@karpathy did a great job with auto researcher. Using a similar harness AI is able to build a @risc_v CPU better than humans using @YosysHQ: github.com/FeSens/auto-arch-…

CPU demand for agents is real. I my experience 6x more flops are needed than before. Before I had one MacBook and was happy. Now I have 2 MacBooks, 2 Desktop PCs, 1 server with 20 cores and 128GB or Ram and it still not enough. The agent demands more.

AI agents designed hardware that runs Qwen on an old FPGA github.com/FeSens/openTPU. @aviflombaum sees a bigger story: what tiny, fast AI hardware could mean for medical devices.

I just ran an LLM on an FPGA. Now I'm wondering: how far are we from building our own TPU? Not in simulation. Not on a GPU. On a real Xilinx Kintex-7 FPGA with 4GB RAM. A few days ago, I came across openTPU, an open-source AI accelerator project created by Felipe Sens Bonettoe with the help of AI agents. The idea was too interesting to leave on GitHub. So I decided to put it to the test. 🔧 From GitHub to real hardware: → Connected the FPGA board to MINISFORUM PC via PCIe → Configured the hardware → Built the software stack → Brought up the DMA drivers → Loaded real model weights → Started testing inference And it actually worked. ✅ FPGA hardware detected and initialized ✅ PCIe DMA communication working ✅ Memory subsystem operational ✅ Hardware self-tests passing ✅ Real LLM inference running, including Qwen The most interesting part wasn't the performance. It was realizing that you can take an open-source accelerator architecture, bring it onto real hardware, and start experimenting with your own AI inference engine. Of course, this isn't an H100 competitor. And an FPGA prototype is still a long way from a production ASIC. But that's exactly why I find it exciting. 🔬 What's next? A few years ago, building your own AI accelerator sounded like something only a semiconductor company could attempt. Today, open-source hardware, affordable FPGA boards, and AI-assisted engineering are making that idea worth experimenting with. Maybe the next wave of AI innovation won't just be about building better models. Maybe it will also be about who gets to build the hardware that runs them. 👏 Huge credit to Felipe Sens Bonetto for creating openTPU. nitter.siddz.com/reasoningTokens 🔗 Original project: github.com/FeSens/openTPU #FPGA #AIHardware #OpenSource #ASIC #LLM

#openTPU is second on @hackernews!

Will we ever see @Etched benchmarked on @SemiAnalysis_ Inference X? It would be very interesting

Open Inference on Open Hardware. #OpenTPU is now able to do @Alibaba_Qwen 3.5 inference at +30 tok/sec! And a 44% increase in prefill speed driven by fully automated agents research in the last 24h! github.com/FeSens/openTPU

Turns out that AI is now capable of designing its own hardware: 25 tok/s on Qwen 3.5 github.com/FeSens/openTPU

Starting from wallets, gas and transactions - the foundations every Ethereum builder needs. Thank you @reasoningTokens!

They are really coming for open models, this is sad. As Dario says AI is really dangerous, too dangerous to leave it to the government to decide, to abuse, to regulate and to monopolize.

Fable is not back… yet 😔

I’ve being poking around with agent harness and fully autonomous companies and I think I’ve seen end game

CLI tools is quickly becoming the standard for agents. If your company don’t have a cli tool, you will be left behind

Its an unpopular opinion but @openclaw is amazing and unreliable. It's amazing when it works, but it often don't

Day 23 of 2026: I haven’t written a single line of code. Edited 3 lines. It’s over. Time to reinvent myself.

Quick new post: Auto-grading decade-old Hacker News discussions with hindsight I took all the 930 frontpage Hacker News article+discussion of December 2015 and asked the GPT 5.1 Thinking API to do an in-hindsight analysis to identify the most/least prescient comments. This took ~3 hours to vibe code and ~1 hour and $60 to run. The idea was sparked by the HN article yesterday where Gemini 3 was asked to hallucinate the HN front page one decade forward. More generally: 1. in-hindsight analysis has always fascinated me as a way to train your forward prediction model so reading the results is really interesting and 2. it's worth contemplating what it looks like when LLM megaminds of the future can do this kind of work a lot cheaper, faster and better. Every single bit of information you contribute to the internet can (and probably will be) scrutinized in great detail if it is "free". Hence also my earlier tweet from a while back - "be good, future LLMs are watching". Congrats to the top 10 accounts pcwalton, tptacek, paulmd, cstross, greglindahl, moxie, hannob, 0xcde4c3db, Manishearth, and johncolanduoni - GPT 5.1 Thinking found your comments to be the most insightful and prescient of all comments of HN in December of 2015. Links: - A lot more detail in my blog post karpathy.bearblog.dev/auto-g… - GitHub repo of the project if you'd like to play github.com/karpathy/hn-time-… - The actual results pages for your reading pleasure karpathy.ai/hncapsule/

Since this post I've been querying @antigravity with "How would a google senior engineer implement XYZ" and its output have been much better

If money couldn't buy me the freedom to work on Omarchy for the sheer love of computers, what good would it be?

Actively doing nothing is better than doom scroll TikTok. Boredom is incredibly important to the brain.