#Knowledge Graph

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#Knowledge Graph Reel by @marc.kaz (verified account) - This changed the way I explore codebases forever. Before: Scattered folders, massive token burn, and zero visibility into real connections. After: O
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@marc.kaz
This changed the way I explore codebases forever. Before: Scattered folders, massive token burn, and zero visibility into real connections. After: One command → full knowledge graph with Obsidian vault, interactive wiki, and plain-English answers like “What calls this function?” The bridge? **Graphify** — built in 48 hours after Karpathy asked for it. Handles code, PDFs, images. 71.5x fewer tokens per query. Open source. Zero setup. Save this if you build or research! 👉 https://github.com/safishamsi/graphify #Graphify #KnowledgeGraph #AICoding #DevTools #OpenSourceAI
#Knowledge Graph Reel by @the_ai_dude (verified account) - Comment 'Graph' to get the full document! . . . . In RAG (Retrieval-Augmented Generation), a knowledge graph organizes data as interconnected entitie
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@the_ai_dude
Comment 'Graph' to get the full document! . . . . In RAG (Retrieval-Augmented Generation), a knowledge graph organizes data as interconnected entities and relationships, enabling better context understanding and retrieval. It helps LLMs fetch precise, structured insights from linked information, improving reasoning, accuracy, and relevance of generated responses beyond traditional text-based document retrieval. . . . #rag #knowledgegraph #ai
#Knowledge Graph Reel by @tectalks.io (verified account) - Your notes aren't just notes anymore, they're alive. This turns an Obsidian vault into a living 3D mind map, where ideas stop sitting in folders and
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@tectalks.io
Your notes aren’t just notes anymore, they’re alive. This turns an Obsidian vault into a living 3D mind map, where ideas stop sitting in folders and start behaving like a real thinking system. In one view, everything orbits a single core idea. In another, thoughts self-organize into themed hubs. And in the wildest version, there’s no center at all, just a fully distributed network where ideas explain their own relationships to each other. It feels less like knowledge management and more like watching cognition happen in real time. Your brain, but spatial. Your thoughts, but navigable. This is what thinking looks like when AI stops organizing information and starts revealing how ideas actually connect. Built by @poetengineer_ #AI #KnowledgeGraph #Obsidian #SecondBrain #3DVisualization
#Knowledge Graph Reel by @charlieautomates - Comment "AI Agent" for your FREE Claude Code ToolKit & the breakdown video coming later today! Claude isn't inefficient by accident. It scans files
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@charlieautomates
Comment “AI Agent” for your FREE Claude Code ToolKit & the breakdown video coming later today! Claude isn’t inefficient by accident. It scans files every time. High tokens, low context. Graphify fixes this by turning your files into a persistent knowledge graph. It connects your assets automatically, so Claude understands relationships instantly. Less searching. More reasoning. Result: smarter outputs, 70x fewer tokens. #AItools #ClaudeCode #KnowledgeGraph #Automation #AIAgents
#Knowledge Graph Reel by @theharis.ai - I turned ALL my ChatGPT + Claude conversations into a searchable knowledge graph 🧠 - completely free. No Claude Code. No subscriptions. Just free t
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@theharis.ai
I turned ALL my ChatGPT + Claude conversations into a searchable knowledge graph 🧠 — completely free. No Claude Code. No subscriptions. Just free tools. Here’s exactly what I used: ✅ AntiGravity (free — replaces Claude Code) ✅ Obsidian (free graph view) ✅ Your Claude + ChatGPT export Full free guide in my bio 👆 (PDF included — with the exact prompt I used) ────────────────── 🔁 Save this if you use ChatGPT or Claude daily 💬 Comment “BRAIN” and I’ll send you the guide #obsidian #chatgpt #claudeai #aiagents #harisai
#Knowledge Graph Reel by @itsaiandy - GitNexus builds a 3D knowledge graph of your codebase enabling AI tools to provide smarter, faster coding help.
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@itsaiandy
GitNexus builds a 3D knowledge graph of your codebase enabling AI tools to provide smarter, faster coding help.
#Knowledge Graph Reel by @alassafi.ai (verified account) - Comment "BRAIN" and I'll DM you the full guide 🧠 Your AI is resetting every single session. Every breakthrough. Every decision. Every pattern you s
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@alassafi.ai
Comment “BRAIN” and I’ll DM you the full guide 🧠 Your AI is resetting every single session. Every breakthrough. Every decision. Every pattern you spent hours debugging. Gone. Not because Claude isn’t smart enough — because you never gave it a memory system. Here’s what changes when you do: 👾 CLAUDE.md — The first thing Claude reads every session. Not a config file. A teaching document. Your architecture, your conventions, your hard nos. It shows up to every session already knowing who you are. 💾 Auto-memory directory — Claude starts writing down what it learns. Patterns it noticed. Things you corrected. Solutions that worked. Organised into topic files. Persistent across every session from now on. 🧠 Obsidian + MCP — Your vault becomes a live knowledge graph it can search at runtime. Two MCP servers: smart-connections for semantic search, qmd for structured queries. The key? Name notes as claims, not categories. Not `memory-systems.md` — `memory graphs beat giant memory files.md`. The titles alone tell Claude if something’s relevant before it reads a word. ”brain-ingest pipeline” — One command. Paste a YouTube link, a voice memo, a meeting recording. It downloads, transcribes locally, extracts the key claims and frameworks, and drops a structured note straight into your Obsidian inbox. That’s the insight from a talk you watched 6 weeks ago — available to Claude today. Each layer compounds on the last. Skip one and the others degrade. Comment “BRAIN” — full guide with MCP config, vault structure, and setup checklist 👇
#Knowledge Graph Reel by @arjay_the_dev (verified account) - Knowledge Graph vs Vector Database Both of these tools can be used by LLM to pull in information to answer a users question (RAG). Using a knowledge
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@arjay_the_dev
Knowledge Graph vs Vector Database Both of these tools can be used by LLM to pull in information to answer a users question (RAG). Using a knowledge graph is best for discovering relations between things, and using a vector database is best for finding similar things. #coding #programming #rag
#Knowledge Graph Reel by @dailydoseofds_ - 8 RAG architectures for AI Engineers 🧠 (explained with usage) 1️⃣ Naive RAG Retrieves documents purely based on vector similarity between query emb
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@dailydoseofds_
8 RAG architectures for AI Engineers 🧠 (explained with usage) 1️⃣ Naive RAG Retrieves documents purely based on vector similarity between query embedding and stored embeddings. Best for: Simple, fact-based queries where direct semantic matching suffices. 2️⃣ Multimodal RAG Handles multiple data types (text, images, audio) by embedding and retrieving across modalities. Best for: Cross-modal retrieval tasks like answering text queries with both text and image context. 3️⃣ HyDE (Hypothetical Document Embeddings) Generates a hypothetical answer document from the query before retrieval. Best for: When queries are not semantically similar to documents. 4️⃣ Corrective RAG Validates retrieved results by comparing them against trusted sources (e.g., web search). Best for: Ensuring up-to-date and accurate information. 5️⃣ Graph RAG Converts retrieved content into a knowledge graph to capture relationships and entities. Best for: Enhanced reasoning with structured context alongside raw text. 6️⃣ Hybrid RAG Combines dense vector retrieval with graph-based retrieval in a single pipeline. Best for: Tasks requiring both unstructured text and structured relational data. 7️⃣ Adaptive RAG Dynamically decides if a query requires simple retrieval or multi-step reasoning chain. Best for: Breaking complex queries into smaller sub-queries for better coverage. 8️⃣ Agentic RAG Uses AI agents with planning, reasoning (ReAct, CoT), and memory to orchestrate retrieval from multiple sources. Best for: Complex workflows requiring tool use, external APIs, or combining multiple RAG techniques. 👉 Over to you: Which RAG architecture do you use the most? #ai #rag #machinelearning
#Knowledge Graph Reel by @alassafi.ai (verified account) - Comment BRAIN and I'll DM you the full guide 🧠 Your AI is resetting every single session. Every breakthrough. Every decision. Every pattern you spe
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@alassafi.ai
Comment BRAIN and I’ll DM you the full guide 🧠 Your AI is resetting every single session. Every breakthrough. Every decision. Every pattern you spent hours figuring out. Gone. Not because OpenClaw isn’t powerful enough — because you never gave it a memory system. Here’s what changes when you do: 👾 SYSTEM.md — The first thing OpenClaw loads every session. Not a config file. A teaching document. Your workflows, your conventions, your hard nos. It shows up to every session already knowing who you are. 💾 Auto-memory directory — OpenClaw starts writing down what it learns. Patterns it noticed. Things you corrected. Solutions that worked. Organised into topic files. Persistent across every session from now on. 🧠 Obsidian + MCP — Your vault becomes a live knowledge graph it can search at runtime. Two MCP servers: smart-connections for semantic search, qmd for structured queries. The key? Name notes as claims, not categories. Not `memory-systems.md` — `memory graphs beat giant memory files.md`. The titles alone tell OpenClaw if something’s relevant before it reads a word. 🔄 brain-ingest pipeline — One command. Paste a YouTube link, a voice memo, a meeting recording. It downloads, transcribes locally, extracts the key claims and frameworks, and drops a structured note straight into your Obsidian inbox. That’s the insight from a talk you watched 6 weeks ago — available to OpenClaw today. Each layer compounds on the last. Skip one and the others degrade. Comment BRAIN — full guide with MCP config, vault structure, and setup checklist 👇
#Knowledge Graph Reel by @keshavsuki (verified account) - Comment "graph" for the exact setup 👇 Do not set up Claude Code without adding this to your Obsidian vault first. Claude Code starts every session
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@keshavsuki
Comment “graph” for the exact setup 👇 Do not set up Claude Code without adding this to your Obsidian vault first. Claude Code starts every session blind. No codebase knowledge. It greps, explores, reads files to figure out what connects to what. Wasted tokens. Broken dependencies. The fix is a knowledge graph inside your Obsidian vault. GitNexus: one command, scans your codebase, maps every function and dependency, drops into Obsidian as linked markdown files. Code architecture lives next to your business knowledge. Same vault. CodeGraph: one install, persistent map Claude reads before every session. Already knows your codebase structure before you type a word. 6.8x fewer tokens on code reviews. 49x fewer on daily tasks. Add the graph location to your CLAUDE.md. Claude loads it at session start, understands the blast radius of every change, stops breaking things it wasn’t supposed to touch. Three layers. One Obsidian vault. Business knowledge. Personal notes. Code architecture. Comment “graph” for the exact setup 👇
#Knowledge Graph Reel by @infranodus (verified account) - Navigate through your #Obsidian notes with #InfraNodus #pkm #knowledgegraph #ai #dataviz #rhizome #lyt #notetaking #productivity
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@infranodus
Navigate through your #Obsidian notes with #InfraNodus #pkm #knowledgegraph #ai #dataviz #rhizome #lyt #notetaking #productivity

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