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An experimental synth for playable generative sound Discussion | Link

Everyone can generate a website now. Type a prompt, get a decent page — that part is a commodity. The question nobody's answering is what happens on day 2 : the leads start arriving, a line of copy needs a tweak, someone asks for a section you forgot. That's when a website stops being a design project and becomes a thing you have to run — and where most tools hand you yet another dashboard to log into and dread. Sitelas makes a different bet. Because a Sitelas site lives inside Claude through an MCP connector, the same chat that built the site also runs it . You don't open an admin panel to see who filled out your form, write back, or change the page. You just ask. Here's what "running your site from a chat" actually looks like. First, the 30-second why Claude connects to outside tools through MCP connectors — you already use the ones for Gmail, Calendar, and Drive. Sitelas has one too. Add it once (in claude.ai: Customize → Connectors → Add custom connector , and paste https://sitelas.com/api/mcp ), and Claude can do things with your site, not just talk about it: publish it, read its submissions, restyle it, add a section. Your site becomes an automation endpoint sitting next to your other connectors — the thing a Webflow or Squarespace site can't be. New here? Start with How to Build a Website From a Claude Chat . "Did anyone fill out my form today?" That single question is the whole idea. You ask; Claude reads your site's submissions, surfaces the new lead — Maya, a bakery owner — and drafts a warm reply in your voice. One message, no tabs. It works because every form on a Sitelas site captures submissions to your inbox automatically — no integration required. You can open that inbox in the dashboard any time: …but running your site from a chat means you rarely need to. Claude reads those same submissions straight from your site, so "who wrote in today, and what do they want?" is answered in the thread you're already in — not in a panel you have to remember to ch

My CLAUDE.md has one line near the bottom that I wrote months ago and mostly forgot about until I started actually paying attention to what it does: ## Important Note after your work done codex will review what you done. Terse, no punctuation, clearly typed in a hurry. But it's a real instruction that fires on every session in this repo: I finish a change, and a second model reviews it before I consider the work done. I added it half as an experiment. A few months in, it's changed how I work more than almost anything else in the setup, and not in the way I expected. I thought it would catch bugs. Mostly it doesn't, not directly. What it actually does is force a triage decision on every single piece of feedback, and getting that triage wrong is where all the pain lives. The three buckets Early on I treated every review comment the same way: read it, do it. That lasted about a week before I was silently making changes I didn't agree with because a second AI suggested them, and separately burning a stupid amount of time re-litigating comments that were just wrong or out of scope. What actually works is sorting every comment into one of three buckets before touching code: Fix it, no discussion. The comment is unambiguous, low-risk, and doesn't touch anything architecturally significant. Just do it and move on. Ask first. The comment is ambiguous, or it touches something that would require a real judgment call, or the "fix" would be a bigger refactor than the comment implies. Stop and get a human decision before acting. Skip silently. The comment is a duplicate of something already handled, or genuinely doesn't apply. Don't reply just to say "not doing this," don't leave a comment thread as evidence of having read it. Silence is the correct response to a non-issue. The failure mode I kept falling into before I had these buckets explicitly was collapsing 2 into 1: treating "ambiguous" as "just pick an interpretation and go." That's the actual source of review fatigue, not

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

RGB TVs are the latest hotness in the world of televisions, and I’ve tested many of the latest models to see which you should buy.

It’s not the upgrade Ring 4 owners have been waiting for, but it’s easily the best smart ring Oura has ever made.

Build landing pages with Aceternity UI Discussion | Link

75 ready-to-use email templates for work & job search Discussion | Link

Why this matters I’ve worked on a few frontend projects where code reviews turned into style debates—tabs vs spaces, semicolons, quote styles… you name it. It slows everything down and adds zero value. At some point, I realized this shouldn’t even be a discussion. So now, whenever I start a project, I set up Prettier + ESLint + Husky on day one. No debates. No manual fixes. No messy PRs. This post is exactly how I do it. 🧰 What each tool actually does Prettier → formats your code automatically ESLint → catches bad patterns & enforces rules Husky → runs checks before commits (so no one skips them) Together → clean, consistent code without thinking ⚙️ Step 1 — Install dependencies npm install -D prettier eslint husky lint-staged 🎯 Step 2 — Setup Prettier Create: prettier.config.js module . exports = { semi : true , singleQuote : true , trailingComma : ' all ' , tabWidth : 2 , }; Create: .prettierignore node_modules dist build 🔍 Step 3 — Setup ESLint Initialize: npx eslint --init Then tweak your config: .eslintrc.js module . exports = { extends : [ ' eslint:recommended ' , ' plugin:react/recommended ' , ' prettier ' ], rules : { ' no-unused-vars ' : ' warn ' , ' react/react-in-jsx-scope ' : ' off ' , }, }; 👉 Important: "prettier" disables ESLint rules that conflict with Prettier. 🔗 Step 4 — Connect ESLint + Prettier Install: npm install -D eslint-config-prettier That’s it. Now ESLint won’t fight Prettier. 🐶 Step 5 — Setup Husky Initialize Husky: npx husky init Add pre-commit hook: npx husky add .husky/pre-commit "npx lint-staged" 🚀 Step 6 — Setup lint-staged Add to package.json : "lint-staged" : { "*.{js,jsx,ts,tsx}" : [ "eslint --fix" , "prettier --write" ] } 💡 What happens now? Every time you commit: ESLint checks your code Prettier formats it Only clean code gets committed No more: “fix formatting” PR comments broken lint rules in main branch inconsistent code styles 🧠 Real impact (from experience) After adding this to a team project: PR noise dropped a lot reviews

Now hide individual files + folders for more private desktop Discussion | Link

AI Spreadsheet for Business Discussion | Link

An activity log tells us what an agent did. A decision log should also tell us what it considered and rejected. Without rejected options, a later reviewer sees a clean path that never existed: model B was selected, the task restarted, the result succeeded. Missing are the reasons model A was unsuitable, why staying put was worse, and what new evidence would change the choice. That information matters for trust and recovery. It lets people challenge a decision without reconstructing the entire session. Execution history is necessary, but different The MonkeyCode model-switch record at commit c58bcd4 stores the task and user, from/to model IDs, request ID, whether to load the session, success, message, session ID, and timestamps. The switch use case creates that switch record, restarts the task with the target configuration, and records the result. That is valuable execution history. It answers “what switch was requested and what happened?” The expanded rejected-options structure below is my design proposal , not a claim about MonkeyCode's current schema or interface. Add the decision before the outcome A reusable record can separate choice from execution: { "decision_id" : "task-42-model-switch-7" , "context" : "The task needs the required tool-call contract." , "chosen" : { "option" : "model-b" , "reason" : "Passed the declared capability contract" , "evidence" : [ "evaluation/capability-model-b.json" ] }, "rejected" : [ { "option" : "model-a" , "reason" : "Required tool-call case failed" , "evidence" : [ "evaluation/capability-model-a.json" ], "revisit_when" : "Adapter version changes" } ], "execution" : { "request_id" : "req-switch-7" , "result" : "success" , "session_id" : "session-9" } } The key field is revisit_when . “Rejected” should not mean universally bad. It should mean unsuitable under a specific context and evidence set. Design the interface for progressive disclosure Do not paste this JSON into the main task timeline. Use three layers: Timeline: Switch

The live AI employee inside your product, serving every user Discussion | Link

I'll Be Honest: The Internet Already Has Translators I know. Language translation isn't a new idea. There are already huge translation platforms out there. So when I started working on a translator for my tools website, I wasn't thinking: "I'm going to reinvent translation." Not at all. My thought was much simpler: "Can I make quick translation feel less distracting?" My Frustration Was Actually Pretty Simple Sometimes I just need to translate text. That's it. I don't want to: Create an account Open five different menus Break a long text into tiny pieces Jump between multiple tools I want to paste the text... Choose a language... And get the translation. So I Built My Own Version 👉 https://allinonetools.net/language-translator/ The tool currently supports 200+ languages and language variations . You can: Detect the source language Select the target language Translate long text Upload text Use voice input Listen to the result Copy or share the translation And I wanted to keep the text experience simple without forcing users into tiny input limits. Just: Enter → Choose Language → Translate 200+ Languages Sounded Simple Until I Saw the List English. Hindi. Gujarati. Spanish. Arabic. These are the languages most people immediately think about. But then I started going through the full language list. Abkhaz. Acholi. Afar. Alur. Aymara. Baluchi. And many more. Honestly... I hadn't even heard of some of them before building this. That was probably my biggest learning moment. I Realized How Small My Own View of the Internet Was As a developer, it's easy to build around the languages we personally know. For me, seeing English, Hindi, and Gujarati feels normal. But the internet is much bigger than my own experience. Someone somewhere may be trying to understand a sentence in a language I've never even heard spoken. That changed how I looked at this tool. The Hard Part Wasn't Adding a Dropdown A dropdown with 200+ options looks impressive. But that's not the real problem. The

I usually write here about Next.js, caching, and the bugs that steal your sleep. But this post is...

The error looks like a typo in the remote URL. Usually it isn't. On a machine with more than one GitHub account signed in, this message is GitHub's way of saying wrong identity, not wrong address. The symptom A repo clone that has worked for months suddenly can't fetch or pull. The remote URL hasn't changed. The repo hasn't been renamed or deleted; you can open it in the browser just fine. Yet the command line insists otherwise: $ git pull remote: Repository not found. fatal: repository 'https://github.com/<org> /<repo>.git/ ' not found Why GitHub's error is misleading For a private repository, GitHub won't confirm or deny that the repo exists to a caller who isn't authorized to see it. Confirming would leak information about private repos to anyone probing URLs. So instead of a clear 403 Forbidden , an unauthorized request gets treated the same as a repo that truly doesn't exist: a 404 , which git renders as Repository not found . "Repository not found" on a private repo almost always means the credential attached to this request can't see it. It's rarely a wrong URL. The usual cause: two accounts, one keychain This shows up most on machines used for both personal and organization-owned work: a personal GitHub account for side projects, and a separate account (or SSO identity) that actually holds access to the org's private repos. Credential helpers cache one token per host. If the cached token belongs to the personal account, every git operation silently authenticates as that account, including ones against the org repo it has no rights to. personal-account --(switch)--> org-account Active, no repo access Has repo access Diagnose it First, confirm the remote itself is fine. $ git remote -v If the URL opens in a browser while logged into the right account, the remote isn't the problem. Next, check which credential is actually cached. On macOS with the default helper: $ git credential-osxkeychain get <<< $'protocol=https \n host=github.com' username=personal-account

Five litmus tests for “this will raise your intelligence” claims A pocket BS detector for brain-training ads, LinkedIn gurus, and your own wishful thinking. 1. Which dial moved? Intelligence talk smuggles four dials into one word: Dial Rough meaning g / fluid ability Harder to move; overclaimed constantly Knowledge & skill Moves with practice and education Acute sharpness Sleep, illness, mood, substances Long-horizon brain health Aging, disease risk, lifestyle If the claim does not say which dial, it is marketing soup. 2. What was the control? “People got better” is almost worthless. Better than last week of the same game is practice. Better than an active control that also gets attention, novelty, and expectation is interesting. Lumosity-style lawsuits exist because companies sold soup as medicine. 3. Near or far? Near transfer: you got better at this and close cousins. Far transfer: the effect jumped to something distant (school grades, matrix reasoning, life outcomes). Far transfer is scarce. Second-order metas on cognitive training keep finding near yes, far ≈ no once bias is handled. That is not cynicism. It is how human learning usually works. 4. Who was the sample? A processing-speed protocol that helps older adults does not automatically mint IQ points for a 24-year-old optimization bro. Deficient populations respond differently than well-nourished ones. Age and baseline matter more than branding. 5. Is the score flattering the seller? If the outcome is “our app score,” the app got better at measuring app use. Prefer outcomes that hurt to fake: standardized batteries against active controls, academic scores, carefully logged real-world performance. What I built instead of another vanity mini-game IntelligenceMax is a live reasoning gym: frontier models write fresh distinction-style items at your edge, and scoring is transparent / IRT-style. That is deliberate practice under honest difficulty, not a clinical IQ battery and not a promise that general intellige

My 2nd article this week, I'm supposed to keep it to just once per week but whateverrr, I've had this...

Apple’s revamped Siri is more than a voice assistant; it’s now the backbone of the iPhone user experience. You can try it now through the iOS 27 public beta.

Most teams test email by not testing it. The send path gets a mock — expect(transport.send).toHaveBeenCalledWith(...) — and everyone agrees that's "good enough." The receive path gets skipped entirely, because there's no honest way to assert on a real inbox from a test runner. So the one part of your system that talks to the outside world over an unreliable, asynchronous, third-party channel is the part with the least coverage. That's backwards. The reason email is hard to test isn't the sending. It's the asserting . You can fire POST /messages/send all day, but to prove the message actually left, rendered correctly, and arrived with the body you expected, you need a real mailbox you control — one you can read programmatically and throw away when the run finishes. Shared Gmail test accounts almost get you there, but they bring OAuth on the runner, catch-all races between parallel workers, and a 90-day token that expires the night before a release. This post is about a different fixture: a disposable Agent Account created at the start of a CI run and deleted at the end. You mint a real mailbox per run (or per test), point your application at it, send and receive real mail, assert on the actual message body, and tear the whole thing down. No OAuth. No shared inbox. No leftover state. What an Agent Account gives you here An Agent Account is just a Nylas grant with a grant_id . That's the whole trick, and it's worth saying plainly because it's what makes this pattern cheap: an Agent Account works with every grant-scoped endpoint you already know — Messages, Drafts, Threads, Folders, Attachments, Webhooks. There's nothing new to learn on the data plane . If you've ever called GET /v3/grants/{grant_id}/messages , you already know how to read a test inbox. The difference from a normal grant is provisioning. A regular grant needs a real human to complete an OAuth flow. An Agent Account is created with a single API call — no OAuth screen, no refresh token, no human. It's a m