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News
Tabular LLMs: An Introduction to the Foundation Models That Predict Your Spreadsheet
3+ hour, 31+ min 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
8+ hour, 1+ min 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…...
Most RAG Hallucinations Are Extraction Errors: Seven Patterns for a Typed Generation Contract
1+ day, 5+ 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…...
Why Adding More AI Agents Made Our System Slower
1+ day, 8+ 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
2+ day, 3+ 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
2+ day, 6+ 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…...
Detecting Vulnerabilities in Agent Skills with SkillSpector: From Green Checkmark to Real Security Judgment
2+ day, 8+ hour ago (1806+ words) Static analysis nailed the malicious skill and over-flagged the useful one. The gap between those results is where human judgement actually earns its keep. An agent skill is a simple package of instructions for an AI agent. In practice, it…...
Prompt Engineering Isn’t Enough: How Four Bricks of Context Engineering Stop RAG Hallucinations
3+ day, 3+ hour ago (743+ words) Enterprise Document Intelligence [Vol.1 #9bis] – Your RAG isn’t hallucinating, it’s answering the wrong context faithfully. On real NIST and World Bank documents, watch each of the four bricks break, and the contract that closes it In the previous two articles (Part…...
I Tried Fine-Tuning a Robot AI Model on Colab. Here Is What Worked | Towards Data Science
3+ day, 5+ hour ago (1499+ words) A reproducible 100-step LoRA fine-tuning run for OpenVLA, with dataset checks, Colab setup, training metrics, and W&B evidence. Fine-tuning a robotics model sounds expensive, fragile, and hard to verify. I wanted a smaller test: can I run a real…...
Are Your ML Experiments a Mess? Here’s the Fix
3+ day, 8+ hour ago (843+ words) A hands-on guide to tracking experiments, logging models, and reproducing results with ML Flow. To ensure that the right models are in production, we need a way to manage various models and versions, track performance metrics, and reproduce results. This…...