Getting started with no-code agentic automations, popular use cases and tips

The state of best practices for context engineering, focused on AI-coding as a leading use case

Enabling secured flexibility to your agents and MCP servers

techniques to combine classic search with agentic retrieval

The story of building Suna - the open source alternative of Manus the generalist autonomous agenthttps://github.com/kortix-ai/suna

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

Is this Agent2Agent new protocol by Google going to be the next MCP?

How to handle long docs like PDF and Docx effectively with LLMs

Tips for AI-driven coding

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

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

Example of product specification required from an AI PM for a new feature

all you need to know about MCP and how to get started as a developer

this episode has 2 aspects - best practices to start a new product in 2025 and what tools can accelerate your process

how to build a text to sql agent

Deep dive on why GraphRAG approach was created and how it works

advanced RAG techniques like RAPTOR and using Clues

Cursor, RepoAgent, Aider, and some more coding agents - overview and interesting architectural concepts

Technical review of the new Realtime API introduced on the dev day

How to migrate the llm provider in your AI app safely

If you need to build an llm-app that uses an open-source llm, this episode is for you. Intermediate level

end to end guide on how to get started and deploy to production your llm app

Build LLM-app with legal documents that have many references and domain verbiage

Getting started with LangGraph, the most popular multi agent framework

Utilize no-code tools for fast POCs

Review of why and how to build a multi-agent system. Assaf is Head of R&D @ Wix and GPT-researcher open source creator.

everything about adopting open source code and models for your llm app

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

examples and practices of advanced agents, and use of LangGraph for effective tool usage by agents

how to solve latency issues with minimal compromise on quality and cost

The 12 most common challenges around building an effective RAG pipeline and the best practices for solutions

exploring the reranker component in the 2 stage retrieval system

how to define effective requirements for llm apps, and first steps after going to production

What are transformers, why it is so expensive to train a Transformer-based model and what is the architecture of the future LLMs

LCEL, LangGraph, LangSmith

Indexing knowledgebases with KG for RAG applications

ragas framework for evaluating unstructured retrievals and generations

Retrieval Augmented Generation process, Llama index vs LangChain, indexing

OpenAI DevDay announcements recap

Evaluating LLMs and AI pipeline in dev and production environments. How to work with datasets

multimodality is a type of model that can analyze multiple data types like language, images, voice, etc.

Find the most suitable project for volunteer support "Iron swords" war efforts

Advanced prompt engineering techniques: skeleton of thoughts, directional stimulus prompting, graph of thoughts, augmentations

Full stack LLM app development, Typescript vs Python

Finetuning techniques (SFT, RLHF...), pros & cons, tools (technical episode)

why should you care about open source models, when and how to finetune, inference best practices, deployment & serving tools, models overview

Drill down on a summarization task use case

Save time building applications with LLMs by utilizing tools like LangChain, Llama index, Guidance, GPTCache and many more

Why you might need a VectorDB, which features to consider when choosing a provider, and use cases examples

From an agent POC with LangChain to production challenges