Most text analysis solutions fall into one of two problems: Too expensive — OpenAI API costs money for every call Too complex — Hosting your own Hugging Face model requires infra, GPU, maintenance I built TextAI Pro — a lightweight REST API that does the job without the overhead. Two endpoints: POST /analyze Sentiment: positive / negative / neutral Confidence score (0–1) Top keywords Word count PO
J'ai un aveu à faire : pendant longtemps, quand un dev me montrait fièrement son app Python avec un bouton gris carré et une Listbox qui sentait Windows 95, je hochais la tête poliment. Aujourd'hui, j'ai arrêté. Pas parce que je suis devenu méchant. Parce que PyQt6 existe, et qu'il n'y a plus aucune excuse. Cet article, c'est ma tentative de te convaincre — toi qui ouvres encore tkinter par réflex
AI Can't Fix What It Can't See: How cdk diagnose Enables Autonomous CDK Remediation Current Behavior vs. What We Want Today, when a CDK deployment fails through a pipeline, the remediation loop looks like this: Developer ──▶ Push code ──▶ Pipeline ──▶ CFN deploy ──▶ ❌ Fails │ ┌───────────────────────────────────────────────
In my last article, I mentioned that my SAST tool uses regex-based pattern matching instead of AST parsing, and that this was a deliberate tradeoff. A few people asked me to go deeper on that decision — because on the surface, it sounds like I took a shortcut. I didn't. Or rather — I did, but it was an informed shortcut, and there's a meaningful difference. Let me explain what AST parsing actually
All Algorithms implemented in Python
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LLMs hallucinate. That's not news. What's underdiscussed is how that failure mode behaves in long working sessions: confident reconstruction that looks fluent, cites specifics, and feels right — until three sessions later when something supposed to be true turns out not to be. This is week 5 of an 8-week deep dive on CRAFT for Cowork, a structured working environment for Claude. The QA framework t
You don’t notice the problem right away. Everything runs smoothly in MySQL… until a new report shows up. Then queries slow down, dashboards lag, and you start realizing you’re stretching the database beyond what it’s good at. That’s usually when BigQuery enters the picture. So the real question becomes: How do you actually move data between them without turning it into a side project? Let’s w