Everything you need to learn, build, and actually ship AI.
One honest hub instead of ten browser tabs — the courses that hold up, the tools people really use, the reference shelf worth bookmarking, and the practices that separate shipped systems from forever-pilots. No pay-to-rank, no course-mill upsell. Where a claim is a fact, it links to its source.
The courses serious practitioners actually study.
A path, not a credential mill — from the math under the hood to shipping LLM systems in production. Almost all free; all from primary sources.
Foundations
math · code · probabilityKhan Academy
FreeStatistics & probability from zero — the literacy every model decision rests on.
Open →3Blue1Brown
Free · videoThe best visual intuition for linear algebra, calculus, and neural nets anywhere.
Open →The Python Tutorial
Free · docsThe official language tour. If you only learn one stack, learn this one.
Open →NumPy quickstart
Free · docsArrays, broadcasting, vectorization — the substrate under every ML library.
Open →Machine learning
the classics that still matterfast.ai
FreePractical Deep Learning — top-down, build-first. The fastest route to competence.
Open →Andrew Ng — ML
Audit freeThe canonical introduction. Still the clearest mental model of how learning works.
Open →Intro to Statistical Learning
Free · bookISLR/ISLP — the readable bridge from statistics to ML. Free PDF + labs.
Open →Hugging Face — Learn
FreeNLP, transformers, diffusion, and agents — hands-on with real model weights.
Open →Deep learning & LLMs
transformers · prompting · RAGNeural Nets: Zero to Hero
Free · videoKarpathy builds backprop, then a GPT, from scratch. Required viewing.
Open →PyTorch tutorials
Free · docsThe framework most research and production runs on. Start at "60-minute blitz."
Open →Anthropic — courses
FreePrompt engineering, tool use, and MCP from the team that builds Claude.
Open →DeepLearning.AI — short courses
FreeBite-size, vendor-built courses on RAG, agents, evals, and fine-tuning.
Open →OpenAI Cookbook
Free · codeCopy-paste recipes for embeddings, function calling, structured output, evals.
Open →Agents, evals & production
the part that actually shipsModel Context Protocol
Free · specThe open standard for wiring models to tools and data. The agent plumbing layer.
Open →Made With ML
FreeDesign, build, and deploy production ML — testing, CI/CD, and serving included.
Open →OWASP Top 10 for LLMs
FreePrompt injection, data leakage, and the rest — read before you ship anything.
Open →NIST AI Risk Mgmt Framework
FreeThe reference governance language your legal team will eventually ask for.
Open →The mainstream stack — named, sorted, and linked.
Categories reflect how teams actually buy software in 2026, not who paid for placement. Multimodal is the default now; agents have moved from demo to production.
Assistants & chat
general-purpose modelsChatGPT
OpenAIThe default assistant for most people — writing, code, analysis, voice, images.
Open →Claude
AnthropicStrong on long-context reasoning, writing, and coding; favored for careful work.
Open →Gemini
GoogleDeeply wired into Workspace, Search, and Android; huge context windows.
Open →Microsoft Copilot
MicrosoftThe assistant embedded across Windows and Microsoft 365.
Open →Grok
xAIReal-time, X-native assistant with a looser personality.
Open →DeepSeek
Open weightsStrong open-weight models that reset the price-performance curve.
Open →Coding
where the productivity is realGitHub Copilot
GitHubIn-editor completion and chat; the most widely deployed coding assistant.
Open →Cursor
AnysphereThe AI-first editor — multi-file edits and agentic refactors in one place.
Open →Claude Code
AnthropicAgentic coding in the terminal and IDE — plans, edits, runs, and verifies.
Open →Windsurf
EditorAgentic IDE with a "flow" model that keeps context across your whole repo.
Open →Replit
Cloud IDEBuild, host, and ship from the browser — Agent turns prompts into apps.
Open →v0
VercelPrompt-to-UI — generate React/Tailwind components and full front-ends.
Open →Image & design
generate, edit, brandMidjourney
ImageStill the quality bar for stylized, art-directed image generation.
Open →Adobe Firefly
AdobeCommercially-safe generation built into Photoshop and the Creative Cloud.
Open →Canva Magic Studio
CanvaAI design for non-designers — decks, social, and layered generation.
Open →Ideogram
ImageThe one that actually renders legible text inside images.
Open →Figma AI
DesignAI features inside the tool product teams already design in.
Open →Leonardo.Ai
ImageFine-grained control and game-asset workflows for production art.
Open →Video & audio
the fast-moving frontierRunway
VideoText- and image-to-video with real editing controls — a studio staple.
Open →Pika
VideoFast, playful video generation and effects for social-first creators.
Open →Synthesia
AvatarsAI presenters for training and explainer video at enterprise scale.
Open →ElevenLabs
VoiceThe most natural text-to-speech and voice cloning available.
Open →Descript
EditingEdit audio and video by editing the transcript. Overdub fixes mistakes.
Open →Otter.ai
TranscribeLive meeting transcription, summaries, and action items.
Open →Work & productivity
where AI meets the day jobNotion AI
DocsQ&A, writing, and autofill across your whole workspace.
Open →Microsoft 365 Copilot
OfficeAI inside Word, Excel, Outlook, and Teams — plus deployable agents.
Open →NotebookLM
GoogleGrounded research over your own sources — with audio overviews.
Open →Zapier
AutomationConnect 6,000+ apps; Agents take multi-step actions across your stack.
Open →n8n
AutomationOpen-source, self-hostable workflow automation with LLM nodes.
Open →Slack AI
ChatThread summaries and channel search where work conversations live.
Open →Search & research
answers with sourcesPerplexity
SearchCited, conversational answers — the closest thing to "search that explains."
Open →Elicit
PapersFind, screen, and extract from research papers at review scale.
Open →Consensus
PapersSearch engine that surfaces what the studies actually conclude.
Open →Semantic Scholar
FreeAI-powered scholarly search across 200M+ papers.
Open →Kagi
SearchAd-free paid search with built-in AI summaries you can trust.
Open →Julius AI
DataChat with your spreadsheets — analysis and charts from plain questions.
Open →The shelf worth bookmarking.
Where to track the field without drowning in hype — primary research, honest benchmarks, signal-heavy newsletters, and the communities that catch things first.
Research & papers
go to the sourcearXiv (cs.AI / cs.LG)
PreprintsWhere the research lands first. Skim the abstracts; the field moves here.
Open →Papers with Code
FreePapers paired with implementations and state-of-the-art leaderboards.
Open →Hugging Face Papers
DailyThe day's most-discussed papers, curated and community-voted.
Open →Stanford HAI — AI Index
AnnualThe most-cited yearly data on AI investment, capability, and adoption.
Open →Benchmarks & leaderboards
how good, really?LMArena
Human votesBlind, head-to-head model voting — the least gameable ranking around.
Open →Artificial Analysis
FreeIndependent quality, speed, and price comparisons across every major model.
Open →SWE-bench
CodingCan the model fix real GitHub issues? The benchmark coding agents chase.
Open →Open LLM Leaderboard
Open weightsStandardized evals for open models you can actually self-host.
Open →Newsletters & communities
signal, not slopThe Batch
WeeklyAndrew Ng's measured weekly read on what actually happened.
Open →Latent Space
EngDeep, practitioner-grade interviews and analysis for AI engineers.
Open →Simon Willison's blog
DailyThe clearest running commentary on what new models can and can't do.
Open →r/LocalLLaMA
CommunityWhere open-weight news, jailbreaks, and hardware tips break first.
Open →What separates shipped systems from forever-pilots.
The patterns the 5% who make it to production share — and the honest reasons the other 95% stall. Facts link to their sources.
Start with evals, not vibes
If you can't measure quality, you can't improve it — you can only demo it. MIT found ~95% of enterprise GenAI pilots delivered no measurable ROI; the ones that shipped built a scoring harness first.MIT ’25
RAG before you fine-tune
Most "we need a custom model" is really "we need better retrieval." Grounding answers in your own documents is cheaper, more accurate, and explainable — and it's become the enterprise default for 2026.RAG ’26
Engineer the context, not the prompt
The 2026 shift: from clever one-off prompts to governed context — what data the model sees, where it comes from, and who validates it before it reaches an agent. The stack is only as good as the data layer under it.
Keep a human on anything that acts
Agentic AI is surging, but only about one in five companies has a mature model for governing autonomous agents. If your AI can take an action, a human should be able to take it back — design the kill switch first.Deloitte ’26
Govern shadow AI before it governs you
About 35% of employees have pasted proprietary information into public AI tools, and most security leaders believe a leak has already happened. Give people a sanctioned tool — or they'll use an unsanctioned one.Writer ’26
Treat every prompt as untrusted input
Prompt injection, data exfiltration, and insecure output handling are the new top vulnerabilities. The OWASP Top 10 for LLM Apps is the checklist to run before launch — not after the incident.OWASP
Ship narrow, then widen
One workflow, end to end, with a measured outcome beats ten half-wired pilots. The gap between individual wins and org-level ROI is structural transformation — not more tools.
Buy the platform, not the press release
Plan AI as part of your enterprise architecture, with shared standards for data, identity, and audit — not as isolated department experiments that quietly multiply your attack surface.
Field notes from the donkey chase.
Short, true, and occasionally rude. The things people learn the expensive way, so you don't have to.
A pilot that never ends isn't a pilot. It's a hobby with a budget.
If you can't measure it, you're not deploying it — you're demoing it.
The model is not the product. The workflow around it is.
RAG your own docs before you fine-tune. Most "custom model" projects are better-retrieval projects in disguise.
If your agent can take an action, make sure a human can take it back. Add the kill switch on day one.
Don't paste the contract into a chatbot you don't control.
"AI-first" with no eval harness is just "vibes-first" with a press release.
Pick the tool for the job, not the logo. Three sharp tools beat one that does everything badly.
Read the OWASP LLM Top 10 before the pen-test, not after the breach.
The demo always works. Production is where the friction lives — and where the value does too.
Cite your sources. An AI answer with no link is a confident guess.
If the roadmap is three slides and a vibe, it's not a roadmap.
Spotted something out of date, or a tool that earned its place? Tell us — this hub updates monthly. The AI Donkey Chase commentary is ours; every factual claim links to its source.
Every model that matters — and when to actually reach for it.
The frontier labs, the open-weight challengers, and the tools to run them yourself. What each one is, what it’s good at, and the job it’s built for. Pick the tool for the job, not the logo.
Frontier / flagship
closed-weight · API & apps · the sharpest toolsClaude
AnthropicFrontier assistant tuned for long-context reasoning, coding and tool use, with a safety-first design. Tiers: Opus (deepest), Sonnet (balanced), Haiku (fast).
Best for: agentic coding, analysis, long documents, careful writing.GPT / ChatGPT
OpenAIThe most widely adopted assistant, with the broadest app, voice, image and plugin ecosystem around it.
Best for: general-purpose work, multimodal tasks, fast brainstorming.Gemini
GoogleGoogle’s frontier family, wired into Search, Workspace, Android and Pixel, with strong multimodal and very long context.
Best for: Google-stack users, research, image/video understanding.Copilot
MicrosoftGPT-class models embedded across Windows, Microsoft 365 and GitHub — AI placed where the work already happens.
Best for: enterprise productivity, in-IDE coding, Office documents.Grok
xAIxAI’s assistant with real-time access to X and a lighter touch on guardrails.
Best for: real-time and social context, fast takes.Open-weight
download · self-host · fine-tuneLlama
MetaThe most widely used open-weight family — permissive license, vast tooling, runs almost anywhere.
Best for: self-hosting, fine-tuning, on-prem and private deployments.Mistral / Mixtral
Mistral AILean, efficient European open models (including mixture-of-experts) with strong performance per parameter.
Best for: cost-efficient deployment, EU data residency.Gemma
GoogleLightweight open models distilled from Gemini research — small enough to run on a laptop or phone.
Best for: on-device use, fine-tuning, experimentation.Qwen
AlibabaA strong open family with standout multilingual and coder variants, from tiny to very large.
Best for: multilingual work, coding, local inference.DeepSeek
DeepSeekOpen reasoning and coder models that punch far above their cost.
Best for: heavy reasoning and coding on a budget.Phi
MicrosoftMicrosoft’s “small language models” — surprisingly capable at a fraction of the size.
Best for: edge/on-device, cheap high-volume inference.Run it yourself
local runners, hubs & servingOllama
Local runnerOne command to pull and run open models locally, with an OpenAI-compatible API. Your data never leaves the machine.
Best for: private, free local inference and prototyping.LM Studio
Desktop appA friendly desktop app for downloading and chatting with local models — no terminal required.
Best for: no-code local experimentation.Hugging Face
Model hubThe hub for models, datasets and the Transformers library — the npm of machine learning.
Best for: finding, fine-tuning and serving almost anything.vLLM
Inference serverA high-throughput server that makes open models fast and cheap to serve at scale.
Best for: production serving of open-weight models.llama.cpp
Edge inferenceBlazing C/C++ inference that runs quantized models on CPUs, laptops and edge devices.
Best for: lightweight local inference on modest hardware.Capabilities and model names move fast — treat this as a map, not a spec sheet. Every link goes to the source. The AI Donkey Chase commentary is ours.