Open any data science tutorial online. Not a Python script. Not a GitHub repo. A tutorial with explanation, code, output, visualizations, and commentary all woven together in one document. That document is a Jupyter Notebook. The best ML papers show their experiments in notebooks. Kaggle grandmasters share their approach in notebooks. Every data science course teaches in notebooks. When you get a
The grey enemy and the friend to save me from it If you're a software developer and you are on GitHub, you already know what I'm talking about: the contribution graph. That public heatmap on your profile that tracks your every commit, PR, and review you've ever made. That grid of gray and green squares that (in some cases looks like a well maintained patch of grass) tells a story about your codi
Specsmaxxing – On overcoming AI psychosis, and why I write specs in YAML
If you have ever built something with an LLM API like Gemini or OpenAI, you know the experience people expect: text that streams in word by word, like the model is thinking in real time. Achieving that in PHP has traditionally meant a lot of plumbing. You end up wrestling with raw curl handles, manual buffer flushing, or even reaching for a Node.js sidecar just to handle the stream. I built Hibla
Most Web3 tutorials give you a token contract and stop there. I went furtherand built the full stack: a Solidity ERC-20 token, a Hardhat test suite, and a React dApp with MetaMask integration and transaction history. Here is every technical decision I made. GitHub: https://github.com/Carter254g/harambee-dapp HarambeeCoin (HBC) is a custom ERC-20 token. The dApp lets you connect MetaMask, check you
A week of intent-based trading for AI agents: five threads from the Hashlock Markets desk The Model Context Protocol surface for crypto trading filled out fast over the last few weeks. Bybit shipped MCP coverage. Gemini added an agentic platform. Alpaca, Kraken, Hummingbot, TraderEvolution, and a handful of community wrappers are all in the same SERP now. The category is real, and it is crowding
OpenAI revenue is still the number people reach for when they want a leaderboard. But the cleaner frame is different: Anthropic appears to be building a different kind of AI business, one centered on enterprise customers, safety positioning, and less dependence on mass-market fame. That distinction matters because public discussion keeps collapsing three separate things into one scorecard: revenue
A journalist recently called out DeepSeek for its "serious lying problem" — the model can write a beautifully crafted biographical sketch in classical Chinese style, but the person's birthplace, mother's surname, and life events are all fabricated. This isn't an isolated incident; it's one of the most stubborn bugs in the LLM industry, and it has a name: AI Hallucination. Right after the May Day h