48 related articles
CS Self-Study Guide: The 74K-Star Comp…
A 74K-star GitHub project by Peking University students curates MIT, Stanford, and CMU open courses into a complete CS self-study roadmap covering algorithms, OS, databases, and more.

A tailored ML guide for control theory learners covering reinforcement learning, data-driven control, Learning-based MPC, and a three-stage roadmap with practical advice.

A practical guide to consolidating scattered automation scripts into a local AI Agent hub. Covers Function Calling, Ollama+Qwen2.5 deployment, tool orchestration architecture, and a complete implementation roadmap.

OpenAI's internal model Astra reportedly achieved 10 breakthroughs in math and theoretical CS. We analyze the rumors, compute infrastructure trends, real AI research assistant experiences, and AI's limits in original research.

Kimi-K3 scores 60.4% on ARC-AGI-2, far surpassing most LLMs. This article analyzes what ARC-AGI-2 tests, what this score means for abstract reasoning, and its implications for the AI industry.

GitHub Trending July 30: Microsoft AI-For-Beginners holds #1, Rust terminal code review tool tuicr surges 338 stars, WhatsApp API library Baileys shows strong real-world adoption.

DeepSeek founder Liang Wenfeng reveals a five-step AGI roadmap—from chain-of-thought to embodied intelligence—under a 20,000-GPU constraint, using the TileLang compiler to break domestic substitution challenges while API cash flow backs AGI exploration.

DeepSeek founder Liang Wenfeng reveals a five-step AGI roadmap from chain-of-thought to embodied intelligence. How does TileLang crack domestic GPU substitution under a 20,000-card constraint?

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.

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.

Alibaba open-sources a 2.4 trillion parameter Qwen model and launches the Qwen Token Plan. Chinese models surge, Kimi K3 tops global rankings, and China's AI is reshaping the global competitive landscape.
From Math to AI Research Engineer: A D…
A GitHub project called maths-cs-ai-compendium surpassed 6,000 Stars with a roadmap for becoming an AI/ML Research Engineer. Here's what makes it worth following.
GitHub Daily · July 19: The Dual Advan…
GitHub Trending July 19: ktransformers tops the list with heterogeneous inference optimization, while jcode, cua, and AstrBot signal a maturing Agent ecosystem.

A four-stage AI Agent development roadmap: from core theory and ReAct paradigm to multi-agent collaboration and production deployment. Covers DeepSeek, Coze, Dify, and more.
Build Your Own X: The Hardcore Learnin…
Explore build-your-own-x, the 520K-star GitHub project that teaches developers to rebuild databases, OSes, and compilers from scratch — and why it matters more than ever in the AI era.

This article synthesizes two MSR India Summit talks, exploring two key paths to better AI reasoning: test-time scaling with variable granularity search, and a formal verification framework for trustworthy agent execution.
The Complete AI Researcher Learning Ro…
A structured AI/ML learning roadmap covering Python, math, machine learning, deep learning, and MLOps — with timelines, milestones, and free resource recommendations.
The Complete Guide to Breaking Into Da…
A complete guide to breaking into data science: learning resources, degree vs. online courses, building a portfolio, and career prospects. Ideal for career changers and upskilling professionals.
The 'One-Step Trap' in AI Research: Wh…
What is the 'One-Step Trap' in AI research? A deep dive into how greedy thinking locks research directions, the limits of incremental improvements, and how multi-step planning and exploration-exploitation balance enable real breakthroughs.

Already know math and Python? Learn the complete machine learning roadmap: from data science tools and classical algorithms to deep learning frameworks and specialization.