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An open-source GitHub repo curates 30+ legally free AI/ML classic books covering deep learning, RL, NLP, computer vision & more, with automated link checking.
Terence Tao on AI and Mathematics: For…
Fields Medalist Terence Tao analyzes AI's impact on math research, discussing LLM-assisted proofs, Lean formal verification, large-scale collaboration, and the future of math education in the AI era.

Not every data science problem needs ML. This guide offers a decision framework across four dimensions — rule complexity, data quality, prediction needs, and interpretability — to avoid over-engineering.

Generative AI is profoundly disrupting the legal profession. This article explores AI's impact on law, law school curriculum reform, and the core competencies future lawyers need, including critical judgment, AI proficiency, and ethical literacy.

Awesome Free AI Books is an open-source repo with 30+ legally free AI & ML classic textbooks covering deep learning, reinforcement learning, NLP, LLMs, and more — all linking to official sources with weekly automated link checks.

Veteran AI practitioner Remy breaks down the leap from chat models to AI agents: how agents work, the three pillars of context, tools, and skills, MCP connections, and hands-on architecture to make you a 100x employee.

Can AI coding tools really earn you $3K/month with zero experience? We break down the marketing hype around Codex freelancing, examine the 4-week roadmap, and reveal what AI-assisted coding can realistically offer.

Reddit users report Gemini feeling "worse over time." This deep dive explains the real causes—silent updates, safety over-alignment, and compute throttling—plus how to rationally evaluate AI model performance.

jlens-gguf is an open-source tool bringing Anthropic's Jacobian Lens interpretability method to GGUF and llama.cpp, enabling internal observation, real-time steering, and abliteration for both dense and MoE models.

A comprehensive guide to OpenAI's new AI coding agent Codex: from concept and comparison of its four forms, to installing Git/Node.js/VS Code, configuring the API Key, and creating a workspace.

Five key AI industry trends: Doubao surpasses 180 trillion daily calls, OpenAI's in-house AI chip, NVIDIA's $3-4 trillion compute forecast, China catching up, and the GPT-5.6 cheating scandal.

A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.

xAI releases Grok 4.5, ranking #1 on SWE Marathon and outperforming Claude Opus. Explore benchmark scores, Agent capabilities, free access, and CLI installation.
Kronos Financial Foundation Model: Usi…
Kronos is the first open-source foundation model treating candlestick data as the "language of financial markets," using an autoregressive Transformer and earning 32K GitHub Stars. A deep dive into its principles, applications, and limits.

A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.

A focused guide to core LLM application engineer interview topics, covering agent architecture, Multi-Agent, Langfuse evaluation, security, and RAG optimization.

From the autocomplete nature of LLMs, tokens, and context windows to RAG vector databases, the MCP protocol, and AI agent loop design — this article uses vivid analogies to unpack the reality of AI engineering.

A deep dive into Agent Tuning: from LLM hallucination and staleness issues to RAG vs. Agent architecture, the 4-step fine-tuning process, and cost analysis for building your own AI agent.

Can manually vectorized paired data train AI models? This guide explores the value, technical feasibility, and monetization paths for practitioners holding professional domain data.

Java developers can build AI apps too! Learn LangChain4j fundamentals including RAG, Agents, Function Calling, and hands-on projects — no Python required.