Learnetto AI directory
A compact directory for people who want practical AI skills and up-to-date official docs: prompting, automation, coding agents, MCP, model selection, AI product work, RAG, evals, model internals, and production AI engineering.
AI Q&A
Short, crawlable answers connected to fuller guides, educators, docs, courses, and videos.
Best AI resources for model release notes and migrations
Follow the official changelog or release-note page for every provider you actively use, plus its deprecations or shutdown page if available. OpenAI, Anthropic, Gemini, DeepSeek,...
Best AI resources for model release notes and migrations
Use OpenAI deprecations, Claude model deprecations, Gemini deprecations, DeepSeek updates and pricing, Mistral changelog, and xAI release notes. Pair those with the provider's m...
Best AI resources for model release notes and migrations
Watch the release notes, keep evals for your core workflows, compare the replacement model on your own prompts, and only then update the default model or alias. Treat migrations...
Best AI agent courses
AI Agents in LangGraph is the best first pick for developers who want explicit state, tool use, and workflow control. Add a broader agents course after that if you want to compa...
Best AI agent courses
Learn a general agent loop first if you are new to tool calling. Choose LangGraph when you need stateful engineering patterns. Choose crewAI when your use case naturally splits ...
Best AI agent courses
It should teach planning, tool schemas, state, retries, tracing, guardrails, and failure handling. A course that only shows a polished autonomous demo is not enough for producti...
Recently added
New directory additions across educator videos, newsletters, social feeds, official model docs, agents, coding tools, and model-selection references. Refreshed Sep 7, 2026.
Choose how to scan the same daily picks: keep the current layout, mix formats, or read it like a feed.
You want OpenAI's September 6, 2026 evidence and measurement framework for how coding agents are changing research workflows, including task delegation, parallel agent use, capability tracking, and the limits of interpreting productivity signals.
You want GitHub's September 4, 2026 technical explanation of runtime model orchestration for coding tasks, including plan decomposition, draft-critique-revise patterns, model cascading, evaluation design, and quality-versus-cost tradeoffs.
You want a concise, hands-on course for replacing vague vibe-coding prompts with project constitutions, feature specs, plan-implement-verify loops, legacy-code workflows, and a portable custom agent skill.
You want Anthropic's September 3, 2026 workflow for declaring agents, environments, skills, memory stores, and scheduled deployments in a repository, previewing changes, and applying them reproducibly in CI.
You want a measured guide to agent-friendly CLI design, including structured output, retry-safe commands, independent grading, and benchmarks showing where higher-level tools reduce calls and token use.
You want reusable, open-source agent skills for training, fine-tuning, evaluating, quantizing, and publishing models with Codex, Claude Code, Gemini CLI, or another skills-compatible coding agent.
You want a September 3, 2026 walkthrough of funes, an open-source local memory layer that indexes agent traces, preserves provenance, and lets Claude Code, Codex, pi, and Hermes recall decisions across sessions and machines.
You are building a GPT-6 Astra workflow where users need to add requirements or redirect a running response over the Responses API without waiting for the current turn to finish.
You want Qwen's September 3, 2026 practical guide to its latest coding-agent workflows, including learning-oriented output styles, scheduled tasks, a browser terminal, safer workspace writes, skills, MCP stability, and current vision-model support.
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Video · Silicon Valley Girl
Watch this first when you want current AI operator examples for moving from prompts to repeatable team workflows.
Research report · OpenAI
You want OpenAI's September 6, 2026 evidence and measurement framework for how coding agents are changing research workflows, including task delegation, parallel agent use, capability tracking, and the limits of interpreting productivity signals.
Model orchestration research preview · GitHub
You want GitHub's September 4, 2026 technical explanation of runtime model orchestration for coding tasks, including plan decomposition, draft-critique-revise patterns, model cascading, evaluation design, and quality-versus-cost tradeoffs.
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Video · Claude
Use this for Anthropic's current Claude Code direction, agent workflow framing, and developer tooling roadmap.
Short course · DeepLearning.AI
You want a concise, hands-on course for replacing vague vibe-coding prompts with project constitutions, feature specs, plan-implement-verify loops, legacy-code workflows, and a portable custom agent skill.
Agent infrastructure guide · Anthropic
You want Anthropic's September 3, 2026 workflow for declaring agents, environments, skills, memory stores, and scheduled deployments in a repository, previewing changes, and applying them reproducibly in CI.
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Video · Fireship
Use this as a fast technical recap of Google I/O 2026 and the Gemini-era product/model changes worth tracking.
Agent tooling and evaluation guide · Hugging Face
You want a measured guide to agent-friendly CLI design, including structured output, retry-safe commands, independent grading, and benchmarks showing where higher-level tools reduce calls and token use.
Open-source skills and tutorial · Hugging Face
You want reusable, open-source agent skills for training, fine-tuning, evaluating, quantizing, and publishing models with Codex, Claude Code, Gemini CLI, or another skills-compatible coding agent.
Open-source guide and repo · Hugging Face
You want a September 3, 2026 walkthrough of funes, an open-source local memory layer that indexes agent traces, preserves provenance, and lets Claude Code, Codex, pi, and Hermes recall decisions across sessions and machines.
API guide · OpenAI
You are building a GPT-6 Astra workflow where users need to add requirements or redirect a running response over the Responses API without waiting for the current turn to finish.
Silicon Valley Girl posted a video pick
2026 · ai adoption · business workflows
Watch this first when you want current AI operator examples for moving from prompts to repeatable team workflows.
OpenAI added research report
You want OpenAI's September 6, 2026 evidence and measurement framework for how coding agents are changing research workflows, including task delegation, parallel agent use, capability tracking, and the limits of interpreting productivity signals.
GitHub added model orchestration research preview
You want GitHub's September 4, 2026 technical explanation of runtime model orchestration for coding tasks, including plan decomposition, draft-critique-revise patterns, model cascading, evaluation design, and quality-versus-cost tradeoffs.
Claude posted a video pick
2026 · claude code · coding agents
Use this for Anthropic's current Claude Code direction, agent workflow framing, and developer tooling roadmap.
DeepLearning.AI added short course
You want a concise, hands-on course for replacing vague vibe-coding prompts with project constitutions, feature specs, plan-implement-verify loops, legacy-code workflows, and a portable custom agent skill.
Anthropic added agent infrastructure guide
You want Anthropic's September 3, 2026 workflow for declaring agents, environments, skills, memory stores, and scheduled deployments in a repository, previewing changes, and applying them reproducibly in CI.
Fireship posted a video pick
2026 · gemini · frontier models
Use this as a fast technical recap of Google I/O 2026 and the Gemini-era product/model changes worth tracking.
Hugging Face added agent tooling and evaluation guide
You want a measured guide to agent-friendly CLI design, including structured output, retry-safe commands, independent grading, and benchmarks showing where higher-level tools reduce calls and token use.
Hugging Face added open-source skills and tutorial
You want reusable, open-source agent skills for training, fine-tuning, evaluating, quantizing, and publishing models with Codex, Claude Code, Gemini CLI, or another skills-compatible coding agent.
Hugging Face added open-source guide and repo
You want a September 3, 2026 walkthrough of funes, an open-source local memory layer that indexes agent traces, preserves provenance, and lets Claude Code, Codex, pi, and Hermes recall decisions across sessions and machines.
OpenAI added api guide
You are building a GPT-6 Astra workflow where users need to add requirements or redirect a running response over the Responses API without waiting for the current turn to finish.
AI video library
Current picks across coding agents, MCP, model selection, RAG, evals, and production AI.
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Silicon Valley Girl · 2026, ai adoption, business workflows
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Claude · 2026, claude code, coding agents
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Fireship · 2026, gemini, frontier models
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Zen van Riel · 2026, agentic engineering, claude code
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Peter Yang · 2026, ai agents, claude code
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Teacher's Tech · 2026, claude code, ai agents
New directory
Find reusable skill registries, local agent harnesses, and AI worker products before choosing what to learn or buy.
Skill system
OpenClaw · Official docs
Use this to understand the OpenClaw skill format: markdown instruction files in directories with SKILL.md frontmatter and tool-use guidance.
Skill marketplace
OpenClaw · Registry
Use this when you need a public registry for OpenClaw skills and plugins rather than hand-copying skill folders.
Agent harness
OpenClaw · Open-source agent harness
Use this when you want an agent runtime you can shape with local skills, plugins, and workspace-level behavior.
Self-improving agent
Nous Research · Open-source agent
Use this when persistent memory, skill creation from completed tasks, and remote agent operation matter more than a simple chat UI.
AI assistant builder
Lindy · AI executive assistant
Use this when the job is business admin across apps rather than custom coding or local agent infrastructure.
AI coworker
Viktor · Slack and Teams AI employee
Use this when the adoption surface should be chat-native and team-facing rather than a separate automation builder.
Sorted for range: founders, product teams, developers, evals, model internals, and production systems.
| Educator | Best for | Skills | Start with |
|---|---|---|---|
| Indie founders, product builders | Founder workflows, Product ops, Automation | Read the public notes and examples before deciding whether the paid material matches your business. | |
| Founders, service business owners | Founder workflows, Systems, Automation | Look for workflow breakdowns and implementation examples. | |
| Product managers, founders | Product strategy, Writing, AI adoption | Review the Maven syllabus and compare it to your current product workflow. | |
| Product teams, founders | Product, Growth, Team adoption | Browse the How I AI interviews and copy the workflows that match your role. | |
| Knowledge workers, educators, leaders | AI literacy, Workplace adoption, Strategy | Read recent essays on using AI as a collaborator and on organizational adoption. | |
| Developers, AI engineers | AI engineering, Agents, Developer tools | Watch AI Engineer talks for production patterns and tool choices. | |
| Everyone from beginners to builders | Prompting, Agents, RAG, ML foundations | Start with ChatGPT Prompt Engineering for Developers, then pick a RAG or agents course. | |
| Coders learning deep learning | Deep learning, PyTorch, Model training | Practical Deep Learning for Coders, lesson 1. | |
| Developers, data scientists | Practical ML, Ethics, Education | Use fast.ai essays and course material alongside hands-on notebooks. | |
| Programmers who want model internals | Neural networks, Backprop, LLM internals | Watch micrograd, then makemore, then the GPT video. | |
| Developers, technical generalists | LLM tools, Prompting, AI safety, Local models, Model selection | Read the recent model-roundup posts, then try the llm command-line tool with two or three different providers. | |
| Developers building LLM apps | Structured outputs, Extraction, RAG | Try the Instructor examples for extraction and validation. |
Use these as quick routes into the directory.
Useful for: Everyone
Learn: What LLMs can and cannot do; Tokens, context windows, hallucinations; Privacy and data handling
Resources: DeepLearning.AI , Learn Prompting , Ethan Mollick
Useful for: Writers, operators, PMs, founders
Learn: Clear task framing; Examples and constraints; Editing, synthesis, and critique loops
Resources: Learn Prompting , DAIR.AI , Peter Yang
Useful for: Founders, operations teams, developers
Learn: Trigger-based workflows; Tool calling; Agent failure modes; Human review points
Resources: Brian Casel , Josh Pigford , AI Engineer
Useful for: PMs, designers, founders
Learn: AI UX patterns; Use-case selection; Prototyping; Measuring quality
Resources: Peter Yang , Lenny Rachitsky , Shreya Shankar
Useful for: Software developers
Learn: Codebase navigation; Test generation; Refactoring; Reviewing AI output
Resources: Simon Willison , Addy Osmani , Matt Pocock , AI Engineer
Useful for: Developers using coding agents and tool-connected workflows
Learn: How to structure instructions, memories, and skills; When to use MCP servers versus retrieval or file search; Subagents, hooks, and tool boundaries; How to keep agent context small, relevant, and testable
Resources: Anthropic , Hugging Face , OpenAI , Matt Pocock , Simon Willison
Useful for: Developers building AI apps
Learn: Chunking and retrieval; Structured extraction; Reranking; Grounded answers
Resources: Jason Liu , Hamel Husain , Eugene Yan
Useful for: AI product teams
Learn: Test sets; Human review; Regression checks; Quality metrics
Resources: Hamel Husain , Shreya Shankar , Chip Huyen
A rotating mix of official docs, hands-on guides, model catalogs, and practical AI engineering resources.
| Resource | Type | Level | Use when |
|---|---|---|---|
|
Claude Code skills
Anthropic
|
Guide | Intermediate | You want Anthropic's official guide for packaging reusable Claude Code skills instead of leaving long instructions inline in every session. |
|
Gemini coding agents with MCP and Skills
Google AI for Developers
|
Guide | Intermediate | You want the newest official Gemini guide for wiring docs MCP, installable skills, and coding-agent best practices into software workflows. |
|
Gemini Deep Research Agent
Google AI for Developers
|
Guide | Intermediate to advanced | You want the latest official Google docs for long-running research agents, cited reports, background execution, and MCP-aware investigation workflows. |
|
Claude Code best practices
Anthropic
|
Engineering guide | Intermediate to advanced | You want Anthropic's own recommendations for scaling Claude Code across larger codebases, parallel sessions, and stronger context discipline. |
|
OpenRouter MCP Server
OpenRouter
|
MCP guide | Intermediate | You want OpenRouter's official MCP path for pulling live models, prices, rankings, docs, and test calls into Codex, Claude Code, or another coding assistant while you compare providers. |
|
Designing the hf CLI for agents
Hugging Face
|
Agent tooling and evaluation guide | Intermediate | You want a measured guide to agent-friendly CLI design, including structured output, retry-safe commands, independent grading, and benchmarks showing where higher-level tools reduce calls and token use. |
|
OpenAI model guide
OpenAI
|
Model docs | Beginner to advanced | You need to choose between current GPT-5.6 variants, smaller GPT-5.4 variants, reasoning levels, tool support, and cost-sensitive API paths, including the current default starting points of GPT-5.6 Sol, Terra, and Luna. |
|
OpenAI Tool search
OpenAI
|
Guide | Intermediate to advanced | Your agent has a large function, namespace, or MCP catalog and you want GPT-5.4 or later models to load only the tool definitions needed at runtime, reducing context use while preserving prompt-cache efficiency. |
