I'm working on an AI Data Analyst in MLJAR Studio. The idea is simple: you ask a question in natural language, AI writes Python code, executes it, and shows the result. But recently I found a small example that reminded me why AI data analysis needs more than code generation. I was testing a medical data analysis use case with a diabetes CSV file. The first task was simple: load data from this URL
Vendredi matin, 9 h 15. Françoise est dans son cockpit — trois écrans, à gauche l'Excel-pointeuse qu'elle tient à jour depuis quinze ans, à droite Sage, et au milieu Rembrandt depuis trois semaines. Sa tasse à la main, celle avec sa tête imprimée dessus que quelqu'un lui a offerte à Noël. Elle pivote sur sa chaise et me lance depuis son bureau : « Michel, combien on a d'inscrits pour la rentrée, d
« Hold on, we need to talk, this doesn't add up » Friday morning, 9:15 AM. Françoise is in her cockpit — three screens: on the left the Excel attendance sheet she's kept up to date for fifteen years, on the right Sage, and in the middle Rembrandt for three weeks now. Cup in hand, the one with her face printed on it that someone gave her at Christmas. She swivels in her chair and calls over from
Lee Powell · Architect of Scrivener and Scapple · Lumen & Lever Most AI document pipelines fail before the model is ever called. Tables become paragraphs. Lists collapse into prose. Annotations are detached from context. Page references disappear. Source traceability is replaced by a confidence score. The structure that gave the document its meaning is gone before retrieval runs, and no retrieval
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Building a Translation Pipeline for International Contract Bidding If your company bids on international contracts, you've probably dealt with the translation bottleneck. Technical proposals need precise translation, certified documents have strict formatting requirements, and procurement deadlines don't wait for anyone. After seeing how UK public procurement translation requirements can make or
Nexus-Open-CLI Nexus-Open-CLI is an App Store-style extensible CLI ecosystem infrastructure. In the process of daily development and using productivity tools, I have identified a long-standing issue: There are many CLI tools, but they are fragmented and difficult to manage in a unified way. For example: Different tools need to be installed separately, and their commands must be memorized indivi
Practical post for engineers who've hit the wall where an AI proof-of-concept works on clean data but can't connect to the legacy systems that hold actual production data. Disclosure: I work at Ailoitte, which builds AI integration layers connecting legacy infrastructure to production AI. Sharing what the engineering actually looks like. AI models expect structured, consistently formatted data. Le