标签：#Product

Nothing ruins a great night of sleep faster than getting too hot. We slept on a myriad of cooling mattresses to find which ones drew the heat away best.

Turn product screenshots and prompts into motion videos Discussion | Link

Product analytics told you what happened. We handle it. Discussion | Link

Being a developer, we all create many projects for learning, work, and experiments. Over time my machine started filling up — not with source code, but with generated junk : node_modules target dist / build framework caches like .next , .angular , .nuxt and more of the same across every cloned repo Every few months I would hunt folders manually, delete something, free a few GB, then the same problem would come back. Only learning about “clean your disk” tips doesn’t help much. Building something for the problem does. So I ended up building ArtifactSweep — a small open-source tool for this everyday developer issue. The real problem As developers we regenerate these folders all the time: npm install cargo build ng build They are not our source of truth. But they sit on the SSD for months. The painful part is not only size. It is: Finding them across many project roots Knowing how big they are before delete Not deleting the wrong folder by mistake I wanted something that could: Scan a folder tree Show sizes Let me clean with more control Work on my day-to-day machines (Windows, Linux, Mac) Step 1: Start with a CLI I started with the command line first. Why CLI? Fast to build and test Fits terminal-first workflow Easy to script and share The CLI is called sweep . Basic usage: # Safe: only list junk under a path sweep scan . # Preview deletes sweep clean . --dry-run # Delete sweep clean . On one of my project folders alone, it reclaimed nearly 5 GB . That was enough validation: this is not a fake problem. Every active developer hits it. Step 2: Then came the desktop app CLI is great when you already know the path and trust dry-run. But sometimes I wanted to: See a list of folders and sizes Filter by type Confirm before delete Click through without remembering flags So I added a desktop app on top of the same idea (same cleanup job, different UI). Flow is simple: Choose folder Scan Review results (and filters if needed) Clean with confirmation If you like GUIs for this ki

Turn screen recordings into engaging product videos Discussion | Link

I could have built another developer portfolio. You know the one. Dark background. Glowing buttons. "Full Stack Developer | AI | Cloud | DevOps" Six project cards. GitHub link. Done. But honestly, that doesn't feel like me. Before I was interested in AI, software engineering, cloud, automation and all the other things I keep breaking and rebuilding, I was just a kid who loved Spider-Man. And the older I got, the more I realized that I didn't actually relate to Spider-Man because he was a superhero. I related to Peter Parker . The curious kid. The awkward kid. The kid who builds things. The kid who experiments. The kid who fails and somehow keeps going. That felt familiar. So when I started building my portfolio, I wanted it to represent that. I called it: 🧪 Peter Parker's Lab The idea is that my portfolio is basically my digital lab. A place where I can show what I'm building, what I'm learning and what I'm experimenting with. 🕷️ Peter Parker → curiosity 🕸️ Spider-Man → persistence 💻 Developer → everything I'm building today And honestly, "lab" describes my development journey pretty well. I build something. It breaks. I investigate why. I fix it. Then I get another idea and break something else. 😂 That's the fun part. I'm currently interested in building things around: AI AI agents automation full-stack applications developer tools cloud infrastructure DevOps local-first software I'm not trying to pretend I've mastered all of it. I'm trying to keep learning by building real things . That's what I want this portfolio to show. Not just a list of technologies. Not just a list of GitHub repositories. But the problems I'm curious about and the things I'm actually trying to create. 🌐 Peter Parker's Lab https://peterparker-lab.vercel.app/ This is version one. I'll keep changing it as I change. New projects. New experiments. New ideas. Probably new bugs too. Because maybe the best portfolio isn't one that says: "Look how much I know." Maybe it's one that says: "Look what I

Record doctor visits and get clear summaries Discussion | Link

Nothing ruins a great night of sleep faster than getting too hot. We slept on a myriad of cooling mattresses to find which ones drew the heat away best.

Ever since I joined the platform, I wanted to post about a topic I was really passionate about....

The biggest mistake in choosing an AI coding tool is comparing autocomplete latency. Cursor and Windsurf are editors with agent abilities. Claude Code works mostly through a terminal on your local repository. GitHub Copilot spans IDEs, GitHub, code review, and a cloud agent. Replit Agent connects generation to a hosted environment where the app actually runs. CodeGeeX provides affordable IDE help for Chinese-language development. They execute in different places. That means a single "best AI coding tool" ranking is a category error — the right question is where the AI should run your work. The four execution models IDE assistants and agentic editors (Cursor, Windsurf, Copilot IDE features, CodeGeeX) stay close to your current edits. Feedback is immediate, and you stay in control of scope. The cost is that complex work still consumes your attention, and two overlapping AI editor subscriptions rarely make sense — run a two-week crossover pilot and keep one. Local terminal agents (Claude Code) read repositories, edit files, and run commands on your machine. This fits debugging, dependency migrations, and test loops. The security docs describe a read-only default with permission requests, and you should keep that default: start read-only, smallest directory, no broad allowlists for network, deletion, or deployment commands. Cloud coding agents (GitHub Copilot cloud agent) work in an ephemeral Actions-powered environment and come back with commits or a pull request. Good for bounded issues, tests, and docs. Budget is not just the seat — AI credits and Actions minutes are separate. Hosted application environments (Replit Agent) go from natural language to a running prototype in the browser. Great for education and proof-of-concept. Test git import/export, database migration, and code export before you depend on it. Quick decision table Primary workflow Evaluate first Main risk Frequent coding inside one AI editor Cursor Editor migration; broad changes still need review Cr

How long does it take to publish one article? If you run a one-person company, the honest answer isn't "the few hours of writing." It's everything that comes after — moving it into your newsletter, copying it to one dev community, pasting it into another, translating it for an English audience, cutting a short version for X, dropping a note somewhere else. That relay of copy-paste-reformat costs more energy than the writing itself. This week I killed that chore. Now I finish a draft, drop it into a single spreadsheet, run one command, and it fans out to eight platforms on its own. But I don't want to talk about "I built an automation tool." That's the small part. I want to talk about three judgments that are worth far more: where a solo founder's real bottleneck lives, how this machine should actually be built — and how it nearly lied to me. Writing isn't the bottleneck. Distribution is. Most people assume the hardest thing about a one-person company is content output. It isn't. AI has already crushed the marginal cost of writing to the floor. What actually eats your time is the repetitive labor of pushing the same piece across a dozen platforms: log in, paste, fix formatting, upload the cover, edit the tags, publish, log it. Five minutes per platform, eight platforms, forty minutes — every single day. This labor has one especially miserable property: it creates no new value. It's pure hauling of things that already exist from one place to another. And yet you can't skip it — no distribution means even great writing goes unread. So when you work alone, the first thing to industrialize isn't "write faster." It's "hand the entire distribution chore — the certain, repetitive, judgment-free part — over to the machine." A central dispatcher plus plug-in channels I didn't build one giant "fully automatic publisher" for this. Those look great in a demo, but they're a nightmare to change — adding a single platform means touching the whole flow. Instead I split it into two l

Most discussions about AI coding tools start with "which model is best?" I've found that's the wrong first question. The better question is: which of my tasks actually need the strongest model, and which ones don't? In my earlier posts I wrote about building a small evaluation suite for AI coding models and a falsification loop for reviewing AI-generated refactors. This post is the missing piece between them: a routing layer that decides, per task, whether a free-tier model is good enough — and a way to measure whether that decision was right, instead of trusting vibes. The problem: paying frontier prices for boilerplate work When every prompt goes to the most expensive model by default, two things happen: You burn budget on tasks a weaker model handles fine (renaming, boilerplate, docstrings, simple test generation). You never build intuition for where the strong model genuinely matters, because you never see the failure distribution of the cheap one. The fix isn't a blog-post benchmark. It's a per-task routing rule plus a log you can audit weekly. Step 1: Classify tasks by blast radius, not difficulty Difficulty is subjective. Blast radius — what breaks if the output is wrong and you don't catch it — is not. I use three tiers: Tier Task examples Failure cost Default route Low Rename/refactor with compiler backing, boilerplate, doc comments, unit test scaffolding, commit message drafts Caught by compiler/CI in seconds Free/cheap model Medium New function in an existing module, bug fix with a clear reproducer, small migration script Caught by code review or tests, costs an hour Free model first, escalate on failure High Concurrency changes, auth/payment logic, schema migrations on live data, security-sensitive parsing May reach production silently Strongest available model + mandatory human review Two rules make this table work: Escalation is cheap, so bias toward the free tier. If the free model's output fails your checks, you escalate that one task. You lose minut

AI that builds a digital version of everyone you work with Discussion | Link

You look at the bill and it makes no sense. You did not feel like you worked more than usual, you asked the same kinds of questions, and the counter doubled anyway. Nobody tells you where it went, so you assume you must be the one asking too much. It is not you. I measured 681 of my own sessions: nine requests out of ten cost almost nothing. What drains your subscription is the moments when the AI keeps hammering the same file. That is one request in seven, and it eats four tenths of everything it produces. What I did I kept a record of all my work with an AI for four months: 681 sessions, across 41 different projects, between 17 April and 10 August 2026. Every exchange leaves a trace of what it consumed. So I did not guess anything: I added it up. Fair warning: part of the result proved me wrong. Nine requests out of ten cost almost nothing That is the first finding, and it changes everything. When you ask your AI for something ordinary — add a page, fix this text, explain that to me — it barely registers on your subscription. You can do plenty of it. It is the remaining 10% of requests that eat more than half of everything. One bad request can cost as much as thirty good ones. So the question is not "am I talking to it too much". The question is: what happens in those moments? The moment that costs: when it keeps hammering I looked at what happens inside those requests. It is always the same scene. You ask it to fix something. It edits a file. It does not work. It edits the file again. Still nothing. It edits it again. And all of that without you saying a word in between. Here is the weight of it: What is happening Out of 100 requests Share of your subscription It touches the same file 3+ times 15 41% It touches the same file 5+ times 8 21% One request in seven eats four tenths of everything. And comparing a hammering request to a normal one: it produces six times more text to end up in the same place. In almost every case I re-read, the final result was already w

Create AR experiences with AI, no code required! Discussion | Link

Stops AI agents before they break something Discussion | Link

Not a flying drone (though count to think of it, that might work), I mean a drone system tray app,...

When I first started testing APIs, Postman was one of the first tools I reached for. It was...

Figma for markdown Discussion | Link

Let AI agents transcode and process video Discussion | Link