InsForge GitHub repo: https://t.co/ACu2d1oaJ1(don't forget to star it ⭐ ) ...

Finally, devs can vibe code all the way to production!There's a frustrating pattern that every developer using AI coding agents has experienced.The Agent builds a beautiful frontend in mins. It sets up working API routes and lays out the component architecture.But then it hits a wall the moment backend configuration is needed.For instance, to enable auth, devs need to manually create Firebase projects, click through auth tabs, enable OAuth providers one by one, and copy credentials between dashboards.That's ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/KpZaIUqSk2Avi Chawla (@_avichawla):Turn any Autoregressive LLM into a Diffusion LM.dLLM is a Python library that unifies the training & evaluation of diffusion language models.You can also use it to turn ANY autoregressive LM into a diffusion LM with minimal compute.100% open-source. https://t.co/sJGYiy009u ...

dLLM GitHub: https://t.co/rqn6t5fZYdGet a free PDF (380+ pages) with 150+ core AI Engineering lessons: https://t.co/sF1iVFFNNU ...

Turn any Autoregressive LLM into a Diffusion LM.dLLM is a Python library that unifies the training & evaluation of diffusion language models.You can also use it to turn ANY autoregressive LM into a diffusion LM with minimal compute.100% open-source. https://t.co/sJGYiy009u ...

RT Avi Chawla (@_avichawla)RAG was never the end goal.Memory in AI agents is where everything is heading. Let me break down this evolution in the simplest way possible.RAG (2020-2023):- Retrieve info once, generate response- No decision-making, just fetch and answer- Problem: Often retrieves irrelevant contextAgentic RAG:- Agent decides if retrieval is needed- Agent picks which source to query- Agent validates if results are useful- Problem: Still read-only, can't learn from interactionsAI Memory:- Read AND ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/IjHXhR8fpYAvi Chawla (@_avichawla):RAG was never the end goal.Memory in AI agents is where everything is heading. Let me break down this evolution in the simplest way possible.RAG (2020-2023):- Retrieve info once, generate response- No decision-making, just fetch and answer- Problem: Often retrieves https://t.co/3NcnfhDAbR ...

Cognee GitHub repo: https://t.co/Ken9bmXWY9(don't forget to star 🌟) ...

RAG was never the end goal.Memory in AI agents is where everything is heading. Let me break down this evolution in the simplest way possible.RAG (2020-2023):- Retrieve info once, generate response- No decision-making, just fetch and answer- Problem: Often retrieves irrelevant contextAgentic RAG:- Agent decides if retrieval is needed- Agent picks which source to query- Agent validates if results are useful- Problem: Still read-only, can't learn from interactionsAI Memory:- Read AND write to external knowledg ...

RT Avi Chawla (@_avichawla)I boosted my AI Agent's performance by 184%...using a 100% open-source technique.Now you can automatically find the best prompts for any agentic workflow you're building.So you don't need to manual prompt engineering at all!The snippet below explains this using Comet's Opik.The idea is simple:1. Start with an initial prompt & eval dataset2. Let the optimizer iteratively improve the prompt3. Get the optimal prompt automatically!And this takes just a few lines of code.Why use Opik?O ...