Chinese open-source AI models are worth tracking when the repo, license, release cadence, and model artifacts stay legible enough for a builder to ver...
Article list
A practical support guide for founders and PMs to monitor China AI startup news in English. Learn which sources to watch, how to verify signals, and w...
A practical support article for builders and PMs tracking China AI chip and compute updates. Learn what to verify, when to act, and which sources to t...
Builders, PMs, and researchers can track China AI policy updates in English using a focused source stack and decision frame. Skip the jargon, watch fo...
Track China AI updates in English without noise. Learn to distinguish model benchmarks, policy shifts, and marketing packaging. For builders, PMs, fou...
Without task-level agent evaluations, model upgrades are just guesswork. This guide covers building evaluation test suites, designing process tracing,...
MCP deployment isn't just about protocol compatibility—permissions, audit logging, and state rollback are hard requirements for production. This artic...
AI coding agents can burn millions of tokens per task. Learn how engineering teams set cost guards, monitor usage, and optimize strategies—before cred...
Browser agents automate browser tasks—but aren't right for every use case. This guide breaks down practical boundaries and implementation steps for fo...
How multi-model routing cuts costs for developers in 2026: route tasks intelligently across draft, review, and execution models—paired with a unified ...
Before exposing internal APIs to AI agents, enforce these 6 constraints: idempotency, authorization, revocability, audit logging, rate limiting, and s...
When should you upgrade to multimodal RAG? This guide gives developers a practical decision framework—3 clear signals + 4 implementation steps—leverag...
Developers often overcomplicate rule files—length doesn't equal effectiveness. This guide shows how to write concise, high-impact CLAUDE.md and AGENTS...
Stop guessing why Agents fail. Use the 5-layer problem tree—Prompt, Tools, Code, Knowledge, and Model—to systematically identify root causes, with act...
The Advisor architecture uses collaborative LLMs to balance intelligence and cost in 2026. This guide covers use cases, implementation steps, and key ...
A step-by-step guide to GitHub Copilot, Wenxin Quick Code, and other top AI coding tools—covering setup, best practices, and real-world collaboration,...
Not just hype—these 7 actively maintained, production-ready AI projects on GitHub are transforming dev workflows in 2026, covering local deployment, c...
A practical 2026 AI skills checklist for developers: Agent development, multimodal programming, and lightweight model deployment—plus essential tools ...
Build a reliable AI trend tracking stack with three clear layers: routing, discovery, and verification. Use this support page to design the stack, the...
Use this checklist to judge whether an AI trend tracking site is worth your attention. The goal is not more feeds, but better routing, faster verifica...
Run AI trend tracking in 20 minutes a week by separating routing, verification, and watchlist updates. This support page gives the weekly cadence, whi...
Build an English-language source stack for China AI updates by separating official release surfaces, policy framing, English reporting, and builder-fa...
Use this workflow to verify China AI model releases in English without mixing repos, policy, and media context together. The page is intentionally nar...
Verify English-language China AI coverage with a simple checklist across model proof, policy framing, and packaging readiness. This page narrows the j...
Learn to classify GitHub AI repos into demo, workflow, or deployable types—and use our 4-step method to quickly assess real-world value and deployment...
How product engineering teams can rapidly assess whether to adopt April 2026's top GitHub Trending AI open-source projects—using a practical 7-step fr...
Skip long-term memory if your agent handles one-off Q&A. This article gives 4 actionable signals—and verifiable external evidence—to decide whether AI...
A practical guide to layering RAG systems—when and why to add retrieval, re-ranking, compression, and routing layers for production-grade performance.
RAG in 2026 isn't just about the buzzword 'Agentic'—it's evolving in multimodal retrieval, verifiable citations, and end-to-end evaluation. We break d...
RAG has evolved to version 3.0 in 2026. This guide traces its journey from basic retrieval to agentic architectures—helping product teams assess readi...