(New here? This is the follow-up to The US Government Just Took Anthropic's Best AI Model Offline. Start there if you missed the original.)

The short version

When I wrote the first piece, the honest bet -- was that Fable 5 comes back, soon, with more process wrapped around it. That's exactly what happened. The ban lasted 18 days. On June 30, Commerce Secretary Howard Lutnick withdrew the export controls. On July 1, Fable 5 came back globally.

But "it's back" is the least interesting part. What actually happened is that the most powerful AI on Earth got pulled, negotiated over for two and a half weeks, and returned with a new safety classifier, a tiered restoration, and -- most importantly -- the beginnings of an industry-wide rulebook for governing this stuff. The sledgehammer got everyone's attention. What quietly got built in its place is a scalpel.

Let me walk you through it.

The field notes: June 12 to July 1

June 12 -- the off switch. The Commerce Department orders Anthropic to block Fable 5 and Mythos 5 for any foreign national, anywhere. Because no company can verify nationality in real time, Anthropic shuts both models off for everyone. (That's the original story.)

June 15 -- talks fail. Anthropic meets with officials to resolve it. The talks go nowhere. The administration hardens its position instead of softening it -- reportedly because a national security agency had weighed in and assessed that Fable's safeguards could be pushed toward the more dangerous Mythos-class capabilities underneath. The government's ask, at its peak, was essentially: guarantee the model can't be jailbroken at all. Every security expert alive will tell you that's technically impossible.

June 26 -- Mythos comes back first, for a chosen few. The first thaw. The government approves redeploying Mythos 5 -- but only to a set of pre-cleared US organizations, concentrated in critical infrastructure and defense, most of them already in Anthropic's vetted Project Glasswing program. The general public still has nothing.

June 30 -- the full lift. Lutnick withdraws the June 12 export controls entirely. Per CNBC, a license is no longer required to export either model.

July 1 -- Fable is back. Anthropic redeploys Fable 5 globally across Claude.ai, the Claude Platform, Claude Code, and Claude Cowork. Cloud marketplaces (AWS, Google Cloud, Microsoft Foundry) follow behind.

Eighteen days, off to on.

Why it really happened (it wasn't just "a jailbreak")

The original story was "a jailbreak spooked the government." The fuller version is more human, and more uncomfortable.

The trigger was a phone call from Amazon. According to Fortune and TechCrunch, Amazon CEO Andy Jassy was already on a scheduled call with White House officials on June 11 about something unrelated. His researchers had found a technique that got Fable 5 to identify software vulnerabilities -- and in one case produce code showing how one could be exploited. He raised it. Officials told him to take it straight to Treasury Secretary Scott Bessent. He did. By Friday evening, Lutnick's letter landed on Anthropic's desk.

Sit with the irony: Amazon is one of Anthropic's largest investors, billions deep. The company bankrolling Anthropic's compute is the same one that told the government its model was dangerous.

The accelerant may have been China. Semafor and Fortune reported that part of the government's urgency came from suspicions that a China-linked group had gained unauthorized access to Mythos, raising reverse-engineering fears. Worth being precise here: that's reporting, and Anthropic's own statements don't mention it. But if true, it reframes everything -- "a jailbreak other models can also do" is a policy debate; "an adversary is already inside the most capable AI on the planet" is a five-alarm fire. That gap probably explains why the government moved at the speed it did.

Anthropic's counter, which mostly held up. Anthropic argued the technique was routine defensive security work that weaker models -- its own testing named Opus 4.8, GPT-5.5, and Kimi K2.7 -- could all do without any bypass. The government's own testers at the Center for AI Standards and Innovation eventually agreed Anthropic's safeguards were, in their words, extraordinarily strong. In other words: the model came back partly because the "too dangerous" framing didn't survive contact with the evidence.

Hype check: what's different now that it's back

Fable 5 did not return unchanged. If you build on it, three things matter.

A new classifier. Anthropic trained a targeted safety classifier that blocks the specific technique from Amazon's report in over 99% of cases. When a request trips it, you're notified and rerouted to Opus 4.8.

More false positives -- for now. The tradeoff is real: the new safeguards flag more benign coding and debugging requests than before. Anthropic says it's actively tuning that down. If you hit a wall on legitimate work, that's why.

A free window with a hard deadline. Pro, Max, Team, and select Enterprise plans get Fable 5 included for up to 50% of weekly usage limits through July 7, after which it moves to usage credits. (Standard Enterprise seats don't get an included allowance -- check the footnotes.) At $10/$50 per million tokens, Fable is roughly double Opus 4.8, so that window is worth using deliberately -- more on exactly how below.

The precedents that will outlast the outage

(Paid subscribers -- the ban is over. What it permanently changed is the part worth paying attention to.)

The 18-day outage resolved. The precedents it set did not. Here are the five that will still be shaping your world a year from now.

Read more

Anthropic just dropped an AI coworker into Slack. There's one catch: you probably can't get it yet. Here's the honest head-to-head -- and the teammate you can hire today.

On June 23, Anthropic launched Claude Tag -- type @Claude in a Slack thread and an AI agent reads the conversation, breaks down the task, and even jumps in on its own to flag things you forgot. It remembers. It works while you're logged off. Anthropic's own framing is that it feels like "working with a real colleague."

If that pitch sounds familiar, it's because there's already a company built entirely around it. Viktor's tagline is literally "Not a tool. A hire." -- an AI employee that lives in Slack and Microsoft Teams, plugs into 3,200+ apps, and ships actual work: reports, dashboards, code, campaigns.

So, as the "AI teammate" category heats up: which one do you actually put in your workspace? Here's the read between the lines.

Claude Tag vs Viktor: Head-to-Head Comparison

---

WHAT IT IS:

Claude Tag: @Claude agent inside Slack threads
Viktor: an AI "employee" in Slack + Teams

WHERE IT RUNS:

Claude Tag: Slack only (more platforms "in coming weeks")
Viktor: Slack AND Microsoft Teams, today

WHO CAN GET IT:

Claude Tag: Claude Enterprise & Team customers, in beta
Viktor: anyone -- free $100 in credits, no card

PRICING:

Claude Tag: per-seat, ~$20--30/user/mo with a 5-seat minimum (Team), or custom (Enterprise)
Viktor: a flat $50/mo shared workspace, no per-seat charge

INTEGRATIONS:

Claude Tag: Anthropic connectors (Slack, GitHub, Drive, warehouses via admin)
Viktor: 3,200+ tools

MODEL:

Claude Tag: Claude (Opus 4.8)
Viktor: Claude, OpenAI, and others

SECURITY:

Claude Tag: admin-controlled access, per-channel scoping, full audit trail
Viktor: SOC 2 compliant, DPA on Enterprise

---

Where Claude Tag Wins: Agent Identity, Security, and Ambient Mode

Let's be fair -- the credible answer matters more than the convenient one.

Claude Tag's agent identity model is legitimately clever, and the part nobody else has cleanly solved. Instead of the AI borrowing your permissions (and quietly turning a shared channel into a side door to your private docs), Claude gets its own scoped account per channel. The legal channel's Claude can't read the engineering repo. Admins control exactly what it touches, and every action lands in an audit log. If you're a large org with a security team that asks hard questions, that architecture is a real advantage.

It's also pure Claude -- if your team is already all-in on Anthropic's models and you're on Enterprise, Tag is the native, no-new-vendor option.

Where Viktor Wins: Microsoft Teams, Flat Pricing, and Day-One Access

Here's the thing the launch coverage buried: Claude Tag is beta, Slack-only, and locked to Enterprise and Team plans. A huge share of the people reading about it this week -- solo operators, small teams, anyone on Microsoft Teams, anyone without an Enterprise contract -- literally cannot use it yet.

Viktor fills exactly that gap:

• You can actually start. $100 in free credits, no credit card, no sales call. Claude Tag wants you on a qualifying plan first.

• It runs on Teams, not just Slack. If your company lives in Microsoft 365, Tag isn't an option today. Viktor is.

• The math favors small teams. Claude Team is per-seat with a 5-person minimum -- call it $125+/month before you've done anything. Viktor is a flat $50/month shared workspace.

• It's model-agnostic. Viktor routes across Claude, OpenAI, and others, so you're not betting the whole workflow on one lab.

My Two Cents: What It's Actually Like Using Viktor Every Day

I'm not neutral, so let me say it plainly: my company, Pictor.pro, runs Viktor every day -- across our dev team, me on product, our C-suite, even alongside customers -- and the link in this post is an affiliate link. I'd still point a friend to it. Here's the honest version of why.

The thing nobody tells you about a tool like this is how it arrives. Viktor didn't get "rolled out." It took root. It earned its way in, one skeptic at a time.

Exhibit A: one of our devs flat-out doubted it could tell an actual bug from human error on a support ticket. Viktor found the relevant line number in the code, explained the likely cause, suggested the fix -- and offered to make the change itself. (We haven't handed it that key yet.) It didn't close the ticket; it gave the team a running start on triage in seconds.

Exhibit B: I needed to understand a very specific corner of how our product worked, and I didn't know the answer. I told Viktor. Instead of guessing, it went and found the teammate who'd actually written that part of the code, asked him pointed, specific questions, and handed me back exactly what I needed to update our support docs. That teammate -- who started out treating Viktor like an alien in the building -- now talks to it like a colleague.

That's the part that's hard to describe without sounding like I'm overselling. Viktor behaves like the teammate who's almost annoyingly good -- relentlessly proactive, weirdly eager, always connecting dots you didn't ask it to connect -- except it has zero interest in the credit. It's not angling for your job. It wants you to win. You end up liking it despite yourself.

Can Viktor Survive Anthropic? Why the Real Moat Is Temperament, Not the Model

Here's the uncomfortable truth underneath this whole comparison: almost every breakout AI startup of the last few years is renting someone else's brain. They're built on top of OpenAI's or Anthropic's models. And those labs are watching their ecosystems find product-market fit and then -- politely or not -- walking into the very same category. Claude Tag is exactly that: Anthropic stepping into the "AI teammate in Slack" space that companies like Viktor mapped out first. If the labs are feeling generous, they acquire you. They usually don't have to. It was their brain you were borrowing.

So what's actually defensible? From where I sit, it isn't the model -- it's the temperament. Viktor's moat is that it nails the feel of a great proactive teammate in a way I haven't seen anyone else, the labs included, reproduce yet. That's a product-and-taste problem, not a model problem, which is exactly why it's hard to copy.

The one place this bites me: my other team, Outsnapped, lives in Google Workspace and Google Chat -- not Slack. So I can't drop Viktor in the way I have at Pictor. I've been hand-tuning a Hermes agent to get somewhere in the neighborhood, and I'll be honest: it's been nearly impossible to come close to the magic Viktor has harnessed -- the proactivity, the eagerness, the dot-connecting. Whoever ships a real Google Chat version of this first is getting my money. Viktor, Claude Tag, anyone -- that's a wide-open door.

The Verdict: Should You Choose Claude Tag or Viktor?

If you're an enterprise already standardized on Claude, with an admin team that cares about per-channel permission scoping, Claude Tag is the elegant native answer -- once you're off the beta waitlist.

For everyone else -- small teams, Teams-first shops, anyone who wants an AI coworker doing real work this afternoon instead of in coming weeks -- the practical pick is the one you can hire right now.

Hire Viktor -- new readers get $50 off ->

The "AI teammate" era didn't start this week. Anthropic just made everyone notice it. The good news: you don't have to wait for a beta invite to live in it.

Disclosure: Pictor.pro is a paying Viktor customer, and the Viktor link above is an affiliate link -- if you sign up through it, Artificially Intimidating earns a commission, at no extra cost to you, and you still get the $50 off. I only run affiliate offers for tools I'd actually point a friend to. Claude Tag details are from Anthropic, Reuters, and TechCrunch reporting (June 23--24, 2026); pricing is current as of publication and may change.

Go Deeper: The Full Claude Tag vs Viktor Research Report

If you want the ground-level version of this matchup, I packaged it into a Deep Research Report -- and it goes well past what's above:

• What practitioners are actually saying on Reddit, Hacker News, Product Hunt, and YouTube (including the "The end of Viktor?" thread and the HN debate).

• The full pros and cons for both tools -- token-cost anxiety, governance gaps, the Teams lockout, credit-burn opacity, and more.

• An 18-row head-to-head feature matrix covering memory model, eligibility, audit logs, pricing, traction, and SOC 2.

• The real-money numbers: Viktor's $20M ARR and $75M Series A, and how Anthropic's launch reframes the whole category.

Paid subscribers get the full report below. It's the kind of thing I'd otherwise keep to myself -- consider it the upgrade.

Read more

On June 15th, Salesforce announced it was buying Intercom — now rebranded “Fin” — for about $3.6 billion.

We’d already left.

Not because we saw the acquisition coming. We left because we were paying $451 a month to be one customer in a zillion, and the marquee feature we were paying for kept lying to the people we were trying to help.

Let me back up.

For Pictor.pro — our AI photo booth software — Intercom was the front door. It was how users reached us when something broke, when they were confused, when they were deciding whether to trust us with their event. That chat widget mattered. And the pitch that sold us was Fin, Intercom’s AI agent: it would resolve 80%+ of support requests on its own. Salesforce is now citing 76% in their press release — and pointedly contrasting it with their own Agentforce’s 62%. Big, confident numbers.

Here’s what that number looked like from our side of the glass: a confident AI handing our customers incorrect information.

And that’s the part nobody tells you about “AI resolution rates.” A ticket marked resolved doesn’t mean the person got helped. Half the time it means they were deflected — they gave up, or worse, they walked away believing something false about our product. In support, a confidently wrong answer is more expensive than no answer at all. “A human will get back to you in an hour” protects trust. “Here’s the wrong setting, good luck” destroys it.

So when I do the math, I wasn’t getting $451 of value a month. I was paying for the privilege of having an AI hallucinate on my behalf. I wasn’t costing Intercom anything close to that to serve, either. The price wasn’t tethered to value — it was tethered to what a market leader could charge while you had nowhere convenient to go.

Then I went looking, and I learned something about incentives.

When a company is in acquisition mode, the whole organization is optimizing for one thing: getting the numbers up, as high as possible, as fast as possible, to look as attractive as possible to a buyer. That is a completely rational thing for them to do. It is also almost never aligned with your experience as a small customer. You are a data point in a growth chart, not a relationship. The Salesforce deal didn’t create that misalignment. It just made it impossible to ignore.

I want to be fair: Intercom is a genuinely good product for the right company. If you’re a 200-person org living inside Salesforce already, Fin folding into Agentforce might be exactly what you want. This isn’t a hit piece. It’s a fit story. And we weren’t the fit anymore.

So I did what any of us should do more often: I compared the alternatives. Zendesk, the usual Intercom competitors, the whole enterprise lineup. And then, almost by accident, in an open-source conversation, I bumped into Chatwoot.

What got me excited wasn’t the feature list. It was that when I asked questions, the founders answered — almost instantly. Not a ticket queue. Not a bot. The people who build the thing, telling me directly that they cared whether my business succeeded.

Is Chatwoot perfect? Definitely not. But I’ll take an imperfect tool run by people who answer my tweets over a polished one where I’m a rounding error.

We started self-hosted, just to test the software for real. Once I trusted it, I looked at their paid hosted plans — and the per-seat pricing was genuinely reasonable, so we moved to that and skipped the maintenance burden. Here’s the part that actually matters, though: we can port our data back and forth whenever we want. If we ever need extreme customizations, or if the pricing ever rockets past what we think it’s worth, we move back to self-hosted. On our terms.

That’s the thing Intercom never gave us. Even leaving was harder than it needed to be — exporting your data means shipping it off to AWS buckets and similar hoops. Friction on the way out is a tell. It says the lock-in was the strategy.

So here’s what I actually want you to take from this, whether you use Intercom or not:

Software gets bloated, then it gets expensive, then it gets acquired, then it gets worse. A smaller tool is often a better fit than the market leader. And the vendors worth your money are the ones who still need you — not the ones who’ve already won and are now just managing you toward an exit.

Which raises the real question. Not “should I leave Intercom?” — that’s specific to me. The real question is: how do you evaluate any critical vendor so you never get caught flat-footed when they get acquired, jack up prices, or quietly stop caring?

I built a scorecard for exactly that. It’s the checklist I now run before I’ll trust any tool with a core part of my business — and there’s one question on it that has saved me more than all the others combined.

The Vendor Evaluation Scorecard

Here’s the framework I run before committing to any tool that touches a core part of my business. Six checks. Run every candidate through all of them before you sign up — and absolutely before you let something become your “front door.”

Read more

Good day, humans. Half of America is now chatting with AI — and most of them quietly think it's going to end badly. Meanwhile, Estonia just decided your AI assistant deserves its own national ID number. Big Tech signed a safety pact, Anthropic put Claude Design on a diet, and we've got a blueprint for agents that run themselves. Let's get into it.

📬 Before we dive in: The sharpest AI Brief tips come from readers who are actually in the weeds. If you spot a story worth covering, drop it in the Discord. The best tips make tomorrow's edition.

Half of America Now Talks to Chatbots

Pew Research Center

📍 Editor's note: I was in the audience for this one — Pew's Monica Anderson unveiled this data live yesterday at Shared Futures: The AI Forum, the first time it was shared publicly.

What happened: A new Pew Research Center survey of 5,119 U.S. adults finds about half now use AI chatbots like ChatGPT, Gemini, or Copilot — up from a third in 2024. Roughly one in four use them daily, and ChatGPT alone reaches 44% of adults.

Why it matters: AI went from early-adopter toy to mainstream habit in about 18 months. Searching for information (42%) and work tasks (38% of employed adults) are the most common uses — this is now infrastructure, not novelty.

What everyone's saying: The adoption curve is the headline, but the skepticism is the real story: 63% say AI is advancing too quickly, and 71% expect it to make their personal data less secure. More Americans think AI will hurt society than help it.

My read between the lines: The most telling number isn't adoption — it's that adults under 30, the heaviest users, are the most pessimistic (48% expect AI to harm society). Familiarity isn't breeding comfort; it's breeding dread. People are leaning hardest on the very thing they fear most.

📖 Further reading: AI Is a Trust Problem, Not a Tech Problem — Pew just quantified it — public distrust, not capability, is the real ceiling on AI.

Be honest: you've been meaning to set up your own AI agents for months — but between running everything yourself, who has the time?

Viktor skips the setup entirely. It already lives in Slack, connects to 3,000+ tools, and does the real work: reports, dashboards, code, campaigns. Not a chatbot you have to babysit — a coworker that just ships.

New users get $50 off their first month, so it costs almost nothing to let it prove itself. Start your trial →

Estonia Wants to Give AI Agents a National ID

CSO Online

What happened: Estonian Prime Minister Kristen Michal approved a plan to make Estonia the first country to issue government-backed digital IDs to AI agents — each autonomous agent getting its own personal ID code, separate from the human or company it serves.

Why it matters: Most agentic tools today demand blanket access to your accounts. Estonia's model would scope an agent to specific actions — view this, draft that, pay up to a fixed limit — all traceable. It's a concrete answer to “who's accountable when the bot does something?”

What everyone's saying: It's a natural extension of Estonia's famous e-government stack — digital ID, online voting, e-Residency. Vendors have floated their own agent-identity schemes, but as CSO Online notes, none carry government backing.

My read between the lines: There's no launch date, no liability framework, and no answer for what happens when an agent blows past its authorized scope. Estonia is brilliant at being first; the hard part — who pays when your AI lawyer hallucinates a contract — is conveniently filed under “analysis pending.”

📖 Further reading: Your laptop has been in the way this whole time — the case for managed agents with scoped permissions — exactly what Estonia is now trying to legislate.

Big Tech Agrees on AI Safety — On Paper

Linux Foundation

What happened: The Linux Foundation launched the Appia Foundation, a vendor-neutral effort to build open specifications and conformity tests so companies can prove their AI systems meet safety, trust, and compliance requirements. Google, Microsoft, OpenAI, Arm, Mastercard, and Siemens have signed on as members.

Why it matters: As governments worldwide roll out AI rules, companies face a patchwork of overlapping audits. Appia wants conformity evidence to be reusable across the supply chain — test once, satisfy many regulators — which could meaningfully cut duplicate compliance costs.

What everyone's saying: It's part of a coordinated standards push: the Foundation also just launched DocLang, an AI-native document format founded by IBM, Nvidia, and Red Hat. The framing across both is that the next AI bottleneck is coordination and trust, not raw model capability.

My read between the lines: When OpenAI, Google, and Microsoft all cheerfully join the same “safety standards” body, read it as the incumbents drawing the map before regulators show up with their own. Self-certification frameworks have a way of certifying exactly what the biggest members were already planning to ship.

📖 Further reading: The US Government Just Took Anthropic's Best AI Model Offline — Here's Why — what AI safety enforcement looks like when it's mandatory, not a voluntary industry pact.

Anthropic Rebuilds Claude Design to Burn Fewer Tokens

VentureBeat

What happened: Anthropic shipped a major overhaul of Claude Design, the AI design tool it launched in April, adding two-way Claude Code integration (“code round-trips”), brand-compliance controls, enterprise export — and, notably, much lower token consumption.

Why it matters: Designers can now hand a mockup to Claude Code, get back production-ready code that stays faithful to the design, and round-trip changes both directions. It pushes “prompt to production” closer to reality for teams that don't have a dedicated designer in the room.

What everyone's saying: Per VentureBeat, the token fix is the headline for power users — the original could blow through subscription limits in a few iterations, a sore point after Anthropic's June 15 move to bill agent usage on a separate meter. It's also fending off open-source rivals like Open Design (57k GitHub stars in eight weeks).

My read between the lines: “We made it use fewer tokens” is a quiet admission the original was too expensive to actually use. The real tell is timing: right after Anthropic split agent billing onto its own meter, the token-hungry tool suddenly found efficiency. Funny how that works.

📖 Further reading: OpenAI shipped a physical camera, but that's not the story — where “vibe-coding” is headed when building software stops requiring expertise — Claude Design is the design-side version.

How to Build AI Agents That Run Themselves

Claire Vo

What happened: Product leader Claire Vo broke down four ways to make AI agents run autonomously — heartbeat, cron, hook, and goal loops — and built live examples in Claude Code and Codex.

Why it matters: This is the shift from “AI you prompt” to “AI that runs on a schedule and acts on its own.” Her mental model — treat an agent like onboarding an employee, with work trees, skills, connectors, subagents, and memory — is the clearest beginner map to autonomous AI yet.

What everyone's saying: Practitioners are converging on the same recipe; cron-style “run on a schedule without going off the rails” patterns are all over dev circles right now. Goal-based loops are the holy grail — and, everyone agrees, the most expensive to pull off.

My read between the lines: Vo's own warning is the quiet part out loud: most builders burn a fortune in tokens before a goal loop produces anything useful. Autonomy sounds like freedom until the bill arrives. The agents work fine — it's your budget that needs supervising.

📖 Further reading: I ignored Hermes for two months. Here's what I actually found. — a hands-on reality check on what living with an autonomous agent is actually like.

That's your AI Brief for Thursday. Join the conversation in the Artificially Intimidating community chat.

—Artificially Intimidating

⚡ Update — July 2, 2026:

Fable 5 is back. The government lifted the ban after 18 days, and the model returned globally on July 1 — but not before a warning from Amazon, an NSA assessment, a China scare, and a quiet new rulebook for the whole industry. Read the full follow-up: Fable 5 Is Back — What Actually Happened During the 18-Day Ban →

Nate B. Jones broke this one from an airplane — first class, calm as ever — because it couldn’t wait for wheels-down. He was right. On June 12, the US government forced Anthropic to pull its two most powerful AI models offline for everyone on Earth. This is the first time a frontier model has been rolled back by government order. Here’s the story, his read, and then mine — including the part nobody’s saying out loud.

Watch Nate break it first

Before anything else: credit where it’s due. Nate B. Jones filmed his breakdown from a plane because the story was moving too fast to wait. That tells you everything about how big this is. If you have nine minutes, watch it — it’s the clearest calm-in-the-storm explainer out there:

▶️ Nate B. Jones: “I’m filming this from a plane because this is unprecedented”

What I love about Nate is that he doesn’t do panic. He does precision. So I’m going to give him full credit for surfacing this, walk you through what actually happened, summarize his three-layer read, and then get into the part that’s been rattling around in my head since I watched it: what this really says about the haves, the have-nots, and the future we’re walking into.

What actually happened, in plain English

On Friday, June 12, 2026, at 5:21pm ET, Anthropic received an export control directive from the US government. Citing national security authorities, the order suspends all access to Fable 5 and Mythos 5 — Anthropic’s two most advanced models — by any foreign national, whether inside or outside the United States, including Anthropic’s own foreign-national employees.

Read that last part again, because it’s the whole story. A modern AI company can’t reliably verify the nationality of every user, employee, and contractor touching a model deployed to hundreds of millions of people. So the only way to actually comply with “no foreign nationals” is to shut the model off for everyone. And that’s exactly what Anthropic did. (All of Anthropic’s other models — Opus, Sonnet, Haiku — are unaffected.)

The timing is brutal. Anthropic had just rolled out Fable 5 on June 9, free to all Pro, Max, and Enterprise users through June 22. The model they handed to millions of people for free on Tuesday was switched off for everyone by Friday night.

Why? Per Axios, the Commerce Department moved after another company claimed it had jailbroken Mythos. Anthropic’s version, in its own statement, is more deflating: the government’s evidence was verbal, the “jailbreak” amounted to asking the model to read a specific codebase and flag software flaws, and the vulnerabilities it surfaced were minor, already-known, and — in Anthropic’s words — discoverable by other publicly available models (including OpenAI’s GPT-5.5) without any bypass at all. Anthropic is complying, but openly disagrees, calling it a likely “misunderstanding” and saying it’s working to restore access.

So: the most capable AI model in the world, pulled on the basis of a verbal report about a narrow technique that other models can already do. That’s the factual core. Now the interesting part.

Nate’s Read: Three Layers

Nate frames the whole thing in three layers, and it’s a genuinely useful lens:

1. The safety layer. He doesn’t dismiss it. His sharp point: if a jailbreak method works against one frontier model, the honest default is to assume it works against the class of models until proven otherwise — these systems share enough structure that an attack is evidence about the category, not just the instance. But — and this is the real critique — a vague verbal claim is nowhere near enough to justify an order this sweeping. If the government is going to freeze a private company’s flagship product, there needs to be a transparent statutory path, a clear technical standard, and a way to respond to the actual evidence. Otherwise any frontier model can be frozen on a claim no one can audit.

2. The “foreign national” fig leaf. The phrase sounds surgical — like the government is just controlling who has access, not banning the product. But for a company that sells globally, employs globally, and runs on international cloud infrastructure, “no foreign nationals” isn’t a scalpel. It’s an off switch with export-control language wrapped around it. Everyone involved, Nate argues, almost certainly knows that.

3. The business reality. This is why he thinks it resolves fast. Anthropic and the government have a working relationship and a template for negotiated, trusted access. Anthropic is acting like the access regime broke, not like Fable is dead. Customers want it back. The government doesn’t want to look like it kneecapped a leading American AI lab while loudly insisting America should lead on AI. His bet: Fable comes back, soon, with more process around it.

His takeaway for operators is the line I keep coming back to: frontier model access is now a policy surface. Raw model quality is no longer the only thing that matters. Keep your alternatives warm. Don’t build critical work on the assumption that the best model stays available on yesterday’s terms.

It’s a great breakdown. Go watch the whole thing.

And now — here’s where I want to take it somewhere Nate’s measured, on-a-plane delivery couldn’t quite go.

Between the lines: this is a story about the haves and the have-nots

Here’s the thing that’s been sitting in my chest since I watched Nate’s video.

Read more

Three weeks ago, in the May 18 brief, I said Apple was quietly trying to “own the trust layer” — routing your Siri requests through its own Private Cloud Compute before they ever touch Google’s Gemini.

I wrote it as a throwaway line. After watching WWDC, I think it was the whole ballgame.

Because here’s what Apple actually announced on Monday, underneath the Liquid Glass sliders and the 80%-faster AirDrop and a macOS named, I’m not kidding, “Golden Gate”:

Apple gave up on building the AI. And it might be the smartest move they’ve made in a decade.

They rented the brain

The new Siri AI — the one Apple first promised in 2024, the one they got sued over and settled for $250 million when it didn’t ship — doesn’t run on Apple’s own frontier model.

It runs on Google’s Gemini.

Apple adapted Gemini to run on its devices and servers, slapped an “Apple Foundation Models” label on it, and shipped it. Even the watermark on AI-generated images is Google’s tech (SynthID). The dependency runs deep, and Apple barely tried to hide it.

Now — the easy take is “Apple fell behind and had to go begging to Google.” And sure, there’s some truth there. You don’t pay a quarter-billion-dollar settlement from a position of strength.

But that’s not the interesting part. The interesting part is what Apple built around the rented brain.

The Tell: A Socket

Look at what Apple kept for itself and what it outsourced.

It outsourced the model. But it kept every piece of the layer that wraps around the model: the system orchestrator, the semantic index in Spotlight that knows your photos and your messages, App Intents that let Siri reach into your apps, on-screen awareness, the personal-context plumbing. That’s the hard part. That’s the part nobody can copy, because it’s welded into the OS and your data.

And then there’s the detail that gives away the entire strategy: Apple is building Apple Intelligence so you can swap the underlying model out. Gemini today. Something else tomorrow. A socket on the wall.

You do not design a slot to swap the engine if you think the engine is your advantage.

Apple just told the entire industry, in its own product architecture, that it believes the frontier model is a commodity — a part you buy, like a screen or a battery or a chip from TSMC. The intelligence is interchangeable. The integration is the product.

That is a wildly different bet from OpenAI and Anthropic, who are betting their entire valuations that the model itself stays the moat.

So who’s right?

The squeeze nobody’s talking about

Here’s the thing that makes Apple’s bet so sharp: “the model is a commodity” crushes the middle from both directions at once.

From above, value floats up to whoever owns distribution, data, and trust. That’s Apple. A billion devices. Your photos. Your messages. Fifteen years of “I’ll hand Apple my data and they won’t sell it.” Gemini is just the engine they dropped into a car everyone already owns and already trusts.

From below, the labs don’t sit still. OpenAI and Anthropic spent the last three years watching people build on their APIs, cherry-picking the workflows that got traction, and absorbing them in-house. ChatGPT Search is Perplexity. Projects, memory, Canvas, computer use — every one of those was somebody’s startup first.

So the thin middle gets flattened. And I’ll say the quiet part out loud, even though one of these is a tool I’ve recommended to you:

Perplexity, Jasper, Wispr Flow, the other dictation apps, the standalone “chat with AI” wrappers — I think most of them are dead tools walking. Not because they’re bad. I love many of these and they supported our business and entry into this space …. but because they built their whole business inside the labs’ roadmap, with no distribution the labs can’t match and no data the labs can’t get.

Perplexity is a gorgeous interface sitting directly on top of the one feature OpenAI most obviously wanted. Brand alone doesn’t stop OpenAI. It barely slows it down.

And the agents are next

Here’s where I lose half of you, because this is the part nobody funding the current hype cycle wants to hear.

It’s not just the chatbot wrappers that are dead tools walking. It’s most of the agents everybody’s so excited about right now.

Watch what Siri actually did in the demos. “Give me directions to the arch with a stop at Jeff’s.” “Add just the photos with Bryce to the shared album.” “Send the menu to the Goal Chasers group chat.” That’s not search. That’s an agent orchestrating across Maps, Photos, Contacts, and Messages from one plain-English sentence.

Apple is building the everything-app. Except it’s not an app — it’s the operating system becoming the agent. They don’t need you to download it. It’s already on the phone in your pocket, wired into your data, with App Intents reaching into every app on the thing.

And the second the OS is the agent, every standalone agent startup gets caught in the exact same squeeze as the wrappers — from both sides. The OS owns it from above: Siri has your personal context, your apps, and a billion-device head start no startup will ever match. The labs own it from below: ChatGPT, Claude’s computer use, Gemini’s agents, all racing to do your tasks natively. The standalone “AI agent that runs your life” is a wrapper with extra steps.

That’s the growth plan, by the way. The App Store taxed apps. The everything-agent taxes every action — Apple sitting in the middle of every booking, every purchase, every transaction, taking its cut, privacy as the cover story. It’s the App Store land-grab an order of magnitude bigger.

Now — does Siri actually pull this off out of the box? Honestly, no. It ships in beta later this year, English-only, blocked in the EU, newest-silicon-only, from the company that promised this exact Siri in 2024 and paid $250 million for missing. The ambition is real. The day-one delivery will lag, like it always does. So there’s a window.

But a window is not a moat.

There will be survivors. They’ll just be few and far between. The agents that live are the ones with — say it with me — the moats. Proprietary data the OS can’t get. A real customer relationship the OS won’t bother to win. Or a vertical so specific, so cross-platform, so far outside Apple’s walls that Siri will never show up to compete. (Apple’s agent only works inside Apple’s world. The web, Windows, Android, the messy regulated verticals — that’s open ground.)

Everything in the fat middle — the horizontal consumer agent with a nice UI and someone else’s model underneath — has a clock on it. Run the test one more time: if Siri can do this natively next fall, what do I have left? If the answer is nothing, you didn’t build a company. You built a feature Apple hadn’t shipped yet.

The only moats left

Strip the jargon off everything above and you’re left with a brutally simple map. When intelligence is a cheap utility you rent by the token, exactly three things still defend you — and nothing else does.

Own the customer. The relationship. The audience. The fact that they come to you on purpose. The labs can clone any tool you use, but they can’t clone the ten thousand people who open your email because it’s yours.

Own the data. Something you generate or collect that no lab has and can’t buy — your transactions, your niche, your operational history. The AI is rented. The data is yours.

Own the trust. This is the one nobody puts on the list, and it’s the whole game. An audience isn’t enough — there are huge lists nobody believes. Trust is what lets you stand in the middle: the one they’ll let broker their tools, hold their data, point them at the rented intelligence. Trust is what turns an audience into a position.

That’s it. That’s the list. Everything else — speed, taste, being a clever integrator — is a head start, not a wall. Real ways to make money. Not moats.

And here’s the part that should reorganize your whole week: Apple’s moats and yours are the same three, just at different scale. A billion customers. Their photos and messages as the data. And fifteen years of trust — which is the one Apple just quietly turned into a cash register.

Which means the model going commodity is the best news an audience-owner has ever gotten. For years the moat was the technology, and you couldn’t compete on that — you don’t have a research lab. Now the tech is a socket on the wall, and the three things that are left are the three things a small operator can actually build.

So the strategy writes itself. Stop chasing the best tool. Start building the three things the labs can never take — and steal the one move Apple just used to monetize all three.

Own the three — and steal Apple’s trust trick

Let me get specific, because “build a moat” is useless without a Monday-morning to-do. And to be clear up front: I’m not torching my stack over one keynote. None of this is “cancel your subscriptions today.” It’s where the time and the money go from here.

The filter

Every tool now has to answer one question before it gets another dollar:

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I have a lot of cameras. Like any amount you’re imagining … you should probably double or triple it.

Vintage rangefinders.

Fancy film cameras.

Point and shoot film cameras.

Medium format bodies.

Literal toy cameras (for adults and children).

I was a Lomo ambassador for years — shot an entire series of 30+ portraits with their Daguerreotype Achromat Art Lens, a recreation of one of the first double-element lenses ever made. Got published in their books. Documented nightlife and music scene for 15+ yeas and amassed an archive of 1.47 million photos of people at events. Currently run OutSnapped, a photo booth company known for inventing the AI Photo Booth. I’ve pined over cameras and photography longer than I’ve thought about almost anything.

So when OpenAI quietly dropped an open-source GitHub repo last week for something called ImageGenCam, I paid attention.

The concept: a camera you build yourself for around $100–150 in parts, using an AI coding agent to walk you through every step. You take a photo. The camera immediately transforms it with AI image generation. Instead of capturing reality, it uses reality as a prompt input. Pointless Scribble. Turn to Cheese. Goblin Mode. Every photo you take gets processed through whichever aesthetic you coded/loaded.

Here’s my honest reaction as a photographer: the intent still carries through. Your eye — what you point the camera at, how you frame it — still matters. But the creative discipline has shifted. You can now design your output before you shoot, not after.

Every snap, good or bad, can come back as a beautifully lit studio headshot. Or a children’s drawing. Your composition feeds the AI. The aesthetic is the prompt.

Witness vs. Inventor

People keep reaching for the Lomography comparison. I understand why — Lomo built a movement around cameras that distort reality, light leaks and vignette and unpredictable results. But that comparison undersells what’s different here. Lomo cameras still capture reality. Most of them are actually recreations of historic photographic technology. A Holga, a Polaroid, a Daguerreotype lens — these tools honor the act of recording the world.

The ImageGenCam doesn’t record. It witnesses, then literally reinvents.

And I find that exciting, not threatening. Different tools serve different purposes. I’ll keep my Canon. I’m also going to build this thing.

But the camera is almost beside the point.

The Vibe Hardware Revolution

The real story is what this camera signals about where we’re headed.

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Michael Carney, President of the U.S. Chamber of Commerce Foundation, opened the with something you don’t often hear from people in rooms like that:

“The only thing that’s clear is that we have no idea what’s about to happen.”

I was there. I heard it. And then we all did what you do in those rooms — we moved on, filled our coffee cups, and proceeded to spend eight hours talking about AI with great confidence.

I was lucky enough to be speaker at the US Chamber of Commerce AI + Work Summit this past week. I was also there representing OutSnapped: AI headshots and pet portraits on a 9-foot live screen, just a few feet from the main stage. While nearly 1,000 business leaders debated the future of AI in the workforce, my team was building actual trust with actual people, 90 seconds at a time.

The word underneath every panel, every data point, every debate wasn’t “AI.”

It was trust.

The Compounding Trust Problem

The summit covered a lot of ground: augmentation vs. automation, skills gaps, workforce planning, equity, the role of small business. No shortage of smart people saying smart things.

But when you cut through all of it, every conversation collapsed into the same problem: nobody is sure who or what to trust, and we’re all moving as fast as possible without a trusted foundation under us.

Trust in the models — which Noella Sudbury, founder of Rasa Legal, captured perfectly: her CTO describes AI as “a hungover intern — very, very confident, and definitely not going to be around to pick up any mess.” Her firm uses AI to help people with criminal records expunge them at scale. They can’t afford hallucinations. So they keep humans in the loop, always.

Trust in the vendors building them — who have their own incentives, their own timelines, and their own definitions of “safe.” Trust in leadership to roll this out thoughtfully. Trust in employers not to use AI as a cover for headcount reduction. Trust in education to re-skill people into jobs that will actually exist.

It’s not a stack of separate trust problems. It’s one compounding one. If the foundation of the model is shaky, and the intent of the vendor is opaque, how can the employee trust the mandate?

The AI Photo Booth Made It Obvious

Here’s what I saw at the OutSnapped booth all day: when you give people genuine control over the output — their style, their setting, how tight the crop is, whether they want to use our camera or their own phone — something changes. The hesitation drops. The curiosity takes over.

We weren’t telling anyone what their AI headshot would look like. We were asking: What do you want?

Every person who walked up was making a choice. Opting in on their own terms. And then when their output appeared on the 9-foot screen — whether it was a polished professional headshot or their golden retriever reimagined as a racing car driver — the reaction was pure delight.

Not because the AI was magic. Because they were in control of it.

What was actually running: two OutSnapped.com custom AI Photo Booth & AI Video Booth experiences with cloud-based generative AI — images and video — delivered directly to guests’ phones via email or text in under 90 seconds. The tech was invisible. Human agency was not.

The Neuroscience of Agency

Edwige Sacco, KPMG’s Head of Workforce Innovation, has a neuroscience explanation for exactly why that matters. The brain’s prefrontal cortex works harder — and triggers a threat response — when change is perceived as something happening to you.

Her framework: the goal of any AI rollout is to help the brain reclassify AI from “risk” to “resource.” That reclassification doesn’t happen through mandate. It happens through:

Edwige Sacco’s framework: the brain won’t accept what it perceives as a threat. Give people agency, and the threat signal drops.

• Agency: Having a say in how the tool is used.

• Competence: Feeling capable of operating the tech.

• Trust: Believing the system — and the leader — has your back.

We see the same reclassification happen with PocketSquare.ai users the first time their bespoke agent responds in a way that’s unmistakably calibrated to their business based on their interactions — it’s not generic AI, but something that actually knows their context. The hesitation that was there five minutes earlier is just gone.

The AI Photo Booth was, accidentally, a perfect application of that framework. Every person who walked up chose to be there. Every output was theirs to direct. The AI was in service of their idea of themselves.

The Dishwasher Moment

The people at that summit — the ones filling the hall, attending the panels, walking up to OutSnapped’s AI Video Booth — they’re already engaged. Already curious enough to show up. Already doing the extra work to figure out what’s going on.

The trust problem isn’t for them.

It’s for everyone who wasn’t in the room. The employees who got handed AI as a mandate. The workers who’ve been told, implicitly or explicitly, that this technology is coming to make their role smaller. The small business owner who doesn’t have a $2,000/seat enterprise license and eight hours to spend at a conference figuring this out.

Those people didn’t get to opt in. They got the dishwasher moment: this is your job now, go figure it out.

Nobody trusts something they were told to do. Especially when the people doing the telling don’t fully understand it either. And that’s the part nobody said out loud.

The leaders in that room are going back to their offices on Monday. They’re going to send memos. They’re going to announce AI initiatives. They’re going to share things they learned at the summit.

What most of them won’t do is say: I don’t know exactly where this is going. Here’s what I’m excited about. Here’s what I’m genuinely worried about. Here’s what we’re going to experiment with together.

[Continue reading with a paid subscription...]

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I got tapped to speak at the U.S. Chamber of Commerce Foundation’s national AI + Work Summit in DC this week. Harvard professors, the former U.S. Chief Information Officer, Google, Anthropic, LinkedIn — and me, a guy who started by taking 1.45 million photos of drunk people at parties.

To prep, I had to write a bio that explained how those two things connect.

What I figured out in the process changed how I write everything now.

Here’s what I didn’t do: I didn’t start typing.

This is the thing I’ve changed about how I work that’s made the biggest difference in the last year, and almost nobody talks about it. The blank page isn’t a writing problem. It’s a thinking problem. And typing is one of the worst ways to think.

When you type, the editor in your brain turns on immediately. Every sentence gets judged before it’s finished. You write three words, delete two, write two more, decide the whole paragraph is stupid, close the laptop, make a coffee. You know how this goes.

I’m self-conscious about writing. Always have been. Which probably sounds strange coming from someone who publishes a newsletter. But the inner critic doesn’t care about your credentials — it just watches you stare at the cursor and waits.

What broke it for me was switching from typing to talking.

Not voice-to-text as a dictation trick. As a thinking method. There’s something that happens when you speak your thoughts instead of write them — a different part of your brain activates, the editor quiets down, and the ideas come out the way they actually live in your head: messy, jumping around, honest. My brain runs in about fifteen directions at once. Talking gets that out. Typing compresses it into something too neat too soon.

The workflow is simple. I use SuperWhisper to capture the voice dump — talk for five minutes about whatever I’m trying to write, don’t stop, don’t edit. Then the transcript goes into Claude, not to write for me, but to act as a thinking partner. To reflect my own ideas back at me in a shape I can react to.

That’s the key word: react. You’re not asking AI to write it. You’re using AI to make your thinking visible so you can edit it.

The bio I ended up with? Thirty minutes. Two decades at the intersection of technology, human experience, and data — long before AI made that combination fashionable. The through-line I’d never quite been able to articulate, from photographing NYC nightlife to building AI-powered experiences on event floors and factory floors, finally landed. Because I talked my way to it instead of trying to type my way there.

Here’s the full workflow, exactly how I run it.

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I want to tell you something I should probably be embarrassed about.

I’ve been running seven companies through Paperclip for a few months. Some are humming along. Most have stalled because I haven’t had the bandwidth to keep pushing them — which is a specific kind of irony when you’re supposed to be running an AI-powered operation. I was using Paperclip as my agent OS and largely ignoring Hermes. The creator noise around it felt like hype bait, and I’d already found something that worked.

Then I watched this episode of The Next New Thing — Andrew Warner talking with Justin Brooke about his current setup: Hermes + gBrain + Paperclip. And things clicked in a way they hadn’t before.

Hermes isn’t a Paperclip replacement. It’s the layer that sits on top — a digital twin that knows your goals, tracks what’s stalled, pings you with questions, and gets things moving again through Paperclip. The combo is genuinely different from either tool alone.

I’m a few days into onboarding Hermes right now. This is my real-time take: what’s worth the hype, what isn’t, and what I wish someone had told me before I spent weeks wading through creator content to find out. I have products in this space — MirrorMemory.ai and PocketSquare.ai — so you should know that going in. The goal isn’t to scare you into buying something. It’s to give you an accurate picture.

What Paperclip already gets right

If you haven’t read my Paperclip Day 0 post, the short version: it’s an open-source agent OS built around an org chart metaphor. You hire agents, give them job descriptions, assign tasks, and review what comes back.

The thing that sets it apart — and the reason I stayed — is how honest Dotta and the community are about what it actually is. Paperclip is explicitly designed as a human-in-the-loop system. Agents run, but they require a human to participate. That’s not a limitation. It’s a design philosophy. It means you can give agents real autonomy without handing over the keys to things that would be catastrophic to mess up. That honesty goes a long way when half the internet is telling you AI agents will run your business while you sleep.

I've also gotten genuinely into the community around it — people like Aron Prins, who built Paperclip Vision. Most agent setups fail not because the agents are bad, but because they have no mandate. They don't know what they're allowed to do, who to escalate to, or what the company is actually trying to accomplish. Vision solves this at the source: it interviews the founder (me) and generates two key documents — a VISION.md (think of it as the company’s constitution: mission, revenue model, org structure, CEO mandate, guiding principles) and a CEO_BOOTSTRAP.md (the activation sequence that lets the CEO agent start running without the founder in the loop for day-to-day decisions). It's the kind of primitive the ecosystem needed and that a community member just went and built. Sending bug fixes, joining the Paperclip community — this is what using a real open-source tool should feel like.

What Paperclip alone doesn’t solve

Seven companies. That’s what I’m managing in Paperclip. Some have momentum. Some have stalled — not because Paperclip can’t handle them, but because “I have no self control and keep adding companies before the current ones have traction. Paperclip is excellent at executing tasks I hand it. It’s not as good at asking: “Hey, you haven’t touched the MirrorMemory.ai roadmap in three weeks — want me to start moving on the next phase?”

That’s where Hermes comes in.

The way I now think about it: Paperclip is my company’s operating system — where work happens, where agents live, where tasks get done. Hermes is the layer above it — the thing that knows my goals across all of my companies and personal projects, tracks what’s stalled, pings me with clarifying questions, and feeds work back into Paperclip to get it moving again. Because it holds persistent context on how I actually think and what I’m trying to accomplish, it can come to me with solutions rather than just problems — and eventually, for the lower-stakes decisions, start making them on my behalf using my own knowledge and preferences. Not one tool replacing another. A stack.

What’s actually real about Hermes

Three things stand out from both my onboarding and the Andrew Warner / Justin Brooke episode.

The first is auto-learning skills per persona. Hermes builds and updates skills for each agent based on how you correct it. Your “writer,” “ops,” and “researcher” personas gradually lock into your preferences without you reinstalling configuration every session. It’s slow to notice, but it compounds.

The second is memory that actually persists. This is the part that changes the daily experience. When I first connected Hermes to Mirror Memory — originally intended as an internal memory tool we built at OutSnapped on top of Nate B Jones’s Open Brain framework — Hermes immediately knew who I was. No onboarding checklist. No “tell me about yourself.” It picked up context from the memory layer, asked some clarifying questions, and started acting like it had been on the team for a week and actively wanted to impress. Justin Brooke uses Garry Tan’s gBrain for the same slot in his stack. Either works. The key is having something there — running either tool without a persistent memory layer is like hiring someone with amnesia.

I’ve set up a lot of agents. Each time, you spend the first session re-explaining your business to something that will forget everything the moment you close the tab. The persistent memory experience is genuinely different, and once you have it, it’s truly magical and you don’t want to go back. When set up correctly, it feels like that that rare friend who can finish your sentences.

The third is long-running /goal missions, not one-off prompts. This is the most underrated thing about Hermes. For example, Justin used it for a land sale — Hermes pulled Zillow comps, flagged that their asking price was $50K too low, built a phased marketing plan across Craigslist, Facebook Marketplace, LandWatch, and a dozen other platforms, and generated platform-specific copy and buyer objection scripts. Not “here’s what you should do.” Here’s the plan. Approve phase one. I’ll get started. That’s a real use case with real stakes, not a demo.

The combination of persistent memory, skill evolution, and long-running goals is where Hermes stops feeling like ChatGPT with a Kanban view and starts feeling like a primitive operating system for your work. Stacked on top of Paperclip, it’s the closest thing I’ve found to actually running a small business like a system rather than a sprint.

What the Hermes hype gets wrong

I learn real things from a lot of the big Hermes content creators — the walkthroughs, the use case compilations. I’m not dismissing it. But there are patterns worth naming.

The most common one: the creator presents themselves as running a portfolio of companies in their sleep. When you look closely, it’s usually a funnel into a paid community and a media channel. That is a real business. It’s just not the same kind of empire the thumbnails imply. The gap between “empire” and “one business doing fine” is where you get sold a $2,000 course.

The framing that bothers me more is “learn this NOW or be left behind FOREVER.” Nobody in the Hermes creator space has shown receipts for true compounding, verifiable business outcomes at the scale those scare tactics imply — unless that business is selling the courses themselves. That framing exists to trigger urgency, not to help you. We’ve written about this trap before — the goal is to hand you tools so you can own your own stack, not to make you dependent on someone else’s content.

Then there’s the “237 ways o make using Hermes” video format. Useful as inspiration. Not useful as proof that any of those workflows are producing revenue at real companies. And the smart home flexes — Hermes toggling your lights via Home Assistant — are genuinely cool content that costs more in tokens and complexity than you’ll ever make back.

If you’re evaluating Hermes for your own stack, treat most creator content as sales collateral, not case studies.

My exact setup: what I installed, what broke, what I’d do differently

The base layer: Paperclip on a VPS

Don’t run Paperclip on your laptop. Put it on a VPS — I use Hostinger* so it’s always on, accessible from any device, and not tied to whether your machine is awake. I wrote about the Mac Mini vs. VPS decision here if you want the full reasoning.

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There’s a weird little reality about every agentic AI tool you’re running right now. The AI itself — Claude, GPT, whatever — lives in a data center. The brains are in the cloud. That part everyone knows.

But the execution? The part that actually runs your workflows, keeps sessions alive, coordinates tools, and makes things happen? That’s been running on your laptop. Your $3,000 computer has been playing middleman between you and a billion-dollar AI infrastructure and the whole thing falls apart the moment you close your lappy’s lid.

I’ve been hacking around this problem for months. I run VPS instances of Hermes, OpenClaw, and Paperclip in the cloud so they’re always on. I use JumpCloud to remote into my personal CPU when I’m not in front of it to access Cursor. It works. It’s also a maintenance headache that most of my readers can’t replicate.

Anthropic just solved this with Claude Managed Agents. And it’s the first step toward something bigger.

Your Laptop Was Never Supposed to Be in the Middle

Here’s the architecture problem nobody talks about.

When you run Claude Computer Use, or Claude Code, or most agentic AI workflows — Claude’s thinking happens at Anthropic’s servers. But someone or something has to orchestrate that thinking. Someone has to hand Claude the next file, trigger the next tool, keep the session going. Until now, that “someone” has been your local machine.

That’s backwards. Why would you pay for a cloud AI and then be required to babysit it from a local device?

Claude Managed Agents moves the orchestration layer to Anthropic’s infrastructure. Claude can now read files, browse the web, run code, coordinate sub-agents, and execute multi-step workflows for hours — without your machine being involved at all. Sessions persist through disconnections. Progress saves even if you close everything.

The AI is in the cloud. The execution is in the cloud. Your laptop is finally just a screen.

(A few weeks ago I wrote about running Claude Code off a USB drive — the most local setup possible. Managed Agents is the exact opposite end of that spectrum. Both have their place.)

This Is Happening Faster Than You Think

I don’t think we’re 5 years from this being normal. I think we’re already most of the way there, and Managed Agents is just the first mainstream-friendly version of a pattern that’s been quietly building.

Look at what Apple is doing with iPhone Mirroring. Right now, you can control your iPhone or iPad from your Mac … but now imagine the next iteration where your Mac lives wherever you put it — home office, closet, turned on and connected — and instead your iPhone accesses it from ANYWHERE. I suspect we’re going to quickly move into the “computer” being stationary. The interface is what will move with you.

That’s the template. Next logical step is that all of your compute lives in a data center (or Anthropic’s servers), your portable device is how you interact with it, and the idea of “my computer needs to be on” becomes as dated as “I need to be at my desk.”

Most people think Managed Agents is a developer feature. It’s not. The two misconceptions I keep seeing:

“This requires coding.” It doesn’t. Anthropic shipped 10 ready-to-run templates in early May — pitchbook creation, KYC screening, month-end close work. You pick a template, configure some permissions, and hand it a task.

“It’s just a fancier chatbot.” It isn’t. A chatbot responds. A managed agent executes — for hours, autonomously, across tools and systems you’ve given it access to. It’s categorically different.

What It Costs (and Why the Number Is Surprising)

Pricing is consumption-based: standard Claude API token rates, plus $0.08 per session-hour for active runtime.

Run a 4-hour research and reporting workflow? That’s 32 cents in runtime overhead, plus whatever tokens Claude uses. For most tasks, you’re looking at a few dollars. For a task that would take you two hours to do manually, you’re probably looking at something under $5 total.

The question isn’t whether you can afford it. The question is what your time is worth.

The Task Audit: How to Find Your First Agent Candidate

Before you open the Claude Platform and start clicking around, do this first.

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The premise sounds like it belongs in a spy movie.

Plug a USB drive into a laptop. Run one file. Watch a full Claude Code-style AI coding agent spin up -- on a machine you've never touched, with no installation required, no subscription dialog, and no trace left when you're done.

That's what OpenClaude Portable delivers. It's an open-source project that packages a Claude Code-compatible agent runtime into a self-contained folder you can put on any USB drive. The same folder boots on Windows, macOS, and Linux. Your chat history, config, and output files travel with the drive. The host machine never knows you were there.

I came across it this week and spent some time actually setting it up. Short version: it works. You can have a functional AI coding agent running from a thumb drive in under 15 minutes, for free, using NVIDIA NIM or OpenRouter as your model provider -- or completely offline using a local model like Gemma via Ollama.

Here's the complete walkthrough.

What You're Building

By the end of this guide, you'll have a full Claude Code-style agent running from a USB drive, with cross-platform support (same folder, same config, same chat history on Windows, Mac, and Linux), your choice of free cloud providers or an offline local model, and a web dashboard UI in addition to the terminal agent. No GPU required for the cloud setup. No system-level installs. Everything lives on the drive.

Step 1: What You'll Need

Before you start:

• A USB drive (or external SSD/HDD) with at least a few GB free -- more if you want a local model

• Internet access for initial setup and API key generation

• A free API key from one of the providers below (covered in Step 3)

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Most of what I write here is about AI tools for people who run businesses — solopreneurs, operators, small teams trying to figure out what’s actually worth using and what’s just noise. That’s the lens I write from, and it’s not theoretical. I run OutSnapped, a photo booth company with services across the USA, and Pictor, the AI-powered software platform we built for photo booth operators internationally. We’ve been running generative AI photo and video outputs at live events since 2023 — and watching the quality improve almost weekly since. We’re also patent pending on AI technology for event photography.

So when ChatGPT dropped Images 2.0 this week, I paid attention.

Not because it’s flashy. Because it finally fixed the one thing that’s been the biggest limiter for real-world business use.

For anyone who’s tried using AI image generation for actual business output — not memes, not art, not fun — the wall you always hit was text.

Ask any model to put a name, a logo, a price, a hashtag, or a menu item in an image and you’d get slop. Garbled letters. Fonts that looked like they were designed by someone who’d only ever heard a description of the alphabet. The images themselves could be stunning. The text looked like a stroke victim.

That’s not a minor bug. For small businesses, text is half the point. You’re making a flyer with your hours. You’re generating a social post with your event name. You’re building a photo booth output where someone’s name appears on their custom print. Without readable text, these tools were toys, not tools.

Images 2.0 fixed it — and honestly, the text reliability here is beating what we’ve seen from Google’s enterprise solution so far. That’s not nothing.

OpenAI says the new model can render text accurately in multiple languages — including non-Latin scripts like Japanese, Hindi, and Bengali. It handles dense layouts, small print, UI elements, even multi-panel comic strips. TechCrunch called it “surprisingly good at generating text.” For people who’ve been watching AI images embarrass themselves with the alphabet for years, “surprisingly good” is an understatement.

Here’s the practical implications for anyone running a small business, a service, a side project:

You can now generate marketing materials — actual usable ones — from a text prompt. Menus, flyers, event graphics, social posts, branded photo outputs. No Canva subscription required. No designer needed for one-off stuff. No screwing around with templates that were designed for someone else’s brand.

You can personalize at scale. Ask someone their name and their name can be part of the output. That’s a simple example, but think through what it means: customized customer-facing images, event-specific prints, personalized digital assets — all generated on demand. That wasn’t reliably possible before last week.

You can replace tools you’re currently paying for. Not everything. But some of them.

The catch … and there’s always a catch … is that none of this matters if you don’t know what you’re actually trying to make.

I keep watching people try AI image tools, get back something mediocre, and conclude the technology isn’t ready. The technology is fine. The problem is they sat down to “try AI images” with no clear picture of the output they wanted, or the understanding, that like any tool, prompting takes time and practice.

It’s the Maserati/minivan problem. You can now access remarkable image generation capabilities. But most people want a Maserati with the ease (and price tag) of a minivan. The output quality depends almost entirely on the clarity of your input.

This is where most businesses stall out. Not at the tool. At the brief.

BUILD YOUR AI IMAGE STYLE GUIDE

The businesses that are going to extract real value from Images 2.0 — and from every image model that follows — are the ones who do one thing their competitors won’t bother to do: build a brand style guide for AI prompting.

This isn’t a complicated document. But it’s the single thing that separates people who get consistent, usable outputs from people who waste an hour generating ten mediocre images and give up.

Here’s what goes in it, and exactly how to build yours.

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Apple spent the least on AI data centers. Now Mac Minis are sold out, Perplexity runs on one, and peer-to-peer compute pools are turning friend groups into GPU clusters. Is this Napster for the cloud era?

Neo-Napster: How Apple’s Mac Mini Accidentally Became the World’s AI Infrastructure

I was a college kid when Napster hit.

If you’re old enough to remember, you know the feeling. One day you’re buying CDs at Tower Records. The next day, every song ever recorded is sitting on your roommate’s laptop, free, waiting for you to click download. We all knew it was wrong. We did it anyway. The pull toward democratized access was stronger than any guilt trip the RIAA could throw at us.

Napster didn’t just change music. It revealed something ugly: the labels had been ripping off artists for decades, and the only thing keeping that system alive was that nobody had an alternative. Then suddenly, everyone did.

Here’s what happened next, though. The labels didn’t die. They adapted. They fought back. And somehow — through Spotify, Apple Music, and a web of equity stakes in streaming platforms — they concocted an evil genius plot to ripping off both the consumers and the artists even more than before while somehow looking like the good guy while villainizing the streaming platforms. The middlemen survived. They always do.

I’ve been thinking about this a lot this week. Because something that kinda-sorta-rhymes with Napster is happening right now in AI compute. And I’m not sure most people see it yet.

The $100 Billion Question

Right now, Amazon, Microsoft, Google, and Meta are spending roughly $100 billion per quarter on AI infrastructure. They’re building what I’ve been calling “GPU cathedrals” — massive data centers stuffed with Nvidia chips, cooled by rivers, powered by nuclear plants. It’s the biggest infrastructure buildout since the railroads.

Meanwhile, Apple’s data center spending is down 19%. They’re the runt of the litter in the AI capex race. The financial press has been writing them off. “Apple is late to AI.” “Apple doesn’t get it.” “Apple is falling behind.”

Except... Apple might have already won. They just didn’t do it the way anyone expected.

The Accidental AI Infrastructure

While the hyperscalers were building their cathedrals, Apple shipped over a billion devices running Apple Silicon. M-series chips. Unified memory. Neural Engines. Every Mac, iPad, and iPhone sold in the last four years is, quietly, an AI-capable computer.

This wasn’t supposed to matter. Apple built M-series chips because they wanted better battery life and performance for consumers. The unified memory architecture — where CPU, GPU, and Neural Engine share one big pool of RAM — was a design choice for efficiency, not an AI play.

Then something weird happened.

In late January, an open-source AI agent framework called OpenClaw went viral. It let you run autonomous AI agents locally on a Mac — agents that could control your apps, browse the web, manage your files, handle your messages. The catch? It ran best on a Mac mini with maxed-out RAM. (If you’re wondering how this compares to other tools, check out my breakdown on Hermes vs. OpenClaw.)

Mac minis sold out. CNN called it “the hottest Apple product right now.” Shipping estimates stretched to 10-12 weeks. People weren’t buying Mac minis to edit spreadsheets. They were buying them to run AI agents that work while you sleep. I almost bought a wall of Mac Minis myself before realizing a $6 VPS could handle the same workload in a way that better suited my personal needs.

Apple must have been pleasantly surprised. They built a consumer computer. The market turned it into an agent server.

Then Perplexity Made It Official

Yesterday — literally yesterday, April 16th, 2026 — Perplexity launched “Personal Computer for Mac.” The recommended setup? A dedicated Mac mini with maximum RAM, running 24/7, connected to your local files and apps.

Let that sink in. Perplexity — an AI search company valued in the billions — built a product whose primary hardware recommendation is a $599 Apple desktop computer sitting in your closet with no screen attached.

The “AI computer” is a Mac mini under your bed.

That’s not a joke. It’s a product. It costs $200/month for the Perplexity subscription, and they want you to buy a dedicated box to run it on. Your phone becomes the remote control. Your Mac mini becomes your brain.

And Now: The Pools

This is where it gets really interesting.

Two days before Perplexity’s launch, a company called Hyperspace shipped a feature called “Pods.” The idea is simple and wild: take a handful of laptops and desktops owned by a group of friends, coworkers, or family members and pool them into a single, peer-to-peer AI cluster.

No central server. No cloud provider. No vendor lock-in. Just your machines, talking to each other, running models too large for any single device, exposing a single OpenAI-compatible API endpoint. If the pool can’t handle a job, it falls back to cheap cloud — but only as a last resort.

Your friend group just became a data center.

This is the part that feels like Napster.

Neo-Napster

I’m calling this “Neo-Napster” because the pattern is unmistakable, even though the details are different.

Napster took centralized music distribution and made it peer-to-peer. Neo-Napster is taking centralized AI compute and making it peer-to-peer. In both cases, regular people suddenly have access to something that was locked behind massive capital expenditure and corporate gatekeepers.

There’s no piracy here, though. Nobody’s breaking the law by running AI models on hardware they own. There’s no guilt in this version of the story. You bought the Mac mini. You own the compute. You can do whatever you want with it.

But the energy is the same. That feeling of “wait, I can just... do this? Without asking permission? Without paying rent to Amazon?” That’s the Napster feeling.

And here’s the question that always keeps me up at night — the question I think every founder, builder, and operator should be asking:

How long do we have before this ends up the same way?

Because remember what happened with music. The labels didn’t die. They adapted, cornered the streaming platforms with the muscle of infinite founded or undfounded lawsuits, and rebuilt the toll booth to be even more profitable for themselves than ever before. Are the hyperscalers — are Anthropic ($30B valuation and eyeing an IPO), OpenAI, Google — going to sit by while compute decentralizes?

Of course not.

Three Futures (And Who Wins Each One)

I’ve been thinking about where this goes, and I see three plausible paths. They’re not mutually exclusive — we’ll probably get a messy blend. But understanding each one tells you something about where to place your bets.

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Anthropic just unveiled the most capable AI model they’ve ever built. They also announced — in the same breath — that you can’t have it.

That’s not a PR stunt. It’s a calculated decision rooted in a pretty uncomfortable fact: Claude Mythos is too good at breaking things.

Here’s what it actually is, what it can do, and why this moment matters more than most AI announcements.

What Is Claude Mythos?

Claude Mythos is Anthropic’s newest flagship model — a tier above Opus, built for general intelligence but startlingly capable at one specific domain: cybersecurity.

Announced on April 7, 2026, Mythos Preview isn’t available on Claude.ai. It’s not available in the API. It was quietly handed to roughly 50 organizations operating critical infrastructure, with $100M in model usage credits attached.

The organizations on that list: Amazon Web Services, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorgan Chase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks.

Anthropic called the initiative Project Glasswing.

The framing: give defenders access before attackers figure out how to build something similar. Use the most powerful offensive tool to shore up defenses before the window closes.

What Can It Actually Do?

This is where it gets serious.

Claude Mythos can autonomously find and exploit zero-day vulnerabilities — previously unknown security flaws — in real production code. Not CTF puzzles. Not sandboxed test environments. Real software running on real systems.

It found critical vulnerabilities in every major operating system and every major web browser.

The headline find: a 17-year-old remote code execution bug in FreeBSD (CVE-2026-4747). The flaw lived in FreeBSD’s NFS server. Mythos didn’t just find it — it wrote a working exploit: a 20-gadget ROP chain split across multiple packets that gives an unauthenticated attacker complete root access to the server.

That bug sat undetected in production code for 17 years. Mythos found it and weaponized it, autonomously, in a research setting. It found a needle in a haystack, then turned that needle into a sword.

The UK’s AI Safety Institute evaluated Mythos Preview separately and confirmed the capabilities. The U.S. Federal Reserve and Treasury held an emergency meeting to assess the implications for critical infrastructure.

On April 12, Anthropic delayed the restricted preview indefinitely.

Why Won’t They Release It?

Because the offense/defense math doesn’t work in their favor.

The defenders on the Project Glasswing list are big, well-resourced, and already thinking about these threats. But the list of potential attackers — nation-states, criminal organizations, anyone with API access — is effectively unlimited.

Releasing a model that can autonomously find exploitable zero-days in any piece of software running anywhere in the world isn’t a product decision. It’s a geopolitical one.

Anthropic’s position: restrict access to the organizations responsible for securing the most critical systems, use that access to patch the worst vulnerabilities before the window closes, and keep Mythos out of general circulation indefinitely.

There’s no public release roadmap. No API access date. The model exists. It works. And Anthropic has decided that’s where it stays.

Why This Matters Beyond Cybersecurity

Mythos isn’t primarily a security tool. It’s Anthropic’s most capable general-purpose model.

The cybersecurity capabilities are the headline because they’re the reason for the restricted access. But Mythos is described as a “generational leap” in reasoning and coding — the same underlying capabilities that make it dangerous in offensive security contexts make it extraordinarily powerful everywhere else.

That gap — between what’s possible and what’s accessible — is the real story here. A small cohort of the world’s largest companies just got exclusive access to the most powerful AI model ever built. The rest of the market is working with last year’s models while that cohort uses something categorically different.

That’s the access gap. And it’s already open.

For the full breakdown of what the Mythos access gap means for founders and solopreneurs — and what you can actually do about it — read the companion piece: Claude Mythos and the Solopreneur Access Gap.

Sources: Anthropic red.anthropic.com · Project Glasswing · The Hacker News · VentureBeat · AISI Evaluation

Anthropic built the most powerful AI model ever documented. It can autonomously find zero-day vulnerabilities in every major operating system and every major web browser — not academic puzzles, real production code running on millions of machines. It found a 17-year-old remote code execution bug in FreeBSD that gives any attacker full root access.

Then Anthropic looked at what they’d built and said: no public release.

Instead, they handed it to Amazon, Apple, Broadcom, Cisco, Google, JPMorganChase, Microsoft, and NVIDIA. About 50 organizations total. The framing: this is “defensive use only.” They’re calling it Project Glasswing.

Here’s my reaction: I believe them. And I think it’s worse than it sounds.

The “too dangerous to release” story is real. Mythos isn’t being withheld as a PR stunt — it apparently autonomously discovers and exploits vulnerabilities that human security researchers have missed for nearly two decades. Releasing that to the public would be genuinely catastrophic.

But here’s the part nobody’s saying out loud.

The enterprises in Project Glasswing are now using Mythos to find and patch vulnerabilities in their software. Vulnerabilities that — until Mythos found them — nobody knew existed. The patches will roll out. The CVE numbers will be filed. And the world will quietly become more secure for users of their platforms.

Meanwhile, the rest of us are running on software that will never get that treatment.

This isn’t hypothetical. If you’ve been building with AI — and if you’re reading this, you probably have — your tools are almost certainly less secure than what a trained engineer would have written. Not because AI coding is bad. Because AI coding is fast, and fast means skipping the parts that are slow and unsexy. Security reviews are slow and unsexy.

I’ve built things with AI help. I’m honestly not sure how secure they are. And now I find out that the most capable model in history is actively scanning enterprise software for exactly this kind of hole — and I’m not in that program.

It’s uncomfortable to say. But it’s true.

This has been building for a while. I’ve watched Anthropic quietly raise the floor on what good AI costs. Token limits tightened. Pricing crept up. The models that actually matter — the ones that can do the work worth doing — keep drifting toward enterprise tiers that most solopreneurs can’t justify.

Mythos is just the moment the gap became undeniable.

There’s an older saying in tech: if you’re not paying for it, you’re the product. We all internalized that.

But something’s shifting that I can’t fully articulate yet — and I’m going to write a whole separate piece on it. The short version: a lot of us are paying for it now … And I’m not sure we’re not still the product.

I don’t have a clean resolution to any of this. I don’t think there is one yet.

But I think the worst thing you can do right now is pretend it isn’t happening.

What to actually do about it (the honest version)

I asked myself what advice I’d give a solopreneur who read this and felt the same uncomfortable recognition I did. Here’s where I landed — not a perfect fix, but an honest playbook for the current reality.

1. Audit what you’ve built with AI before Mythos-adjacent tools do it for you.

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Anthropic launched something today that I genuinely couldn’t stop thinking about.

It’s called Claude Managed Agents. And if you’ve ever tried to build an AI agent — like actually deploy one that does real work while you sleep — you know exactly why this is a big deal.

Most “agents” we’ve seen so far are brittle. They live in a browser tab. They lose their train of thought if you refresh the page. Or, if you’re a developer, you know the nightmare: the persistent state, the session handling, the multi-step coordination. It usually takes months of engineering just to make sure the thing doesn’t fall over when it hits a snag.

Here’s what Managed Agents does: you describe an agent in plain English (or a YAML file if you’re into that), tell it what tools it can use and what guardrails to follow, and Anthropic’s infrastructure runs the whole thing. Long sessions. Autonomous work. Persistent state across disconnections. Multi-agent coordination for complex parallel work.

Anthropic just made all of that “the infrastructure’s problem.”

While in private beta, Notion used it to ship AI features 10x faster. Rakuten stood up agents across product, sales, marketing, finance, and HR in a week per deployment. Asana built “AI Teammates” that work alongside humans inside project management workflows.

Think about what that means for a small operator. The stuff enterprises are using to eliminate whole departments — it’s now available to you with an API key and a prompt.

We are moving away from “AI as a Chatbox” and toward “AI as Infrastructure.” You don’t talk to these agents. You employ them.

My Personal Hit List

Here’s where it gets interesting for someone like me: I’ve been looking for a way to replace Intercom for Pictor. It’s expensive. It requires a team to manage. And it still doesn’t do exactly what I need. With Claude Managed Agents, I could spin up a fully customized support agent — one that knows our product docs inside out, escalates the right things, and never takes a sick day — and have it running in days, not months.

For OutSnapped, I’d love to spend the $20k per year I spend on Hubspot on literally ANYTHING else. Ideally, they would bring their pricing back to reality before I have to renew in December, but in the meantime … I’m thinking about a sales agent that warms up old clients. Identifies who hasn’t booked in 12 months. Drafts personalized outreach. Follows up automatically. All the things a sales admin would do, running in the background while I’m focused on everything else.

This is the direction everything is going. The question isn’t whether to build with agents. It’s how fast you can get there before this becomes table stakes.

If You Build ONE Thing...

Stop thinking about AI as a way to write better emails and start thinking about it as a high-level data scientist you just hired.

The Revenue Intelligence Agent: upload every messy piece of customer data you have — emails, support tickets, CRM notes, Stripe history, lost-deal notes — and have an agent work through all of it to tell you where the money is hiding.

Not a summary. Actual conclusions: who’s about to churn and why, which dormant accounts look revivable, what your highest-LTV customers have in common that you’re not actively selling.

This used to require a $150k/year hire or a specialized SaaS tool that costs $2k a month. Now? It’s a managed agent away.

The Honest Part About Pricing

But — and this is the part I want you to actually read carefully — there’s a catch.

Currently Claude Managed Agents is API-only and I don’t see this changing.

Not Claude Max. Not your existing Claude subscription. You need an Anthropic API account, and you’re billed separately: standard token rates (Sonnet 4.6 is $3/million input, $15/million output) plus $0.08 per session-hour of runtime.

For a one-hour coding session with Opus 4.6 using 50K input and 15K output tokens? Anthropic’s own example puts the cost at about $0.70 per agent.

That’s not outrageous. But it’s not free. And the people framing this as “is it cheap?” are asking the wrong question.

The right question is: what is your time worth?

If a Claude agent can replace 10 hours a week of manual work — answering support tickets, drafting outreach, processing documents — and it costs you $50/month to run it, that’s one of the best hires you’ve ever made.

One more thing worth flagging: watch out for “teaching to the test” syndrome — where an agent optimizes for looking efficient rather than being efficient. It’s rare, but real. That’s what the session trace in the Console is for. Always review it.

Setting up the API account? Not hard. The bigger question is whether you’re willing to pay for it.

That’s how I’m thinking about this.

Why This Is Nothing Like ChatGPT

Before I walk you through setup, I need you to understand the actual difference — because if you’re thinking “this is just another AI chat thing,” you’ll underuse it badly.

ChatGPT is a conversation. You send a message, it responds, you send another. Every time you close the tab, the slate is wiped. It can’t run without you. It can’t use tools in sequence. It can’t pick back up where it left off. It definitely can’t work autonomously for three hours while you’re at dinner.

Claude Managed Agents is a runtime. You define what an agent is allowed to do, give it tools (web search, file access, code execution, API calls), and it executes multi-step workflows on its own — for as long as the task takes. Sessions persist through disconnections. State is maintained between runs. The platform handles the infrastructure: sandboxing, authentication, error recovery, audit logs.

The meaningful difference: you stop being in the loop.

With ChatGPT, you are always the operator. You write the prompt, you read the output, you copy it somewhere, you paste it somewhere else, you write the next prompt. It’s useful, but it’s still you doing the work.

With Managed Agents, you define the work once. The agent runs it, uses tools, handles errors, and delivers an output. You review the result — you don’t manage every step.

That’s not a feature upgrade. That’s a different category.

Step-by-Step: Your First Agent in Under an Hour

Step 1: Get your API account (5 minutes)

Go to platform.anthropic.com. Create an account. Add a payment method. You don’t need to load a huge balance — start with $20, you’ll use maybe $2–5 learning the ropes.

This is the thing that trips people up: you can’t use your Claude.ai login here. It’s a separate account. Think of Claude.ai (including Max) as the consumer product, and the Claude Platform as the enterprise/developer infrastructure. Same AI, completely different billing and access.

Step 2: Open the Claude Console and find Managed Agents (2 minutes)

Once you’re logged into console.anthropic.com, you’ll see Managed Agents in the left navigation. It’s in public beta — you may need to click through a brief onboarding confirmation.

Step 3: Define your first agent in plain English (10 minutes)

Click “New Agent.” You’ll see two options: natural language or YAML. Use natural language first.

Here’s an example definition you can copy, paste, and edit:

Read more

Update — April 11, 2026

Three days after this piece went live, the story moved. Here’s what happened.

The repo nearly doubled — from 23,000 to 41,200 GitHub stars — and shipped v3.1.0. More importantly, the team quietly walked back the 100% claims. The README now leads with 96.6% R@5 on LongMemEval in raw mode, and acknowledges that the community “caught real problems” within hours of launch. The AAAK token example, “30x lossless compression,” and “+34% palace boost” have all been corrected or removed.

An independent benchmark reproduction on an M2 Ultra confirmed what this piece argued: raw mode hits 96.6%, but the palace architecture (wings, rooms, halls) actually makes retrieval worse. The fancy structure regresses performance. ChromaDB is doing the heavy lifting, not MemPalace.

The story also got darker. Kotaku reported that a MemPalace crypto token appeared on pump.fun with a 50% creator reward split between Jovovich and Sigman — pumped and dumped within 24 hours of launch. They also flagged a deleted GitHub account (”aya-thekeeper”) that originally pushed the repo, raising questions about who actually wrote the code. Sigman’s role as CEO of Bitcoin Libre, a crypto lending marketplace, adds context.

Meanwhile, Forbes ran an entirely uncritical puff piece — no mention of the benchmark controversy, the deleted contributor, or the crypto coin. And as of today, the maintainers posted a warning about fake MemPalace websites impersonating the project.

The original analysis below stands. The tech is real. The 96.6% raw score is now independently verified. But the story around it keeps getting stranger.

My first reaction when MemPalace hit r/LocalLLaMA was not “interesting project.” It was LOL WHAT?

Milla Jovovich — Leeloo from The Fifth Element, Alice from Resident Evil — spent months building an AI memory system with developer Ben Sigman. The same problem I’ve been obsessing over for months. The same architecture decisions. The same benchmark targets.

I’m a solopreneur who runs two companies and lives inside AI tools all day. I’ve lost real work because context didn’t carry between tools. I built Mirror Memory because I got tired of re-explaining myself every single session, across every single tool. And apparently, so did a movie star.

We don’t need smarter models anymore. We need models that actually remember who we are.

OK. So what did they actually build?

MemPalace is a genuinely interesting idea. Let me explain what it does before I tell you about the benchmarks.

The project — open-source, MIT-licensed, free — gives AI agents persistent long-term memory. It stores your full conversation history in ChromaDB and SQLite, locally on your machine. No cloud. No subscription. No monthly memory tax. pip install mem-palace and you’re running.

The architecture is inspired by the ancient memory-palace mnemonic technique — the one where you imagine walking through a building and placing memories in specific rooms. In MemPalace, that translates to: wings (people or projects), halls (memory types), rooms (specific topics), closets (compressed summaries), drawers (verbatim originals).

The core insight is smart: rather than burning LLM tokens to decide what’s “worth remembering,” MemPalace keeps everything verbatim and lets semantic search surface it later. That’s the right instinct. It’s also the one that actually holds up in testing — more on that in a moment.

v3.0.0 ships with MCP integration supporting 19 tools, so it’s not just for one agent anymore. Claude, ChatGPT, and others can theoretically connect to it.

Now the benchmarks. This is where it gets interesting.

The headline: 100% on LongMemEval. The reality: a lot of fine print.

LongMemEval (ICLR 2025) is the gold standard benchmark for AI memory systems. It tests five things: information extraction, multi-session reasoning, temporal reasoning, knowledge updates, and knowing when to say “I don’t know.” Hard to game. Which makes what happened here worth unpacking.

MemPalace claims 100% on LongMemEval. Developers aren’t buying it. And when you read the GitHub issue dissecting the methodology, it’s hard to argue with them.

Here’s what the audit found:

The 100% LoCoMo score retrieves everything. The LoCoMo benchmark’s conversation sessions contain 19–32 items. MemPalace ran it with top_k=50. When your retrieval window is larger than the entire candidate pool, you can’t miss — you literally retrieve every conversation. As the GitHub auditor dial481 wrote: “Setting top_k=50 retrieves the entire conversation. The ‘memory system’ contributes nothing.” The honest LoCoMo ceiling, after correcting ~99 dataset errors, is around 93–94%.

The metrics aren’t what you think. The LongMemEval scores measure recall_any@5 — whether the right memory is retrieved — not whether the system actually answers the question correctly. These are different things. A system that retrieves perfectly but answers wrong still scores 100% under this metric.

Both 100% scores require the Claude API. The marketing leans hard on “no API key, no cloud.” The configurations that hit 100% use paid Claude API calls. Two different things being conflated in the same pitch.

So what’s the real score? The auditor’s honest numbers: 60.3% R@10 with no reranking; 88.9% R@10 with hybrid retrieval and no LLM. That’s still competitive with other local-only memory systems like Mem0 and Zep — it’s not a fraud, it’s a legitimate system with an aggressively marketed ceiling.

The concept is sound. The verbatim-storage approach works. The architecture is thoughtful. The 100% headline is not what it looks like.

Respect the tech. Don’t trust the poster.

So who should install MemPalace?

Builders. People who run local LLM setups, enjoy owning their stack, and want a single-machine memory system they can audit and extend. If that’s you, the 88.9% hybrid score is real, the architecture is elegant, and the price is right.

But most of the people who read this newsletter aren’t that person.

Most of you are operators. You use Claude for one thing, ChatGPT for another, Cursor while you code. MemPalace now has MCP, which means it can technically talk to multiple tools — but it still runs on your local machine. It doesn’t follow you between environments, devices, or team members. Your context stays on whatever machine the ChromaDB lives on.

And here’s the thing nobody mentions when open-source AI tools go viral: they work great on day 1. They break on day 60. ChromaDB needs maintenance. SQLite databases get weird. Dependencies drift. The tool that felt effortless in April becomes a Saturday afternoon by September.

That maintenance tax is invisible in the GitHub star count. It shows up in your calendar.

My honest take: MemPalace is the right answer for the wrong person most of you are trying to be.

The r/LocalLLaMA crowd loves it because they are that person — they build things, they own their infra, they read benchmark BENCHMARKS.md files on Saturday mornings. That community is real and MemPalace serves them well.

But the founders and operators I built Mirror Memory for are different. They want persistent memory that follows them across Claude, ChatGPT, Cursor, and whatever tool ships next week — across devices, not just across tabs. They want it working tomorrow, not after a weekend of configuration. They want something managed, so the maintenance tax never shows up on their calendar.

The three questions that tell you which AI memory solution is actually right for you.

I’ve been deep in this space for months, testing everything from custom RAG pipelines to managed memory APIs. Here’s the framework I wish I’d had when I started:

Read more

On April 7, 2026, Anthropic quietly dropped something called Project Glasswing.

It’s a cybersecurity initiative built around Claude Mythos Preview — the frontier model everyone’s been waiting for. Here’s what Mythos can do: it can “surpass all but the most skilled humans at finding and exploiting software vulnerabilities.” It isn’t just a chatbot — it’s an autonomous vulnerability hunter. It’s already discovered thousands of zero-day vulnerabilities in major operating systems and browsers before the developers even knew they existed. Partners include AWS, Apple, Google, Microsoft, Cisco, and CrowdStrike.

But look at the pricing. This is where the canary starts to sing.

$25 per million input tokens. $125 per million output tokens.

Let that last number sit.

Now add three other things that broke the same week:

Anthropic’s annualized revenue crossed $30 billion, officially passing OpenAI’s $25 billion. They now hold 40% of enterprise LLM API spend. Two years ago they were the underdog. Now they’re first.

The Trump administration formally blacklisted Anthropic as a “supply chain risk.” Anthropic filed suit in response. The AI internet cheered. Free press. Principled stand. David vs. Goliath.

And Anthropic is now evaluating an IPO at a $380 billion valuation, potentially by this October.

Most people reading those headlines are celebrating. I think they’re missing the point.

Let’s start with Glasswing, because it’s the one that actually tells you something.

Anthropic has spent three years building the “responsible AI” brand. Their founding documents literally say they might be building one of the most dangerous technologies in human history. Dario Amodei has given more speeches about existential risk than any other active tech CEO. They built Constitutional AI. They published safety research. They were the ones you trusted to be thoughtful about this.

And then they shipped a model that can autonomously discover and exploit zero-day vulnerabilities in your operating system.

Defensively. For security research. Sure. I believe that’s the intent.

But here’s the honest thing I keep coming back to: where does the good guy end and the bad guy start? It’s genuinely hard to tell. Anthropic standing up to the government publicly looks principled — and it probably is. It also generated more free advertising than a 10 Super Bowl spots. The safety brand is real. It’s also commercially brilliant. It’s likely why AWS, Google, and Microsoft chose to partner with them instead of building it themselves.

I’m not calling them evil. I think they’re complicated in a way most companies aren’t honest about. And Dario, to his credit, has put his money where his mouth is more than any other AI CEO I’ve watched in recent times. That still counts for something.

But “complicated” is different from “safe to assume everything stays the same.”

Here’s the thing nobody’s saying out loud in the revenue celebration.

Right now, Anthropic needs solopreneurs and small businesses. Not because we’re their biggest customers — we’re not — but because we’re the word-of-mouth engine. We’re the characters in the story of “look what AI can do” while the major corporations are still trying to figure out how to get a Claude subscription approved by procurement & security teams. We’re why Claude feels human and nuanced compared to the competition. We’re why their brand means something to actual people instead of just procurement departments.

That calculus is about to change.

Public companies get measured on one thing: revenue growth. And when you’re at $30 billion, the fastest path forward isn’t winning more $20/month Pro subscribers. It’s closing more contracts like Glasswing — enterprise security firms, government agencies, the organizations that pay $125 per million output tokens without flinching.

Glasswing’s pricing isn’t just a product decision. It’s a signal. It tells you exactly what Anthropic believes an enterprise customer will pay for frontier performance. And when the enterprise floor goes up, the prosumer ceiling doesn’t stay still forever.

The IPO accelerates this. Every public company that’s had both a consumer and enterprise product line has eventually made the same call: enterprise clients spend more, complain less, and renew more predictably. The consumer product gets maintained. It doesn’t get prioritized.

If you’re heavily reliant on Claude for your daily operations, you need to realize you’re currently getting a massive discount on frontier intelligence. That discount may not last.

Claude isn’t going dark. The tool you use today will still work tomorrow. But the incentive structure is changing — and you should know your exposure before it does.

This is where most posts would tell you not to worry. I’m going to tell you something more useful.

The Claude Dependency Audit

Most people who read this will nod, feel vaguely unsettled, and then open a new Claude tab and continue exactly as before.

Don’t be most people.

Here’s the 30-minute exercise worth doing this week. I’m running it on my own businesses right now — OutSnapped, Pictor, Mirror Memory, and even this newsletter. Here’s how to do it.

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Last week I spent real money on extra Claude credits. Not because I was doing more work. Because I wasn't paying attention to the work I'd already set up.

I've been building automations for months. Scheduled tasks that run every day. Skills that kick in when I need them. Prompts I tuned until they did what I needed. When they worked, I moved on. Build it, ship it, next. That's the job.

What I never did was go back.

Nate Jones published a piece last week on token waste — four levels of it, from rookies loading raw PDFs to advanced users drowning their agents in context. Reading it, I had a moment of recognition: I'm doing this. Not the rookie stuff. The other stuff. The stuff you don't notice because you built it months ago and it's been quietly "working" ever since.

Here's the thing about working: it doesn't mean efficient. It means the output looks right. That's a much lower bar.

We were trained to think the internet is free. You open a browser, you search, you scroll, you watch 22 hours of YouTube videos a week, you scroll endlessly on TikTok — and none of it costs you anything obvious. Data centers are running, energy is being burned, infrastructure is being maintained, but that's someone else's problem. Your tab is free.

AI compute doesn't work like that.

Every token is a real cost — in money, in your usage limit, and in actual energy consumed. And unlike the internet, where your behavior has no visible consequence, here the bill shows up fast. Hit your limit. Buy more credits. Do it again next week.

I spent a fortune on extra credits last week. When I sat down to figure out why, I found the answer everywhere.

Screenshot habit. Every task I'd built was taking screenshots at intermediate stages to "see what's there." Screenshots to confirm a page loaded. Screenshots to read text off a page. Screenshots to verify steps the previous step already confirmed. get_page_text exists. It returns clean text in one call. I wasn't using it.

PDF habit. Same idea, different format. Raw PDFs are one of the most expensive ways to feed information into Claude. I know this. I was still doing it.

But the biggest one — the one I'd never thought about at all — was my recurring tasks.

I have scheduled tasks that run every day. They were built to solve a problem, they solved it, and I moved on. I never looked at whether they were doing it efficiently. Why would I? They worked.

When I finally looked at one — a daily task that exports Facebook group member requests to a Google Sheet and approves them — I found 11,800 tokens of waste in a single run.

Three ToolSearch round-trips the task didn't need (the prompt never declared which tools it used, so every run started by going looking for them). Screenshots where get_page_text would do the same job in one call. Five navigation steps to find the last row in a spreadsheet, when a single JavaScript call returns it directly. A re-copy step that only existed because the clipboard was getting clobbered between steps that should have been atomic.

At daily cadence: 350,000+ tokens wasted per month. On one task.

I have a lot of tasks.

Here's the category Nate didn't cover, and I think it's the biggest one for anyone who's been building seriously: automated task bloat.

Prompts running in the background. Producing correct output. Burning tokens on patterns nobody ever audits because correct output and efficient output look identical from the outside.

Build-and-forget is a great strategy for shipping. It's a terrible strategy for token efficiency. The thing you shipped six months ago has never been revisited. It's doing everything it was told — including every wasteful habit you had when you wrote it.

And even if you're not hitting your limits: this matters. The compute doesn't disappear. The energy doesn't disappear. "I'm not paying for it directly" is not the same as "it doesn't cost anything." Do your part.

The fix was obvious, and I did it the obvious way — laziness. Instead of auditing every task manually, I wrote two prompts. One to optimize all my skills. One to optimize all my scheduled tasks. Drop them into a fresh Cowork session and they do the work.

Both are below.

Prompt 1: Audit and optimize all your skills

Paste this into a new Cowork session. It reads every SKILL.md in your skills directory and rewrites the instructions to cut token waste per run — without touching what the skill actually does.

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