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ANTHROPIC'S LEAD ENGINEER WON A $1.2M BONUS FOR A SYSTEM THAT TURNS ANY DATA CHAOS INTO A GRAPH IN 8 STEPS raw chaos in - self-updating graph out - and the agent gets +42% productivity from day one Load → Extract → Graph → Index → Query → Memory...

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Sprytix

7,393 subscribers

485,299 просмотров • 1 месяц назад •via X (Twitter)

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Комментарии: 33

Фото профиля Yel
Yel1 месяц назад

I am the lead engineer at Anthropic and this is not true. Please stop spreading misinformation.

Фото профиля Fleeting Yeets
Fleeting Yeets1 месяц назад

Stop with the unrelenting “anthropic engineer did x” clickbait bullshit engagement farming.

Фото профиля ViceSol
ViceSol1 месяц назад

Combining vector search, keyword search, and graph traversal can be more effective than relying on any one method alone

Фото профиля Karl Toussaint
Karl Toussaint1 месяц назад

Are you kidding me?

Фото профиля Quentin Chapelain
Quentin Chapelain1 месяц назад

Checked your sources. 18% accuracy gain: vs raw images, not RAG. 85% cost cut: vs direct file ingestion, not RAG. "Right graph beats bigger model every time": the paper you cite says the opposite. Citing real papers with fake baselines is still making numbers up.

Фото профиля Carry
Carry1 месяц назад

'bookmark and paste into claude code' is doing a lot of heavy lifting for a $1.2M system

Фото профиля Brosko
Brosko1 месяц назад

ngl the wildest part is anthropic paying 1.2m for somethin that still needs YOU to hit paste who got that bonus tho bc i have questions 😭

Фото профиля Nate Daly
Nate Daly1 месяц назад

No he didn’t and it isn’t that simple.

Фото профиля Agent Naeem
Agent Naeem1 месяц назад

Sure $1.2M bonus and one can just paste prompt to get this 💀

Фото профиля AI Mastery Guide
AI Mastery Guide1 месяц назад

A graph that updates itself while you sleep is genuinely next level.

Фото профиля Magicfit
Magicfit1 месяц назад

graphs are becoming the real agent memory

Фото профиля Marshall
Marshall1 месяц назад

Slop

Фото профиля TTD 🇮🇩
TTD 🇮🇩1 месяц назад

Agreed

Фото профиля rvb_
rvb_1 месяц назад

WHY ARE WE SHOUTING?!?

Фото профиля MAIL
MAIL1 месяц назад

well put together. saving this for later

Фото профиля Wallchain Community Hub
Wallchain Community Hub1 месяц назад

bullish on agents actually shipping useful stuff for once

Фото профиля Web4 News
Web4 News1 месяц назад

Graphs could become the foundation for smarter AI agents. I read about it in @TheUpsideAI this morning

Фото профиля RJxDetroit
RJxDetroit1 месяц назад

Where is the link?

Фото профиля Jeffrey Hannel
Jeffrey Hannel1 месяц назад

Send this to me

Фото профиля Rezzi
Rezzi1 месяц назад

graph pipelines are durable signals

Фото профиля ClariLayer
ClariLayer1 месяц назад

This sounds exaggerated, is it real

Фото профиля Mike Schinkel
Mike Schinkel1 месяц назад

And in other news, many people are having a hard time even putting food on the table in today's haves-vs-have-nots economy. #justsaying

Фото профиля Binx
Binx1 месяц назад

MAAT!

Фото профиля jiosphone (三哥)
jiosphone (三哥)1 месяц назад

我特别喜欢这种图形界面等操作，然后ai使用一目了然。

Фото профиля Matthew Nicoletti
Matthew Nicoletti1 месяц назад

Turning raw data chaos into a graph in eight steps is efficient.

Фото профиля rewind
rewind1 месяц назад

zero duplicates is doing serious work here

Фото профиля Abdulmuiz Adeyemo
Abdulmuiz Adeyemo1 месяц назад

Mad

Фото профиля Saman Ahmed
Saman Ahmed1 месяц назад

The real unlock is turning messy information into usable structure.

Фото профиля Rai
Rai1 месяц назад

Same pipeline works for any knowledge base,docs,Slack,codebase Just change the data source,the 8 steps stay identical

Фото профиля Chris.agents
Chris.agents1 месяц назад

saving this for later

Фото профиля Collin Leijenaar
Collin Leijenaar1 месяц назад

Clickbait

Фото профиля SportsKoat
SportsKoat1 месяц назад

Visual helped me understand thank you

Фото профиля Frezz
Frezz1 месяц назад

Curious how this compares to a well built vector only RAG stack in terms of latency and maintenance costs.

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Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

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Andrew Ng built agentic knowledge graph that runs agents loops from scratch: his agents searched thousands of tools through the graph, found the "right" one by meaning, and called it agent efficiency increased by 90% here's how: step 1 → embed every tool, search by meaning - not just keywords step 2 → but "similar" ≠ "correct" - it grabs the wrong one, skips the step before it step 3 → map tools as a graph - nodes are tools, edges are what runs before what step 4 → retrieve the path, not one tool - every tool the task needs, in order step 5 → bake the process into the graph - the sequence becomes data, not a guess most agents fail here and no one knows why - 1-hour brilliant course about it watch & bookmark - then read full course about graph engineering ↓

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17,040 просмотров • 1 месяц назад

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Google just released free 2-hour course on full Graph & Loop engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 0% → 0:35 - Graph engineering from scratch 30% → 31:17 - build your first agents graph 45% → 43:40 - run hundreds agents in parallel 75% → 1:04:58 - Loop engineering: route, check, repeat 100% → 1:30:09 - self-improving graphs that work while you sleep most people keep stuffing instructions into one agent - the real upgrade is a graph that routes, checks, and rebuilds the workflow at runtime learn graph from Google, ship your first - then unlock the complete system design below ↓

codila

348,236 просмотров • 1 день назад

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Hanako

53,692 просмотров • 1 месяц назад

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141,580 просмотров • 2 месяцев назад

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This is one of the coolest open-source AI agent projects I've seen in a while: 'Understand Anything' It's a plugin for Claude Code, Codex, OpenCode etc. that analyzes your codebase and turns it into a knowledge base that you can interact with. It explains the codebase to you, rather than showing you the structure. It seems like it's designed for code but I opened my Obsidian vault of podcast highlights in Claude Code, then ran /understand. The result is a knowledge graph that I can search of highlights from 888 podcast episodes and 144K lines of markdown text.

Dan McAteer

174,887 просмотров • 2 месяцев назад

He built Claude Code in 7 days and made $3.2M last month - and in 30 minutes revealed everything you need to know about AI: 00:54 - how Anthropic engineers write prompts 06:14 - Graph Engineering makes AI 10x smarter 14:47 - $100k+/month business built with Opus 5 after watching I deleted all my old prompts and started from scratch - first $20k in a month. Bookmark & watch - read the article below and build your first Graph system
17:54

He built Claude Code in 7 days and made $3.2M last month - and in 30 minutes revealed everything you need to know about AI: 00:54 - how Anthropic engineers write prompts 06:14 - Graph Engineering makes AI 10x smarter 14:47 - $100k+/month business built with Opus 5 after watching I deleted all my old prompts and started from scratch - first $20k in a month. Bookmark & watch - read the article below and build your first Graph system

Noisy

32,735 просмотров • 1 месяц назад

THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this
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THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this

Yarchi

56,177 просмотров • 3 месяцев назад

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!
3:46

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,435 просмотров • 2 лет назад

Holy sh*t this is gold THE FULL GRAPH ENGINEERING PIPELINE, GROUNDED IN REAL, OPEN SOURCE WORK. Representation. Ontology. Entity extraction. Relations. Events. Quality gate. Fusion. Embeddings. Memory your agent can actually traverse. Real repos already cover this end to end. Strwythura walks through the full ontology pipeline with entity resolution and embedding-enhanced GraphRAG. llm2kg covers entity and relationship extraction against an ontology, Neo4j storage with vector embeddings, and a ReAct agent that queries the graph directly. No PhD required. Real, working code you can run against your own data today.
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Holy sh*t this is gold THE FULL GRAPH ENGINEERING PIPELINE, GROUNDED IN REAL, OPEN SOURCE WORK. Representation. Ontology. Entity extraction. Relations. Events. Quality gate. Fusion. Embeddings. Memory your agent can actually traverse. Real repos already cover this end to end. Strwythura walks through the full ontology pipeline with entity resolution and embedding-enhanced GraphRAG. llm2kg covers entity and relationship extraction against an ontology, Neo4j storage with vector embeddings, and a ReAct agent that queries the graph directly. No PhD required. Real, working code you can run against your own data today.

CyrilXBT

49,462 просмотров • 1 месяц назад

Andrew Ng just released a 1-hour course on what comes after RAG. How to turn raw documents into agent memory: 00:00 - Why larger context windows are not enough 02:18 - Extract entities, relationships, and sources 14:31 - Coordinate specialized agents inside one graph 30:43 - Build it with Google ADK and Neo4j 47:25 - Turn knowledge graphs into persistent memory Most people are still learning RAG. Andrew Ng is already teaching the layer that comes after it. Documents → Entities → Relationships → Agents → Knowledge Graph More context is the old workflow. Persistent graphs are the new memory layer. This 1-hour watch is worth more than most paid GraphRAG courses. Bookmark and watch it today Then read the full graph engineering roadmap below
1:00:53

Andrew Ng just released a 1-hour course on what comes after RAG. How to turn raw documents into agent memory: 00:00 - Why larger context windows are not enough 02:18 - Extract entities, relationships, and sources 14:31 - Coordinate specialized agents inside one graph 30:43 - Build it with Google ADK and Neo4j 47:25 - Turn knowledge graphs into persistent memory Most people are still learning RAG. Andrew Ng is already teaching the layer that comes after it. Documents → Entities → Relationships → Agents → Knowledge Graph More context is the old workflow. Persistent graphs are the new memory layer. This 1-hour watch is worth more than most paid GraphRAG courses. Bookmark and watch it today Then read the full graph engineering roadmap below

Lunar

42,118 просмотров • 1 месяц назад

SpaceXAI engineer, Lauren Tan: "the upgrade was never a smarter model, it was wiring 20 Grok Bots into one graph they hand work to each other now, with zero input from me that took me from 6 tasks a day to 200+, my team ships overnight while I sleep" in a 55-minute session, a SpaceXAI engineer wires a full team of Grok Bots into one graph from scratch worth more than a $500 course on agentic engineering watch today, then read how to give that fleet a shared memory so it stops repeating your mistakes in the article below
55:50

SpaceXAI engineer, Lauren Tan: "the upgrade was never a smarter model, it was wiring 20 Grok Bots into one graph they hand work to each other now, with zero input from me that took me from 6 tasks a day to 200+, my team ships overnight while I sleep" in a 55-minute session, a SpaceXAI engineer wires a full team of Grok Bots into one graph from scratch worth more than a $500 course on agentic engineering watch today, then read how to give that fleet a shared memory so it stops repeating your mistakes in the article below

KingWilliam

29,071 просмотров • 5 дней назад

Loops vs. Graphs, clearly explained! loops are great, but they have a ceiling: a loop makes one unit of work better. it cannot decide which units exist. so you end up with a very good agent running the wrong three steps, in the wrong order, one at a time. Graph engineering fixes this by moving the decision up a layer: what runs, what runs at the same time, and what never runs at all. you need both. here's how it works: a graph splits your system into two kinds of decision. ↳ inside a unit: the loop. produce, check, correct, repeat until green ↳ between units: the graph. split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs you get parallel work, isolated contexts, and steps that stop running when nothing needs them. the trick is being selective about what becomes a node. only spend a model where judgment lives. merging, ranking, deduping and schema checks are edges, and edges are code. free, instant, and they cannot be argued out of a verdict. a graph where every edge is an agent pays rent on its own wiring. one thing to know before you scale it. a graph has two return paths, and almost everyone builds one. ↳ the correction edge is short. a gate rejects one unit back to the step that produced it, and it fixes the run you are in ↳ the learning edge is long. an accepted result goes back to the splitter as a constraint, and it fixes every run after skip the second and you get a graph that is fast and never gets smarter. next week it starts from the same place with the same blind spots. and a smaller one that eats whole nights: when a unit fails, return that unit, not the batch. send back four slices because one failed and you have just rewritten three correct ones. do it twice in a run and the run never converges. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓
0:41

Loops vs. Graphs, clearly explained! loops are great, but they have a ceiling: a loop makes one unit of work better. it cannot decide which units exist. so you end up with a very good agent running the wrong three steps, in the wrong order, one at a time. Graph engineering fixes this by moving the decision up a layer: what runs, what runs at the same time, and what never runs at all. you need both. here's how it works: a graph splits your system into two kinds of decision. ↳ inside a unit: the loop. produce, check, correct, repeat until green ↳ between units: the graph. split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs you get parallel work, isolated contexts, and steps that stop running when nothing needs them. the trick is being selective about what becomes a node. only spend a model where judgment lives. merging, ranking, deduping and schema checks are edges, and edges are code. free, instant, and they cannot be argued out of a verdict. a graph where every edge is an agent pays rent on its own wiring. one thing to know before you scale it. a graph has two return paths, and almost everyone builds one. ↳ the correction edge is short. a gate rejects one unit back to the step that produced it, and it fixes the run you are in ↳ the learning edge is long. an accepted result goes back to the splitter as a constraint, and it fixes every run after skip the second and you get a graph that is fast and never gets smarter. next week it starts from the same place with the same blind spots. and a smaller one that eats whole nights: when a unit fails, return that unit, not the batch. send back four slices because one failed and you have just rewritten three correct ones. do it twice in a run and the run never converges. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Hanako

73,867 просмотров • 22 дней назад