OPEN LINK ↗
// 53d agoOPENSOURCE RELEASE
Unified map ties LLM knowledge stack together
A free, MIT-licensed GitHub guide that tries to unify the modern LLM knowledge stack into one practical framework. It spans 11 chapters and around 21K words, covering RAG, long context, knowledge graphs, context engineering, harness engineering, MCP, agent memory, skill systems, progressive disclosure, and the Chinese AI ecosystem. The core pitch is that these topics are not separate trends but layers of the same runtime architecture for building better AI systems.
// ANALYSIS
The angle is strong because it reframes the current “RAG vs. long context vs. agents” debate as a systems problem, not a feature checklist.
- –The positioning is timely: the guide argues RAG is still relevant, but only as one layer in a broader knowledge-runtime stack.
- –The best part is the connective tissue across topics that are usually documented in silos, especially skills, memory, and harness design.
- –The Chinese ecosystem section is a useful differentiator because most English-language roundups still under-cover tools like Dify, RAGFlow, DeepSeek, and Kimi.
- –The launch reads more like a research-heavy reference asset than a product demo, so its audience is builders, tinkerers, and technical writers rather than casual users.
- –I could not find a Product Hunt listing or badge for the repo, so the PH field should stay `NONE`.
// TAGS
ragcontext-engineeringharness-engineeringmcpagent-memoryskillsknowledge-managementllmopen-source
DISCOVERED
53d ago
2026-04-06
PUBLISHED
53d ago
2026-04-06
RELEVANCE
8/ 10
AUTHOR
choeng_919
