Turn your viral game idea into $100,000. Introducing the Astrocade Creator Competition. - Three weeks. 25 creators. - Build the most viral content. - One winner takes the full $100K. Applications close 11/20.

Delete all your prompts. You only need two letters now. When you send an email, you can add viktor@viktor.com in the CC field, the "copy" line. That puts an AI on the thread like a regular colleague. He reads the whole email conversation, figures out what needs doing and does it, then replies right there in your inbox. The email thread already has all the context, so you don't have to explain anything. Simple idea. Weird that nobody did it earlier. Just email viktor@viktor.com @viktor_com Paid Partnership

Absolutely incredible. AI just took its first real science exam. Claude Opus 5 scored 90.9% on scientific coding. Open-weight DeepSeek-V4.1-Flash beat GPT-6.1 and Claude at hypothesis discovery, 21.3% vs 4.9% and 7.3%. Science's Last Exam: 50+ real research tasks across 9 disciplines, checked by hidden tests the agent never saw. This is a big moment for all of AI research.

Delete all your prompts. You only need two letters now. When you send an email, you can add viktor@viktor.com in the CC field, the "copy" line. That puts an AI on the thread like a regular colleague. He reads the whole email conversation, figures out what needs doing and does it, then replies right there in your inbox. The email thread already has all the context, so you don't have to explain anything. Simple idea. Weird that nobody did it earlier. Just email viktor@viktor.com @viktor_com Paid Partnership

Every AI engineer needs this. I spent months collecting everything I wish I had when I started building agents. Now it's free for everyone. Inside the repo: > A 7-module agents course, from zero to production > 5 deep handbooks > 114 vetted resources > 18 skills, 72 slash commands, 6 subagents and a Claude Code plugin It's updated regularly, and every month there's a new "New this month" section with what changed. Repo: Website:

Nobody warns you about this when you start running multiple agents The more agents you add, the more they forget. Each one is smart on its own, but none of them knows what the others did, so you end up as the messenger between them. But once you get it right, the setup feels almost unfair: OpenClaw sits at the front as the router. Every task goes to it first, and it decides who handles it. It also keeps research, notes, and planning for itself. Claude Code gets the thinking-heavy stuff: architecture, big refactors, the nasty bugs. Codex gets execution: well-defined tasks, tests, cleanup. Fast and cheap for the boring 80%. Each one does what it's actually good at, and you stop paying top-model prices for grunt work. Even Simon Willison, the co-creator of Django, runs Claude Code and Codex side by side and sends the riskier tasks off to Codex Cloud. The catch is that they don't share memory. Each one starts from zero, and you end up re-explaining the same project. I fix that with @FulcraDynamics. It's a shared context layer you own: every agent reads and writes the same memory, so one picks up exactly where the other left off. The router sees what's already done and hands off with full context. No handoff prompts, no re-briefing. You decide what each agent sees, down to a single folder, read-only. Your data is encrypted, exportable, and never sold, and the first 5 GB are free. And because every agent plugs into the same context layer, connecting a new one for handoffs takes minutes and not a custom integration. Split the work, share the context. Fewer tokens, way more done. npx skills add fulcradynamics/agent-skills

Andrej Karpathy just built his own YouTube in about 2 hours. His pipeline works for almost any app I broke down how it works: He didn’t write a smarter algorithm. He let a crowd that has been recommending things for 20 years do the ranking, and had Claude read it The whole thing rests on one idea: “a link posted in a reply is a vote” So the work splits cleanly: > the crowd picks the items > Claude reads and counts > he does only what needs his accounts The pipeline has 6 steps: 1. Annoyance: a feed that serves you badly 2. Source: years of threads where people share links 3. Filter: keep what comes back again and again, drop one-offs 4. Enrich: one API key, Claude pulls each item’s details 5. Build: a small site, no logins 6. Ship: server and domain, then hand Claude the IP The filter is where the value is. 43M Hacker News comments became 6,454 videos, a list no person could make by hand The site stays small on purpose. Likes and history live in your browser, so there are no accounts to build or secure. Thumbnails are a frame from the middle of the video, not the A/B-tested cover Shipping took one prompt: “update, secure, harden, deploy and optimize” He shares the full analysis as one zip, so you can point your Claude at it and build your own version His setup: Claude Code on Mac, Opus 5.5 on Medium effort. His only manual work was an API key, a VPS and a domain Swap Hacker News for any community that has argued about books, tools or papers for years, and the same pipeline works

Your coding agent is quietly burning money on tools it never touches. Microsoft just released a plugin that fixes it, it installs in about 2 minutes. It learns from every session in your repo and makes the next one cheaper. Up to 30.9% lower cost on SWE-Bench, with comparable pass rate. Works with Claude Code, Codex, Copilot CLI and Pi.

someone under my 10 ways post asked which one is easiest to land clients for review replies how i'd get the first one: - bot finds local shops with unanswered google reviews - drafts 3 replies to their worst one - i send it as the pitch bot qualifies, human sends also a fix: one stream is ~$250/week, not $1000 you stack 3-4

Garry Tan, the CEO of Y Combinator, open-sourced 55 ready-made agents for Claude Code Each agent is a slash command in plain Markdown with its own role and rules. 134k stars, MIT. What is inside: > 7 for browser work and scraping > 6 for product planning > 6 for design > 6 for memory and retros > 5 for code review and debugging > 5 for security and guardrails > 5 for QA and performance > 5 for shipping and deploy > 5 for iOS apps > 3 for docs They hand work to each other. /office-hours writes a design doc, the plan reviewers read it, /qa and /ship check against it. Clone it into ~/.claude/skills/gstack, run ./setup, then try /review on any branch.

I made a full, detailed guide on AI agents. Plus plugins for fast setup. Free 7 modules, 21 lessons. From a single prompt to a production agent, read in order. 5 handbooks, 44 pages. One per layer, open the one you need: every technique, the tools and step by step builds. Each module teaches the idea. Its handbook holds the full menu. Course: Plugins: Free and open source

Peter Steinberger, the creator of OpenClaw, open-sourced his entire agent setup The core idea is one folder that every agent on his machines reads from. A single AGENTS.MD holds his rules, and a script symlinks it into ~/.claude/CLAUDE.md and ~/.codex/AGENTS.md This way Claude Code and Codex always follow the same instructions. Every other repo gets one line at the top: READ ~/Projects/agent-scripts/AGENTS.MD BEFORE ANYTHING. Change a rule once and every project picks it up. Skills work the same way. 69 of them live in one place, each with a short description the agent reads to decide what to load, and scripts/sync-skills links them into both agents. Fork it, replace his rules with yours, and delete the skills you do not need. His AGENTS.MD is full of his own hosts and accounts. 6.6k stars, MIT -

A Google Developer Expert put an entire AI engineering course on GitHub. 523 lessons, from linear algebra to agent swarms. Free What is in there: > 20 phases, ~342 hours, in Python, TypeScript, Rust and Julia > Every algorithm built from raw math first, then with the real library > Every lesson ships something reusable: a prompt, a skill, an agent or an MCP server > 396 skills and 99 prompts you can drop into Claude Code, Cursor or Codex If you're new to AI, start with Phase 0. If you already code and want agents - Phase 14, about 60 hours Repo:

This paper might change your life Researchers from MIT, UCLA and Princeton built an entire trading firm out of AI agents. It beat every classic strategy they put it against. It copies how a real trading desk works: > Four analysts read fundamentals, news, sentiment and charts > A bull and a bear researcher debate the case > A trader weighs both sides and makes the call > A risk team and a portfolio manager can still veto it The paper explains how the firm is built: what each role does, how the agents hand work to each other, and how it held up against buy-and-hold and the usual indicator strategies. The repo is the working framework, 98k stars. You pick a ticker, a date and a model, and the agents write their reports and land on buy, sell or hold. Repo and paper in the first reply DYOR

I put Jev in front of my agent, and now it cuts about 90% of my token spend without any drop in quality. Here is the setup Most of what an agent gets is a decision. Is this email worth answering, does this ticket need a human, etc. I was paying frontier prices for every one of them. So I added a gate. Every request goes to Jev first. Jev does not write anything. You send it two things: > State: any text, like with any other model > Questions: not open ones, only ones with a known set of answers There are three types of questions: > Noul: yes or no > Choice: one option from a list > Score: a scale from 2 to 10 It answers in 70 to 500 milliseconds, at $0.042 per million input tokens, and output is free. So, how my setup works: 1. A request comes in and goes to Jev, never straight to the main model 2. Jev answers one question: does this need real work 3. If it is confident the answer is no, the request is closed right there 4. If yes, or if it is not sure, it goes to the frontier model. Only that model writes anything About 10% of requests make it through. The other 90% never touch the expensive model, and the ones that matter still get it. The saving comes from what you skip.

This paper might change your life Researchers from MIT, UCLA and Princeton built an entire trading firm out of AI agents. It beat every classic strategy they put it against. It copies how a real trading desk works: > Four analysts read fundamentals, news, sentiment and charts > A bull and a bear researcher debate the case > A trader weighs both sides and makes the call > A risk team and a portfolio manager can still veto it The paper explains how the firm is built: what each role does, how the agents hand work to each other, and how it held up against buy-and-hold and the usual indicator strategies. The repo is the working framework, 98k stars. You pick a ticker, a date and a model, and the agents write their reports and land on buy, sell or hold. Repo and paper in the first reply DYOR