Not enough people are building consumer AI products e.g. everyone is launching AI lawyers to help lawyers NOT AI lawyers for consumers so they don’t need a lawyer to begin with. Same with accountants The only space crushing this so far is AI developers like Lovable & Replit

Former Scale AI founder and newly appointed @Meta Chief AI Officer @alexandr_wang dropped a bombshell during his YC conversation with @garrytan, and you could almost hear the tech leadership world go quiet. He said Meta has already seen this internally: Build the right agentic loop, give it an evaluation system and metrics that let it optimize itself, and a group of AI agents can complete more work than a team of 100 senior engineers. And they do it “very easily.” But the most interesting part wasn’t the 100-engineer comparison. It was how simple the system underneath it actually is. You’d expect some insanely complex, almost alien architecture powering a swarm like this. Instead, Wang described it with a few almost comically basic words: “Markdown files, cron jobs, goal, metrics, data.” Once you strip away the hype, the implications for traditional software engineering become pretty clear: 1️⃣ It’s not that the models are magically smarter. The eval loop is doing the heavy lifting. Traditional approach: humans write prompts, run the code, inspect the output, and hope nothing broke. Meta’s approach: turn the business goal into something a machine can score automatically. The agent submits its work. The system runs tests, calculates metrics, finds what’s wrong, and sends it back for another pass. Repeat until it passes. Nobody has to babysit every step. The metric becomes the supervisor. 2️⃣ The real alpha is burning 1,000x more tokens inside the feedback loop. A lot of people are still optimizing for the cost of a single AI call. The frontier labs are playing a different game: spend 1,000x or even 1,000,000x more tokens in the background so agents can constantly review, rerun, challenge, and verify each other’s work until they reach a reliable business outcome. Token cost is fixed. The payoff is a pipeline that keeps running. 3️⃣ Memory doesn’t need some fancy database. Persistent memory can live in Markdown files. Scheduling can be handled by the server’s built-in cron jobs, running overnight. The simpler the scaffolding, the more robust the system can be. Less infrastructure also means fewer ways for context to fall apart. This is a pretty brutal change in how technical organizations work. The ceiling for a tech lead used to be partly about how many people they could manage, how many meetings they could sit through, and how many teams they could coordinate. The leverage for the next generation of technical leaders may look very different: Can you turn a messy business objective into a rigorous set of metrics that an AI can evaluate automatically? If 100 people’s output can be replaced by a few cron jobs, Markdown files, and a well-designed eval loop, the era of “just take the ticket and write the code” is coming to an end. The people who can design the evals and orchestrate the swarm aren’t just holding a new tool. They’re effectively running a virtual company.

im increasingly convinced that the best way to bet on a particular sector is simply to own the top 1 or 2 dominant players. the laggards may feel cheap and u tell urself they have much more room to grow, but they r often at a massive disadvantage in terms of economy of scale and network effects. good companies stay good and bad companies stay bad.

Study Morphic Resonance

This feels inevitable

Too many people are bearish on vibe coding

The inventor of 2FA has wasted a million human lifetimes. All in 10-30 second intervals

What have you created today?

The next 10-15 years are gonna be fun

Extremely interesting writeup - AI accelerating science is critical, but it doesn’t directly address the bottleneck of *human* clinical trials. Need AI w predictive validity for efficacy in humans (Phase II trials) & that AI requires massive public datasets for human biology!

This is big. OpenAI and Retro used a custom model to make cellular reprogramming into stem cells ~50× better, faster, and safer. Similar Wright brothers’ glider to a jet engine overnight. We may be the first generation who won't die. Let's take a look at what they did. 🧵

The way new entrants view btc at $120,000 is the same way I viewed it at $1,000 in 2017 “I can never own enough of this to make it matter” It’s literally 100x more true now, don’t let the maxis fool you

Forte brings protocol composability and native automation directly to the network. Forte is coming: • Testnet: September 17 • Mainnet: October 22 Watch @dete73 announce Forte, the future of consumer crypto and AI, at the @EthGlobal Pragma main stage⤵️

Just heard from the team in NYC that @flow_blockchain was the most built on out of all platforms at @ETHGlobal , not just L1/L2 chains 🤯 Seemed like the people liked your talk @dete73 Forte (& friends) coming October 2025 to put Flow-powered apps and agents in every pocket 🤖

Just signed & wired to lead a new deal in LA. AI-native apps will have their @Snapchat and @Tinder moments & many will be built here. Breakout apps take time in every platform shift. For AI, that time is now. And LA is ready.

Forte 👀

New themes just dropped in the editor - Dune Dark - Tokyo - Light - Dark Let me know which ones you'd like to see me create next

This guy is single-handedly building Cursor but for Jupyter Notebooks. Classic notebook feel, supercharged with AI Follow him now to join the whitelist early

oh yeah btw intellisense supported too

Agentic mode in the chat side bar - can read and write files available in the notebook instance