Row-hite Mon

[darkzodchi @zodchiii]

Anthropic engineer:

"No need to write prompts anymore. The new job is to write and learn graph engineering."

In 47 minutes he shows how Anthropic engineers run Claude Code day to day: the workflows and the parts everyone gets wrong.

Watch it, then save the article below on graph engineering 👇

[Nexven Ai @Tec_Nexven]

𝟭𝟲 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗔𝗜 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼𝘀 𝗶𝗻 𝟮𝟬𝟮𝟲

Bookmark this list before your next AI project. These repositories cover everything from coding agents and RAG to OCR, image generation, and LLM deployment.

1. OpenClaw
Personal AI agent that runs on your devices and connects to 50+ messaging platforms.

2. AutoGPT
Platform for building, deploying, and running autonomous AI agents.

3. Hugging Face Transformers
The model framework for state-of-the-art ML across text, vision, audio, and multimodal.

4. Ollama
Run powerful LLMs locally on your hardware with a single command.

5. LangChain
The foundational framework for building agents and LLM-powered applications.

6. Open WebUI
Self-hosted, offline-capable ChatGPT alternative with built-in RAG and plugin system.

7. ComfyUI
Node-based visual workflow builder for AI image and video generation applications.

8. Sim
Open-source drag-and-drop workflow builder for creating and deploying AI agent pipelines.

9. Opik
Open-source platform to trace, evaluate, and monitor LLM apps and agentic workflows.

10. Firecrawl
Turn any website into LLM-ready markdown or structured data.

11. Airweave
Open-source context retrieval layer that syncs 50+ data sources for AI agents.

12. vLLM
High-throughput, memory-efficient LLM serving engine for production deployments.

13. Unsloth
Fine-tune and run open models 2× faster with 70% less memory.

14. OpenPipe ART
Train multi-step AI agents for real-world tasks using RL.

15. OpenCode
Open-source, provider-agnostic AI coding agent built for the terminal.

16. Chandra OCR (by Datalab)
State-of-the-art OCR model for complex tables, forms, handwriting, and 90+ languages.

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@Tec_Nexven
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[divyansh tiwari @DivyanshT91162]

10 GitHub repos so good they probably shouldn't be free.

1. OmniRoute
One endpoint. 231 AI providers. 50+ free-tier providers.

Connect Claude Code, Codex, Cursor, and Cline to Claude, GPT, and Gemini for free.

Compresses tokens by 15–95% with automatic fallback if a provider goes down.

GitHub:
https://t.co/DyQkj165aY

2. OfficeCLI
The first Office suite built for AI agents.

Control Word, Excel, and PowerPoint from a single command line.

No Microsoft Office installation. No dependencies. Just one binary.

GitHub:
https://t.co/q2D8nVPj9u

3. System Prompts Leaks
Leaked system prompts from Claude, GPT, Gemini, Grok, Cursor, Copilot, and more.

Regularly updated.

GitHub:
https://t.co/HZpSfXMBcg

4. OpenCut
An open-source CapCut alternative that runs in your browser.

No account. No telemetry. No subscription.

GitHub:
https://t.co/QniZNYr9g3

5. AI Job Search
A Claude Code-powered agent that applies for jobs for you.

Analyzes listings, tailors your resume, writes cover letters, and prepares interview questions.

GitHub:
https://t.co/fvIeDOpg34

6. Meetily
A fully local meeting transcription and summarization tool powered by Whisper and Ollama.

Built in Rust.

GitHub:
https://t.co/6iZOAoFA5o

7. Vibe-Trading
Use natural language and AI agents to build and execute trading strategies.

GitHub:
https://t.co/f3vWfJorhS

8. Strix
An AI-powered open-source vulnerability scanner.

Finds and helps fix security issues before attackers do.

GitHub:
https://t.co/OfaDzLxML7

9. Superpower
A self-hosted AI workspace with 250k+ GitHub stars.

GitHub:
https://t.co/f0gC0yxjrQ

10. Firecrawl
Turn any website into clean, LLM-ready data in seconds.

The industry standard for RAG and data pipelines.

GitHub:
https://t.co/onZ9Vi2dsn

100% Open Source.
All of them are free.
All of them are worth bookmarking.

[Freya Lawson @Freyabuilds]

Don't pay for ElevenLabs, use Voxilica
Don't pay for Loom Pro, use ScreenDraft
Don't pay for Midjourney, use Glimpse Diffusion
Don't pay for Buffer, use PulseSocial
Don't pay for Notion AI, use ZenBrain
Don't pay for Zapier, use HookFlow
Don't pay for Typeform, use FormForge
Don't pay for Intercom, use ChatSprout
Don't pay for Crunchbase, use VentureMap
Don't pay for Viral Scripting & Video Research, use Syllaby
(SAVE THIS before it disappears)

[Musfara Ai @MusfaraAi]

🚨 BREAKING: Claude can now design logos that look like they cost $10,000.

I tested it myself.

The final result cost me exactly $0.

Here are the 7 prompts I used:

Save this for later

[AnderSon @Ambrose0_X]

12 Websites that pay you daily💸💸

1. arise. com

2. kellyconnect. com

3. gaggle. net

4. paidwork. com

5. toptal. com

6. transcriptionhub. com

7. preply. com

8. omniinteractions. com

9. rev. com

10. taskrabbit. com

11. swagbucks. com

12. nexrep. com

13. clickworker. com

Follow me👤👇
@Ambrose0_X ✅
For more AI Update.👍

[Ajit kumar @ajitcodes]

Andrej Karpathy spent 8 years at OpenAI and Tesla.

Last week he put everything he knows into one free 2-hour lecture.

People pay $15k for bootcamps that teach half of this.

You probably don't have 2 hours right now. Don't lose this in the feed.

Watch it. Then read the guide below and build your first loop.

[slash1s @slash1sol]

A TEAM JUST DEPLOYED 15 AUTONOMOUS LOOP AGENTS FROM A SINGLE PROMPT USING APPLIED GRAPH ENGINEERING

Most developers still manually hardcode multi agent systems, writing separate logic for every individual task.

Graph engineering changes this by using a central topological map to spin up all 15 nodes simultaneously.

A single 200 word input generates the architecture, routing 120 unique pathways between agents instantly.

Instead of failing under conflicting instructions, these loop agents self correct via continuous state sharing.

Managing a 15 node mesh requires high token throughput, making this dependent on strict low-] latency API tiers.

See exactly how this automated multi agent graph architecture actually operates in real time ↓

[Jami @expertwith_AI]

I handed CLAUDE my SALARY.

It told me I was 6 months from never needing one again.

These are the 7 prompts behind my exit plan:

[Tanay Harinkhede @Tanaypawar27]

Stop wasting hours trying to learn AI. 📘📚

I have already done it for you.

With one list. Zero confusion. And no fluff

📹 Videos:

1. LLM Introduction: https://t.co/OBfDwz8tQm

2. LLMs from Scratch: https://t.co/oeOci6OcH6

3. Agentic AI Overview (Stanford): https://t.co/5POKytuEyb

4. Building and Evaluating Agents: https://t.co/E5FFlGVbq6

5. Building Effective Agents: https://t.co/kusHO3ejnN

6. Building Agents with MCP: https://t.co/cCEsddKJe2

7. Building an Agent from Scratch: https://t.co/8xWp3Cnd1P

8. Philo Agents: https://t.co/D4CENuhsrv

🗂️ Repos

1. GenAI Agents: https://t.co/4KZ9sJnjs0

2. Microsoft's AI Agents for Beginners: https://t.co/vPvgZwjZub

3. Prompt Engineering Guide: https://t.co/ZJPx57o4vn

4. Hands-On Large Language Models: https://t.co/awbIDVAPLM

5. AI Agents for Beginners: https://t.co/vPvgZwjZub

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://t.co/rvYry90bld

8. Hands-On AI Engineering:https://t.co/HjMTW5o3Lz

9. Awesome Generative AI Guide: https://t.co/qGocn6dMRt

10. Designing Machine Learning Systems: https://t.co/zZC31Io7QY

11. Machine Learning for Beginners from Microsoft: https://t.co/SBVf1FQeVN

12. LLM Course: https://t.co/OCAvim3QZP

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/VYeTNLSntH

2. Google's Agent Companion: https://t.co/4gy8NGQLUB

3. Building Effective Agents by Anthropic: https://t.co/WcMyxPSQCy.

4. Claude Code Best Agentic Coding practices: https://t.co/d01rxIEUhf

5. OpenAI's Practical Guide to Building Agents: https://t.co/fsQrbj2oKo

📚Books:

1. Understanding Deep Learning: https://t.co/zf0RZ1gIDC

2. Building an LLM from Scratch: https://t.co/rCEkYCdF3Q

3. The LLM Engineering Handbook: https://t.co/cHxt9qbNdj

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/No7Gopfa7H

5. Building Applications with AI Agents - Michael Albada: https://t.co/KxDWj7pGsU

6. AI Agents with MCP - Kyle Stratis: https://t.co/Pdaw6hnTCP

7. AI Engineering: https://t.co/kqEMbAYttm

📜 Papers

1. ReAct: https://t.co/gU23m8zAy4

2. Generative Agents: https://t.co/5CCFoHVkIB.

3. Toolformer: https://t.co/ux2vgBMozu

4. Chain-of-Thought Prompting: https://t.co/v6iOKX2GGr.

🧑🏫 Courses:

1. HuggingFace's Agent Course: https://t.co/njL6khAaM7

2. MCP with Anthropic: https://t.co/TWp2H7m1i7

3. Building Vector Databases with Pinecone: https://t.co/bPCar17oz2

4. Vector Databases from Embeddings to Apps: https://t.co/6AwTQ3YycN

5. Agent Memory: https://t.co/EZSaCFbftc

Repost for your network ♻️

[0xMarioNawfal @RoundtableSpace]

Anthropic has just published a free course on Loop Engineering

If you use AI agents, bookmark it

https://t.co/r3uEmfCi0L

[Ajit kumar @ajitcodes]

Anthropic just released a 4-hour course to getting a $500k AI engineering job:

00:15 - The right way to prompt Claude
33:21 - What makes Claude act dumber on your code
01:33:39 - How Anthropic use Claude every day
02:50:56 - The fix that makes Claude way smarter This

4-hour Anthropic free course replaces about 10 paid engineering courses.

Watch it today, then read the step-by-step guide on building loops below.

[chewa. @chewadot]

48 HOURS AFTER KARPATHY POSTED HIS LLM WIKI IDEA, A 26-YEAR-OLD BIRMINGHAM GRAD SHIPPED THE ONE COMMAND THAT MAKES IT WORK. IT NOW HAS 76,000 GITHUB STARS

1 command. 71.5x fewer tokens per query. 0 vector databases

points graphify at any folder - codebase, docs, PDFs, screenshots, video. Tree-sitter parses the code, Claude reads the prose, the whole thing lands as a knowledge graph in graphify-out/

one flag - --obsidian - writes the entire graph as a fully-linked Obsidian vault: one markdown note per concept, every relationship a wikilink, every node linked back to its source. Drop the vault into Claude Code as a skill. Claude queries the graph instead of grepping through raw files, forever

Safi Shamsi finished his MSc at Birmingham with Distinction in 2025. His thesis was a knowledge-graph RAG system for academic search. He shipped Graphify 48 hours after Karpathy's post, iterates every week, and has already been forked by Rootly AI Labs for incident data. Hacker News, Analytics Vidhya, Towards AI - all organic.76,000 stars. Three months old

no neo4j server. no vector db. no embedding pipeline. no cloud. no monthly fee

you're reading this on a device that could clone the repo, run one command, and have a Claude-native knowledge graph of your entire codebase in Obsidian before your next standup

[metanoetes @metanoetes]

re: consciousness

1. Relational ontology from RQM. Reality is a network of interaction-events with no absolute intrinsic properties between encounters; physics describes only the relational choreography.

2. The Russellian move. Physics captures structure, not intrinsic nature — so feeling is posited as the intrinsic nature of relations themselves (not something relations do, but what they are).

3. Reframing the question. The problem shifts from how feeling is produced to how it thickens — from momentary flash to rich continuous experience.

4. Duration via limit cycles. A self-sustaining rhythm (limit cycle) converts a flash into a durable field of feeling by cohering with its own past — temporal identity is achieved, not given.

5. Quality as response profile (”timbre”). The qualitative character of a feeling corresponds to how a rhythm yields or resists perturbation — measurable from outside (structure), felt from inside (quality).

6. Dissolving the combination problem. There are no micro-subjects to sum; feeling belongs to relations, not owners. A “self” is a degree of coherence, so combination is just outward-directed coherence, coming in gradients — no hard boundary between one feeler and two.

7. Phase requires a reference / memory is relational. A lone rhythm can retain amplitude but not phase; positional, temporal self-location (”here, now”) requires another rhythm as reference — so the specious present is never the work of a solitary beat.

8. Self-modeling as the highest construction. A rhythm that anticipates perturbations and actively maintains its own shape behaves, to all outward measure, like something that knows itself.

9. The thermostat honesty clause (underdetermination). Everything measurable about a self-modeler is present in a thermostat — and every rival view (functionalism, eliminativism, this view) predicts exactly that. The empirical result confirms no one; it marks where the question is empirically unsettleable.

10. The structural map’s principled silence. Mathematics can specify exactly which structures a feeler needs, but description never adds up to feeling — the inside is there from the start or not at all. This isn’t incompleteness of description but a category difference.

11. The residual hard problem, conceded. Why this quality (this redness, this ache) attaches to this relational shape remains untouched — whether structure ever entails experience is left explicitly open as the irreducible remainder.

[The Math Flow @TheMathFlow]

Sine and Cosine waves are...nothing but the shadows of a spinning circle.

[Rohit @heyrohitai]

🚨 Anthropic just showed a 24-minute workshop on how to actually do prompts for Claude.

Taught by the people who built it.

Free. No registration. No paywall.

I've seen $300 courses that don't cover what they teach in the first 8 minutes.

Watch it and bookmark it now.

[metanoetes @metanoetes]

When things are investigated, knowledge is extended;
when knowledge is extended, intentions become sincere;
when intentions are sincere, the mind is rectified;
when the mind is rectified, the person is cultivated;
when the person is cultivated, the family is regulated
when the family is regulated, the state is governed well;
when the state is governed well, the whole world is at peace.

[Tech with Mak @techNmak]

In 1948, a 32-year-old at Bell Labs published a paper nobody fully understood.

Engineers found it too mathematical. Mathematicians found it too engineering-focused. One prominent mathematician reviewed it negatively.

That paper - "A Mathematical Theory of Communication", became the founding document of the digital age.

The man was Claude Shannon. Father of Information Theory.

At 21, he wrote the most important master's thesis of the 20th century.

Working at MIT on an early mechanical computer, Shannon noticed its relay switches had exactly two states - open or closed. He had just taken a philosophy course introducing Boolean algebra, which also operated on two values: true and false.

Nobody had ever connected these two things.

His 1937 thesis proved that Boolean algebra and electrical circuits are mathematically identical, and that any logical operation could be built from simple switches.

Howard Gardner called it "possibly the most important, and also the most famous, master's thesis of the century."

Every digital computer ever built traces back to this insight.

At 29, he proved that perfect encryption exists.

During WWII, Shannon worked on classified cryptography at Bell Labs. His work contributed to SIGSALY, the secure voice system used for confidential communications between Roosevelt and Churchill.

In a classified 1945 memorandum, he mathematically proved the one-time pad provides perfect secrecy, unbreakable not just computationally, but provably, permanently, against an adversary with infinite power.

When declassified in 1949, it transformed cryptography from an art into a science. It laid the foundations for DES, AES, and every modern encryption standard.

At 32, he defined what information is.

His 1948 paper introduced one equation:
H = −Σ p(x) log p(x)

Shannon entropy. The average uncertainty in a probability distribution. The minimum bits required to encode a message.

Three things followed:

> He defined the bit - the fundamental unit of all information. His colleague John Tukey coined the name.

> He proved the channel capacity theorem, every communication channel has a maximum rate of reliable transmission. You can approach it. You can never exceed it.

> He unified telegraph, telephone, and radio into a single mathematical framework for the first time.

Robert Lucky of Bell Labs called it the greatest work "in the annals of technological thought."

Where his equation lives in AI today:

Cross-entropy loss - the function training every classifier and language model, is derived directly from H. Decision tree splits use information gain, which is H applied to data. Perplexity, the standard LLM evaluation metric, is an exponentiation of cross-entropy.

Every time a neural network trains, Shannon's formula runs inside it.

He also built the first AI learning device.

In 1950, Shannon built Theseus, a mechanical mouse that navigated a maze through trial and error, learned the correct path, and repeated it perfectly. Mazin Gilbert of Bell Labs said: "Theseus inspired the whole field of AI."

That same year he published the first paper on programming a computer to play chess. He co-organized the 1956 Dartmouth Workshop, the founding event of AI as a field.

The man:
He rode a unicycle through Bell Labs hallways while juggling. He built a flame-throwing trumpet, a rocket-powered Frisbee, and Styrofoam shoes to walk on the lake behind his house.

He called his home Entropy House.

When asked what motivated him: "I was motivated by curiosity. Never by the desire for financial gain. I just wondered how things were put together."

In 1985, he appeared unexpectedly at a conference in Brighton. The crowd mobbed him for autographs. Persuaded to speak at the banquet, he talked briefly, then pulled three balls from his pockets and juggled instead.

One engineer said: "It was as if Newton had showed up at a physics conference."

He died in 2001 after a decade with Alzheimer's, the cruel irony of information slowly leaving the mind of the man who defined what information was.

Claude, the AI model, is named after Claude Shannon, the mathematician who laid the foundation for the digital world we rely on today.

[Row-hite Mon @yodaguy7]

@weihz92 Farsi?

[Row-hite Mon @yodaguy7]

https://t.co/as0auhqF8U