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News
How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs. In-Memory ANN Indexes
12+ hour, 15+ min ago (675+ words) Architecting cost-effective infrastructure by navigating the latency and storage trade-offs of HNSW, SPANN, and DiskANN In this article I would like to dive into the details of what actually makes semantic search fast and efficient: approximate nearest neighbor (ANN) algorithms,…...
The Fluid Simulator That Doesn’t Solve the Fluid Equations
14+ hour, 15+ min ago (1689+ words) A hands-on Lattice Boltzmann Method tutorial: from statistical mechanics and kinetic theory to a C++ implementation and supercomputer scaling The image above shows a Kármán vortex street (the alternating swirls that form behind any blunt object in fast-moving flow). You’ve…...
Tabular LLMs: An Introduction to the Foundation Models That Predict Your Spreadsheet
1+ day, 10+ hour ago (1158+ words) A 28M-parameter model you don’t train beats tuned XGBoost. An introduction to tabular foundation models, with an independent reproduction. A tabular foundation model predicts the missing column of any table, zero-shot, the way a language model completes text. On the…...
Context Windows Forget What Matters — I Built a Usage-Reinforced Decay Engine for AI Agent Memory
1+ day, 15+ hour ago (1843+ words) How to build a usage-reinforced memory decay engine for AI agent memory that outperforms naive recency-window pruning on long-running, multi-session tasks — with a deterministic, zero-dependency Python implementation you can verify yourself. The issue here is structural, not something you can…...
When Data Science Makes Us Sad: The Story of an Overbooked Flight
2+ day, 10+ hour ago (1131+ words) $8 million vs $5k + Potentially Going Viral I recently came across a post on social media about a passenger bumped from an airplane because of overbooking. If you’re a frequent flyer, you may come across a similar situation. If you’re unlucky enough,…...
Most RAG Hallucinations Are Extraction Errors: Seven Patterns for a Typed Generation Contract
2+ day, 12+ hour ago (982+ words) Enterprise Document Intelligence [Vol.1 #8ter] – Naming the RAG error correctly matters: model reads the context, so a wrong answer is an extraction error, not a hallucination. Seven typed-contract patterns keep the generation brick honest, with a decomposition rule for small models…...
Lessons Learned After 8.5 Years of ML
2+ day, 13+ hour ago (760+ words) However, I think that there are still some lessons that are applicable regardless of the field one actually is doing machine learning research or machine learning practices in. Frequently, I take the opportunity of another half year of progress in…...
Why Adding More AI Agents Made Our System Slower
2+ day, 15+ hour ago (1477+ words) hidden cost of asynchronous systems, how tiny CPU tasks quietly became our biggest bottleneck while scaling hundreds of LLM agents. You’re building a product around LLMs. You’re working on a previously impossible challenge that is now possible to tackle using…...
Loop Engineering for RAG Generation: iterate top-k one at a time
3+ day, 10+ hour ago (927+ words) Enterprise Document Intelligence [Vol.1 #8bis] – Two regimes for sending retrieved candidates to the generation brick, the sufficiency signal that picks between them, and the per-question type dispatch that makes it cheap Naive RAG ships batch by default. Retrieval returns top-5, the…...
Build an LLM Agent That Can Write and Run Code
3+ day, 13+ hour ago (1364+ words) A hands-on walkthrough of code execution with the OpenAI Agents SDK and Docker Code execution is probably one of the most useful capabilities we can give to an LLM agent. Once the agent can write and execute code, many tasks…...