that's a nice question; here is a post about Algeria by an Algerian. link in the next tweet. 👇
PROOF OF EFFORT “when everyone is spreading their effort wider and wider, deep, concentrated effort becomes rare, obvious, and attractive. If you don't want to compete on volume, the best thing you can do is choose not to.” creatorscience.com/proof-of-effor…
We’re committing a portion of Anthropic’s shares to the Anthropic Public Benefit Fund, a new wholly owned entity dedicated entirely to the public benefit. It will start off with 5% of our shares. For every 50% increase in the company’s value over a set baseline, we can contribute up to an extra 2%, subject to our board’s discretion. In total, this means that roughly 20% of Anthropic’s shares are committed to the public good. Our co-founders have already pledged to donate 80% of their equity, and around half our employees are donating a portion of their equity as part of a donation matching program we’ve run since our founding. In the post below, we share more details about our new fund. anthropic.com/news/public-be…
I've been slowly broadening Claude's scope as it works 24/7 autonomously on my SaaS app, and a suggestion it recently added to my backlog really highlights what these models are capable of with the right setup. For context, my product, Preceden, is a timeline maker. People can build timelines by hand or use the AI generator on the homepage to create one about any topic, then sign up to keep it. However, free accounts are limited to 10 events, and AI-generated timelines usually contain more than that. So people sign up and immediately find they can't add any events of their own. Without me pointing it at this problem, Claude realized this funnel was broken. It dug into the relevant code, site copy, and production data, and proposed several fixes: - Let free users add 5 events of their own to an AI-generated timeline, even if it's over the free plan limit - Restore the thumbnail of their timeline on the signup page, a feature that had been silently broken since 2023 - Make the upgra…
This is Pele, my new rendering library for diagrams and charts, compatible with the latest Mermaid syntax. - 100-500× faster than mermaid.js - 6× smaller bundle - No dependencies - Easy to theme - Responsive to device width In pursuit of making Obsidian insanely fast and cohesively designed, I always found the Mermaid library to be too big, too slow, and too hard to theme. Pele will be the new renderer in the next version of Obsidian, but you can start using it now. Pele [ˈpɛlɛ] is named after the goddess of volcanoes and fire. It's open source under the MIT license. Try the playground!
Some observations about the "total" automation of software engineering: 1. There are more software engineers now than in 2025, and demand is growing. 2. Software engineers have never worked harder than they do now. 3. They largely don't write code anymore, agents write ~100% of the code. 4. The bottom line of the companies that employs these engineers and agents (i.e. the terminal purpose of all this code) is typically not budging as a result of AI adoption. This is why you have to think in terms of systems. Automating one aspect of a system creates its own friction and moves the bottlenecks elsewhere.
Banger paper from Tencent on recursive self-improvement for terminal agents. (bookmark it) This is a great read if you are tracking research on training terminal or coding agents with RL Starting from Qwen3.8-27B, they alternate two phases over five stages. One phase fine-tunes on rewritten versions of trajectories that passed the verifier. The other runs PPO with a critic inherited from the previous stage. The four-suite Terminal-Bench average goes from 20.68% to 50.58%, with 86.52% on Terminal-Bench 2.1 and 79.00% on Terminal-Bench Hard. The reward audit is just as useful. Under a dense progress reward, failed runs collected 50.09% of the reward, and 48 failures passed every executed check but still missed the final test. Separating strict success from partial credit cut that share to 28.72%. arxiv.org/abs/2610.22192 Chat with Paper: academy.dair.ai/papers/termina…
The NSA mathematician who made the numbers protecting your passkeys died in 2023 without ever remembering how he made them. Cryptographers chased the answer for years and put up a $36,864 bounty. Claude figured it out in under 30 minutes, and the answer was the clock on his computer. His name was Jerry Solinas. Back in 1997 he generated the starting numbers for P-256 and a few sibling curves, the math underneath your passkeys and a big chunk of the secure websites you visit every day. NIST published them in 2000 with no explanation of where they came from, and for a long time people mostly shrugged. Then 2013 happened. The Snowden files showed the NSA had slipped a backdoor into a different crypto standard, and suddenly every unexplained number the agency had ever handed out looked suspicious. These were the most widely used ones of all. Jerry's own story was that he'd hashed a joke sentence, something along the lines of "Give Bob and Jerry a raise," and then lost it when his work…
OpenCode is currently under a sustained cyberattack, and mid-incident they ran into the same problem Hugging Face did: the frontier models they were using for defense began refusing to help fight attacking agents because analyzing the attacks started triggering safety guardrails.
