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  • React
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NEW

Grep Beats Embeddings for Some AI Coding Tool Tasks: Use Deterministic Search Before You Build RAG

Watch: Is RAG Still Needed? Choosing the Best Approach for LLMs by IBM Technology Short version: A SWE-Bench Verified agent using only grep and find reached the top of the leaderboard without any semantic indexing. A single exact-name query can use far fewer tokens with grep than broad embedding…
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NEW

Build an AI Agent in 60 Lines of Python Before Reaching for LangChain

Watch: Build an AI Agent From Scratch in Python - Tutorial for Beginners by Tech With Tim Understanding the agent loop stopped being optional the moment you tried to change one thing in a framework demo and couldn't find where the loop lived. Quickstarts run fine until you need to touch them. A…
Thumbnail Image of Tutorial Build an AI Agent in 60 Lines of Python Before Reaching for LangChain

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NEW

Express for AI Inference in 2026: Build a Low-Latency RAG API with PGVector or FAISS

Latency stopped being optional the moment you put retrieval in front of an LLM. Every millisecond your vector search burns is a millisecond before the first token appears. Express stays lean by design, which leaves the vector store owning most of your retrieval headroom. This guide is a project…
Thumbnail Image of Tutorial Express for AI Inference in 2026: Build a Low-Latency RAG API with PGVector or FAISS
NEW

Build a Production AI App: Where Vibe Coding Platforms Break Down

Your prototype stopped being optional the moment a real user typed a real password into it. Before that, it was a demo. Now it's a liability with a login screen, and the habits that got you this far won't carry you further. Vibe coding is a good way to explore. It can accelerate exploration, reduce…
Thumbnail Image of Tutorial Build a Production AI App: Where Vibe Coding Platforms Break Down
NEW

AWQ in Practice: Quantize LLMs for Faster AI Inference Without Killing Output Quality

AWQ cuts 70B model serving costs from $0.757 to $0.253 per million tokens by moving from two GPUs to one. Llama 3.3 70B quantized to INT4 with AWQ drops 2 MMLU points and 3 HumanEval points versus full precision. AWQ INT4 brings a 70B model's VRAM down from 140GB to roughly 40GB, fitting it on a…
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