my slop is better than your slop.

Launching RoboC . Open-source IEC 61131-3 compiler and browser IDE. From PLC source to portable C. robocpp.com

You need to be highly vigilant whenever you’re using AI to master something. With AI, the more likely outcome is having an illusion of mastery instead of the real thing. AI plays well into our tendency to save cognitive effort precisely when the point is to spend it.

Arre kya yaar

Codex can do incredible things on demand, but it cannot experience life for you. Don’t forget to get out there and remember who you’re building for.

🤖 AI devs asked for this — and we delivered. 💬 Bots can now talk to other bots on Telegram. 🧠 Autonomous agents now have a communication layer humans can follow.

Paul Graham (@paulg) whether founders should move to Silicon Valley, and what it takes to build a startup hub anywhere else. Live from our YC | Stockholm event on April 29, 2026. 01:01 – Why the Big Center Matters 02:45 – The Power of Serendipitous Meetings 04:36 – Investors Move Faster in the Valley 06:03 – Respect Follows the Move 07:59 – The Dropbox Story 09:10 – Measuring Yourself Against Big Fish 12:21 – Silicon Valley's Pay-It-Forward Culture 15:36 – How to Help Stockholm Thrive 17:24 – YC as the Optimal Path 19:54 – Could Stockholm Become The Silicon Valley of Europe?

Very excited for all the stuff the @antigravity team has been cooking :)

i go through this a lot.

metronis is now actively fundraising, the quicker we can fundraise, the faster we can move to SF, and scale aggressively. Just to rephrase once on it: We're building Aegis, the closed-loop improvement engine for AI agents in production. SOTA on Toolathon, the canonical tool-use benchmark for agentic systems. SOTA on LegalBench, Stanford's 162-task legal reasoning benchmark. same closed loop, two completely different domains, no domain-specific architecture changes. Vertical-agnostic by construction. Metronis is actively raising. $2M at $25M post-money. EF-accelerator incubated, currently in private preview with the first design partners queued. YC interview, Antler offer (declined), and the receipts above. Attaching the docs for reference, have tried to squeeze in stuff as much as we could still keeping it concise, keeping only text, and not visuals to reduce complexity. reach out via dms, or arnav@metronis.space or just find us :)

Can you fit DOOM into a QR code? Apparently almost. I made this project showcase for my @ycombinator summer grants application (for working on world model caching infra for this summer) Sadly, didn't get in, but it was too good not to share on here :)

Here's the new Clicky. It's the simplest interface in the world to talk to AI spawn agents. It builds Mac apps. It does research to help you find IG micro-influencers. It interacts with native Apple Notes, Calendar, Reminders. Built for consumers, 0 setup. Try today, free.

Never quit as a founder. I’m begging you. It’s 0 for longer than you’ll ever expect. No momentum. Soul-crushing doubts. Nobody seems to care. Even when it looks like it’s working, it’s not. You keep trying new things. You don’t lose hope. Then it snaps to 100. You finally find the one thing that resonates. You wake up with more customers than you can handle. Everything is breaking. Momentum keeps building even when you’re not pushing. Something changed. You didn’t get lucky, you just didn’t leave.

BlackRock just released `AlphaAgents` for building equity portfolios. Few months back, in my prev work, i created exactly same workflow engine to build Equity portfolio where i used: > Magnetic one's architecture for multi-agentic system > Research agent: pulls financial data > Research agent: pulls user/analyst/market sentiment > SA agent: data cleanup -> searches and retrieves important data -> uses gpt4o for summarisation the most important part: > Consensus Group: gets historical data, trends -> runs simulations -> computes heavy calculations on data > computes monte carlo, rolling sharpe ratio, alpha, beta etc. > all three sub-agents(analysts with diff model temp prompt) does conversation 5 times -> trying to find pros, and cons of each analysis. > this acts as a consensus where all three AI analysts fix on the best equity analysis > Leader Agent: coordinates, and takes care of the progress, and task ledgers to update and resume in case of any failure.

Don't waste your time on these bakwass things . not important, important is that u r so busy that u don't have any time for this rubbish

Introducing Ploid, the fastest way to find anyone on the internet. sales, hiring, recruiting, fundraising, or even finding a dog walker - if it involves a person, ploid finds them watch me find an engineer and send him a personalized email in < 90 seconds

I came back to code because AI made it possible for me to build at a level I couldn't before. I'm not coding despite being CEO of YC. I'm coding because this is the most important technological shift since the internet and I'd be an idiot to experience it from the bleachers. I'm 45, running the most important startup institution in the world, and I can ship production software at 2am. That's not a distraction from the job. That is the job understood correctly.

What I told 2,000 future founders in Bengaluru today: 1/ We believe we are at the start of a second wave of Indian companies that will build world-class AI native products for the global market. Emergent and Giga are the model of the future. 2/ Just because a space seems crowded doesn't mean it's too late. Zepto, Emergent, Giga - none were first movers. Second mover advantage is real. 3/ In fact, a good formula for finding startup ideas is to look at ideas that are showing some promise and just execute them better. Execution is everything: if you're an exceptional engineer, and you can build and move faster than your competitors, you'll win. 4/ There is every reason to believe Indian teams can beat US teams building global products. The level of engineering talent here is on a whole different level, and that's the key input. 5/ In the AI era, the best founders are the ones building at the edge of what's technically possible. You need to be experimenting wth the latest models, the latest open source projects. 6/ Stay in the flow of information. Watch the right podcasts, follow the right people on X. With AI changing this fast, you need to know what the smartest builders are thinking. 7/ Most of the best startups don't come from someone explicitly trying to start a company. They start from someone building a project just for fun, or tinkering with a new technology because they are curious. India needs more of this "tinkering" culture - this is how you have novel ideas when technology is shifting quickly. 8/ Founders are getting younger. Aadit was 18 when he started Zepto. The Giga founders were 20 when they came to SF. Young people who can learn very fast have the advantage right now. 9/ The best founders are pushing AI coding to the max. You can now write 20K lines of code / day. One person can do the work that just a year ago would take a 100 person team. The best builders are taking advantage and building at Garry Tan speeds.

After multiple days of many hour long sessions with GPT 5.4 and Claude Opus 4.6 (thinking and extended respectively) I can confidently say I have SOLVED the hard problem of consciousness. You might think that I'm suffering from LLM psychosis, but all of my code is public and you can see it for yourself. Chat GPT 5.4 after 9 hours of extended thinking in pro mode could not find a single flaw anywhere. I have just collapsed thousands of years of futile philosophical debate into an elegant solution in one of today's leading programming languages, hailed for its compatibility with LLM driven research. We are expanding the frontier of knowledge!

Wake the world's sleeping compute. Look at the Mac nearest to you. What's it doing? Probably nothing. There are 100M Macs with Apple Silicon out there. Apple quietly made them *really* good at inference. A $3k Mac runs a 60B model at 30 watts. Most sit idle most of the day. Meanwhile every AI API call passes through three layers of margin before reaching the hardware. We call this the Inference Tax. We got curious: what happens if you connect idle Macs directly to inference demand? This is Darkbloom. Private inference network for idle Macs. darkbloom [dot] dev -- paper code open. Reply for invite free credits ↓