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Measuring Japanese Read-Aloud Speed with AmiVoice Timestamps — A Coaching App That Doesn't Stop at STT-to-Claude

📝 Originally published in Japanese on Zenn. This is the English version. Canonical: https://zenn.dev/uya0526_design/articles/main_article_reading-speed-meter Introduction — What I Built I built a web app that lets you read a Japanese passage aloud, measures your speed and fluency, and has an AI coach return a one-line piece of feedback. 🌐 Demo: https://reading-speed-meter.vercel.app/ 📦 Repository: https://github.com/uya0526-design/reading-speed-meter The flow is simple. You read a passage aloud into the mic (up to 10 seconds) while looking at the script — I prepared the opening lines of two Japanese classics, The Tale of the Heike and Hōjōki — and when you press "Measure," : AmiVoice API recognizes the audio, the code computes your pure speaking speed (characters/min) and stagnation rate from that result, and Claude Haiku returns coaching as "one compliment + one improvement." (Measurement starts on a button press after recording — it never runs automatically.) This article aims to be a single, self-contained piece covering the whole picture, the design decisions, and the reproduction steps . 💡 Where this sits in my journey I'm an ex-Java engineer learning TypeScript and Python in public. This was my first project where I deliberately adopted "AI-collaborative development" as a clear mode. Throughout, I'll drop in comparisons to Java — hopefully useful for anyone coming from a similar background. What You'll Get From This Article How to design evaluation logic that fully exploits the per-word timestamps AmiVoice returns How to build a BFF (Backend for Frontend) so API keys never reach the browser A "two-stage" design where code does the math and Claude Haiku only does the wording First-hand findings you only learn by verifying — e.g., "I thought I'd optimized it, but it wasn't actually working" I include concrete endpoints, parameters, and environment variables so you can reproduce it yourself. My Learning Style (AI Transparency) 💡 Learning companions & how this art

2026-06-18 原文 →
AI 资讯

Why Are We Still Writing Code When No One Is Going to Read It?

One of the oldest axioms of software engineering is that code is written primarily for humans to read, and only secondarily for machines to execute. Clean code, expressive variable names, and architectural elegance all serve a single purpose: to ensure that the next developer - or our six-months-older self - can understand what on earth happened. Code has always been a cultural artifact, a shared language, a bridge between human intent and silicon. But what happens to this bridge in the era of vibe coding? We are rapidly moving toward a reality where code is generated by LLMs and pull requests are reviewed by LLMs. When the resulting string of characters is spawned by a machine and audited by a machine, human readability immediately ceases to be a primary metric of quality. This forces a radical question upon us: If code no longer needs to be human, does the code itself need to change? Why do we still cling to Python, to micro-frontends, or to neatly structured repositories? We invented these structures to accommodate the cognitive limitations of the human brain - to keep ourselves from drowning in complexity. An AI doesn’t need these training wheels. To an LLM, a 50,000-line monolithic spaghetti-code mess, completely impenetrable to a human eye, is just as easy to parse as the most pristine clean architecture. So, what is the point of the code itself? Is it possible that code is merely a transitional, obsolete interface—a form of "digital carbon monoxide" that we will soon phase out entirely, replacing it with pure intent and mathematical weights? I ran this exact thought experiment in practice recently when I built a tiny, native macOS utility called Portia over a single weekend (you can check it out here: getportia.app ). The app's function is dead simple: when a port gets stuck (EADDRINUSE), it frees it up with a single click. Throughout development, I was almost exclusively in vibe coding mode. I wasn’t thinking about syntax; I was thinking about the problem an

2026-06-18 原文 →
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Ky 2.0 Fetch API Wrapper with Revamped Hooks, Smarter Timeouts, and Built-In Schema Validation

Ky 2.0 is an open-source JavaScript HTTP client built on the Fetch API, featuring significant updates such as consolidated hook handling, enhanced timeout management, and improved URL processing. The release includes response validation through schema validation libraries and addresses migration from earlier versions. It aims to provide a lightweight alternative to axios. By Daniel Curtis

2026-06-18 原文 →
AI 资讯

Embedding Forbidden Text in Spyware to Discourage AI Analysis

At least one malware developer is adding text about nuclear and biological weapons to their spyware, in an effort to stop automatic AI analysis. Details : The _index.js payload begins with a large JavaScript block comment containing fake system instructions and policy-triggering content. Because it is inside a comment, it does not affect JavaScript execution. The runtime skips it. The real malware begins after the comment with a try{eval(…)} wrapper around a large character-code array and a ROT-style substitution function. This header appears designed for AI-mediated analysis, not for Node, Bun, or Python. It attempts to derail scanners or analyst copilots that feed the beginning of a file to a language model without clearly isolating the content as untrusted data. In weak pipelines, this can cause refusal behavior, prompt confusion, context pollution, or premature classification before the scanner reaches the actual malware...

2026-06-18 原文 →
AI 资讯

Geoengineering still faces major practical challenges

Solar geoengineering is often portrayed as a sort of emergency brake. Something along the lines of Pull in case of climate emergency to scatter light-reflecting particles to bounce sunlight out of the atmosphere and cool the planet. But it might be less like a simple brake and more like a complicated, entirely unsolved puzzle. Some…

2026-06-18 原文 →