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News
Loop Engineering for RAG Generation: iterate top-k one at a time
3+ hour, 42+ min 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
6+ hour, 15+ min 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
8+ hour, 12+ min 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
1+ 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
1+ 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
1+ 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…...
How Much of a Data Science Workflow Can Run on a GPU Today? Part 1: Accelerating Data Preparation
1+ day, 6+ hour ago (1282+ words) Exploring GPU acceleration with cuDF, cudf.pandas, and the Polars GPU Engine A lot of discussions around GPUs focus on deep learning and large language models. However, a lot of data scientists still spend most of their time working with…...
Loop Engineering with Adaptive Parsing in Action: Parsing Flat Tables with Azure and Figures with a Vision LLM
2+ day, 5+ hour ago (1750+ words) Enterprise Document Intelligence [Vol.1 #10B] – The LLM as last line of defence, then two real escalations walked end to end: a flat table to Azure, a figure to a vision model 📓 The runnable companion walks both escalations end to end: you…...
How to Work Effectively with GPT-5.6
5+ day, 3+ hour ago (798+ words) Maximize the latest OpenAI model In this article, I will give my first impressions of the newest OpenAI model, GPT-5.6. The model was released a few days ago, and I’ve gotten the chance to test it extensively since the release…...
Context Engineering Isn’t Enough — A Loop Engineering Experiment With No LLM Inside the Loop
5+ day, 6+ hour ago (1794+ words) The Scope: This isn’t a benchmark pitting agent frameworks against each other, nor does it claim this controller is “smarter.” It claims something narrower and much more defensible: that failure isolation is a real, measurable architectural property. It’s a Pattern,…...