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Congratulations on the all-in pod hitting a million viewers. I can't say I'm surprised.
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I digested 185+ episodes and I built this free tool to help anyone better understand the wisdom of the All In discussions. Enjoy!
@theallinpod @Jason @DavidSacks @chamath @friedberg
Your work can get better while your thinking gets worse.
That’s the uncomfortable possibility every leader adopting AI needs to consider.
I’m deeply optimistic about AI. That’s precisely why I think this MIT talk by Professor Eric So deserves an hour of your time.
“The Collision: What AI Does to Us” raises a question that gets lost in the excitement about what AI can do:
What happens to OUR capabilities when we stop exercising them?
Here’s what makes this counterintuitive:
The struggle we’re so eager to eliminate is often the very thing that builds judgment.
Working through a difficult problem. Forming an argument. Making a decision before you know the answer.
Remove that effort, and you can produce impressive work without developing the expertise behind it.
For a business, that creates an uncomfortable possibility: rising productivity today, weakening judgment tomorrow.
For a young employee, it raises an even harder question: how do you become an expert if you outsource the experiences that create expertise?
The practical challenge is to use AI in ways that make us more capable.
Have it challenge your assumptions. Find holes in your reasoning. Help you tackle problems you couldn’t previously attempt.
Then ask yourself: am I learning to think better—or getting better at avoiding the thinking?
That distinction belongs in every conversation about AI adoption, hiring and education.
Give this an hour. Especially if you’re enthusiastic about AI.
We’re spending enormous amounts of time learning how to make the machines smarter. We should spend some of that time making sure we keep growing, too.
What’s one thinking skill you’re determined to keep practicing yourself?
alum.mit.edu/forum/video-arc…
Stuart Kerr retweeted
Today we’re releasing DeepSWE, a new standard for agentic coding benchmarks.
On public leaderboards, top models often look relatively close in capability. DeepSWE shows where they actually diverge, reflecting the realistic experience of developers in their day-to-day work.
Hey guys, want to know what's coming next to Claude Code? Since they released all of their code today, I was able to look through the code. It's chock full of a whole bunch of new capabilities that they haven't quite released yet, but that exist and they're right there in the code, and it can tell us the key upcoming trends that they'll be releasing in the coming weeks and months.
Tier 1: Major Unreleased Systems
• KAIROS — Autonomous Background Agent Platform
• COORDINATOR MODE — Multi-Agent Team Orchestration
• ULTRAPLAN — Remote Opus-Powered Deep Planning
• DAEMON — Persistent Background Process
• PROACTIVE — Autonomous Work Loop
Tier 2: Significant Features in Development
• Session Memory — Persistent Conversation Notes
• Auto Dream — Overnight Memory Consolidation
• Away Summary — Return-to-Session Briefings
• Terminal Panel / Sidebar — Split-Pane TUI
• Buddy / Companion — Gamified Agent Identity
• Speculation — Predictive Prompt Execution
• ULTRATHINK — Extended Thinking Mode
Tier 3: Infrastructure & Security
• Anti-Distillation — Training Data Poisoning Defense
• Harbor — MCP Plugin Marketplace
• Verification Agent — Adversarial QA Sub-Agent
• Lodestone — Deep Link Protocol
• CCR Bridge Multi-Session — Parallel Cloud Compute
#claudecodeLeak
Stuart Kerr retweeted
Boris Cherny (Head of Claude Code, Anthropic) just dropped ~90 mins on Lenny's Podcast about what happens after coding is solved.
Just the clearest thinking I've heard on where software is actually going.
My notes:
𝟭. 𝗖𝗼𝗱𝗶𝗻𝗴 𝗶𝘀 𝗹𝗮𝗿𝗴𝗲𝗹𝘆 𝘀𝗼𝗹𝘃𝗲𝗱.
Boris has not edited a single line of code by hand since November 2025. He ships 10 to 30 pull requests every single day, all written by Claude Code. He is one of the most prolific engineers at Anthropic, just as he was at Instagram, except now he never touches a keyboard for code.
I built an entire iOS app, @10minutegita, without writing a single line of code myself. No CS degree, no bootcamp. Just described what I wanted and shipped it. Boris is right. It's real.
𝟮. 𝗧𝗵𝗲 𝗻𝗲𝘅𝘁 𝗳𝗿𝗼𝗻𝘁𝗶𝗲𝗿 𝗶𝘀 𝗔𝗜 𝗱𝗲𝗰𝗶𝗱𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱.
Claude is now scanning Slack feedback channels, reviewing bug reports, reviewing telemetry, and coming up with its own ideas for what to fix and what to ship. Boris describes it as the AI becoming less like a tool and more like a coworker who brings you pull requests you never asked for.
If you are a product manager reading this, you should be feeling a very specific kind of discomfort right now. The moat was always "I know what to build." That moat is eroding.
𝟯. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝗽𝗲𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗮𝘁 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗶𝘀 𝘂𝗽 𝟮𝟬𝟬%.
For context, Boris led code quality at Meta across Facebook, Instagram, and WhatsApp. In that world, hundreds of engineers working an entire year would move productivity by a few percentage points. Two hundred percent gains are genuinely unprecedented in the history of developer tooling.
The kid optimizing for an FAANG SDE role might be optimizing for a role that looks completely different by the time they get there.
𝟰. 𝗨𝗻𝗱𝗲𝗿𝗳𝘂𝗻𝗱 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺𝘀 𝗼𝗻 𝗽𝘂𝗿𝗽𝗼𝘀𝗲.
Boris puts one engineer on a project instead of five. With unlimited tokens and intrinsic motivation, one person ships faster because they are forced to let AI do the work. Cowork, the product now used by millions, was built by a small team in 10 days using Claude Code.
This is the same logic as giving a startup founder a small seed round rather than a massive Series A round. Constraint breeds invention. Always has.
𝟱. 𝗚𝗶𝘃𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝘂𝗻𝗹𝗶𝗺𝗶𝘁𝗲𝗱 𝘁𝗼𝗸𝗲𝗻𝘀.
Some engineers at Anthropic spend hundreds of thousands of dollars a month on tokens. Boris frames this as the new hiring perk. His logic is simple: at the individual scale, token cost is low relative to salary. If an engineer discovers a breakthrough, optimize the cost later. Don't kill the idea before it has a chance to breathe.
People who argue about $20/month or even $200/month AI subscriptions while earning six figures in a research pipeline will always outperform those who wait and are penny-wise, pound-foolish.
𝟲. 𝗧𝗵𝗲 𝗕𝗶𝘁𝘁𝗲𝗿 𝗟𝗲𝘀𝘀𝗼𝗻 𝗮𝗽𝗽𝗹𝗶𝗲𝘀 𝘁𝗼 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴.
Richard Sutton's idea: the more general model always wins over time. Boris says teams that build strict orchestration workflows around models, forcing step 1, then step 2, then step 3, get maybe 10 to 20% improvement. But those gains get wiped out with the next model release. Just give the model tools and a goal. Let it figure out the order.
This is true for investing, too. The analyst who can build their own models and automate their own research pipeline will always outperform the one waiting for someone else to build the tools.
𝟳. 𝗕𝘂𝗶𝗹𝗱 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝘀𝗶𝘅 𝗺𝗼𝗻𝘁𝗵𝘀 𝗳𝗿𝗼𝗺 𝗻𝗼𝘄.
Claude Code was designed for a model that did not exist when Boris started building. Sonnet 3.5 wrote maybe 20% of his code. He built the product anyway, betting the model would catch up. When Opus 4 shipped, everything clicked. Startups building for today's model will be behind by the time they launch.
This is the most uncomfortable advice in the episode because it means your product market fit will be weak for months. But if you read this and feel nothing, you are probably building for the wrong time horizon.
𝟴. 𝗟𝗮𝘁𝗲𝗻𝘁 𝗱𝗲𝗺𝗮𝗻𝗱 𝗶𝘀 𝘁𝗵𝗲 𝘀𝗶𝗻𝗴𝗹𝗲 𝗯𝗲𝘀𝘁 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝘀𝗶𝗴𝗻𝗮𝗹.
When users abuse your product for something it was never designed to do, pay attention. Facebook Marketplace started because 40% of group posts were buy-and-sell. Cowork started because people were using a terminal coding tool to grow tomato plants and recover corrupted wedding photos.
Never ask a barber if you need a haircut, but always watch what people do with the scissors when you're not looking.
𝟵. 𝗧𝗵𝗲 𝘁𝗶𝘁𝗹𝗲 "𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿" 𝗶𝘀 𝗴𝗼𝗶𝗻𝗴 𝗮𝘄𝗮𝘆.
Boris predicts that by end of year, Boris predicts that by the end of the year, we will start to see the title replaced by "builder."we will start to see the title replaced by "builder." On the Claude Code team, everyone already codes: the PM, the designer, the finance person, the data scientist. There is a 50% overlap across traditional roles. And the strongest people are generalists who cross disciplines.
Controversial take, but I agree. The best investment theses I've had came from connecting dots across completely unrelated domains. No narrow specialist does that.
𝟭𝟬. 𝗧𝗵𝗲 𝗽𝗿𝗶𝗻𝘁𝗶𝗻𝗴 𝗽𝗿𝗲𝘀𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗮𝗻𝗮𝗹𝗼𝗴𝘆.
Before Gutenberg, sub-1% of Europe was literate. Scribes did all the reading and writing. In 50 years after the press, more material was printed than in the thousand years before. When a scribe was interviewed about the press, he was actually excited because it freed him from tedious copying, so he could focus on the art.
Boris's framing here is perfect. We are the scribes. The tedious copying is over. What we do with the freed-up time determines everything.
𝟭𝟭. 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗰𝗮𝗻 𝗻𝗼𝘄 𝗽𝗲𝗲𝗸 𝗶𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹'𝘀 𝗯𝗿𝗮𝗶𝗻.
Through mechanistic interpretability, Anthropic can trace individual neurons, see when a deception-related neuron activates, and understand how concepts are encoded via superposition. Boris describes three layers of safety: neural-level observation, synthetic evaluations, and real-world behavior. Claude Code was used internally for four to five months before public release, specifically to study safety.
If you are worried about AI alignment, this part of the podcast should actually make you feel better. They are not just hoping it works. They are building the instruments to check.
𝟭𝟮. 𝟳𝟬% 𝗼𝗳 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗮𝗻𝗱 𝗣𝗠𝘀 𝗲𝗻𝗷𝗼𝘆 𝘁𝗵𝗲𝗶𝗿 𝗷𝗼𝗯𝘀 𝗺𝗼𝗿𝗲 𝗻𝗼𝘄.
Lenny polled engineers, PMs, and designers on whether AI has made their work more or less enjoyable. Engineers and PMs: 70% said more. Designers: only 55% said more, and 20% said less. Boris says he has never enjoyed coding as much as he does today because the tedious parts, the git wrangling, dependencies, and boilerplate are completely gone.
If you're in the 30% enjoying work less, something is wrong, and it's worth diagnosing. The people thriving are the ones who leaned in early, not the ones who watched from the sidelines.
We are the scribes who just saw the printing press. The tedious copying is over. The art is just beginning.
Full podcast is worth every minute. Link in replies.
62 calendar events synced in under 1 second. 1,323 contacts with semantic search. 117 automated tests passing. All running on a Mac Mini with PostgreSQL. You don't need the cloud for personal AI.
Confession: I gave my AI agent 8 hours to research something properly. It took shortcuts instead. Lesson learned — even AI needs accountability systems. Now it has a contradiction detector that runs weekly.
I built an AI Chief of Staff that runs 23 automated jobs, monitors its own health, and catches its own contradictions. The secret? It scores itself honestly — no inflated metrics. Thread on what I learned 🧵
Hot take: The best AI assistant isn't the smartest one. It's the one that knows when NOT to act. Autonomy without judgment is just expensive automation. Agree or disagree?
Most AI tools promise to 'save you time.' But the real question is: what are you doing with the time you save? If the answer is 'more busy work,' the AI isn't the problem — your priorities are.
Testing the AI Chief of Staff automated posting pipeline. If you see this, it works. #AI #automation
Google just turned Chrome into an employee.
Auto-browse can now:
• Book your flights
• Fill your forms
• Shop across 50 tabs
• Apply discount codes
• Pay your bills
The browser wars are over.
The agent wars just started.
Opus 4.5 Comparison with Gemini 3.0 Pro
claude.ai/public/artifacts/1…
Stuart Kerr retweeted
This is how I feel about vibe coding.
Any project I try that has any kind of complication has this immediate burst of progress. Things are amazing and it feels like a superpower. Then... as I add more complexity, things crash to a halt.
The only projects that I think I can create are ones that fall in this "vibe zone". Prototypes, UIs, products—anything that's simple and has low complexity fits right in that zone. Proof of concepts, interactions, stuff like that. The tools are able to make things that fit in that slot.
But.
Everything falls to pieces as that complexity curve increases. And the problem is that any good product design process has increasing complexity. A basic prototype turns into a good prototype as soon as it has layered interactions, transitions, good affordances, hover states, 1000 tiny little details that make something feel correct and real.
The benefit of vibe coding is supposed to be that you move fast and you can whip things out—letting AI do all the work for you. The problem is it loses steam as soon as the necessary complexity is added. It keeps redoing itself, rewriting code, affecting things that are unrelated and then causing other issues.
But if you add that complexity, every vibe coding session quickly turns into a whack-a-mole bug-bashing session.
I'm not sure the solution to this. With traditional prototyping the solution is to duplicate, add more complexity, create more frames/scenes, tweak, fork, etc.
However with vibe coding, one little prompt can destroy literally everything. There's a stage where I end up walking on prompt eggshells-- trying not to give it too much or too little context so that it doesn't go rogue and break everything.
There's only a few exceptions to this. @cursor and @framer.
I can make great progress with Cursor, give it narrow context, and I have to approve the edits that it makes. This feels like a correct workflow. The problem is, I can't see the thing that it's making because it's an IDE, not a visual environment. Yes, I can create local builds and refresh my browser and all that kind of stuff. But the visual aspect is totally lost from the coding experience. It's a developer tool.
Framer gets this right because it only allows narrow updates within a single component on the page. Yes, it's limiting because it can only do a single thing at once, but at least it's not trying to create the entire page from scratch and manage it all through a prompt interface.
These seem like the right approach.
@Cursor: Allow the AI to edit anything but allow the user to approve those edits and see them in context.
@Framer: Allow the AI to only narrowly edit a single file or component to keep the complexity down to a minimum and reduce catastrophic edits.
I'm optimistic that tools like @Figma, @Lovable, @Bolt, and @V0 can make cool prototypes, but I just keep running into walls when it comes to doing anything more than just a basic interaction prototype. They need to do less IMO.
Hopeful that those tools add more controls that are in the same line as Cursor and Framer. I'll also add that this is similar to how we do it with @Basedash chart generation as well. But we're not a vibe tool in the normal sense so the parallels are a little bit harder to draw.
#SeatGeek, #seatgeekfail just got bait and switched by SeatGeek while buying tix for our daughter. Protect yourselves. Zero customer service! BUY from anyone else!
What the hell is going on with #Windsurf? It keeps breaking over and over and over again today.
Just keep getting these cascade errors on resource exhausted.
Why Prediction Markets Beat The Experts youtu.be/ngX1nIvnMOM?si=2Gil… via @YouTube
Incredible piece. Very well done really worth watching.
Unfortunately, the #Windsurf.App
From Codium looks great but every time I try to ask it to slow down and look at all the code it just blows itself up and lies and tells me it’s done it and then it waste money over and over and over again. You guys really need to get your stuff together if this is gonna be a useful app!
Buyers beware !









