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Ashpreet Bedi

@ashpreetbedi • 20,718 subscribers

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Agents with gpt-4o from scratch 🔥 in 11 minutes we'll build: 🌎 Web Search Agent (2:40) 📈 Finance Agent (3:30) 🫡 Hackernews Agent (5:50) 📊 Data Analysis Agent (8:10) 🗒️ Research Agent (9:35) code:

Agents with gpt-4o from scratch 🔥 in 11 minutes we'll build: 🌎 Web Search Agent (2:40) 📈 Finance Agent (3:30) 🫡 Hackernews Agent (5:50) 📊 Data Analysis Agent (8:10) 🗒️ Research Agent (9:35) code:

697,448 görüntüleme • 2 yıl önce

Building the LLM OS by Andrej Karpathy with gpt-4o The speed + quality is 🔥🤯 code:

Building the LLM OS by Andrej Karpathy with gpt-4o The speed + quality is 🔥🤯 code:

251,707 görüntüleme • 2 yıl önce

Lets build `Auto-RAG` where we let the LLM pull the data it needs from different sources. 🔎 The user asks a question. 🤔 LLM decides whether to search its knowledge, memory, internet or make an API call. ✍️ LLM answers with the context. Code:

Lets build `Auto-RAG` where we let the LLM pull the data it needs from different sources. 🔎 The user asks a question. 🤔 LLM decides whether to search its knowledge, memory, internet or make an API call. ✍️ LLM answers with the context. Code:

174,580 görüntüleme • 2 yıl önce

🚀 Say hello to the new & improved phidata 🚀 Build, ship, and monitor Agents with blazing-fast memory, knowledge, tools & reasoning 🔥 ⚡️ 70% faster memory & knowledge 🛠 100+ tools 🧠 Reasoning Agents 🤝 Multi-agent collaboration 📊 Built-in monitoring and Agent UI

🚀 Say hello to the new & improved phidata 🚀 Build, ship, and monitor Agents with blazing-fast memory, knowledge, tools & reasoning 🔥 ⚡️ 70% faster memory & knowledge 🛠 100+ tools 🧠 Reasoning Agents 🤝 Multi-agent collaboration 📊 Built-in monitoring and Agent UI

114,568 görüntüleme • 2 yıl önce

🌶️ Hot take: The only way Autonomous Multi-Agent Systems work is by adding Reasoning & Agentic Context. I've tried it all, and here are my learnings👇

🌶️ Hot take: The only way Autonomous Multi-Agent Systems work is by adding Reasoning & Agentic Context. I've tried it all, and here are my learnings👇

91,710 görüntüleme • 1 yıl önce

This was a great read. So I started building an open-source data agent with 6 layers of context: 1. Table usage 2. Human annotations 3. Query patterns 4. Institutional knowledge 5. Memory 6. Runtime context Early version if you want to follow along:

This was a great read. So I started building an open-source data agent with 6 layers of context: 1. Table usage 2. Human annotations 3. Query patterns 4. Institutional knowledge 5. Memory 6. Runtime context Early version if you want to follow along:

51,251 görüntüleme • 9 ay önce

Generate dashboards on-demand using agents Agents can now generate beautiful, just-in-time dashboards. Dynamic software is the future

Generate dashboards on-demand using agents Agents can now generate beautiful, just-in-time dashboards. Dynamic software is the future

28,345 görüntüleme • 6 ay önce

🚀 Introducing the first-ever Agent UI 🚀 This is hands-down my favorite product! Chat with local Agents tailored to my needs. Local memory, storage, knowledge and tools 🔥 ⚡️ Your data, your control 🧠 Compatible with any LLM 🤝 Run multiple agents or a team of agents

🚀 Introducing the first-ever Agent UI 🚀 This is hands-down my favorite product! Chat with local Agents tailored to my needs. Local memory, storage, knowledge and tools 🔥 ⚡️ Your data, your control 🧠 Compatible with any LLM 🤝 Run multiple agents or a team of agents

69,015 görüntüleme • 2 yıl önce

Prompt In → Video Out Introducing Vibe-Video: a multi-agent team that turns curiosity into motion-graphics videos. Built with HeyGen Hyperframes and Opus 4.7 Researches topics via Parallel Web Systems, explains GitHub repos, renders the result. Free and open-source ↓

Prompt In → Video Out Introducing Vibe-Video: a multi-agent team that turns curiosity into motion-graphics videos. Built with HeyGen Hyperframes and Opus 4.7 Researches topics via Parallel Web Systems, explains GitHub repos, renders the result. Free and open-source ↓

25,185 görüntüleme • 6 ay önce

Fully local RAG with Llama 3 on ollama & Streamlit Learn how to: 🦙Run Llama 3 using ollama 📄Add Website & PDFs to a knowledge base 💻Build an AI App using Streamlit 🏬Store chat history in a database PostgreSQL YT:

Fully local RAG with Llama 3 on ollama & Streamlit Learn how to: 🦙Run Llama 3 using ollama 📄Add Website & PDFs to a knowledge base 💻Build an AI App using Streamlit 🏬Store chat history in a database PostgreSQL YT:

70,390 görüntüleme • 2 yıl önce

Super impressed with the new Ollama updates! Adjusting my rating from 30-40% to a solid 80-90% -- thats how good it is now for building Agents 🙌 Exceptional work by the ollama team, not sure how they got this perf from 8b models 🔥

Super impressed with the new Ollama updates! Adjusting my rating from 30-40% to a solid 80-90% -- thats how good it is now for building Agents 🙌 Exceptional work by the ollama team, not sure how they got this perf from 8b models 🔥

39,380 görüntüleme • 1 yıl önce

Introducing `Personalized Agentic RAG` - where the LLM remembers key details about the user and automatically chooses the tool for RAG. We'll build: 👏 ChatGPT like memory ⚙️ Function calling 🧙 Multi-Agent orchestration code:

Introducing `Personalized Agentic RAG` - where the LLM remembers key details about the user and automatically chooses the tool for RAG. We'll build: 👏 ChatGPT like memory ⚙️ Function calling 🧙 Multi-Agent orchestration code:

45,035 görüntüleme • 2 yıl önce

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