This hybrid search stack I mentioned in the post is actually implemented in this open-source context retrieval layer for agents.GitHub repo: https://t.co/iU6P0KoaRf ...

Vector search is not always the answer.A 30-year-old algorithm with zero training, zero embeddings, and zero fine-tuning still powers Elasticsearch, OpenSearch, and most production search systems today.It's called BM25, and it's worth understanding why it refuses to die.Let's say you're searching for "transformer attention mechanism" in a library of ML papers.BM25 scores documents using three core ideas:1) Word rarity matters more than word frequencyEvery paper contains "the" and "is" so those words carry n ...

15-16) Set max_workers and pin_memory in DataLoader.PyTorch dataloader has two terrible default settings. Update them according to your config.Speedup is shown in the image below 👇 https://t.co/BBuk5RSqLS ...

14) Use momentumIn gradient descent, every parameter update solely depends on the current gradient. This leads to unwanted oscillations during optimization.Momentum reduces this by adding a weighted average of previous gradient updates to the update rule.Check this 👇 https://t.co/77X9rwRyOF ...

I have been training neural networks for 10 years now.Here are 16 ways I actively use to optimize model training:(detailed explanation ...🧵) https://t.co/5HyMgEOIks ...

Generative vs. discriminative models in ML:(a popular ML interview question) https://t.co/fVVwZBkCVR ...

4 strategies for Multi-GPU training, explained visually: https://t.co/o5Z2Ni9qHk ...

RT Avi Chawla (@_avichawla)The ultimate Full-stack AI Engineering roadmap to go from 0 to 100.This is the exact mapped-out path on what it actually takes to go from Beginner → Full-Stack AI Engineer.> Start with Coding Fundamentals.> Learn Python, Bash, Git, and testing.> Every strong AI engineer starts with fundamentals.> Learn how to interact with models by understanding LLM APIs.> This will teach you structured outputs, caching, system prompts, etc.> APIs are great, but raw LLMs still need the latest inf ...

The ultimate Full-stack AI Engineering roadmap to go from 0 to 100.This is the exact mapped-out path on what it actually takes to go from Beginner → Full-Stack AI Engineer.> Start with Coding Fundamentals.> Learn Python, Bash, Git, and testing.> Every strong AI engineer starts with fundamentals.> Learn how to interact with models by understanding LLM APIs.> This will teach you structured outputs, caching, system prompts, etc.> APIs are great, but raw LLMs still need the latest info to be effective.> Learn h ...

RT Avi Chawla (@_avichawla)Your embedding stack forces a 100% re-index just to change models.And most teams treat that as unavoidable.Imagine you built a RAG pipeline with a large embedding model for high retrieval quality, and it ships to production.Six months later, your application traffic and your embedding model costs are soaring while your pipeline struggles to scale. You want to switch to a model that prioritizes cost and latency in order to meet this new demand.But your existing embeddings live in o ...