I've got @antonosika co-founder of @lovable coming on the pod this week. What should I ask him?
CODE
-
Foundational approach to book with potential RL bonus material
By
–
Thanks for the comment! I tried to keep the book more "foundational" rather than chasing the latest research (that's what I do in my blog). But yeah, maybe I can add some bonus material to the GitHub repo on implementing RL with verifiable rewards. (I don't want to do just
-
Large Reasoning Models Excel at Competitive Programming Tasks
By
–
Competitive Programming with Large Reasoning Models El-Kishky et al.: https://
arxiv.org/abs/2502.06807 #ArtificialIntelligence #DeepLearning #ReinforcementLearning -
31 Production-Ready RAG Implementations with LangChain
By
–
Advanced RAG Techniques Discover a powerful collection of 31 production-ready RAG implementations, from basic to advanced, built with LangChain. Each technique comes with detailed documentation and practical code examples to enhance your RAG systems. Level up your RAG
-
LangChain RAG Doc Assistant Built with LangGraph Orchestration
By
–
LangChain Doc Assistant A powerful RAG system for querying LangChain docs, built with LangGraph orchestration. Available as both a live service and reference implementation, it features source-backed answers and customizable models. Build your own doc assistant today!
-
Google Cloud LangChain Production Server Enables Enterprise AI Applications
By
–
Gen AI Toolbox Google Cloud and LangChain's production-ready server enables AI applications with native database support. Integrated with LangGraph, it delivers enterprise security, observability, and optimized database connectivity. Build smarter with databases
-
Developer Innovation Accelerated by Speed in AI
By
–
Our favorite part about all of this is seeing what the developers build! With speed comes innovation!
-
Python package installation options with pip and uv
By
–
If they have Python installed separately, they can still do `pip install` or `uv pip install` etc, whatever works for them.
-
Secure RAG Systems with LangChain and OpenFGA Authorization
By
–
Secure RAG with OpenFGA Build secure RAG systems with document-level authorization using LangChain and OpenFGA. The FGARetriever implementation provides:
– Authorization layer
– Vector store integration
– RAG pipeline Learn how to implement it: https://
auth0.com/blog/building-
a-secure-rag-with-python-langchain-and-openfga/
… -
Conda speed advantage lost when using PyPI packages with pip
By
–
It's only fast if you use conda packages. But most AI/ML packages need to be installed from PyPI, so in that case you would have to use pip in your conda env, which is again slower than uv.