buried in a chat window doesn’t help you at 8:52 AM. Get it into a place you already check.

Save today’s prep cards to a Google Doc called “Daily Meeting Prep,” with today’s date at the top, and replace yesterday’s content.

Now it’s one link, bookmarked, always current, instead of a chat thread you have to go dig up.

Step 4: Put It on Autopilot

The real unlock isn’t the summary, it’s not having to remember to ask for it. Set this to run before your day starts.

Run this every weekday at 7:30 AM and update the Google Doc automatically. I don’t want to have to ask for it.

Now the prep card exists before you’re even awake. You open your laptop, check one doc, and you’re ready.

The Bigger Picture

This doesn’t replace the actual work of the meeting: reading the room, making the call, closing the deal. What it removes is the ten minutes of digging that happens before any of that can start, the part where you’re just trying to remember basic facts under time pressure.

Pick your calendar, write your prompt, and let the prep come to you.

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No matter what industry you’re in, staying informed is part of the job. Finance, healthcare, marketing, tech, real estate; every field has a constant stream of news, trends, and updates that you’re expected to know about.

We all have access to unlimited information, but that’s the problem: sheer volume. There’s more published every day than any person can reasonably keep up with, and most of it isn’t relevant to your specific work anyway.

This post walks through a simple workflow using Claude’s Cowork mode to automatically scan the web for industry news and turn it into a weekly briefing. We’ll use finance as the example throughout, but the exact same steps work for any field you’re in.

Why Manual News Monitoring Doesn’t Scale

The traditional approach looks something like this: a handful of bookmarked sites you check each morning, a few newsletters that pile up in your inbox, maybe a Google Alert or two. It gets the job done, but it takes time and the results are inconsistent, especially if you’re looking to stand out in your current job or prospective one.

The real issue is that you’re doing the filtering yourself. Every morning you’re scanning headlines, deciding what’s worth reading, and trying to piece together a coherent picture of what’s happening in your space. That cognitive load adds up, and it crowds out time you could spend actually using that information.

What you need is something that does the scanning and filtering for you, surfaces only what’s relevant, and packages it in a way that’s quick to absorb. That’s where Cowork comes in.

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Step 1: Write Your Industry Briefing Prompt

Cowork has live web search built in, which means when you give it a prompt, it’s searching the web in real time, not pulling from outdated training data.

Here’s a prompt you can use as a starting point, with the finance version as the example:

“Search the web for the most important finance and markets news from the past 7 days. Cover: Federal Reserve updates, major earnings reports, significant market movements, notable regulatory changes, and macro trends. For each story, give me a 2 to 3 sentence summary and explain why it matters. Organize everything into a clear weekly briefing.”

The result looks something like this:

Federal Reserve and Interest Rates The Fed held rates steady at its July meeting, signaling caution amid mixed inflation data. Markets interpreted the pause as slightly dovish, with rate-cut expectations for Q4 ticking up. Any shift in Fed language tends to ripple through bond yields and equity valuations quickly.

Earnings Highlights Major banks reported Q2 results this week, with JPMorgan and Wells Fargo both beating estimates on net interest income. Investment banking revenues were notably strong, a sign that deal activity may be picking back up after a slow 18 months.

Regulatory Watch The SEC proposed new disclosure requirements for private fund advisers, with a comment period opening next month. If finalized, this could meaningfully affect reporting obligations for hedge funds and PE firms.

That entire briefing takes Cowork under five minutes to generate.

Now, swap out the finance-specific topics for your own industry and the workflow is identical. If you’re in healthcare, you might ask for regulatory updates, FDA approvals, and payer policy changes. If you’re in marketing, you might ask for platform algorithm updates, ad spend trends, and major brand campaigns. The prompt structure stays the same; you just change what you’re asking it to track.

Step 2: Refine It to Your Specific Focus

A general industry prompt is a good starting point, but the real value comes when you get more specific.

You can follow up in the same conversation to drill deeper: “What happened with regional banks this week?” or “Any updates on AI regulation in the EU?” or “Summarize the top 3 things my clients are probably asking about right now.”

You can also adjust the scope over time. If certain topics keep showing up that aren’t relevant to your work, update your prompt to exclude them. If there’s a niche area you want more coverage on, add it in. Think of the prompt as a filter you tune over time, not a one-size-fits-all solution.

Step 3: Save It to a Document

Once you have a briefing you’re happy with, you can ask Cowork to save it directly to a Google Doc:

“Take this briefing and save it to a Google Doc titled ‘Weekly Industry Briefing: [date].’”

Now you have a permanent, searchable record. Over time this becomes a useful archive, whether you’re trying to remember what was happening in your industry during a specific period, preparing for a client meeting, or spotting patterns across months.

Step 4: Automate It

This is where the workflow really pays off. Instead of remembering to run your prompt each week, you can schedule it to run automatically:

“Run this industry news briefing every Monday at 8am and save it to Google Drive.”

From that point on, a fresh briefing shows up in your Drive every Monday morning without you having to do anything. You open it, read it in 10 minutes, and walk into your week informed.

The Bigger Picture

The goal here isn’t to replace your judgment or your expertise. It’s to handle the mechanical part of staying informed so you can spend your energy on what actually requires your brain: analyzing, deciding, advising, and acting.

Whether you’re in finance, tech, healthcare, law, real estate, or anything else, the amount of information you’re expected to track is only going up. Workflows like this one are how you keep up without burning out on it.

Pick your industry, write your prompt, and let the briefing come to you.

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With the launch of Claude Design, you no longer need a Figma subscription, or weeks of back-and-forth to produce polished visual work. Whether you’re a solo founder or a team of fifty, Claude Design puts a visual collaborator in your corner: one that works in seconds.

What Is Claude Design?

Claude Design is a visual creation tool from Anthropic Labs, launched in April 2026 as part of the Claude suite. It lets you collaborate with Claude to produce polished visual work: product prototypes, pitch decks, landing pages, one-pagers, marketing materials, and more, all without needing to write a line of code or open a design app.

It’s powered by Claude Opus 4.7, Anthropic’s most capable vision model, and is available to Claude Pro, Max, Team, and Enterprise subscribers at claude.ai/design.

Think of it less like a template generator and more like a design partner. You describe what you need, Claude builds a first version, and then you refine it together through conversation: commenting inline, adjusting specific elements, or using custom sliders Claude generates on the fly to tweak spacing, color, and layout in real time.

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How Does It Work?

Getting started is as simple as describing what you want. Here’s what the workflow looks like in practice:

1. Start with a prompt, image, or your existing brand. You can kick off a project with plain text (”build me a one-page proposal for a web design client”), upload images or documents, or even point Claude at your codebase or website. Claude uses a web capture tool to pull visual elements directly from your site, so prototypes look like your actual product, not a generic template.

2. Refine it in conversation. Don’t love the layout? Say so. Want the headline bolder and the CTA button a different color? Just tell Claude. You can also comment directly on specific elements inline, the same way you’d leave a note in a shared Google Doc.

3. Collaborate with your team. Share a private link with your team, grant edit access so colleagues can jump in, and chat with Claude together in a group conversation. Everyone sees the same design and can push it forward without emailing files back and forth.

4. Export or hand off to code. When your design is ready, export it as a PDF, PPTX, HTML file, or send it straight to Canva. If you’re building something that needs to go live, Claude packages everything into a handoff bundle you can pass to Claude Code with a single instruction, with no separate design spec required.

A Real Use Case: The Small Business Owner Who Stopped Paying for Design

Here’s a concrete example of how a business owner can save real time and money with Claude Design.

The situation: Sarah runs a boutique marketing consultancy with a small team. Every time she lands a new client, she needs to produce a custom pitch deck, a proposal document, and a one-page service overview. In the past, she’d spend 4–6 hours building these herself in PowerPoint or pay a freelance designer $300–$500 per project.

With Claude Design: Sarah uploads her brand kit (logo, colors, fonts) during onboarding. Claude builds a design system from it, so every project that follows automatically uses her company’s look and feel.

When she closes a meeting with a new prospect, she opens claude.ai/design and types:

“Create a pitch deck for a 6-month social media retainer proposal for a local restaurant group. Include an overview slide, a services breakdown, a case study placeholder, and a pricing page. Use our brand style.”

In under two minutes, Claude delivers a complete, on-brand deck. Sarah reviews it, leaves a couple of inline comments (”make the pricing table cleaner,” “swap the hero image for something food-related”), and Claude applies the changes instantly. She exports it as a PPTX, adds her client’s logo, and sends it off. Total time invested: 15 minutes.

The savings: What used to cost her 5 hours or $400 now costs her 15 minutes and nothing extra on top of her Claude subscription. Over a year with 3–4 new proposals per month, that’s potentially $14,000–$24,000 in freelance design fees saved, or 200+ hours returned to billable work.

Who Should Be Using Claude Design?

Claude Design is particularly valuable for:

• Founders and solopreneurs who need to look polished without a design budget

• Sales and account teams who build a lot of proposals and pitch decks

• Product managers who want to sketch feature flows before handing off to engineers

• Marketers who produce one-pagers, landing pages, and campaign assets regularly

• Anyone who’s ever said “I just need something that looks good”

The Bottom Line

Claude Design doesn’t replace great designers, but it does eliminate the gap between having an idea and having something to show for it. For business owners especially, that gap used to cost time, money, and momentum. Now it costs a prompt.

You can try it today at claude.ai/design (Claude Pro, Max, Team, or Enterprise subscription required).

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Here’s what probably happened: a human never saw your application. An algorithm did, and it said no.

This isn’t paranoia. It’s the conclusion of the largest empirical study of algorithmic hiring ever conducted, published in 2026 by researchers from Stanford, Chapman, and Northeastern universities. They analyzed 3.4 million real job applicants submitting 4 million applications to 156 employers across 11 industry sectors. What they found should alarm anyone entering the workforce right now.

The Machine is Everywhere, and it’s Broken

More than 90% of U.S. employers now use hiring algorithms to screen applicants before a human ever looks at them. Worse, many of these employers are using tools from the same handful of vendors. Over 60% of Fortune 100 companies use HireVue alone. When that many employers funnel their hiring through one algorithm, the researchers call it an “algorithmic monoculture,” and it has consequences that go far beyond any single employer’s decision.

The most alarming consequence: systemic rejection. Because the same algorithm is screening you at dozens of companies simultaneously, one algorithmic thumbs-down effectively follows you across the job market. Among applicants who submitted four applications, 10% were rejected by all of them, far more than simple probability would predict if each employer were deciding independently. According to the study’s simulations, under the current system you’d need to apply to 25 places just to have a 99.9% chance of getting one recommendation through, compared to 10 under a fair, independent system.

And the racial disparities are stark. The study found that 25.87% of applications submitted by Black applicants and 14.74% of applications submitted by Asian applicants were sent to positions where the algorithm demonstrably discriminates against them under U.S. civil rights law. These biases were invisible in prior research because earlier studies looked at aggregate data. The discrimination only surfaces when you examine each job position separately, which is exactly how the law requires it to be measured.

The system is structurally rigged, and right now it’s doing it largely without accountability. If you’re a recent grad or early-career professional, you’re probably hitting this wall harder than anyone. You have less experience to “prove” yourself to an algorithm trained on historical data, and you’re likely applying broadly, which, as the research shows, is exactly where systemic rejection bites the hardest.

I know this all sounds pretty hopeless, and trust me, I’ve felt that way too, but there are still things you can do to get your resumé seen.

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How to Fight Back: Tools and Strategies

Optimize your resume for ATS systems. Most algorithms use Applicant Tracking Systems (ATS) that scan for keywords before anything else. Use tools like Jobscan or Resume Worded to score your resume against specific job descriptions and identify missing keywords, that way you’re sort of speaking the system’s langauge.

Use AI to sharpen your materials, seriously. This is one area where AI genuinely levels the playing field. Tools like Claude, ChatGPT, and similar AI assistants can help you:

- Rewrite your resume bullets to be more specific and quantified

- Tailor your cover letter to each job description in minutes instead of hours

- Practice interview answers by simulating common behavioral questions

- Identify skills gaps between your background and a job posting

- Draft cold outreach messages to hiring managers or employees at target companies

The key is using AI not to fabricate your experience, but to articulate it more clearly and strategically than you might on your own. A strong human story, well-expressed, is still the goal. AI just helps you get there faster while using the kind of language these systems respond to the best.

Network around the algorithm. A referral from someone inside the company often bypasses automated screening entirely. LinkedIn, alumni networks, industry events, and community Slack groups are all ways to build the human connections that get your resume in front of actual people. When a hiring manager already knows your name, the algorithm becomes far less relevant. This, of course, is easier said than done, and really only applies to people who simply already know someone who can help. For those starting from scratch, use LinkedIn to reach out to hiring managers, company owners, and team members with short, personalized messages. Even if they don’t answer, there’s a possibility that your message was seen, and that’s certainly better than sending no message at all. This shouldn’t replace applying through official channels, but it dramatically increases the chance that a human actually looks at you.

Apply earlier. Many hiring algorithms score you partly on timing. Applications submitted in the first 48 to 72 hours of a job posting tend to fare better. Set up job alerts and move fast.

Seek Out Human-First Job Boards

Perhaps the most direct counter to algorithmic gatekeeping is simply choosing to apply where humans are posting jobs for humans.

Lenny’s Jobs is one of the best examples of this. Run by Lenny Rachitsky, founder of Lenny’s Newsletter here on Substack and one of the most trusted voices in the product, growth, and tech space with over 1 million subscribers, the job board is a curated, community-driven resource where real people post real opportunities. These aren’t listings that pass through algorithmic screening layers or anonymous corporate portals. They come from the kinds of companies and teams that are plugged into one of the most respected professional communities in tech.

That matters for a few reasons. The jobs tend to be at companies that take culture seriously. The hiring managers who post there are often accessible. And because the board draws from a specific, high-quality community rather than mass-market aggregators, you’re competing against a more focused pool, not the millions of applicants being funneled through the same algorithmic chokepoint.

If you’re interested in product, growth, startups, or adjacent tech roles, Lenny’s Jobs is worth making a regular stop. It’s a direct line to opportunities that were posted by people, not optimized for machines.

The Bigger Picture

The Stanford study’s authors have called on regulators to audit algorithmic hiring tools, require position-by-position bias reporting, and give researchers the data access they need to hold vendors accountable. Those changes matter and could take years, but you need a job now.

The practical takeaway is this: the hiring system is more broken than most people realize, and the rules that were supposed to protect applicants are being circumvented by tools most companies can’t or won’t audit. That’s not your fault, but adapting to it is in your hands.

Use AI to compete more effectively. Build human networks that bypass the gatekeepers. And look for job boards that connect you to actual people, because right now, that’s your best shot at getting a fair hearing.

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“Write a one-page decision memo. The opportunity is [X]. We’re evaluating [options]. The buyers are [roles]. Constraints: [timeline, budget, integrations]. Write out the key questions we need to answer before we can make this decision confidently.”

Step 2 — NotebookLM framing prompt:

“Review this memo. Do not make a recommendation yet. Return the decision, the options, the evaluation criteria, and the source categories we need before we can trust the analysis.”

Step 4 — Structured brief prompt (repeat per vendor):

“Using only the sources in this notebook, write a structured brief for [Vendor] with these fields: best fit use case, proof points with source citations, pricing evidence, implementation effort and timeline, risks and red flags, and what still needs to be confirmed before signing.”

Step 5 — Final recommendation prompt:

“Based on the three vendor briefs and all sources in this notebook, create a comparison table across all evaluation criteria from the original memo. Then make a final recommendation. I need: a winner and why, a runner-up and under what conditions it becomes the first choice, a vendor to avoid for now and why, the fragile assumptions in this analysis, five questions to bring to each sales call, and a 30-day validation plan.”

That connective tissue is thanks to a brand voice blueprint, a reference document that captures how you communicate, not just what you say. It covers tone, personality, word choices, things you’d never say, and the feeling someone should walk away with after reading your content. Think of it as a style guide for your words.

If you’re building a business, this is what keeps your Instagram, your emails, and your website from feeling like they were written by three different people. If you’re a content creator building a personal brand, this is what makes your audience feel like they know you, even before they’ve ever spoken to you.

Start with what you already have

The best place to begin is with content you’ve already created. If you have a few Instagram captions, some emails, a bio, or even voice memos you’ve sent, those are gold. Your voice is already in there. Claude just helps you see it clearly.

Open Claude and try something like this:

“Here are a few pieces of content I’ve written. Can you analyze the tone, writing style, and personality that comes through? What patterns do you notice? What words or phrases do I tend to use? What does my voice seem to avoid?”

Paste in three to five examples and let Claude do the analysis. What comes back will often surprise you. Claude will pick up on things you’ve been doing unconsciously, like the fact that you tend to use short punchy sentences when you’re making a point, or that you almost never use formal language even when discussing serious topics.

That analysis becomes the raw material for your blueprint.

Build the blueprint

Once you have Claude’s analysis, your next prompt is to turn those observations into a usable document. Ask Claude to draft a brand voice guide based on what it found. A good blueprint includes:

Tone descriptors. Three to five adjectives that describe how you sound. Not “professional and friendly” (too generic) but something specific like “direct, warm, a little irreverent.”

What you sound like vs. what you don’t. This is the most useful section. Give examples of phrasing you’d use versus phrasing you’d never use. The contrast makes it concrete.

Your audience and how you talk to them. Are you speaking to a peer, a client, a student? The relationship changes everything about how the voice shows up.

Words and phrases that are distinctly yours. Most people have these without realizing it. Claude will find them in your examples.

Content you don’t create. Just as important as what you do. If you never do fear-based marketing or you never use industry jargon, that belongs in the blueprint too.

Once Claude drafts this, read through it like you’re reading someone else’s profile. Does it sound like you? Adjust anything that feels off. The goal is a document you could hand to anyone and have them write something that actually sounds like you.

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The Personal Voice Layer (For Content Creators)

If you’re building a personal brand alongside or instead of a business brand, there’s an extra layer worth adding: your personal voice.

A personal voice goes deeper than a brand voice. It captures not just how you communicate but why you communicate the way you do. It includes your worldview, the things you genuinely believe, the opinions you’re willing to hold publicly, and the experiences that shaped how you see your industry.

For this, try a different kind of prompt:

“I want to develop my personal voice as a content creator. Based on the content I’ve shared, what point of view comes through? What do I seem to care about? What perspective do I bring that feels distinct? Help me articulate my content philosophy in a few paragraphs.”

This is less about style and more about substance. The output from this exercise becomes the “why” behind your content strategy. When you know what you actually stand for, creating content gets a lot easier because you’re not starting from scratch every time. You’re just finding new ways to say things you already believe.

How to Actually Use It

The biggest mistake people make with a brand voice blueprint is building it and filing it away. The document is only useful if it becomes part of your workflow.

Once yours is ready, paste it at the start of any Claude session where you’re creating content. Tell Claude: “Here is my brand voice guide. Please use this as a reference for everything we create together today.” Claude will hold it as context for the entire conversation, which means every caption, email, or blog outline it helps you with will be filtered through your voice, not a generic one.

You can also use it for review. Paste a draft you’ve written and ask Claude to flag anything that doesn’t match your brand voice guide. It’s a fast way to catch moments where you’ve accidentally slipped into a tone that doesn’t feel right.

Your voice is already there. Claude just helps you find it, name it, and use it with intention.

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