Building a daily AI news brief in 325 lines of Python I read too many AI newsletters. Most of them are 4,000 words of sponsor copy and "thought leadership" wrapped around two actually-useful items. So I wrote a script that does the compression itself, and now I read it instead. It's 325 lines of Python. It runs once a day on a $5 VPS. It costs about half a cent per brief. The output goes to a pu
What is FastAPI? As the name suggests, FastAPI is a modern Python framework designed for building RESTful APIs with high performance and minimal boilerplate. In 2026, it has become the industry standard because it’s exceptionally fast, reliable, and includes powerful out of the box features — such as automatic interactive documentation and native support for asynchronous programming. These comma
If you've ever tried to learn Python consistently, you know the problem: That's why I built DuCode — a platform that ## How it works Every day a new challenge drops. You get a code snippet like this: def make_counter(start=0): def counter(step=1, *, reset=False): if reset: counter._val = start return counter._val counter._val = getattr(counter,
🚀 Introduction Most text-to-speech systems today are powerful—but they come with a cost: heavy models, GPU requirements, and complex setup. I wanted something different. So I built Kitten TTS — a lightweight, CPU-friendly text-to-speech model that’s fast, efficient, and easy for developers to use. Instead of just shipping a model, I went one step further: 👉 I built a live GUI and deployed it
Until last week the public verify endpoint at GET /api/v1/verify/{signature_id} returned a single boolean: {verified: true} or {verified: false}. That works for the buyer's first-glance UX. It does not work for a regulator or a court trying to figure out what actually went wrong. An older trust-data-infrastructure design we read for context lays this out: their verify can return Valid Document, In
https://raw.githubusercontent.com/sanjaybk7/agentic-guard/main/docs/demo.gif If you're building LLM agents with LangGraph or the OpenAI Agents SDK, your architecture might already be vulnerable — and no runtime tool will catch it The problem nobody is talking about
A rejection is data. Until last week we were throwing it away. If an attacker submitted a forged signature against the public verify endpoint, we returned 404 signature_not_found and that was the entire footprint. Same for a cross-org access attempt on the replay endpoint, an agent that got suspended mid-run trying to sign once more, or a probe walking through random sig_* ids. An older trust-data
While setting up Apache Airflow using Docker on Windows 11 WSL, I needed to extend the image to install some python packages. I created a dockerfile and requirements.txt, but every time I ran "docker-compose up --build", I received the error: ERROR: Invalid requirement: '<package-name': Expected semicolon (after name with no version specifier) or end To fix the error, I needed to change the encod