258 stories
When I work with coding agents I usually have a few things going at once, and after a couple of sessions I no longer know what's finished, what's half done and what's waiting on me. I tried a TODO.md, but I never looked at it. Jira or Linear felt silly for a one-person project.
When a support chatbot gives a wrong answer, the first instinct is to blame the model. But when I looked at how these failures are described in support and operations communities, a different pattern kept showing up: the model was faithfully repeating the documents it was given.
Because I find it difficult to read the textual output of agents after each modification or planning, and relate everything to the big picture, I built this simple application (with the help of Claude Code). It is an Electron application that has an integrated terminal linked to the Chalkboard.
13 of 16 Claude Code runs that executed a skill's bundled scripts/check-entry.sh also loaded its source into context, with Read or cat, although the skills docs label such a script "executed, not loaded".
Что должно быть в pet boarding forms: полный чек-лист для владельца зоогостиницы Если вы держите небольшую зоогостиницу или груминг-салон на 1–10 сотрудников, вы наверняка сталкивались с ситуацией: клиент привозит питомца, а вы лихорадочно ищете, где записана информация о прививках, аллергиях или…
Mấy tháng gần đây, mình thấy team nào dùng AI coding agent (Claude Code, Codex CLI, Cursor, Aider...) cũng gặp chung một vấn đề. Hôm nay agent làm rất tốt. Sang hôm sau, nó lại quên sạch, dùng sai convention, chạy sai lệnh test, sửa nhầm file generated.
For as long as I can remember, I’ve been a night owl. Nick at Nite reruns were my favorite lullaby as a teen, and although I now opt for streaming shows and audiobooks as an adult, my experience remains the same—when the world goes to sleep, I’m wide awake.
Real‑time voice apps—think live streaming, virtual assistants, or interactive gaming—have to juggle a handful of tight constraints. Low latency, high fidelity, and graceful scaling are not optional; they’re the foundation of a great user experience.
Imagine a hamster in its wheel. It runs faster and faster, 30% faster, then 50%. If we measure its performance in revolutions per minute, every indicator is green. Yet it has not travelled a single metre.
By Robin Winters · First published July 30, 2025 · Republished October 2, 2026 Original LinkedIn edition · Readable HTML edition · Robin’s portfolio Original wording, images and captions. Technical observations and opinions retain their original context.
Let’s talk about a silent cultural crisis happening inside modern engineering teams—one that is costing your company thousands of dollars a month in hidden labor and system instability.
We spend a lot of time discussing new AI tools, comparing models, building things, and exploring what’s possible. I do this too. But I want to hear more about what happens after the experimentation. What problem has AI helped you solve, and what difference did that make in your life?
An AI coding agent can inspect your repository, spin up a new endpoint, modify the database schema, wire the frontend, generate unit tests, handle errors, and produce a migration—all in under ten minutes.
Most small businesses don't fail at AI because the models are weak. They fail because they automate the wrong thing first, or they automate a process that only exists in someone's head.
You push code to Git. A few minutes later, your application is running in Kubernetes. No manual kubectl apply. No SSH into servers. No copying YAML files. No "Did someone forget to deploy production?" Sounds good, right? This is the idea behind GitOps.
You have a topic. You open your AI assistant and start typing. First you explain your blog: who reads it, how you write, what you covered last month. Then the topic. The draft comes back sounding like every other draft. You ask for a title. Then a summary. Then a search description and some tags.
Voice‑cloned marketing isn’t science fiction any longer. When you can hand‑craft a brand’s tone, pitch, and even its personality in a few lines of code, you’re giving your audience a more authentic, engaging experience—and you’re doing it at scale.
When I research a topic, I usually end up with too many tabs, a separate notes document, and a chat window. The frustrating part is not collecting information. It is remembering where a quote came from and which of my notes belonged to that source.
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