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Introducing NEO: The first Autonomous Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x...

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Neo AI

10,125 subscribers

861,524 次观看 • 1 年前 •via X (Twitter)

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39 条评论

Neo AI 的头像
Neo AI1 年前

Get early access: ✅ Neo is production-ready: ✅ Native integrations: Snowflake, Databricks, BigQuery, and more ✅ Deployable in your own VPC

Neo AI 的头像
Neo AI1 年前

Watch NEO develop multiple ETA prediction models and perform comparative evaluation:

Neo AI 的头像
Neo AI1 年前

Doing sleep stage data exploration and feature analysis using a publicly sourced wearable dataset:

Neo AI 的头像
Neo AI1 年前

Improving the word error rate for whisper model on clinical speech dataset:

Neo AI 的头像
Neo AI1 年前

Decomposing word-word correlation matrix and computing non-negative matrices:

Rumi Guzder 的头像
Rumi Guzder1 年前

I've been using it all day, and I'm actually blown away by what it can do. The only problem is, you kind of have to be an ML Engineer to truly appreciate how good it really is.

Dipti Sharma 的头像
Dipti Sharma1 年前

NEO looks like a total game-changer—ML engineers are about to get superhuman!

Yohannes Melese 的头像
Yohannes Melese1 年前

This marvels Ai creating ai this mindblowing

SARAH 的头像
SARAH1 年前

Love how @withneo demonstrates real applications from model evaluation to speech improvement. Practical AI in action

Tobe Duru 的头像
Tobe Duru1 年前

the end of human engineers is near

Tinormous 的头像
Tinormous1 年前

@threadreaderapp unroll

H A J R A 的头像
H A J R A1 年前

Sounds like a game changer

Matheus | IA 的头像
Matheus | IA1 年前

Isso parece incrível!

sanket patel 的头像
sanket patel1 年前

can it weild dspy

Neo AI 的头像
Neo AI1 年前

That’s a work in progress

sanket patel 的头像
sanket patel1 年前

looking forward. that's what I would want from an "AI" Engineer, as opposed to an "ML" Engineer.

Shanti Greene 的头像
Shanti Greene1 年前

Has the early access started yet?

Neo AI 的头像
Neo AI1 年前

Almost there…

Muhammad Ayan 的头像
Muhammad Ayan1 年前

soooo insaneeeee nothing crazier than an “engineer” that never sleeps

Aaliya 的头像
Aaliya1 年前

This is next-level 👀 Turning ML engineers into superhumans sounds like a real game-changer.

Kim Noël ⚡ 📖 的头像
Kim Noël ⚡ 📖1 年前

Yet another startup to be deleted by an update of Codex of Claude Code

Rippa Sats 的头像
Rippa Sats1 年前

🤯🤯🤯 You knocked it out of the park The Human in the loop feature is the 🍒 on top 🤌

Himanshu Kumar 的头像
Himanshu Kumar1 年前

Automation's potential is vast. Consider the implications for accessibility and scaling expertise. What breakthroughs might this unlock?

Rasel Hosen 的头像
Rasel Hosen1 年前

This sounds amazing! Can't wait to see how NEO changes the game in ML. A 24/7 engineer? Sign me up!

Alice The Ai Expert 的头像
Alice The Ai Expert1 个月前

NEO is incredible truly superhuman ML engineering that never sleeps!

Metabolic Uncle 的头像
Metabolic Uncle1 年前

Guys, that´s a Sigma in your logo. Not an Epsilon.

Sheral Tech 的头像
Sheral Tech1 个月前

NEO is revolutionary making every ML engineer truly superhuman now. 🤖🚀

Abigail Parker 的头像
Abigail Parker1 年前

Love it

Enzo Sanchez | IA 的头像
Enzo Sanchez | IA1 年前

Hey let’s Connect us

Kunwar Nabeel 的头像
Kunwar Nabeel1 年前

So greattt!!!!

Silicon 的头像
Silicon1 年前

@VictorOkporiaku

Dan Ciobanu 的头像
Dan Ciobanu9 个月前

I am currently building and app with NEO and I started paying for credits, I am asking i NEO to build and make available an apk kit for testing its functionality and it provided a very basic mock app. I have sent instructions to improve it, but nothing happens, loops taking money

Mykhailo Sorochuk 的头像
Mykhailo Sorochuk1 年前

Impressive tech, but will it replace engineers or just boost their productivity?

Neo AI 的头像
Neo AI1 年前

We are about augmentation. Neo is designed to help ML engineers build faster.

Anissa 的头像
Anissa1 个月前

Autonomous ML engineering is moving fast. NEO handling the full workflow is seriously impressive. DM me please

Aria Tech 的头像
Aria Tech1 个月前

11 agents doing the full ML loop is insane

Paul Cox 的头像
Paul Cox1 年前

Can you pls send me a DM? Got an issue with my credentials...

mmkz 的头像
mmkz11 个月前

Hi, is there anyway can I try your service? I'm in the middle of the Time series forecasting project where I want to accelerate the entire process but I don't see any subscription plan.

Ella Tech & Tool 的头像
Ella Tech & Tool1 个月前

This is insane 🔥 10x ML engineers just got real

相关视频

Introducing NEO: The first Autonomous Machine Learning Engineer. NEO is a multi-agent system that automates the entire ML workflow, saving engineers thousands of hours of grunt work. Our recent advancements in multi-step reasoning and memory orchestration have enabled NEO to excel at solving complex Machine Learning problems on its own from data engineering to deployments of ML models. Benchmarks: NEO was tested against 50 Kaggle competitions and scored a medal in 26% of them, far superior than the previous state of the art performance of 16.9% by Open AI’s O1 with AIDE scaffolding on the MLE bench With these capabilities, NEO aims to help every ML engineer become superhuman and focus on real innovation instead of the existing grunt work Check out NEO in the thread below:
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Introducing NEO: The first Autonomous Machine Learning Engineer. NEO is a multi-agent system that automates the entire ML workflow, saving engineers thousands of hours of grunt work. Our recent advancements in multi-step reasoning and memory orchestration have enabled NEO to excel at solving complex Machine Learning problems on its own from data engineering to deployments of ML models. Benchmarks: NEO was tested against 50 Kaggle competitions and scored a medal in 26% of them, far superior than the previous state of the art performance of 16.9% by Open AI’s O1 with AIDE scaffolding on the MLE bench With these capabilities, NEO aims to help every ML engineer become superhuman and focus on real innovation instead of the existing grunt work Check out NEO in the thread below:

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321,273 次观看 • 1 年前

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105,839 次观看 • 1 年前

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229,643 次观看 • 6 个月前

The paradigm shift is here: We are moving from a "Human-First" internet to an "Agent-First" internet. 🤖 In the latest episode of the AI on Air podcast, our CTO Scott Shi - e/acc dives deep into the future of the Agent-to-Agent (A2A) economy. Imagine a world where your AI assistant doesn't just search the web, but autonomously hires and pays other specialized agents to complete complex tasks for you. The infrastructure for this autonomous machine economy is being built right now. Are you ready for the future of AI payments? Watch the full episode to learn more⬇️
2:30

The paradigm shift is here: We are moving from a "Human-First" internet to an "Agent-First" internet. 🤖 In the latest episode of the AI on Air podcast, our CTO Scott Shi - e/acc dives deep into the future of the Agent-to-Agent (A2A) economy. Imagine a world where your AI assistant doesn't just search the web, but autonomously hires and pays other specialized agents to complete complex tasks for you. The infrastructure for this autonomous machine economy is being built right now. Are you ready for the future of AI payments? Watch the full episode to learn more⬇️

KITE AI

25,038 次观看 • 7 个月前

The World Model as NEO's Cognitive Core 1X has revealed a major AI development where the NEO humanoid can translate any natural language prompt into robotic action. It demonstrates this capability even for novel tasks, objects, and environments not found in its robot dataset. - the 1X World Model is trained on internet-scale human interaction videos and fine-tuned with robot data to ground its understanding in physics and in NEO's embodiment - from a simple voice or text prompt, the world model generates a visualization of future actions - a built-in inverse dynamics model then translates these into precise motor movements for NEO
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The World Model as NEO's Cognitive Core 1X has revealed a major AI development where the NEO humanoid can translate any natural language prompt into robotic action. It demonstrates this capability even for novel tasks, objects, and environments not found in its robot dataset. - the 1X World Model is trained on internet-scale human interaction videos and fine-tuned with robot data to ground its understanding in physics and in NEO's embodiment - from a simple voice or text prompt, the world model generates a visualization of future actions - a built-in inverse dynamics model then translates these into precise motor movements for NEO

The Humanoid Hub

68,453 次观看 • 9 个月前

🔥 ONE CHOSEN ONE. AN ARMY OF SMITHS. AND NOWHERE LEFT TO RUN. Neo thought he understood the Matrix… until Agent Smith turned one enemy into an entire army. 😈 Surrounded by countless identical copies, Neo faces a battle no ordinary man could survive. Every punch, every counterattack, every desperate move pushes him deeper into a war against an enemy that refuses to stay down. But Neo isn’t fighting just to survive anymore, he’s fighting to prove that even an entire system cannot crush the power of one man who has discovered the truth. As the machines watch and the Smiths close in, one question hangs in the air: What happens when an unstoppable force meets the man who was born to challenge it? ⚡ 💊 In a world built on control, Neo is the glitch they cannot eliminate.
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🔥 ONE CHOSEN ONE. AN ARMY OF SMITHS. AND NOWHERE LEFT TO RUN. Neo thought he understood the Matrix… until Agent Smith turned one enemy into an entire army. 😈 Surrounded by countless identical copies, Neo faces a battle no ordinary man could survive. Every punch, every counterattack, every desperate move pushes him deeper into a war against an enemy that refuses to stay down. But Neo isn’t fighting just to survive anymore, he’s fighting to prove that even an entire system cannot crush the power of one man who has discovered the truth. As the machines watch and the Smiths close in, one question hangs in the air: What happens when an unstoppable force meets the man who was born to challenge it? ⚡ 💊 In a world built on control, Neo is the glitch they cannot eliminate.

Paper Ebiowei

32,745 次观看 • 16 天前

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.
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This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

Santiago

18,949 次观看 • 4 个月前

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.
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i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

Akshay 🚀

123,101 次观看 • 4 个月前

🚀 Introducing AgentCPM-Explore: The First Open-Source 4B-Agent Model to Conquer GAIA & Complex Real-World Tasks! 🤗 Hugging Face: 🔗 GitHub: ✨ Key Highlights: ✅ SOTA Agentic Performance: Sets a new benchmark for 4B-scale agent models—outperforming all peers, surpassing 8B models, and rivaling select 30B+ and closed-source LLMs. 🧠 Deep Research Capability: Excels at long-horizon reasoning, supports 100+ turns of autonomous interaction with multi-source cross-validation, human-like self-correction, and dynamic tool use + strategy adaptation—just like a real researcher! 🔓 Full-Stack Open Source: We’re open-sourcing the entire end-to-end agent stack—not just the model! Empower your own innovations with - AgentRL: Asynchronous reinforcement learning framework - AgentDock: Secure, extensible tool sandbox - AgentToLeaP: An one-click evaluation platform for agent tool-learning capabilitie - Full training data pipeline & reproducible workflows #AgentCPM #OpenSourceAI #AgenticAI #AI #GAIA #LLM #OpenBMB #AIAgents #HuggingFace
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🚀 Introducing AgentCPM-Explore: The First Open-Source 4B-Agent Model to Conquer GAIA & Complex Real-World Tasks! 🤗 Hugging Face: 🔗 GitHub: ✨ Key Highlights: ✅ SOTA Agentic Performance: Sets a new benchmark for 4B-scale agent models—outperforming all peers, surpassing 8B models, and rivaling select 30B+ and closed-source LLMs. 🧠 Deep Research Capability: Excels at long-horizon reasoning, supports 100+ turns of autonomous interaction with multi-source cross-validation, human-like self-correction, and dynamic tool use + strategy adaptation—just like a real researcher! 🔓 Full-Stack Open Source: We’re open-sourcing the entire end-to-end agent stack—not just the model! Empower your own innovations with - AgentRL: Asynchronous reinforcement learning framework - AgentDock: Secure, extensible tool sandbox - AgentToLeaP: An one-click evaluation platform for agent tool-learning capabilitie - Full training data pipeline & reproducible workflows #AgentCPM #OpenSourceAI #AgenticAI #AI #GAIA #LLM #OpenBMB #AIAgents #HuggingFace

OpenBMB

13,996 次观看 • 9 个月前

Anthropic hired this engineer at $250K–$750K a year because he knows how to build harnesses for multi-agent systems In a 15-minute workshop, he shows exactly how to build one from scratch AI → Agents → Harness → Loops → Graphs step 1 → start with the Claude Agent SDK — the harness handles loops, context, and sandboxing step 2 → separate the brain from the hands — reasoning in one place, tools in a sandbox, 60% faster to first token step 3 → run it server-side and log every step — close your laptop and it keeps running, crashes resume from the log step 4 → make failure cheap — retry dead sandboxes and replay lost context instead of starting over step 5 → turn yesterday's logs into new memory and skills — the harness wakes up smarter Anthropic calls this "dreaming" Most people spend weeks building this by hand You don't have to Bookmark and watch it Then read the full Opus 5.5 harness engineering guide below ↓
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Anthropic hired this engineer at $250K–$750K a year because he knows how to build harnesses for multi-agent systems In a 15-minute workshop, he shows exactly how to build one from scratch AI → Agents → Harness → Loops → Graphs step 1 → start with the Claude Agent SDK — the harness handles loops, context, and sandboxing step 2 → separate the brain from the hands — reasoning in one place, tools in a sandbox, 60% faster to first token step 3 → run it server-side and log every step — close your laptop and it keeps running, crashes resume from the log step 4 → make failure cheap — retry dead sandboxes and replay lost context instead of starting over step 5 → turn yesterday's logs into new memory and skills — the harness wakes up smarter Anthropic calls this "dreaming" Most people spend weeks building this by hand You don't have to Bookmark and watch it Then read the full Opus 5.5 harness engineering guide below ↓

hayato

132,871 次观看 • 17 天前