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Discover▾

Not sure where to start? This week’s hottest software, models and stacks in three minutes.

AI Apps▾

AI software people use: from assistants like ChatGPT to tools for coding, video and workflows, picked by purpose.

For Agents▾

Software for AI: tools that let your agent (Claude Code, Codex, Cursor…) browse, read docs, run code and remember.

Models▾

Choose models, estimate cost, self-host: prices, rankings, benchmarks, free tiers and compute.

Solutions▾

A goal or an error message? Ready-made stacks, steps and fixes by scenario.

Learn▾

A systematic course from zero to the frontier, with an algorithm library, must-read papers and tutorials.

Frontier▾

Daily AI news, weekly highlights, the latest papers and how AI is changing every discipline.

// I want to solve · Students / Individuals / Developers / Researchers

🎓 Learn AI (roadmaps, tutorials)

Learning paths from complete beginner to developer and researcher, plus curated free external tutorials and the software you need along the way.

4 steps3 alternatives2 stacks · 1 problemschecked 2026-10-22

How to do it

  1. 01Pick a route: use AI / build AI apps / do research
  2. 02Follow our learning paths stage by stage with a small project each
  3. 03Install Ollama or LM Studio and run models hands-on
  4. 04Read classic papers and weekly picks to keep up

What it costs

Free start

Our courses and curated tutorials are free

Individual paid

Use free APIs or local models for labs

Compute

Rent cloud GPUs by the hour for training labs

Prices change; the vendor’s official page wins. Subscriptions compared · API prices

Alternatives: which to pick

OptionChoose it when
学 AIOur learning paths
外部教程Curated free courses
LM StudioZero-setup local experiments

✓ Good fit

  • From beginner to developer or researcher

Ready-made stacks

All stacks
Run LLMs on your own computerBeginner From one-click installs to command-line tuning: download open-weight models, run them locally and expose an OpenAI-compatible API to other tools. 5 tools Student learning stack Explain, practice, note-taking and exam review in one line. 4 tools

Software that helps

All software

🖥️ Local inference & serving👩‍💻 Coding agents & AI IDEs📐 Skills & rules

Ollama Local inference & servingv0.40.8 ★ 184k Get up and running with Kimi, GLM, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. Open source CLIAPI llama.cpp Local inference & servingb11804 ★ 132k LLM inference in C/C++ Open source vllm Local inference & servingv0.31.0 ★ 95k A high-throughput and memory-efficient inference and serving engine for LLMs Open source APICLI cherry-studio Local inference & servingv2.1.4 ★ 53k AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs Open source App LocalAI Local inference & servingv4.11.0 ★ 50k LocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required. Open source API exo Local inference & servingv1.0.71 ★ 48k Run frontier AI locally. Open source CLIAPI sglang Local inference & servingv0.5.21 ★ 38k SGLang is a high-performance serving framework for large language models and multimodal models. Open source API mlx Local inference & servingv0.32.3 ★ 29k MLX: An array framework for Apple silicon Open source SDK llamafile Local inference & serving0.10.6 ★ 26k Distribute and run LLMs with a single file. Open source CLIAPI ktransformers Local inference & serving ★ 20k A Flexible Framework for Experiencing Heterogeneous LLM Inference/Fine-tune Optimizations Open source TensorRT-LLM Local inference & servingv1.4.0rc1 ★ 15k TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way. Open source SDKAPI ai-dynamo/dynamo Local inference & servingv1.5.0-kimi-k3-post.1 ★ 8.4k A Datacenter Scale Distributed Inference Serving Framework Open source API lmdeploy Local inference & servingv0.18.0 ★ 8.1k LMDeploy is a toolkit for compressing, deploying, and serving LLMs. Open source APICLI mlx-lm Local inference & serving ★ 7.4k Run LLMs with MLX Open source llama-swap Local inference & servingv262 ★ 5.9k Reliable model swapping for any local OpenAI/Anthropic compatible server - llama.cpp, vllm, etc Open source API gpustack Local inference & servingv2.2.3 ★ 5.9k A GPU cluster manager for high-performance AI model serving (vLLM, SGLang) and on-demand SSH-accessible GPU instances. Open source API lms Local inference & serving ★ 5.4k LM Studio CLI Open source CLI llm-d Local inference & servingv0.10 ★ 4.9k Achieve state of the art inference performance with modern accelerators on Kubernetes Open source API ramalama Local inference & servingv0.25.0 ★ 3.1k RamaLama is an open-source developer tool that simplifies the local serving of AI models from any source and facilitates their use for inference in production, all through the familiar language of containers. Open source CLI LM Studio Local inference & serving0.4.26（Homebrew 当前） Desktop app to download and run local LLMs with an OpenAI-compatible server. Closed / commercial Free macOSWindowsLinux NVIDIA driver Local inference & serving615.78.08 Open source CUDA Toolkit / cuDNN Local inference & servingCUDA 13.4.2 Closed / commercial stable-diffusion-webui Local inference & servingv1.10.1 ★ 165k Stable Diffusion web UI Open source API textgen Local inference & servingv4.9 ★ 48k Open-source desktop app for local LLMs. Text, vision, tool-calling, OpenAI/Anthropic-compatible API. 100% private. Open source API

202 more by category →

Problems & solutions

All problems

Needs in this scenario

  • IND-07Structured learning path plus free compute and required software

    Individual developers & researchers · People search for: llm learning roadmap; mcp tutorial; rag tutorial

Learn

All tutorials

Data updated: Solutions & stack guides(UTC+8)