Deep dives on AI systems, architecture, and production deployments.
After context engineering comes the loop: bounds, event journals, sandboxes, permission classes and verifiers. The mechanisms, parameters and failure modes of the code that turns a model into an agent.
A seven-step field guide to production RAG and context engineering: three laws of the model, a reference architecture, seventeen variants, and the criteria that decide between them.
I built an explainable heuristic agent for Kaggle's Pokemon TCG AI Battle competition. One reordered rule took the win rate from 6.3% to 83.3%, and every change had to survive a 300-game statistical gate before it shipped.
One frozen open-source model, sixteen agent harnesses, two task types, 150 graded runs. A controlled experiment on whether harness complexity pays — plus a structured catalog of every common agent pattern, mapped to the framework you already use.
A hands-on guide to building an automated multi-agent system for code review using local LLMs (Ollama) with structured Agent-to-Agent (A2A) messaging.
Lessons from deploying conversational analytics at Stellantis Financial Services with latency, accuracy, and compliance guardrails.
A field report on pairing sensor data with knowledge bases to power explainable maintenance copilots for connected vehicles.
Breaking down the experimentation, models, and change management that lowered fare evasion for SNCF.
Reframing : Are you solving the right problems ?ML Design Pattern — Problem representation“If a problem can’t be solved within the frame it was conceived, the solution lies in reframing the problem.” ...
Recently i moved in a new apartment and i had no decoration, and i didn’t want to just use random posters from Amazon, i wanted to create something myself. The only issue is, i am not the most artisti...
Having too much data is problem every data practitioner wishes of having but it’s still a problem nonetheless.A single solution might not work to all requirements and so different solutions can work i...
OpenCV EAST model and Tesseract for detection and recognition of text in natural scenesSourceGoogle has digitized books ang Google earth is using NLP to identify addresses, but how does it work exactl...
https://www.dezyre.com/article/spark-mllib-for-scalable-machine-learning-with-spark/339MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy....
Apache Spark is a must for Big data’s lovers. In a few words, Spark is a fast and powerful framework that provides an API to perform massive distributed processing over resilient sets of data.Prerequi...
OCR with Python, OpenCV and PyTesseractOptical Character Recognition (OCR) is the conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document,...
Natural language processing and sentiment analysisSentiment Analysis is a common NLP task that Data Scientists need to perform. This is a straightforward guide to learn the basics of NLP and to create...
Whether you are working on data science, machine learning projects, you are probably going to need to extract data from the web in your line of work. So how do we do to actually pull stuff out from th...
