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IT / Business analysis / System analysis / Solution Architecture / Trading / Finance / TON: UQCIQAoG1ub2c7hsFEQ6lo-5CTe1o3_N16d_dfU0Plu8v2xJ facebook.com/psysoev Москва Joined February 2012-
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INSTEAD OF WATCHING NETFLIX TONIGHT. Spend 1 hour with this. Claude AI FULL COURSE that teaches you how to BUILD and AUTOMATE anything. The people who watch this tonight will wake up tomorrow with a new skill. Watch it and Bookmark it now.
Claude Tag is the next evolution of agents. It's a proactive, multiplayer agent with memory and identity, built on top of Claude Code. Learn more about how Claude Tag works and best practices for using it in this deep dive.
How to build an agent that gets better over time: There are 3 areas an agent can learn from: 1. The model: Only works for code and math, where a computer can score right vs. wrong. Leave this to the big labs. 2. The harness: These are the steps, tools, and safety checks you build around the model. This is easy to control and will give you a huge payoff now. 3. The context: This is a plain-text representation of what the agent has learned. Probably the simplest place to start. But there's something else that most people miss: Your agent should learn from its users. You want to learn from every time a user fixes the agent's decision. Nothing can replace feedback from real usage.
El CLAUDE. md de Karpathy alcanzó el #1 en las tendencias de GitHub. Más de 220,000 estrellas. La mayoría de los desarrolladores aún no lo han leído. Son 65 líneas. Llevó la precisión en la codificación con IA del 65% al 94%. Las 4 reglas dentro: -> piensa antes de codificar expón tus suposiciones. pregunta cuando no estés seguro. nunca adivines. -> simplicidad primero escribe el código mínimo que resuelva el problema. sin abstracciones que nadie pidió. -> cambios quirúrgicos no toques código no relacionado con la solicitud. cada línea cambiada debe rastrearse hasta lo que se pidió. -> ejecución orientada a metas convierte instrucciones vagas en criterios de éxito verificables antes de escribir una sola línea. eso es todo. 65 líneas. 4 reglas. 94% de precisión. guarda esto antes de que sea demasiado tarde
Claude Code feels completely different once you install this. Anthropic quietly released an official plugin called claude-code-setup and it basically turns Claude Code from “pretty good” into an actual AI dev environment. It scans your project and recommends: → hooks → skills → MCP servers → subagents → automations Then sets everything up step-by-step for you. Most people are using Claude Code completely vanilla… which is why their experience feels messy. The real power comes from the ecosystem around it. Install: /plugin install claude-code-setup@claude-plugins-official Bookmark this before you forget it.
Your AI is average. Mathematically If you're using the same ChatGPT as 100M other people with zero context about your actual work, you're getting average intelligence. Generic answers to specific problems. I watched my team copy-paste the same project context between Claude,
A senior Anthropic engineer just dropped 11-page PDF on "Loop Engineering" for agentic systems. The shift: you stop prompting the agent. You build the system that prompts it instead. Schedule → Discover → Build → Verify → Repeat Every loop runs one turn, five moves: • Discovery: it finds its own work - failing CI, open issues, recent commits - instead of being handed a list. • Handoff: each task gets an isolated git worktree so parallel agents don't collide. • Verification: a second agent, told to assume the code is broken, reviews the first. The "thing that can say no." • Persistence: results get written to disk, never left in a context window that gets flushed. • Scheduling: an automation wakes it on a timer. That's what makes it a loop. The key insight: an agent grading its own work always praises it. This 11-page PDF changed how I'm building agentic systems today. Read it now, then explore the article below.
Claude Code creator: "100% of our pull requests at Anrtopic are run by Claude Code. 80–90% of code review too. The feature I’m using the most today is /loops. I’m not prompting Claude anymore - I’m building loops" in 1-hour interview, Boris reveals his setup, which helps him build the #1 coding tool of this year. Worth more than a $500 vibe-coding course.
// HeavySkill // One of the cleaner takes on agentic harness design I've read. They argue that what actually drives agent harness performance is not the orchestration code. It's a single inner skill: parallel reasoning followed by deliberation. If you can internalize that into the model and most of the scaffolding becomes optional. The paper systematizes this as a two-stage pipeline you can run beneath any harness, then trains it as a learnable skill via RLVR. The numbers: > GPT-OSS-20B jumps from 69.7% (M@K) to 85.5% (HM@4) on LiveCodeBench under the heavy-thinking variant. > R1-Distill-Qwen-32B nearly doubles on IFEval, from 35.7% to 69.3%. > Several models reach Pass@N-level performance with HeavySkill. Harness wins start to look like model wins once you can train them in. If parallel-reasoning-plus-deliberation really is the inner skill, the long arc is models that come with it baked in, not orchestration glue around them. Paper: arxiv.org/abs/2605.02396 Learn to build effective AI agents in our academy: academy.dair.ai
Goodbye Claude Code subscription fees. Someone just built a proxy that runs Claude Code completely free... and it's wild. You literally plug in a free NVIDIA API key and point Claude Code at localhost. That's it. It handles everything: - Converts Anthropic API calls to NVIDIA NIM format - Unlocks 40 requests/min for free - Supports Kimi K2, GLM 4.7, MiniMax M2, Devstral and more - Streams thinking tokens and tool calls live - Even includes a Telegram bot so you can run Claude Code from your phone No API bill. No rate limit panic. No vendor lock-in. Honestly, this goes beyond router tools like OpenRouter. It doesn't just swap the model... it turns Claude Code into a free agent you can control remotely. The project is open-source on GitHub. It's called free-claude-code.
Uni-1 is no longer just a model. It’s now an API. That means the same multimodal reasoning engine creatives have been using to generate insanely consistent visuals can now be built directly into products, workflows, and custom tools. This changes everything. Why Uni-1 stands
office workers after reading this article:
5 upgrades you can do to Karpathys LLM wiki save this blueprint and send it to your agents
new update to the LLM Knowledge base shipped 5 upgrades to my Claude + Obsidian second brain today: - hot cache → new sessions start with a summary of the last one - /save → turns any conversation into a filed wiki note - /autoresearch → multi-round loop that searches,
MIT JUST LEAKED THE EXACT DOCUMENT WALL STREET QUANT FIRMS USE TO HIRE PEOPLE. It is called the MIT Quant Bible. And it covers everything the $500,000 a year quants actually know that you do not. Here is what is inside: Probability fundamentals. Conditional probability, Bayes theorem, expected value, variance, joint distributions. The mathematical foundation every trading decision at Jane Street and Citadel is built on. Stats fundamentals. The Law of Large Numbers. Central Limit Theorem. Confidence intervals. The tools quants use to know when a signal is real and when it is noise. Quant research and data science. Least squares, regressions, dimensionality reduction. Real case studies from Two Sigma, QuantCo, and CitiBikes. Quant trading and market making. What market making actually is and how the theory translates into real trading decisions. And then the part that makes this document worth more than most finance degrees. A question bank with real interview questions from Jane Street, Virtu Financial, Optiver, Akuna Capital, Citadel, Hudson River Trading, Two Sigma, Five Rings, and SIG. Not prep questions. The actual questions these firms ask. With answers. The firms charging $50,000 a year for access to this type of preparation are not going to be happy this exists. Bookmark this before it disappears. Follow @cyrilXBT for every elite resource that gives you access to what the top 1% actually know.
ANTHROPIC JUST RELEASED THE OFFICIAL PLAYBOOK FOR BUILDING A COMPANY WITH CLAUDE CODE. 30 minutes. free. from the engineers who built it. Bookmark this before you forget. CEO: 1 human. Employees: AI agents. Operations: fully automatic. The zero-headcount company is no longer a joke.
This CLAUDE.md turns Claude Code into a senior engineer who never forgets your standards. Boris Cherny (creator of Claude Code at Anthropic) shared the internal workflows his team actually uses daily. Someone turned those X threads into a structured file you can drop into any project. What’s inside: > Subagent orchestration > Verification gates before marking tasks done > Autonomous bug fixing loops > Self-improving rules from your own corrections That last one is the whole game. Every time you correct Claude, the rule gets encoded permanently. > Next session it doesn’t repeat the mistake. > Next month it matches how you think. > Next year you’re not managing Claude. It’s working like someone who’s been on your team for years. Drop it in any project. Start today.
SOMEONE JUST DROPPED A NEW AGENT HARNESS THAT CLAIMS TO BLOW PAST CLAUDE CODE AND CODEX quick context: an agent harness is the layer between you and the AI and it handles prompts, tools, memory, and parallel sessions (e.g. codex cli & claude code) it's called jcode AND it's free and open source on github claims to be: > 20x more memory efficient than claude code > spawns 63x faster than codex CLI > more customizable than every existing harness > built in memory system, no extra setup > built specifically for running parallel sessions and agent swarms > handles background tasks, browser use, and codebase searching natively the part everyone is losing their minds over is that you can log in with your existing claude code or codex oauth and just use it as the harness so you keep your max plan, but you run it through a faster, more efficient interface that can spawn 20 agents at once if you want the catch is that anthropic has banned people for using claude oauth with third party tools. so this lives in a grey zone right now but if you're already running multiple claude code sessions in tmux or paying for codex separately, this is built to replace both with one harness the open source community is moving way faster than the big AI companies. again.
ANTHROPIC JUST PROVED MOST PEOPLE HAVE NO IDEA HOW TO PROMPT CLAUDE. Their applied AI team dropped a 24 minute free workshop. Not a creator who reverse engineered it. Not a Reddit thread. ANTHROPIC. The people who wrote the weights. And what they showed is uncomfortable. There are 6 elements to a properly structured Claude prompt. Most people are using 1. Maybe 2. That is not a skill issue. That is an information issue. And it has been quietly costing you every single day. The outputs that felt slightly off. The responses you had to rewrite 4 times. The prompts that worked once and never again. All of it traces back to the same 6 missing elements. The people who watch this 24 minute workshop tonight will understand something about Claude that most daily users still do not know exists. The people who skip it will keep getting 30% of what the tool is actually capable of and wonder why the results never quite land. I watched it twice. Then I built a Claude Skill that applies all 6 elements to every prompt automatically. No more thinking about structure. No more guessing what Claude needs. The framework runs in the background every single time. Full breakdown and skill setup is below. Bookmark this now. Watch the workshop first. Then read the guide. This is the one that compounds. Follow @cyrilXBT for the exact prompt architecture, Claude skills, and systems I use to get outputs most people do not believe came from one person working alone.
Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow @codewithimanshu for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow @codewithimanshu. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on @codewithimanshu. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. @codewithimanshu. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on @codewithimanshu. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. @codewithimanshu. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. @codewithimanshu. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow @codewithimanshu for more high-signal content that actually moves your AI engineering career forward.
If I had to land a $200K AI engineer job in 90 days, I would not get a degree. I would master these 10 GitHub repos. 1. awesome-llm-apps The production AI playbook. RAG, agents, multimodal apps, all in working code. 106K+ stars. Repo → github.com/Shubhamsaboo/a… 2. LangChain The foundational framework. Used in production by Klarna, Replit, Elastic, and most AI startups in 2026. Repo → github.com/langchain-ai/l… 3. LangGraph The orchestration layer powering production agents. The skill on every senior AI engineer job description. Repo → github.com/langchain-ai/l… 4. CrewAI Multi-agent coordination. The framework most Fortune 500 teams reach for first. Repo → github.com/crewAIInc/crew… 5. Ollama Run any open-source LLM on your own machine. The fastest way to learn how models actually work. Repo → github.com/ollama/ollama 6. awesome-mcp-servers MCP is the standard every major AI lab adopted in 2026. Knowing it puts you ahead of 99% of engineers. Repo → github.com/punkpeye/aweso… 7. Qdrant The vector database used for production RAG at scale. Embeddings and semantic search are non-negotiable for AI roles. Repo → github.com/qdrant/qdrant 8. AI-Agents-for-Beginners Microsoft's free 12-lesson course on building agents. Real code, real exercises, real prep. Repo → github.com/microsoft/ai-a… 9. system-design-primer Production AI is system design. The repo FAANG engineers use to prep for interviews. Repo → github.com/donnemartin/sy… 10. awesome-claude-code The playbook for the tool now used inside FAANG, OpenAI, Anthropic, and most YC startups. Repo → github.com/hesreallyhim/a… Here's the wildest part: A $200K AI engineer in 2026 isn't paid for a degree. They are paid for what these 10 repos teach. The market doesn't care where you learned it. It only cares if you can ship. 90 days. 10 repos. One portfolio that proves you can do the work. That's it. That's the whole game. Save this before you forget. 100% free. 100% open source.
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