标签：#tools

The average knowledge worker switches between apps 1,200 times per day, according to a 2024 Harvard Business Review analysis. Each switch is small. The cumulative cost is not. For freelancers managing their own tool stack, the problem is both a productivity drain and a billing leak. What the Research Actually Says The most cited figure comes from Gloria Mark at the University of California, Irvine: it takes an average of 23 minutes and 15 seconds to fully refocus after an interruption. That number gets quoted a lot, but the context matters. Not every app switch is a full context switch. Checking Slack for two seconds is different from switching from deep coding work to a client call. A more useful framing comes from the American Psychological Association, which distinguishes between task switching (changing what you are working on) and tool switching (changing which app you are using for the same task). Both have costs, but tool switching is uniquely wasteful because it does not change the work -- only the interface. You are still working on the same problem but spending cognitive effort navigating a different app. For freelancers, the most expensive switches are the ones between a task manager and a time tracker, between a calendar and a task list, and between a project view and a communication tool. These happen multiple times per hour during active work, and each one breaks the low-level focus that produces billable output. How to Audit Your Current Tool Stack Before consolidating tools, figure out what you actually use. For one week, keep a simple log: every time you open an app to do work (not social media or entertainment), note it. At the end of the week, tally the list. Most freelancers find they use 6-10 tools daily. The typical list looks something like this: Task manager (Todoist, Asana, Notion) Time tracker (Toggl, Clockify, Harvest) Calendar (Google Calendar, Outlook) Communication (Slack, email) File storage (Google Drive, Dropbox) Invoicing (FreshBook

Automatic customer context for product teams and agents. Discussion | Link

Emojis and GIFs, Anywhere on Your Mac Discussion | Link

Computational language built for humans and AI agents Discussion | Link

Automatic short-form video editing Discussion | Link

Android becomes an intelligence system Discussion | Link

AI replies that are trained to sound like you Discussion | Link

Learn new words from the world around you Discussion | Link

AI agents are making software development faster. That is great. But there is a problem I do not think we are talking about enough: testing is not speeding up in the same way. In many teams, testing is still held together by spreadsheets, meeting notes, screenshots, chat messages, and the memory of a few experienced QA engineers. That worked when delivery was slower. It becomes fragile when one developer can use multiple agents to change code across several modules in a single afternoon. The bottleneck is no longer "can we write more test cases?" The bottleneck is: Can the team prove what was tested, why it was tested, what failed, what was fixed, and whether the release is safe? That is the problem I built testboat for. The Most Dangerous Sentence Before A Release The sentence I worry about most is not: We did not test this. At least that is honest. The dangerous sentence is: I think we tested this. That sentence usually means the team has test artifacts, but they are disconnected: requirements live in a doc test cases live in a spreadsheet automation scripts live somewhere in the repo execution results live in CI logs or chat bugs live in an issue tracker release reports are written manually before sign-off Each piece may be useful on its own. But when a Tech Lead asks, "Which requirements are not covered?" or a founder asks, "Can we release today?", the team has to reconstruct the answer manually. That is not a testing process. That is institutional memory under pressure. AI Makes This Gap Worse AI agents are very good at increasing throughput. They can: implement a feature faster refactor code faster generate UI faster write automation faster fix bugs faster But faster change creates more testing uncertainty. If an agent changes the authentication module, what should be rerun? If a test fails, is it a product bug, a flaky automation script, or an environment issue? If a developer says "fixed", has the failed test actually been rerun? If a release report says "ma

See if Japanese AI search recommends your brand Discussion | Link

Turn your Mac notch into an intelligent active workspace Discussion | Link

Automated profanity censoring for video & audio Discussion | Link

Every developer has done it. You hit a README, you see the install command: curl -fsSL https://example.com/install.sh | sh And you run it. Maybe you skim the script first. Maybe you don't. But you run it. I've been doing this for years. And each time, a small voice in the back of my head says: you have no idea what that script actually does. You just piped a stranger's code straight into your shell. Eventually I got tired of ignoring that voice. What the pattern actually is curl | sh is not a bad pattern — it's a fast, convenient pattern with a real trust gap. The script runs with your permissions, in your shell, right now. It can: Install something with sudo Delete files with rm -rf Write to your disk with dd Access your SSH keys or .env files Set up a cron job or a systemd service that runs again next reboot Decode and run a payload with base64 | eval Most install scripts do none of these things maliciously. But many do several of them legitimately — and you wouldn't know which ones until something went wrong. --- ## What I built instead I'm a solo founder based in Ouagadougou, Burkina Faso. I build with heavy AI pairing — I'm not a trained engineer, I work with Claude, review the output, and ship. This tool ( peek ) was AI-paired and reviewed by me before release. peek is a ~130-line POSIX shell script that sits in front of the pattern: # Instead of: curl -fsSL https://example.com/install.sh | sh # Do: peek https://example.com/install.sh Before anything runs, peek: Fetches the script Scans it for risky patterns Prints a risk score and the exact dangerous lines Asks you to confirm — and refuses to auto-run a HIGH-RISK script unless you type RUN You can also pipe into it, or run it in analysis-only mode: curl -fsSL https://example.com/install.sh | peek # analyze from a pipe peek --print ./downloaded.sh # never runs, analysis only What it flags (and what it doesn't) The patterns peek checks: Root escalation — sudo , running as root Destructive file ops — rm -rf , fi

OS runtime unifying GA4, PostHog, + Stripe into a local db Discussion | Link

Powerful computer built into lightweight see-through glasses Discussion | Link

Turn any HTML/CSS design into an editable InDesign file Discussion | Link

AI Drum Machine MPC in your browser Discussion | Link

AI for API Design & Testing in 2026: Speakeasy vs Swagger AI vs Postman AI — I Built 3 APIs with Each API design is one of the most tedious parts of backend development. Writing OpenAPI specs, generating SDKs, testing endpoints, maintaining documentation — it's all hours of work that could be automated. In 2026, three tools are fighting for your API workflow: Speakeasy , Swagger AI , and Postman AI . I spent 4 weeks building the same REST API with each one, tracking time savings, code quality, and actual developer experience. Here's what actually happened. The Test Setup: Building a Real API I built a simple but realistic ecommerce API (GET/POST products, orders, user management) three separate times, measuring: Time to spec — writing the OpenAPI definition Time to SDK generation — generating client libraries Time to testing — setting up test cases and running them Documentation quality — readability, examples, completeness Iteration speed — how fast you can modify the API and regenerate everything All three tools were given the same requirements. Same laptop, same environment, same skill level (senior backend engineer). Speakeasy: The SDK Generation King Setup time: 8 minutes (CLI install, auth, first config) Learning curve: Low — clear docs, straightforward CLI Speakeasy is laser-focused on one problem: turning your API spec into production-ready SDKs. It generates TypeScript, Python, Go, Java, and more from a single OpenAPI definition. The Good SDK quality is exceptional. The generated TypeScript SDK was production-grade immediately — proper error handling, retry logic, request/response typing, retries built in. No cleanup work needed. Multi-language generation is instant. After writing the OpenAPI spec once, I had Python, Go, and TypeScript SDKs in < 2 minutes total. Each one was framework-idiomatic (using popular libraries in each language). Versioning is smart. Speakeasy automatically versioned SDKs and generated changelogs. When I modified the spec, it detect

Run your AI agents over any app, even games. Discussion | Link

Transform songs into retro Gameboy-style game console music Discussion | Link