https://ramdi.fr/tags/knowledge-base/Recent content in Knowledge-Base on Noureddine RAMDIHugoenSat, 23 May 2026 20:41:27 +0000https://ramdi.fr/github-stars/obsidian-llm-wiki-local-local-first-ai-powered-wiki-generation-with-human-in-the-loop-feedback/Sat, 23 May 2026 20:41:14 +0000https://ramdi.fr/github-stars/obsidian-llm-wiki-local-local-first-ai-powered-wiki-generation-with-human-in-the-loop-feedback/obsidian-llm-wiki-local generates interlinked Obsidian markdown wikis using local LLMs. Its standout feature is a rejection feedback loop that refines article quality via user input.https://ramdi.fr/github-stars/how-obsidian-second-brain-transforms-your-obsidian-vault-into-an-ai-maintained-knowledge-base/Tue, 05 May 2026 16:46:42 +0000https://ramdi.fr/github-stars/how-obsidian-second-brain-transforms-your-obsidian-vault-into-an-ai-maintained-knowledge-base/obsidian-second-brain rewrites Obsidian vault pages autonomously using Claude Code agents, detecting contradictions and running scheduled maintenance for a self-healing AI knowledge base.https://ramdi.fr/github-stars/llm-wiki-orchestrating-multi-agent-llm-research-into-persistent-knowledge-bases/Tue, 05 May 2026 13:37:39 +0000https://ramdi.fr/github-stars/llm-wiki-orchestrating-multi-agent-llm-research-into-persistent-knowledge-bases/llm-wiki is a shell-based orchestration layer that turns LLM agents into a persistent, multi-agent research wiki. Supports up to 10 agents, deep investigations, and durable provenance tracking.https://ramdi.fr/github-stars/injecting-domain-expertise-into-claude-ai-a-deep-dive-into-the-email-marketing-bible-skill/Mon, 04 May 2026 10:23:02 +0000https://ramdi.fr/github-stars/injecting-domain-expertise-into-claude-ai-a-deep-dive-into-the-email-marketing-bible-skill/Explore how the email-marketing-bible Claude Code skill injects 68,000 words of sourced email marketing expertise into Claude AI, enabling expert audits, copywriting, and automation advice.https://ramdi.fr/github-stars/llm-wikid-agent-agnostic-ai-knowledge-base-with-schema-driven-compilation-for-obsidian/Mon, 04 May 2026 10:23:02 +0000https://ramdi.fr/github-stars/llm-wikid-agent-agnostic-ai-knowledge-base-with-schema-driven-compilation-for-obsidian/llm-wikid uses a CLAUDE.md schema to control a multi-phase ingest pipeline compiling markdown wiki pages for Obsidian, offering better Q&A at scale than RAG. Supports multiple AI agents and quality checks.https://ramdi.fr/github-stars/openkb-a-persistent-vectorless-wiki-knowledge-base-powered-by-llms-and-pageindex/Mon, 04 May 2026 10:23:02 +0000https://ramdi.fr/github-stars/openkb-a-persistent-vectorless-wiki-knowledge-base-powered-by-llms-and-pageindex/OpenKB compiles documents into a persistent, interlinked wiki using LLMs and PageIndex’s vectorless retrieval, supporting multi-LLM backends and interactive chat with persisted sessions.https://ramdi.fr/github-stars/understanding-math-a-curated-knowledge-base-for-mathematical-literacy-and-notation/Mon, 04 May 2026 10:23:02 +0000https://ramdi.fr/github-stars/understanding-math-a-curated-knowledge-base-for-mathematical-literacy-and-notation/understanding-math is a curated repository aggregating resources on mathematical literacy, notation, and proof techniques, bridging math concepts with programming needs in ML and cryptography.https://ramdi.fr/github-stars/stash-a-shared-agent-memory-with-no-server-side-llm-calls/Mon, 04 May 2026 10:23:01 +0000https://ramdi.fr/github-stars/stash-a-shared-agent-memory-with-no-server-side-llm-calls/Stash captures coding agent session transcripts for teams and builds a shared knowledge base without server-side LLM calls, preserving privacy and cutting costs.https://ramdi.fr/github-stars/inside-llm-wiki-a-desktop-app-for-building-persistent-llm-powered-personal-wikis/Mon, 04 May 2026 10:05:49 +0000https://ramdi.fr/github-stars/inside-llm-wiki-a-desktop-app-for-building-persistent-llm-powered-personal-wikis/llm_wiki uses a two-step chain-of-thought pipeline to build a self-maintaining knowledge base. It combines Tauri, knowledge graphs, and Louvain clustering for a unique personal wiki experience.