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
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
Engineering Craftsmanship: Building a Sovereign Immutable List in Java In an era of "vibe coding" and AI-driven bloat, there is a distinct value in returning to the fundamentals of structural integrity. As I navigate a career pivot toward Site Reliability Engineering (SRE) and Senior Development, I’ve found that the most resilient systems are those built on the principles of data sovereignty and
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A team I worked with shipped their first LLM feature in two weeks. Six weeks later, they got a $47,000 OpenAI bill — for a free tier product. The post-mortem found three things: one tenant ran a script that retried failed requests indefinitely, another had a buggy prompt that asked the model to "respond in ten thousand tokens," and a third was just abusive — they had discovered the API key was eff
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