I spent time finding the best free guides for learning AI agents from zero. If I had to learn again, I would follow them in this order: 1. Understand what an agent is An agent is more than a chatbot. It has a model that makes decisions, instructions that set its job and tools that let it read data or take action. OpenAI's guide explains the basic structure, how to pick a useful business problem and where guardrails or human approval belong. openai.com/business/guides-a… 2. Build one without a framework Tech With Tim builds an agent in pure Python. You see the API call, conversation history, tool calling, file access, shell commands and the loop that keeps it working until the task is done. nitter.net/TechWithTimm/status/20… 3. Learn the seven parts of an agent Paweł Huryn gives a clean visual guide to instructions, model choice, tools, memory, orchestration, interface and evals. Use this after the Python tutorial to see how those pieces fit together. nitter.net/PawelHuryn/status/1933… 4. Learn the agent loop The basic loop is simple: > read the goal > choose the next action > call a tool > inspect the result > repeat or stop The hard part is deciding when the agent should retry, ask a human or end the task. Anthropic's guide shows several patterns, including prompt chains, routing, parallel work and orchestrator-worker systems. anthropic.com/engineering/bu… 5. Add memory without filling the context with junk Short-term memory keeps the current task moving. Long-term memory stores useful facts, past decisions and completed work outside the chat. Do not feed every old message back into the model. Keep the smallest set of information it needs for the next step. Anthropic explains context limits, compaction, external memory and how subagents can return short summaries to the main agent. anthropic.com/engineering/ef… 6. Apply one agent to your existing business Pick one workflow that: > happens often > takes several steps > needs judgment > uses emails, documents or other messy data > has an outcome you can check Good starting points include sorting support requests, researching leads, checking documents, preparing reports or turning a creative brief into first drafts and design tasks. Write the current human process first. Give the agent only the tools needed for that process. Keep approval on before it sends, publishes, buys, deletes or changes customer data. 7. Test the work before adding more agents Create a small set of real tasks and check: > did it finish the right job? > did it use the right tools? > did it invent information? > did it stop when approval was required? > how much time and money did each run use? Fix the failures you can see. A swarm will only multiply a broken workflow. 8. Move to multi-agent systems when the work can split cleanly Anthropic's research system uses a lead agent to plan, send focused jobs to subagents, save progress to memory and decide whether more research is needed. This works for research because several paths can be explored at once. It is a poor fit when every step depends on the previous step or every agent needs the same full context. anthropic.com/engineering/mu… 9. Watch one course that connects everything This Google course covers first-agent setup, short and long memory, long-running loops, MCP and multi-agent systems in 72 minutes. nitter.net/AnatoliKopadze/status/… My suggested learning path: > build one small agent > give it one or two tools > add memory only when the task needs it > test it on real work > add human approval > measure failures and cost > use multiple agents only when parallel work helps Save this as your AI agent learning list.

you are probably using the wrong Claude effort level for half your coding tasks Anthropic's own testing found higher effort helped most when Claude needed to verify its work or catch edge cases. use this instead: → low: brainstorming, rough drafts and small changes → medium: regular feature work → high: debugging old code, testing and code review → max: security checks and tasks you want Claude to finish alone best coding loop: 1. ask Claude to question you about the spec 2. build the first version on low 3. review and fix it on low 4. run the final tests on high low keeps you in the loop. high makes Claude spend more time checking its own work. bookmark this before your next coding session

THIS IS F**KING INSANE jack dorsey (ex-CEO of twitter) just released a free framework for running a business where the agents are full teammates, not tools it is already past 33,000 stars on github how to set it up (5 min): 1. clone the repo 2. deploy your own server, channels, search, git and automations run from there 3. add your agent to a channel like another teammate, set its permissions, and let it work with the team in real time it is a nostr relay, so every message, patch and approval is a signed event, which means an agent gets the same audit trail as a person save this post, repo below

YOUR AI CODED APP LOOKS GENERIC BECAUSE YOU'RE NOT USING THESE REAL COMPONENT LIBRARY 10 UI libraries worth a bookmark before your next build 👇 1. uimaxx.ing components for AI and fintech products: prompt bars, agent chat, terminals, diff viewers 2. ui.halaska.com an AI-product UI kit built on shadcn, one paste into Claude Code or Cursor 3. libraries.dev AI-agent UI libraries shipped as prompts: beam borders, thinking orbs, liquid metal 4. aicss.dev copy-paste blocks for the parts agents show mid-chat: thinking, tool calls, streaming text, tables 5. beautifului.dev crafted primitives for AI-native screens: loading, thinking, tool chips, approval cards 6. elements.ai-sdk.dev Vercel's AI Elements: shadcn components for chat, reasoning, tools and workflows 7. beui.dev 117 animated React and Next components: dock, dynamic island, command palette, otp input 8. kokonutui.com 100+ animated components plus a registry your coding agent installs on its own 9. bencho.dev interactive blocks you tune in the browser before you take them 10. animata.design 155+ animated React components you copy into any project, free and MIT save this before your next build turns into another app that looks like default ai slop site