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Top 10 Free Online Tools Every Developer Should Bookmark

Top 10 Free Online Tools Every Developer Should Bookmark Disclosure: I built these tools after repeatedly running into the same development tasks over and over again. They're free to use, browser-based, and process data locally whenever possible. As developers, we spend a surprising amount of time doing small repetitive tasks: Decoding JWTs Comparing configuration files Converting timestamps Generating UUIDs Building cron expressions Inspecting API payloads Most of these tasks shouldn't requi

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Capstone: A 100-Test Suite, End to End (Playwright + TypeScript, Ch.26)

This is where everything comes together. Over the course we built a layered Playwright TypeScript framework — fixtures, Page Objects, API + UI + integration tests, CI, reporting — against a real dockerized app. The capstone makes it whole: end-to-end journeys, broad coverage, and a suite that's green, fast, and deterministic. ── Run summary ─────────────────────────────── result: passed tests: 100 (✓ 100 ✘ 0 ⤿ flaky 0 – skipped 0) projects: setup 1 api 66 ui 33 Code for thi

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Two Pillars Protocol — A maturity model for AI-era software engineering

Software engineering has had maturity models for forty years. CMMI told organizations whether their processes were repeatable. ISO/IEC 33000 told them whether quality was measurable. Pöppelbuß & Röglinger (2011) gave the field a methodology to evaluate the maturity models themselves. None of them speak the language of the engineer who, last Tuesday, dispatched four agentic flows in parallel, gated three of them with tests, killed one because the spec was wrong, and shipped the surviving merge in

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How I Built a Python Backtesting Engine with 623 Tests

I spent the last year building a backtesting and live trading engine in Python. It started as a personal tool — I was tired of rewriting the same plumbing every time I wanted to test a strategy. Here's how it turned into a real product. Every time I wanted to backtest a trading strategy, I ended up writing the same boilerplate: Pull OHLCV data from somewhere Wire up indicators Write entry/exit logic Track positions, P&L, drawdowns Build some kind of report Hope nothing broke when I changed one t

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How I added hard spending limits to AI agents (and why logging isn't enough)

If you've built an AI agent that calls paid APIs, you've probably I wanted something harder: a policy that blocks the agent before LangChain callbacks, OpenAI traces, CrewAI logs — they're all Even interrupt-based approaches like HumanInTheLoopMiddleware Treat budget as a tool the agent calls before any paid operation: python @function_tool def check_spend(amount: float, category: str = None) -> str: """ Check whether a planned spend is within budget. Returns 'approved' or 'deni

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Introducing React Motion Gallery

React Motion Gallery is a collection of primitives for complex layouts, modals, pointer/touch events, motion and data patterns – a library engineered around two principles: fluid animations and zero layout shift. It all began in the year 2022 with my first side project, an e-commerce template for Next.js. I noticed the bar was set extremely low in terms of UX for e-com sites, even the most profitable ones. So, I took it upon myself to change that, and give developers the tools to make sites fast

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Automating Myself Away… Almost

Automating Myself Away… Almost Hey, it’s the AI era, and I could not help but test the theory. And yes, I automated myself away. Well, not exactly. But almost. It has actually been fun to see how far this can go, and how it might look in the end. Of course, none of this goes anywhere near production systems without proper review, and it definitely does not replace these personal blogs I write myself. I still believe the human is very much important. I have kind of embraced it. AI is fully help

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What is AI? You Are Already Using It - You Just Did Not Know

Pipeline & Prompts | Byte size guides on DevOps, Cloud and AI A decade ago I was selling predictive and prescriptive analytics solutions to enterprise clients. Tools like SPSS Modeler — IBM’s data science platform for predicting future outcomes — and CPLEX, the optimisation engine we talked about in Article 6, which solved complex scheduling and logistics problems for supply chain and warehouse operations. Back then AI was not a word that appeared in everyday conversation. It lived in university

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