פודקסט טכני בעברית על בניית סוכנים ואפליקציות עם מודלי שפה. הצטרפו לקהילת הווטסאפ שלנו לדיונים ושאלות: https://chat.whatsapp.com/JKVFSNFMVQu8G1KWx1jqap ניוזלטר עם סיכום דו-שבועי של כל הנושאים הכי חמים: www.langtalks.ai
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Short and long term memory mechanisms for agents
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Getting started with no-code agentic automations, popular use cases and tips
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The state of best practices for context engineering, focused on AI-coding as a leading use case
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Enabling secured flexibility to your agents and MCP servers
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techniques to combine classic search with agentic retrieval
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The story of building Suna - the open source alternative of Manus the generalist autonomous agenthttps://github.com/kortix-ai/suna
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Sharing our workflow with Cursor, Claude Code, GitHub Copilot and others. Listed the useful MCPs and tools, how to measure efficiency gains, and our vision for the future of coding
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Is this Agent2Agent new protocol by Google going to be the next MCP?
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How to handle long docs like PDF and Docx effectively with LLMs
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Tips for AI-driven coding
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Python or JS? LangChain or Vanilla? In this episode we gave guidelines on important things to notice when picking a tech stack for new project or a company's platform
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Building SWE multi-agent with LangGraph: Zero to Hero | Lee & Gal (LangTalks GenAI 2025 Conference)Lecture video:https://www.youtube.com/watch?v=KroPK5DygWwRepo:https://github.com/langtalks/swe-agent
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Example of product specification required from an AI PM for a new feature
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all you need to know about MCP and how to get started as a developer
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this episode has 2 aspects - best practices to start a new product in 2025 and what tools can accelerate your process
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how to build a text to sql agent
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Deep dive on why GraphRAG approach was created and how it works
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advanced RAG techniques like RAPTOR and using Clues
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Cursor, RepoAgent, Aider, and some more coding agents - overview and interesting architectural concepts
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Technical review of the new Realtime API introduced on the dev day
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How to migrate the llm provider in your AI app safely
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If you need to build an llm-app that uses an open-source llm, this episode is for you. Intermediate level
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end to end guide on how to get started and deploy to production your llm app
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Build LLM-app with legal documents that have many references and domain verbiage
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Getting started with LangGraph, the most popular multi agent framework
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Utilize no-code tools for fast POCs
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Review of why and how to build a multi-agent system. Assaf is Head of R&D @ Wix and GPT-researcher open source creator.
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everything about adopting open source code and models for your llm app
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build trust with users and stakeholders - transparency and explainability, reliability and consistency (how to fail safely), automation vs user control.how to evaluate with uncertainty, prompt engineering, effective bug reports
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examples and practices of advanced agents, and use of LangGraph for effective tool usage by agents
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how to solve latency issues with minimal compromise on quality and cost
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The 12 most common challenges around building an effective RAG pipeline and the best practices for solutions
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exploring the reranker component in the 2 stage retrieval system
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how to define effective requirements for llm apps, and first steps after going to production
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What are transformers, why it is so expensive to train a Transformer-based model and what is the architecture of the future LLMs
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LCEL, LangGraph, LangSmith
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Indexing knowledgebases with KG for RAG applications
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ragas framework for evaluating unstructured retrievals and generations
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Retrieval Augmented Generation process, Llama index vs LangChain, indexing
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OpenAI DevDay announcements recap
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Evaluating LLMs and AI pipeline in dev and production environments. How to work with datasets
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multimodality is a type of model that can analyze multiple data types like language, images, voice, etc.
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Find the most suitable project for volunteer support "Iron swords" war efforts
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Advanced prompt engineering techniques: skeleton of thoughts, directional stimulus prompting, graph of thoughts, augmentations
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Full stack LLM app development, Typescript vs Python
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Finetuning techniques (SFT, RLHF...), pros & cons, tools (technical episode)
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why should you care about open source models, when and how to finetune, inference best practices, deployment & serving tools, models overview
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Drill down on a summarization task use case
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Save time building applications with LLMs by utilizing tools like LangChain, Llama index, Guidance, GPTCache and many more
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Why you might need a VectorDB, which features to consider when choosing a provider, and use cases examples
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