Meet the people behind the posts.

0xAA

WTF Academy founder and Web3 educator

@0xAA_Science

Builds open-source learning materials for Solidity and ethers.js, and works on xAPI, an API gateway for AI agents.

Addy Osmani

Engineer working on Claude Code at Anthropic

@addyosmani

Practical ways to plan, inspect and test software made with coding agents, from a small change to a team's development process.

Amjad Masad

Replit founder and CEO

@amasad

The product and infrastructure thinking behind Replit's move from a coding editor to building software through natural language.

Andrej Karpathy

AI researcher and educator

@karpathy

Explains how language models change software development, from his original description of vibe coding to experiments where an agent changes training code and measures the result.

asta

Crypto trader following AI-token catalysts

@astaso1

Builds bullish token theses around AI products and app incentives. His KLED article connects data collection, user rewards and token demand for an asset he says he holds.

Boris Cherny

Claude Code creator at Anthropic

@bcherny

Explains the design of a coding agent and shares how he uses it in everyday work, including project instructions, reusable commands and ways to verify a change.

Chip Huyen

Computer scientist and author of AI Engineering

@chipro

Explains how to turn foundation models into applications, with attention to tools, planning, evaluation and the cost of running the system.

Chris Olah

Anthropic co-founder and interpretability researcher

@ch402

Studies the internal features and circuits of neural networks, connecting visual explanations to experiments on why a particular model produces a particular response.

Clips

Community-token promoter and AI-project commentator

@clipzfnf

Wrote about Eric Hartford's Samantha companion model and announced a community token initiative intended to support its development through trading fees.

Cryptoboys27

Crypto investor writing about revenue and token narratives

@0xCB27

Discusses revenue-producing crypto projects, blockchain data for AI agents and memecoin narratives.

dunks411

AI-culture humorist

@AdeptCamp

Uses one-line jokes to react to AI-model hype and the rapid arrival of new agent products.

Eugene Yan

Applied AI engineer and creator of AlignEval

@eugeneyan

Explains product-specific evaluations through labeled data, calibrated model judges and repeatable experiments, with an app you can inspect.

François Chollet

AI researcher; co-founder of Ndea and ARC Prize

@fchollet

Created Keras and ARC, and examines how to distinguish learned task performance from the ability to solve unfamiliar problems.

Geoffrey Huntley

Software developer; creator of the Ralph technique

@GeoffreyHuntley

Explores coding agents as repeatable systems, from the Ralph loop to a Lisp application that acquires new capabilities through conversation.

Builds software for running language and speech models on your own hardware. His project documentation connects a model download to a working local program.

Hamel Husain

Machine learning engineer and AI evaluation educator

@HamelHusain

Shows how to inspect real application traces, define useful failure criteria and check automated evaluators against domain experts.

Harrison Chase

LangChain cofounder

@hwchase17

Explains the surrounding software that lets a model take actions: agent loops, durable execution, memory and the context passed between workers.

Illia Polosukhin

NEAR co-founder and NEAR AI founder

@ilblackdragon

Blockchain and AI design, from coauthored machine-learning papers to NEAR's approach to programmable money.

Jason Liu

Creator of Instructor; AI engineer

@jxnlco

Connects structured model outputs and retrieval design to measurable application quality, with practical ways to inspect data and improve search.

Jason Wei

AI researcher at Meta Superintelligence Labs

@_jasonwei

Coauthored research on prompting models to produce intermediate reasoning steps and on testing whether short factual answers are correct.

Jerry Liu

LlamaIndex cofounder

@jerryjliu0

Connects document parsing and retrieval to structured workflows that apply business rules and update other systems.

Johann Rehberger

AI security researcher; author of Embrace The Red

@wunderwuzzi23

Investigates how instructions hidden in webpages, code and tool responses can redirect AI agents, with concrete attack reports and the permissions that made them possible.

Kai Greshake

AI security researcher at NVIDIA

@KGreshake

Explains indirect prompt injection: an attacker places instructions in material an AI application retrieves, then uses the application’s capabilities to influence its behavior.

Lawyered

Business lawyer and legal AI tool builder

@BitGrateful

Builds open-source AI tools for legal work, including cLawyer and a connector for Clio’s practice-management software.

Lilian Weng

Machine learning researcher; author of Lil’Log

@lilianweng

Connects reasoning and reinforcement learning methods to their failure modes, including models that improve an evaluator's score without completing the intended task.

Mark Rizzn Hopkins

AI agent developer and technology writer

@rizzn

Builds self-hosted agent systems and writes about persistent memory, task coordination and the path from an early experiment to the Sanctum stack.

Max Woolf

AI developer and writer

@minimaxir

Publishes detailed image-generation experiments with prompts, outputs and failed constraints. His articles help turn a visual idea into instructions that can be checked against the resulting image.

Mnewbis

Crypto and AI humorist

@Mnewbis

Makes brief jokes about crypto's hunger for a bull run and the everyday awkwardness of AI, including a Grok group-chat punchline.

Monty

Crypto-AI product and market writer

@monty_xbt

Connects AI products with crypto-market narratives, examining agent memory, product economics and the role of a project's token.

Nathan Lambert

Post-training researcher; co-founder and executive director of Trillium Labs

@natolambert

Explains how training examples, preference comparisons and verifiable rewards turn a pretrained language model into a more useful assistant.

Neel Nanda

Google DeepMind mechanistic interpretability team lead

@NeelNanda5

Explains how to inspect and intervene on model internals, separating a useful hypothesis about a mechanism from evidence that a causal test supports it.

Noam Brown

Research scientist at OpenAI

@polynoamial

Researches reasoning, reinforcement learning and multi-agent systems, with earlier coauthored work on poker search and language-guided negotiation.

Patrick Esser

Image and video generation researcher

@pess_r

Coauthors research on directing visual generation with an existing image or video. His papers separate the desired appearance from the structure and continuity that an edit should preserve.

Peter Steinberger

OpenClaw creator; agent builder at OpenAI

@steipete

Publishes detailed accounts of building applications with coding agents and the tools that grew out of that work, including OpenClaw and Oracle.

A solo developer's account of building products with coding agents, remote development machines and interactive previews.

Player1Taco

RePay Loans founder and AI infrastructure discussion host

@Player1Taco

Shares the process of building RePay Loans and hosts conversations about AI agents, decentralized compute and the infrastructure around them.

profit | eca.eth

Crypto market commentator and satirist

@FlippingProfits

Questions launchpad hype with blunt market comments and jokes about feature comparisons and asking Claude to build the next version.

Riley Brown

AI educator and app-building creator

@rileybrown

Explains how a small app can connect AI services to a specific user problem, and how to demonstrate the result.

Robin Rombach

Cofounder and CEO of Black Forest Labs

@robrombach

Connects image-model foundations to research on editing with visual references. His coauthored papers offer a path from latent diffusion to maintaining a subject through successive image changes.

Sander Dieleman

Research scientist at Google DeepMind

@sedielem

Explains how generative models move from noise to an image and how guidance changes that path. His illustrated essays connect a model control to both stronger conditioning and visible distortions.

Sasha Rush

ML researcher at Cursor

@srush_nlp

Makes model research inspectable through annotated code and training reports, from a Transformer implementation to the training and evaluation of a coding model.

Sebastian Raschka

LLM research engineer and author

@rasbt

Explains language models through small implementations, connecting tokens and attention to the code for building, training and adapting a GPT-style model.

shaimo

Technology and product commentator

@shaimo

Comments on autonomous driving and the trust people place in consumer-app interfaces.

Shunyu Yao

Language-agent researcher; coauthor of ReAct and τ-bench

@ShunyuYao12

Studies how models alternate reasoning with actions, then tests whether tool-using agents complete requests consistently across conversations.

Simon Willison

Independent developer; creator of Datasette and LLM

@simonw

Turns experiments with coding agents into explanations of what to build, how to inspect the result and how to keep learning from code a model wrote.

Stella Biderman

AI researcher and executive director of EleutherAI

@BlancheMinerva

Studies how model behavior develops during training, using public checkpoints and controlled experiments on memorization and capabilities.

Steve Yegge

Independent engineer, writer and creator of Beads and Gas Town

@Steve_Yegge

Persistent task tracking and coordination for coding agents that outgrow a single chat session.

Thomas Wolf

Hugging Face cofounder and Chief Science Officer

@Thom_Wolf

Connects open model research and software to questions about what an AI benchmark measures, with work on model compression and scientific reasoning.

Tim Brooks

Media generation lead at Meta Superintelligence Labs

@_tim_brooks

Researches generating and editing images and video. His coauthored projects expose the difference between following an editing instruction and keeping a scene coherent over time.

Tim Dettmers

Assistant professor at CMU; research scientist at Ai2

@Tim_Dettmers

Studies how to fit and adapt large models with less memory. His explanations separate reduced-precision inference from training a small set of additional parameters.

Top Tick Crypto

AI product and crypto-market commentator

@toptickcrypto

Comments on AI products, private inference and the economics of generating model outputs, alongside crypto-market discussion.

Tri Dao

Chief Scientist at Together AI; ML systems researcher

@tri_dao

Studies how model algorithms and GPU hardware fit together, with coauthored explanations of faster attention and alternatives to a growing attention cache.

tut™

Digital artist and AGNTSocial builder

@Tuteth_

Connects digital art with onchain AI agents through AGNTSocial, and writes about the interfaces people use to interact with AI.

zhynx

Crypto and decentralized-AI commentator

@onlyzhynx

Writes about the idea of running large AI models across distributed consumer GPUs and responds to developer demonstrations of decentralized inference.

潜水观察员 (Connectfarm1)

Chinese-language market writer and trading-tool builder

@connectfarm1

Writes about onchain markets and builds monitoring tools. His Code Vault collects trading scanners, wallet alerts and experiments with AI trading.