Загрузка видео...

Не удалось загрузить видео

На главную

this is free f*cking gold for AI engineers 10 repos worth keeping, from python basics all the way to LLMs, agents and production AI. forget the random tutorials 1. Python-100-Days - 100 days through fundamentals, data analysis and web dev ↳ 2. Generative AI for Beginners - LLM basics,...

AnnatarXBT's profile picture

Annatar.md

1,858 subscribers

11,733 просмотров • 4 дней назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 14

Фото профиля ＿和歌山フリ―ター＿
＿和歌山フリ―ター＿4 дней назад

どのリポジトリが特に役立ったか教えてもらえますか？

Фото профиля Fajar M Reza
Fajar M Reza4 дней назад

Curated agent repositories are valuable when failure patterns accompany the success stories.

Фото профиля Paruchh
Paruchh4 дней назад

Bookmarking this asap

Фото профиля ALEXYZ
ALEXYZ4 дней назад

python basics to production ai in one list

Фото профиля Mr. Buzzoni
Mr. Buzzoni4 дней назад

Thanks for sharing

Фото профиля Brian Hadu
Brian Hadu3 дней назад

those 10 repos could really accelerate learning and spark new projects fast

Фото профиля 等一场遇见
等一场遇见3 дней назад

Keep it up

Фото профиля magsimich
magsimich4 дней назад

This is a crazy useful list

Фото профиля 林川OKX50饭佣
林川OKX50饭佣3 дней назад

这波不仅是干货简直是AI行业内参

Фото профиля 星河93范热币
星河93范热币3 дней назад

这才是弯道超车的硬核干货储备

Фото профиля AI Mastery Guide
AI Mastery Guide3 дней назад

Python 100 Days is legit

Фото профиля 林川OKX50高饭佣
林川OKX50高饭佣4 дней назад

这份清单质量很高 省下不少筛选的时间了

Фото профиля Chen
Chen4 дней назад

yeah. i keep underestimating how long the second half takes

Фото профиля WhinyPuppy🐶
WhinyPuppy🐶3 дней назад

Crazy useful! thanks for sharing

Похожие видео

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):
15:38

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):

Matt Dancho (Business Science)

35,452 просмотров • 1 год назад

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 01:45 - Build an LLM from scratch 17:22 - Learn how to actually use AI models 30:03 - Master prompt engineering 52:35 - Coordinate 100 agents as one human Most people are still learning how to prompt one model. Jeff Dean is showing the full stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating agent systems is the new one. This is 27 years of Google AI compressed into one hour. Bookmark and watch it today Then read the full Graph Engineering guide below ↓
1:06:12

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 01:45 - Build an LLM from scratch 17:22 - Learn how to actually use AI models 30:03 - Master prompt engineering 52:35 - Coordinate 100 agents as one human Most people are still learning how to prompt one model. Jeff Dean is showing the full stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating agent systems is the new one. This is 27 years of Google AI compressed into one hour. Bookmark and watch it today Then read the full Graph Engineering guide below ↓

Lunar

102,835 просмотров • 1 месяц назад

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 0% → 01:45 - build an LLM from scratch 30% → 17:22 - learn how to actually use AI models 60% → 30:03 - master prompt engineering 100% → 52:35 - coordinate 100 agents as one human Most people are still learning how to prompt a single model. Jeff Dean is showing the entire stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating entire agent systems is the next one. This is 27 years of Google AI compressed into a single hour. Bookmark it and watch today. Then read the full Graph Engineering guide below ↓
1:06:06

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 0% → 01:45 - build an LLM from scratch 30% → 17:22 - learn how to actually use AI models 60% → 30:03 - master prompt engineering 100% → 52:35 - coordinate 100 agents as one human Most people are still learning how to prompt a single model. Jeff Dean is showing the entire stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating entire agent systems is the next one. This is 27 years of Google AI compressed into a single hour. Bookmark it and watch today. Then read the full Graph Engineering guide below ↓

Lunar

25,126 просмотров • 22 дней назад

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, January 29th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (1570+ registered):
15:38

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, January 29th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (1570+ registered):

Matt Dancho (Business Science)

242,084 просмотров • 1 год назад

Don't waste 2 years learning to become an AI agentic engineer in 2026. Andrew Ng, the godfather of AI, gave the complete playbook to become one from scratch. 1 hour course. Free: • 00:00 - AI agent basics • 12:12 - AI Agentic workflows & design patterns • 53:27 - Practical tips for building AI agents • 1:20:30 - self-improving AI agent loops • 1:30:19 - multi-agent AI systems I watched it last night. Halfway through, I realized I could get into Anthropic in weeks, not years. Bookmark now. Watch it. Then build your own AI agent with the guide below.
1:45:51

Don't waste 2 years learning to become an AI agentic engineer in 2026. Andrew Ng, the godfather of AI, gave the complete playbook to become one from scratch. 1 hour course. Free: • 00:00 - AI agent basics • 12:12 - AI Agentic workflows & design patterns • 53:27 - Practical tips for building AI agents • 1:20:30 - self-improving AI agent loops • 1:30:19 - multi-agent AI systems I watched it last night. Halfway through, I realized I could get into Anthropic in weeks, not years. Bookmark now. Watch it. Then build your own AI agent with the guide below.

Roan

251,464 просмотров • 1 месяц назад

Don't waste 2 years learning to become an AI agentic engineer in 2026. Andrew Ng, the godfather of AI, gave the complete playbook to become one from scratch. 1 hour course. Free: • 00:00 - AI agent basics • 12:12 - AI Agentic workflows & design patterns • 53:27 - Practical tips for building AI agents • 1:20:30 - self-improving AI agent loops • 1:30:19 - multi-agent AI systems I watched it last night. Halfway through, I realized I could get into Anthropic in weeks, not years. Bookmark now. Watch it. Then build your own AI agent.
1:45:51

Don't waste 2 years learning to become an AI agentic engineer in 2026. Andrew Ng, the godfather of AI, gave the complete playbook to become one from scratch. 1 hour course. Free: • 00:00 - AI agent basics • 12:12 - AI Agentic workflows & design patterns • 53:27 - Practical tips for building AI agents • 1:20:30 - self-improving AI agent loops • 1:30:19 - multi-agent AI systems I watched it last night. Halfway through, I realized I could get into Anthropic in weeks, not years. Bookmark now. Watch it. Then build your own AI agent.

Dhairya Karekar

133,284 просмотров • 1 месяц назад

10 GitHub repos with ⭐ 1.2M+ combined stars that help you build your AI agent from scratch 1. n8n 201k⭐ - visual agent builder with 400+ integrations. Wire up multi-step AI workflows without writing a backend. 2. Hermes Agent 231k⭐ - the fastest-growing open-source agent framework of 2026. Self-improving skill loop, persistent memory across sessions. 3. Dify 152k⭐ - visual builder for agentic workflows and RAG pipelines, production-ready out of the box. 4. LangChain 144k⭐ - the standard framework for LLM apps. Chains, agents, retrieval, all in one place. 5. MetaGPT 70k⭐ - simulates an entire software company. PM, architect, engineer agents that build your app together. 6. AutoGen 60k⭐ - Microsoft's framework for multi-agent conversations and coordination. 7. OpenCode 198k⭐ - terminal coding agent, provider-agnostic across 75+ models. The go-to harness for agentic coding in 2026. 8. LlamaIndex 52k⭐ - connects your agents to your actual data. Indexing and retrieval done right. 9. CrewAI 57k⭐ - role-based crews of agents. Reads almost like plain English, no graph to learn. 10. LangGraph 40k⭐ - graph-based orchestration when you need precise control over agent state in production. Save this before you pay for another agent-building course 👇
1:08

10 GitHub repos with ⭐ 1.2M+ combined stars that help you build your AI agent from scratch 1. n8n 201k⭐ - visual agent builder with 400+ integrations. Wire up multi-step AI workflows without writing a backend. 2. Hermes Agent 231k⭐ - the fastest-growing open-source agent framework of 2026. Self-improving skill loop, persistent memory across sessions. 3. Dify 152k⭐ - visual builder for agentic workflows and RAG pipelines, production-ready out of the box. 4. LangChain 144k⭐ - the standard framework for LLM apps. Chains, agents, retrieval, all in one place. 5. MetaGPT 70k⭐ - simulates an entire software company. PM, architect, engineer agents that build your app together. 6. AutoGen 60k⭐ - Microsoft's framework for multi-agent conversations and coordination. 7. OpenCode 198k⭐ - terminal coding agent, provider-agnostic across 75+ models. The go-to harness for agentic coding in 2026. 8. LlamaIndex 52k⭐ - connects your agents to your actual data. Indexing and retrieval done right. 9. CrewAI 57k⭐ - role-based crews of agents. Reads almost like plain English, no graph to learn. 10. LangGraph 40k⭐ - graph-based orchestration when you need precise control over agent state in production. Save this before you pay for another agent-building course 👇

unicode

30,511 просмотров • 1 месяц назад

We’re officially a YOUnicorn! 🦄 Excited to share that You.com just raised $100M Series C at a $1.5B valuation, led by Cox Enterprises. We’ve been heads down building the search infrastructure for the AI and agent future. Soon there will be more AI agents using the web than humans, but today's search wasn't built for this. Agents need deep, contextual information from both public web and internal private data to make real decisions. Our web search API delivers the most up-to-date, accurate, and fastest search results for LLMs and agents. Real benchmarks show we consistently outperform the competition on accuracy and speed while staying cost-effective. Today, we’re answering over 1 billion queries every month across enterprises like DuckDuckGo, Windsurf, Harvey, and many others. There is no other startup of recent years at this scale. Grateful to our wonderful employees, customers, partners, and investors who believe in our vision. The agentic era is here - let's build it together. Try our LLM search APIs.
2:25

We’re officially a YOUnicorn! 🦄 Excited to share that You.com just raised $100M Series C at a $1.5B valuation, led by Cox Enterprises. We’ve been heads down building the search infrastructure for the AI and agent future. Soon there will be more AI agents using the web than humans, but today's search wasn't built for this. Agents need deep, contextual information from both public web and internal private data to make real decisions. Our web search API delivers the most up-to-date, accurate, and fastest search results for LLMs and agents. Real benchmarks show we consistently outperform the competition on accuracy and speed while staying cost-effective. Today, we’re answering over 1 billion queries every month across enterprises like DuckDuckGo, Windsurf, Harvey, and many others. There is no other startup of recent years at this scale. Grateful to our wonderful employees, customers, partners, and investors who believe in our vision. The agentic era is here - let's build it together. Try our LLM search APIs.

Richard Socher

63,660 просмотров • 1 год назад

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.
30:12

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

229,172 просмотров • 5 месяцев назад

🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.
0:28

🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.

Vikas gupta

18,053 просмотров • 18 дней назад

🚨Elon Musk:“In 6-12 months GrokBot will be the #1 tool for building agentic systems. In a year, 100% of code will be written by LLMs.” At SpaceXAI, more than 70% of engineers are already using GrokBot to build self-learning agentic systems. In a 40-minute talk, Elon sits down with SpaceXAI engineers and discusses what the future of AI engineering is really going to look like.This conversation is honestly worth more than 2 hours of any Stanford lecture on AI engineering. Watch it today, then go read the article below on how to build a full Grok bot agents team from scratch. Who’s ready to see the future of AI engineering from the people actually building it?
46:43

🚨Elon Musk:“In 6-12 months GrokBot will be the #1 tool for building agentic systems. In a year, 100% of code will be written by LLMs.” At SpaceXAI, more than 70% of engineers are already using GrokBot to build self-learning agentic systems. In a 40-minute talk, Elon sits down with SpaceXAI engineers and discusses what the future of AI engineering is really going to look like.This conversation is honestly worth more than 2 hours of any Stanford lecture on AI engineering. Watch it today, then go read the article below on how to build a full Grok bot agents team from scratch. Who’s ready to see the future of AI engineering from the people actually building it?

Hosna ⚖️ בניטה

16,111 просмотров • 1 месяц назад

Demis Hassabis, CEO of Google DeepMind: "Agents are the first step to AGI, and we're just getting started. One engineer with agents already does what took 1000 back in the 2000s." This is the shift you can't ignore anymore, unless you want to be left behind. 40 minutes of pure insight from a Nobel laureate on where AI is really headed. Watch it, then read the step by step guide for beginners on building agents below.
39:05

Demis Hassabis, CEO of Google DeepMind: "Agents are the first step to AGI, and we're just getting started. One engineer with agents already does what took 1000 back in the 2000s." This is the shift you can't ignore anymore, unless you want to be left behind. 40 minutes of pure insight from a Nobel laureate on where AI is really headed. Watch it, then read the step by step guide for beginners on building agents below.

Anatoli Kopadze

34,739 просмотров • 2 месяцев назад

We’re back for Episode 14 of TAO Talk 🚨 calanthia from Masa joins 563 and brody this week to chat about new subnets and AI agents sourcing intelligence from Bittensor! The group chats about: - $TAO -pilling AI/ML chads at NeurIPS Conference feat. const Crucible Labs Macrocosmos Manifold and Yuma - JJ teaming up with Cameron Fairchild to form Laτenτ Holdings, which will validate and help scale subnets - Crucible Labs drops a subnet analysis framework - Celium offering H100s cheaper than any other provider - Tao360 releases inaugural research report for their AI-enabled subnet analysis tool @notYourBananaa - Masa unveils the AI Agent Arena on SN59 /// Timestamps: 00:00 Intro 01:10 Subnet 42 and Agent Arena: Masa’s Subnets 03:00 Real-Time Data Networks for AI 04:20 How Masa is Building AI Agent Arenas Inspired by Gladiators 06:10 Decentralized AI and Bittensor: The Growing Ecosystem 08:15 AI Meets Web3: Masa’s Role in Revolutionizing Data Networks 10:00 The Future of AI Agents: Intelligent Societies and Real-Time Data 12:05 Why Masa Chose Bittensor 14:10 $TAO Incentives and the Future of AI Decentralization 16:25 Bittensor and the Rise of Agent Competition: Masa’s Perspective 18:00 Exploring AI Agent Societies 20:30 Creating Competitive AI Arenas: The Agent Arena Subnet Explained 23:00 Calanthia on the Challenges of Web3 AI Development 25:10 Bringing Web2 Developers into Web3: Lessons from Masa 27:30 The Evolution of AI: From Dumb Agents to Intelligent Societies 30:00 AI Agents as the Future of Interaction in Decentralized AI 34:00 The Agent Arena’s Vision: Competition, Incentives, and Innovation
37:00

We’re back for Episode 14 of TAO Talk 🚨 calanthia from Masa joins 563 and brody this week to chat about new subnets and AI agents sourcing intelligence from Bittensor! The group chats about: - $TAO -pilling AI/ML chads at NeurIPS Conference feat. const Crucible Labs Macrocosmos Manifold and Yuma - JJ teaming up with Cameron Fairchild to form Laτenτ Holdings, which will validate and help scale subnets - Crucible Labs drops a subnet analysis framework - Celium offering H100s cheaper than any other provider - Tao360 releases inaugural research report for their AI-enabled subnet analysis tool @notYourBananaa - Masa unveils the AI Agent Arena on SN59 /// Timestamps: 00:00 Intro 01:10 Subnet 42 and Agent Arena: Masa’s Subnets 03:00 Real-Time Data Networks for AI 04:20 How Masa is Building AI Agent Arenas Inspired by Gladiators 06:10 Decentralized AI and Bittensor: The Growing Ecosystem 08:15 AI Meets Web3: Masa’s Role in Revolutionizing Data Networks 10:00 The Future of AI Agents: Intelligent Societies and Real-Time Data 12:05 Why Masa Chose Bittensor 14:10 $TAO Incentives and the Future of AI Decentralization 16:25 Bittensor and the Rise of Agent Competition: Masa’s Perspective 18:00 Exploring AI Agent Societies 20:30 Creating Competitive AI Arenas: The Agent Arena Subnet Explained 23:00 Calanthia on the Challenges of Web3 AI Development 25:10 Bringing Web2 Developers into Web3: Lessons from Masa 27:30 The Evolution of AI: From Dumb Agents to Intelligent Societies 30:00 AI Agents as the Future of Interaction in Decentralized AI 34:00 The Agent Arena’s Vision: Competition, Incentives, and Innovation

TAO τalk 🥩🦍

24,929 просмотров • 1 год назад

Today, I am launching Paper Breakdown. - PBD gets you academic paper recommendations and lets you study CS/ML/AI research with LLM agents. - It highlights relevant sections directly in the actual PDF - generates flowcharts/illustrations too - we provide an in-build screenshot tool to send images to the agent directly from the paper. - we also got agentic paper search that allows you to search our database of 70,000+ CS and ML Arxiv papers in seconds using natural language. I have been building PBD for almost half a year - it all started as a means for me to keep up with research and use AI to produce visuals and scripts for my own YouTube videos. I have developed it enough to confidently recommend it to you. Visit our landing page to learn more.
2:28

Today, I am launching Paper Breakdown. - PBD gets you academic paper recommendations and lets you study CS/ML/AI research with LLM agents. - It highlights relevant sections directly in the actual PDF - generates flowcharts/illustrations too - we provide an in-build screenshot tool to send images to the agent directly from the paper. - we also got agentic paper search that allows you to search our database of 70,000+ CS and ML Arxiv papers in seconds using natural language. I have been building PBD for almost half a year - it all started as a means for me to keep up with research and use AI to produce visuals and scripts for my own YouTube videos. I have developed it enough to confidently recommend it to you. Visit our landing page to learn more.

AVB

14,579 просмотров • 8 месяцев назад

5 open-source AI repos that blew up on github this week → 1. MinerU (opendatalab) - ~69k★ turns any PDF or office doc into clean, LLM-ready markdown - the fix for garbage RAG inputs 2. voicebox (jamiepine) - ~35k★ open-source AI voice studio - clone a voice, dictate, create. the local ElevenLabs 3. ai-website-cloner-template (JCodesMore) - ~22k★ clone any website with one command using your AI coding agent - great for learning and prototyping 4. Anthropic-Cybersecurity-Skills (mukul975) - ~21k★ 800+ ready security skills for AI agents, mapped to MITRE ATT&CK and NIST - works with claude code, cursor, codex 5. agent-native (builderio) - ~3k★ a framework for building apps designed for AI agents from day one, not bolted on after one of these saves you a paid tool. bookmark before you forge
0:59

5 open-source AI repos that blew up on github this week → 1. MinerU (opendatalab) - ~69k★ turns any PDF or office doc into clean, LLM-ready markdown - the fix for garbage RAG inputs 2. voicebox (jamiepine) - ~35k★ open-source AI voice studio - clone a voice, dictate, create. the local ElevenLabs 3. ai-website-cloner-template (JCodesMore) - ~22k★ clone any website with one command using your AI coding agent - great for learning and prototyping 4. Anthropic-Cybersecurity-Skills (mukul975) - ~21k★ 800+ ready security skills for AI agents, mapped to MITRE ATT&CK and NIST - works with claude code, cursor, codex 5. agent-native (builderio) - ~3k★ a framework for building apps designed for AI agents from day one, not bolted on after one of these saves you a paid tool. bookmark before you forge

self.dll

65,079 просмотров • 2 месяцев назад

AI agents just got simple. Like really simple. Building AI agents is now accessible to everyone, without the need for coding, complex setups, or a full tech team. A groundbreaking platform is revolutionizing this narrative. Meet Carla Torres the easiest way to build, manage, and scale AI agents. What makes Airia different? It simplifies everything. ✔️ No technical background needed ✔️ Works with any model GPT, Claude, Gemini, Mistral & more ✔️ Enterprise-grade security ✔️ Built for creators, teams & companies Transform an idea into a functional AI agent in minutes. The part that amazed everyone Airia offers 2,500+ pre-built agent templates. Search. Pick one. Customize. No blank screens. No starting from scratch. Just instant value. This is why founders, creators, engineers, and enterprises are enthusiastically joining the Airia Community every day. Why it matters AI agents are shaping the future of work. Airia makes it easy, fast, and secure for anyone to bring AI to life. If AI agents are part of the 2025 vision, this platform is the perfect place to start. 🔗 Explore the Airia Community 2,500+ ready-to-use agents are waiting at #Airia #AIAgents #AIPlatform #AICommunity #AIForBusiness #AgenticAI #NoCodeAI
6:35

AI agents just got simple. Like really simple. Building AI agents is now accessible to everyone, without the need for coding, complex setups, or a full tech team. A groundbreaking platform is revolutionizing this narrative. Meet Carla Torres the easiest way to build, manage, and scale AI agents. What makes Airia different? It simplifies everything. ✔️ No technical background needed ✔️ Works with any model GPT, Claude, Gemini, Mistral & more ✔️ Enterprise-grade security ✔️ Built for creators, teams & companies Transform an idea into a functional AI agent in minutes. The part that amazed everyone Airia offers 2,500+ pre-built agent templates. Search. Pick one. Customize. No blank screens. No starting from scratch. Just instant value. This is why founders, creators, engineers, and enterprises are enthusiastically joining the Airia Community every day. Why it matters AI agents are shaping the future of work. Airia makes it easy, fast, and secure for anyone to bring AI to life. If AI agents are part of the 2025 vision, this platform is the perfect place to start. 🔗 Explore the Airia Community 2,500+ ready-to-use agents are waiting at #Airia #AIAgents #AIPlatform #AICommunity #AIForBusiness #AgenticAI #NoCodeAI

Aakash Verma

40,334 просмотров • 9 месяцев назад

8 GITHUB REPOS FOR BUILDING AN AI TRADING STACK Want to understand how autonomous trading systems are actually built? Start here. Not “AI predicts the next candle” bullshit. 8 repos covering agents, backtesting, exchange APIs, market making, RL and execution. 1. AI Hedge Fund Multi-agent setup where different agents analyze a trade from different angles before reaching a conclusion. 2. Freqtrade One of the biggest open-source crypto trading bots. Strategies, backtesting and automated execution. 3. CCXT The infrastructure layer for connecting to Binance, Bybit, OKX and dozens of other exchanges through one API. 4. NautilusTrader Trading infrastructure for research, backtesting and live execution. 5. Hummingbot Market making, arbitrage and liquidity automation. 6. Eliza Framework for autonomous agents that can interact with crypto wallets and onchain systems. 7. FinRL Reinforcement learning for financial trading research. 8. Jesse Python framework for developing and backtesting crypto trading strategies. The stack basically looks like: data → strategy → backtest → risk → execution → agent I’d treat these as a research starting point, not a money printer. Build locally. Test without real funds. Understand the system before giving an agent access to your wallet.
0:25

8 GITHUB REPOS FOR BUILDING AN AI TRADING STACK Want to understand how autonomous trading systems are actually built? Start here. Not “AI predicts the next candle” bullshit. 8 repos covering agents, backtesting, exchange APIs, market making, RL and execution. 1. AI Hedge Fund Multi-agent setup where different agents analyze a trade from different angles before reaching a conclusion. 2. Freqtrade One of the biggest open-source crypto trading bots. Strategies, backtesting and automated execution. 3. CCXT The infrastructure layer for connecting to Binance, Bybit, OKX and dozens of other exchanges through one API. 4. NautilusTrader Trading infrastructure for research, backtesting and live execution. 5. Hummingbot Market making, arbitrage and liquidity automation. 6. Eliza Framework for autonomous agents that can interact with crypto wallets and onchain systems. 7. FinRL Reinforcement learning for financial trading research. 8. Jesse Python framework for developing and backtesting crypto trading strategies. The stack basically looks like: data → strategy → backtest → risk → execution → agent I’d treat these as a research starting point, not a money printer. Build locally. Test without real funds. Understand the system before giving an agent access to your wallet.

TeddyCEO

145,658 просмотров • 4 дней назад

At first, prompting seemed to be a temporary workaround for getting the most out of large language models. But over time, it's become critical to the way we interact with AI. On the Lightcone Podcast, Garry, Harj, Diana, and Jared break down what they've learned from working with hundreds of founders building with LLMs: why prompting still matters, where it breaks down, and how teams are making it more reliable in production. They share real examples of prompts that failed, how companies are testing for quality, and what the best teams are doing to make LLM outputs useful and predictable. 0:58 - Parahelp’s prompt example 4:59 - Different types of prompts 6:51 - Metaprompting 7:58 - Using examples 12:10 - Some tricks for longer prompts 14:18 - Findings on evals 17:25 - Every founder has become a forward-deployed engineer (FDE) 23:18 - Vertical AI agents are closing big deals with the FDE model 26:13 - The personalities of the different LLMs 27:26 - Lessons from rubrics 29:47 - Kaizen and the art of communication
31:25

At first, prompting seemed to be a temporary workaround for getting the most out of large language models. But over time, it's become critical to the way we interact with AI. On the Lightcone Podcast, Garry, Harj, Diana, and Jared break down what they've learned from working with hundreds of founders building with LLMs: why prompting still matters, where it breaks down, and how teams are making it more reliable in production. They share real examples of prompts that failed, how companies are testing for quality, and what the best teams are doing to make LLM outputs useful and predictable. 0:58 - Parahelp’s prompt example 4:59 - Different types of prompts 6:51 - Metaprompting 7:58 - Using examples 12:10 - Some tricks for longer prompts 14:18 - Findings on evals 17:25 - Every founder has become a forward-deployed engineer (FDE) 23:18 - Vertical AI agents are closing big deals with the FDE model 26:13 - The personalities of the different LLMs 27:26 - Lessons from rubrics 29:47 - Kaizen and the art of communication

Y Combinator

352,522 просмотров • 1 год назад

I'm excited to introduce my AI Exploratory Data Analysis (EDA) Agent that built EDA reports, performs correlation analysis, and missing data analysis in 20 seconds. Today, I'll share with you how to automate creating EDA reports with the AI EDA Agent, which is available on GitHub. We'll create an EDA Agent focusing on a Customer Churn Problem. I'll guide you through setting up the EDA Agent, creating EDA reports, and analyzing the findings. This AI agent is a huge time-saver! Table of Contents: 00:00 Introduction to EDA Tools Agent 01:20 Get the AI Data Science Team 04:49 Create the EDA Tools Agent 06:35 Using the EDA Tools Agent for Missing Data and Correlation Funnels 10:00 Get SweetViz EDA Reports with AI 12:00 8-Week AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: Business Intelligence with AI Register here (1570+ registered):
13:04

I'm excited to introduce my AI Exploratory Data Analysis (EDA) Agent that built EDA reports, performs correlation analysis, and missing data analysis in 20 seconds. Today, I'll share with you how to automate creating EDA reports with the AI EDA Agent, which is available on GitHub. We'll create an EDA Agent focusing on a Customer Churn Problem. I'll guide you through setting up the EDA Agent, creating EDA reports, and analyzing the findings. This AI agent is a huge time-saver! Table of Contents: 00:00 Introduction to EDA Tools Agent 01:20 Get the AI Data Science Team 04:49 Create the EDA Tools Agent 06:35 Using the EDA Tools Agent for Missing Data and Correlation Funnels 10:00 Get SweetViz EDA Reports with AI 12:00 8-Week AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: Business Intelligence with AI Register here (1570+ registered):

Matt Dancho (Business Science)

31,118 просмотров • 1 год назад