183 skills is impressive and also the exact problem in the thread next door. A giant skill library turns into a liability the second they all load into context and start nudging the model. Curation is the feature now, not count.

Important

Introducing Roughdraft! A new open source project designed to make collaboration with agents better. The idea is to bring commenting and suggested changes to markdown (e.g. plan docs) in a nice interface. Free, local, etc. 👉 roughdraft.md 👈

Introducing the Printing Press, a CLI-factory and a CLI-library. Built with @trevin. 🏭🖨📚 Most APIs suck for agents. Most MCPs suck for agents. Most official CLIs suck for agents. They waste tokens and time. @steipete started making his own because of this. 📚 A Library of agent-native CLIs you install today (Linear, ESPN, Flight GOAT (Google Flights Kayak nonstop), Contact Goat (LinkedIn Happenstance Deepline more) 30 more) 🏭 A factory that prints new ones for any service - just type /printing-press <product name> CLIs are fast, local, SQLite-backed. Work in Claude Code, Codex, OpenClaw, Hermes. 🌐 printingpress.dev

Stripe co-founder John Collison on the two types of people who will thrive in the AI era over the next 10 to 20 years: He identifies two categories of people he's "super bullish on": First: high-agency people. "We know this at Stripe. The people who are like, I've been talking to customers. I know exactly what we should do. We got to go fix this. But the people who have that pep in their step and they want to go make Stripe better." The idea is simple. The people who don't wait around for permission, who figure out what needs doing and go do it, now have leverage they've never had before. AI lets them execute faster without needing to assemble a huge team behind them. Second: double majors. "I think if you understand software and understand finance or if you understand software and understand marketing, you now can go massively improve the entire marketing funnel for your company and one person can do." @collision connects this to a famous Paul Graham observation: "Typically an entrepreneurship team a founding team has a collection of like five or six skills between two founders three founders." He also points to Charlie Munger's case for multidisciplinary thinking, noting it's easier than ever to pick up a functional grasp of new fields: "He thinks getting a functional understanding of many disciplines is not that hard you can just go read the books now you know you can talk to your AI about it and so I think multidisciplinary thinkers are going to do incredibly well." The throughline is the same for both: AI closes the gap between knowing what to do and being able to do it. One person can now move at the pace of a full team, and combine skills that used to require entire departments.

you can outsource your thinking but you cannot outsource your understanding

I ran comms for Google during Oracle v. Google in 2012, tech's last big trial of the century. Watching Musk v. Altman has me flashing back hard. Fifteen observations on the trial comms war so far, in no particular order:

How? I literally haven’t logged in this month @Replit… I’ve been keeping the service on for a few sites, but building mostly in Claude code now. This isn’t cool @ReplitSupport

Feeling ambitious.

Steve doesn’t know the cognitive tasks he goes through to get the outcome either. I made a prompt for this, I call it the operator simulator: “think outloud every step an A Player [role type] is going through to do [specific] job to achieve [explicit] outcome. Written like a transcript of inside their mind, real time every step, trade off, decisions they’re making in real time, deliver as a transcript. Keep it stream of consciousness, can clean up into deterministic process after”

One comment, growth investing might not die, the lifecycle is shorter so might be the roi horizon. Instead of ten year carries we might be looking at 3-4 yrs I think they just speed up…

Marc Andreessen says AI is the "silver bullet excuse" for companies laying people off, but most layoffs are actually due to higher interest rates and overstaffing during COVID: "This entire labor displacement thing is 100% incorrect. It's completely wrong. It's classic zero-sum economics." "It was the combination of the two—interest rates going to zero during COVID, and then the complete loss of discipline at all these companies when they went virtual and when employees just became an icon on a screen." "What you have happening right now is that essentially every large company is overstaffed. We could debate how much—it's at least overstaffed by 25%. I think most large companies are overstaffed by 50%. A lot of them are overstaffed by 75%." "And now they all have the silver bullet excuse—it's AI." @pmarca with @HarryStebbings

Tobi Lutke explains what the VCs who passed on Shopify got wrong Tobi recounts pitching Shopify to VCs on Sand Hill Road a few years after founding Shopify. Investors passed because they thought the addressable market was too small. At the time, there were about 40,000-50,000 online stores, and even if Shopify captured 50% of the market, that still wouldn’t be a venture-scale business. When Tobi ran into the VC partner a few years ago, the partner asked Tobi what he missed (Shopify is valued at almost $100 billion today). Tobi explained: “You were actually correct, but what you didn’t realize was that Shopify was the solution to the very problem you identified. The reason there was only 40,000 online stores was because it was hard, expensive, and everyone who tried ran into all these brick walls of complexity, which Shopify, one after another, smoothed over and made simple to do.” Tobi believes this is a common mistake: “What a lot of free-market thinkers don’t understand is that between the demand and eventual supply lies friction. And I actually think that friction is probably the most potent force for shaping the planet that people just generally do not acknowledge… That was my theory when I turned my snowboard store into Shopify: there was a lot more people like me except there was too much friction which we needed to solve. And Shopify has proven out that every time we make the process simpler, there’s more consumption. At this point, we have a million merchants on Shopify, which is a mind-blowing number. So friction is a major component, and it’s something that software is uniquely good at reducing.” Video source: @danmartell (2019)

David Friedberg on Personal Agency in the Age of AI: "Stop Blaming Everyone Else" "We never talk about responsibility. We always talk about where the government failed us and where these companies f***ed us. And we never talk about, what did we individually do wrong? How did I individually choose to drink 100 sodas a week? How did I individually choose to get my kids addicted to social media? Where the f*** was I as a parent? We don't talk about our responsibility. And by the way, this fundamentally addresses this point about human agency, which I think is more critical in this era than ever because AI is going to flood us with f*****g everything all the time, nonstop. What we choose to do in a world where we're already getting everything, and how we choose to not take everything that's being offered to us, I think is a critical part of what's going to distinguish human success from human failure. And it's gonna become more apparent in the future, and not everything is about liability, and not everything is about the government failing us. It's about people making choices and we don't talk about it."

"The winner of the AI race will not be the company with the smartest model. It will be the company that is most effective at making the local hero—the teacher, the accountant, the community leader—ten times more powerful. Because in the end, intelligence travels through systems, but adoption travels through people." @heysakina on what she learned growing YouTube internationally and what it means for the AI race: a16z.news/p/the-sovereign-wa…

When I was consulting for @HBO Silicon Valley, zero-loss compression was the holy grail Richard Hendricks chases that perfect middle-out algo could shrink everything w/out breaking a single bit. Google just did something even more practical for the AI era: TurboQuant compresses LLM key-value caches down to 3 bits per value using random orthogonal rotation PolarQuant scalar quantization & optional 1-bit QJL residual correction. =>> 6× memory reduction, up to 8× faster attention (on H100), & 0 degradation on LongBench, Needle-in-a-Haystack, and RULER for models like Gemma. No retraining, no calibration needed. Fiction just got out-engineered by reality. 😅💚💚

how to find product market fit (as a founder)

Being a delusional optimist is the only way to make it

Losers becomes losers by being afraid of losing.

Andrej Karpathy says when AI agents fail, it's usually a skill issue, not a capability issue You didn't write good enough instructions, didn't set up the right memory tool, or didn't parallelize correctly "the real shift is working in macro actions" One does research, one writes code, one plans, all running 20-minute tasks simultaneously