Lula Rocha 🙃IQ21

THIS IS INSANE. 🤯 India gave GitHub THREE HOURS to delete BitChat. By the time the deadline hit, the app was already back. On a network with no company to send the order to. Here's what happened: • India orders GitHub to remove BitChat, citing "unlawful communication" • GitHub has 3 hours to comply or face legal action • Within hours, a developer mirrors the entire codebase on Radicle • Radicle has no central server. No company. No CEO to subpoena. No app store to pressure. • The instructions go out: clone it, seed it, keep it alive • Every person who seeds it becomes another copy the government can't touch The part nobody's saying out loud: this is the exact scenario BitChat was built for. It runs on pure Bluetooth. No internet. No SIM. No phone number. It was already unkillable at the app layer, that's why protesters in Nepal, Iran, and Madagascar used it to route around government shutdowns. Now the SOURCE CODE is unkillable too. India didn't delete BitChat. It just handed the entire internet a live demo of why decentralized infrastructure exists. You can order a company to take something down. You can't order a network that isn't owned by anyone. Command to mirror it yourself is already public. People are seeding it right now.

What's happening on the Swedish grid right now is not only hard to believe, but a window into the future regarding the Bitcoin usecase the world embraces first. Here's a snapshot.

Two math olympiad champions wrote a training manual in 1993 on two old Macintosh computers, and every American kid who has won a major math competition in the last decade learned to think from it. Their names are Sandor Lehoczky and Richard Rusczyk. The book is called The Art of Problem Solving. Most people in math know it as AoPS. Since 2015, every single member of the US International Math Olympiad team has been an AoPS student. Not most of them. Every one. That statistic sounds impossible until you understand what the book actually does. Lehoczky and Rusczyk were not professors. They were competitors. Lehoczky earned the sole perfect AIME score in 1990 and led the national first place team. Rusczyk was a USA Mathematical Olympiad winner and a perfect AIME scorer in 1989. They had both survived the same brutal selection process the book was designed to train students for. And the first thing they decided was that almost every existing math textbook was teaching the wrong thing. School math gives you formulas. You memorize them. You apply them. You pass the test. Then you sit down in front of a real competition problem and the formula does not apply, and you have nothing underneath it. That is the gap. The gap is not knowledge. It is thinking. The entire premise of AoPS is that problem-solving is a transferable skill, not a bag of memorized tricks. A student who genuinely understands why a technique works can adapt it, combine it with something else, and deploy it in a context they have never seen before. A student who only memorized the technique freezes the moment the problem looks different. The book teaches the difference between a formula and a method. A formula tells you what to compute. A method tells you how to see. The students who win olympiads are not the ones who know more formulas. They are the ones who have trained themselves to look at an unfamiliar problem and recognize its structure. To see that this problem is secretly asking the same question as a problem they solved three weeks ago, just dressed differently. Rusczyk calls this "learning to read the problem." Not reading the words. Reading what the problem is actually asking underneath the words. The second thing they built into the book is tolerance for being stuck. Most students treat confusion as a signal to stop. The book treats confusion as the starting point. Every chapter pushes students past the point where the obvious approach runs out. That moment of running out is not failure. That is where the actual thinking begins. Lehoczky once described it this way. If you can solve a problem quickly, you are not learning. You are performing. Learning only happens when you are past the edge of what you already know. The book was written on old Macintosh computers in 1993. Rusczyk launched the AoPS website in 2003. Today the community has over one million users. Thousands of students enroll in AoPS online courses every year. Most winners of every major American math competition are AoPS alumni. A platform built by two kids who were good at math competitions has become the infrastructure that produces the next generation of mathematicians, engineers, and scientists who are good at thinking. The formulas you memorized in school will eventually be obsolete. The thinking you trained will not. What is one problem in your life right now that you have been avoiding because you do not yet know the right formula to solve it?

Adobe tried to buy Figma for $20 billion in 2022. The deal collapsed. So Figma went public on the NYSE in July 2025 instead. Ticker FIG. Public company. Quarterly earnings. Wall Street pressure. You know what happens to design tools after they IPO. In March 2025, Figma raised the Professional Full seat 33%. From $15 to $20 a month. Organization seats jumped to $55. Enterprise to $90. Then they took Dev Mode, which was free during beta, and locked it behind a paid seat. Your developers now pay extra to inspect the designs your designers already paid to create. In March 2026, Figma started charging for AI credits on top. If Figma raises prices again, you pay. If Figma gets acquired, you pray. If Figma shuts down, your files die with it. Your design system. On their servers. In a proprietary format only their app can read. To draw rectangles on a screen. There is an open source design platform that runs on your hardware. Stores your files in plain SVG. Costs $0 forever for unlimited users. It is called Penpot. 45,700+ stars on GitHub. A full Figma-grade design platform built on open web standards. Vector editing. Components. Design tokens to W3C spec. Flex and Grid layouts. Real-time multiplayer. Interactive prototyping. Here's what it does: → Real-time collaboration. Live cursors. Comments in line. → Components, variants, shared libraries. → Auto layout, Flex, CSS Grid. The tool outputs production CSS, not lookalike CSS. → Interactive prototypes with overlays, animations, and flows. → Inspect tab. Free. Built in. Every developer grabs production CSS, SVG, HTML without a separate seat. → Plugin ecosystem. Figma import to migrate your files. → Self-host on Docker in one command. Your designs never leave your network. Here's the wildest part: Figma stores your designs in a proprietary format only Figma can read. Penpot files are SVG. The same format your browser has rendered for 25 years. Open them in any editor. Open them in 20 years. Nobody can lock you out. The feature Figma charges your developers extra for, Penpot gives away. Without asking permission. Figma Professional: $20/month per seat. A 10-person team: $2,400/year. Figma Organization: $55/month per Full seat. A 50-person org: $33,000/year. Penpot: $0. Unlimited users. Unlimited files. Unlimited teams. Self-hosted. Free forever. 45,700+ stars. 2,700+ forks. 250+ contributors. MPL-2.0 license. Backed by a community that believes design tools should be free. Your designs. Your files. Your standards. 100% Open Source. (Link in the comments)

Farzapedia, personal wikipedia of Farza, good example following my Wiki LLM tweet. I really like this approach to personalization in a number of ways, compared to "status quo" of an AI that allegedly gets better the more you use it or something: 1. Explicit. The memory artifact is explicit and navigable (the wiki), you can see exactly what the AI does and does not know and you can inspect and manage this artifact, even if you don't do the direct text writing (the LLM does). The knowledge of you is not implicit and unknown, it's explicit and viewable. 2. Yours. Your data is yours, on your local computer, it's not in some particular AI provider's system without the ability to extract it. You're in control of your information. 3. File over app. The memory here is a simple collection of files in universal formats (images, markdown). This means the data is interoperable: you can use a very large collection of tools/CLIs or whatever you want over this information because it's just files. The agents can apply the entire Unix toolkit over them. They can natively read and understand them. Any kind of data can be imported into files as input, and any kind of interface can be used to view them as the output. E.g. you can use Obsidian to view them or vibe code something of your own. Search "File over app" for an article on this philosophy. 4. BYOAI. You can use whatever AI you want to "plug into" this information - Claude, Codex, OpenCode, whatever. You can even think about taking an open source AI and finetuning it on your wiki - in principle, this AI could "know" you in its weights, not just attend over your data. So this approach to personalization puts *you* in full control. The data is yours. In Universal formats. Explicit and inspectable. Use whatever AI you want over it, keep the AI companies on their toes! :) Certainly this is not the simplest way to get an AI to know you - it does require you to manage file directories and so on, but agents also make it quite simple and they can help you a lot. I imagine a number of products might come out to make this all easier, but imo "agent proficiency" is a CORE SKILL of the 21st century. These are extremely powerful tools - they speak English and they do all the computer stuff for you. Try this opportunity to play with one.

The skills: aihero.dev/5-agent-skills-i-…

gork said:

When industrial companies were rewarded by the stock market for moving production to China, it was like a starting gun going off for jobs moving to China. Fade calls that AI will not be that disruptive to white collar jobs; the only real debate is when disruption AI will arrive

Karpathy buried the most interesting observation in paragraph five and moved on. He’s talking about NanoClaw’s approach to configuration. When you run /add-telegram, the LLM doesn’t toggle a flag in a config file. It rewrites the actual source code to integrate Telegram. No if-then-else branching. No plugin registry. No config sprawl. The AI agent modifies its own codebase to become exactly what you need. This inverts how every software project has worked for decades. Traditional software handles complexity by adding abstraction layers: config files, plugin systems, feature flags, environment variables. Each layer exists because humans can’t efficiently modify source code for every use case. But LLMs can. And when code modification is cheap, all those abstraction layers become dead weight. OpenClaw proves the failure mode. 400,000+ lines of vibe-coded TypeScript trying to support every messaging platform, every LLM provider, every integration simultaneously. The result is a codebase nobody can audit, a skill registry that Cisco caught performing data exfiltration, and 150,000+ deployed instances that CrowdStrike just published a full security advisory on. Complexity scaled faster than any human review process could follow. NanoClaw proves the alternative. ~500 lines of TypeScript. One messaging platform. One LLM. One database. Want something different? The LLM rewrites the code for your fork. Every user ends up with a codebase small enough to audit in eight minutes and purpose-built for exactly their use case. The bloat never accumulates because the customization happens at the code level, not the config level. The implied new meta, as Karpathy puts it: write the most maximally forkable repo possible, then let AI fork it into whatever you need. That pattern will eat way more than personal AI agents. Every developer tool, every internal platform, every SaaS product with a sprawling settings page is a candidate. The configuration layer was always a patch over the fact that modifying source code was expensive. That cost just dropped to near zero.