标签：#Product

The open source VAPI alternative Discussion | Link

I read back six months of my own work journal and found three different note-taking systems, only one of which I remember deciding to build. This is what the volume of information actually did to my notes, what got better, what I lost, and how I capture things now.

I tested the best webcams across various prices to find the top option. Here’s what I learned.

These powerful, high-capacity portable batteries are the best ones when laptop charging is a priority. All have at least 20,000 mAh, enough to charge most laptops twice.

As gaming laptop prices continue to rise, it’s increasingly difficult to find affordable options that aren’t terrible. Here are your best options based on performance and cost.

This burgeoning wearable tech lets you talk to an AI assistant, listen to music, or check out a display screen from the comfort of your very own face.

Microplastics are in everything, especially your coffee. A new generation of plastic-free drip coffee brewers is trying to change this.

Take control of files and editors Discussion | Link

The Scenario Every Developer Recognizes It is mid-2026, and you have a feature to ship. You open ChatGPT or Claude, type something like "build me a function that parses webhook payloads and routes them to the right handler," and wait. The model returns something plausible. You paste it in, run it, and it almost works. So you prompt again: "fix the edge case where the payload is missing the event key." Another round. Then another. Forty-five minutes later, you have code that functions, but you also have a conversation thread that looks like a debugging session rather than a build session. You never actually described what you were building. You just started building it. This is the default mode for most developers using AI coding assistants in 2026, and it is expensive. According to McKinsey's State of AI in 2024 report ( source ), organizations that adopt structured design and planning approaches before implementing AI tools report higher success rates and better integration outcomes compared to those using ad-hoc implementation strategies. The pattern holds at the individual developer level too. Jumping straight into prompting skips the step that makes prompting useful: knowing precisely what you want before you ask for it. The fix is not a better model. It is a different sequence. What Design-First Actually Means in Practice Design-first means producing a written artifact that describes your system before you write a single prompt asking an AI to build it. Not a full technical document. A tight, structured description of inputs, outputs, constraints, and edge cases. Think of it as the brief you would hand to a contractor before they start work. The contractor analogy is useful because it reframes the relationship: you are not collaborating with the model in real time, you are commissioning it with a clear scope. Here is what that looks like concretely. Instead of opening Google Gemini and typing "help me build a webhook router," you spend ten minutes writing this

Automate boring work. Extensible and free desktop AI agent. Discussion | Link

A few weeks ago I wrote about building a reproducible test harness for comparing free AI coding models before you commit . That harness answers one question: which model should I use? It does not answer the harder follow-up: once a model generates a patch for my real codebase, when is it safe to merge? This week there was a great discussion on DEV about "understanding over origin" — the idea that it doesn't matter whether code came from a human or a model, only whether someone actually understands it. I agree with the principle, but principles don't survive contact with a busy afternoon. What survives is a checklist with teeth. So here is the pipeline I bolted onto my model harness: every AI-generated patch has to pass through a scripted review gate before I even read it, and the script produces a scorecard that tells me how carefully I need to read it. The problem with eyeballing diffs When a model produces a 40-line diff that looks idiomatic, my brain does a dangerous thing: it pattern-matches on style and skips semantics. The code reads like something I'd write, so I approve it like something I'd write. The failures I've actually shipped from AI-generated code were never syntax errors — the tests even passed. They were things like: A retry loop that retried on the wrong exception type, so real errors got swallowed. A query filter that was subtly wider than the one it replaced (tests passed because fixtures were too small to notice). A dependency added for a one-liner the standard library already covers. All three would have been caught by asking four boring questions before reading the code. So I scripted the questions. The review gate: a reproducible artifact The gate is a small shell script. It takes a patch file, applies it to a throwaway worktree, and runs four checks. It never touches my working branch, and it prints a one-line verdict at the end. #!/usr/bin/env bash # review-gate.sh <patch-file> <base-branch> set -euo pipefail PATCH = " $1 " BASE = " ${ 2 :

Stop Ghosting People You Actually Like Discussion | Link

I had a podcast pipeline I was proud of. It took a transcript, turned it into a two-person conversation with text-to-speech, laid in the music, and produced an MP3 I could publish. I'd built it in one app, and it worked. I loved the output. So when I started a second app that needed the same flow, I didn't want to rebuild the pipeline. I already had one. I just wanted it over there. So I asked the AI to copy it. And it did. Here's the part that matters: I didn't just copy it and hope. I checked. I opened a fresh session (a clean one, no memory of the first) and told it to look at the new pipeline and make sure everything was right. It went and looked. It came back and told me everything was good. Everything looked good. Or so I was told. Then I loaded the first real transcript and ran it. It was wrong. Not a little wrong. The voices were wrong. The music didn't come in when it was supposed to. It didn't cut off when it was supposed to. It didn't fade. It just stopped. The words were all there, every one of them, in the right order. But everything that made the first pipeline good (the timing, the production, the feel) was gone. I walked away from my desk for a bit. It pissed me off, because I'd done what I was supposed to do. I'd asked. It had answered. The check was green. And the check was a lie. Here's what I think I actually got wrong, and it's not "I trusted the AI." It's subtler than that. When I asked a fresh session to "make sure everything's good," I got back a confident yes. But the session had no way of knowing what good sounded like. It never heard the first pipeline. It had no stake in whether the podcast was any good. It reported what it could see (the code looked reasonable) and what it could see was almost never the thing I actually cared about. That's the trap, and it isn't a beginner's trap. I have a whole process built to avoid exactly this: spec, adversarial review, a plan, a build, a code review. And I skipped it, on a task I decided was too sma

Swipe right on your next customer Discussion | Link

I build software with AI all day. A reading app for dyslexic kids. A map that lives on your desktop. A meditation app. A fox in my menu bar. Some of it with Claude, some with Gemini, some at 2am with whatever model was awake. The code was never the problem. The problem was six months later, opening a file and having no idea what we were thinking. Not what it does — the code says that. Why it's like that. What we tried that didn't work. What we weren't sure about. That part evaporated the moment the editor closed. So we started leaving a note. It's called MurphySig , and it's not a tool — it's a comment: // Signed: Kev + claude-sonnet-5, 2026-07-14, Confidence 0.5 (spike; // compiles, on-device run pending), Prior: Unknown // Review: claude-fable-5, 2026-07-14 — the on-device run HAPPENED same // day: gemma-4-12B-it-4bit loads + describes the app icon correctly, // 265 prompt tokens/image, 7333MB peak. Confidence now 0.9 for the // instrument itself (measured live). That's a real one, from M1K3 's codebase. Signed 0.5 in the morning, reviewed 0.9 the same evening, measurement attached. Confidence as a live value, not decoration. The one that sold me on my own convention My favourite signature lives in Cartogram's map engine. Three models worked that file across two months. In June, one of them recorded a performance overhaul: drift updates moved to "1s intervals," 52% CPU down to zero. In July, a newer model read that note, saw the shipped constant was 0.1s, took the mismatch for a bug, and "fixed" it. On hardware, every longer interval was stop-motion. So it reverted — and then wrote this into the file: So 0.1s was not a regression; it is load-bearing, and the 1s in the 06-21 note is the part that was wrong. [...] the standing lesson is that drift cost needs Instruments, not reasoning. The confident note turned out to be the bug. The code was innocent. And the correction is now part of the file's memory, so nobody — human or model — "fixes" that constant again. That

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. Star git-lrc to help devs discover the project. Do give it a try and share your feedback. A few years ago the AI conversation looked like this. "Should I pay $20?" "No, $200." "Actually this new tool is $39/month." My wallet started looking like it had gone through a startup funding winter. Then Google quietly walked into the room and started dropping free AI tools like Oprah handing out cars. "You get an AI IDE!" "You get a workflow builder!" "You get a GitHub coding agent!" ...except nobody really noticed because Google announced them across five different events, Labs pages, GitHub repos, and random blog posts. So I spent some time collecting the ones developers will actually find useful. No "AI that writes your wedding speech." No "AI that guesses your spirit animal." Just tools that can actually help you ship software. Bookmark this one. 1. Pomelli https://labs.google/pomelli If you've ever launched a side project, you already know the painful truth. Building the product is fun. Writing 37 LinkedIn posts explaining the product... not so much. Pomelli takes your website, understands what your product does, builds a brand profile, then generates social posts around it. Think of it as hiring an intern that actually reads your landing page before tweeting. Would I let it post automatically? No. Would I happily let it generate the first draft so I don't stare at a blinking cursor? Absolutely. Perfect for: Indie hackers SaaS founders Open-source maintainers pretending they enjoy marketing 2. Stitch https://stitch.withgoogle.com Remember when designing an app meant opening Figma... ...moving a button 3 pixels... ...asking for feedback... ...moving it back 3 pixels? Stitch skips a surprising amount of that. You describe the interface. Or upload a sketch. Or even paste a wireframe. It generates modern UI designs and can even produce

Proactive sales agent that books, shows up, and runs demos Discussion | Link

Open Source harness for multi-agent coding workflows Discussion | Link

A coding agent with no context usually hesitates, searches, or asks a question. A coding agent with stale context can be much more confident. That is the dangerous case. The file still exists. The instructions look deliberate. The generated JSON is valid. The agent follows it exactly — into a package that stopped owning the feature two weeks ago. Nothing looks broken until the edit is already in the wrong place. I wanted repository context to have an expiration signal that CI could verify, not a date someone had to remember to check. The failure is not missing documentation Imagine a monorepo where packages/auth owns token validation. The repository publishes a machine-readable handoff: { "startHere" : "docs/for-agents/packages/auth.md" , "editRoots" : [ "packages/auth" ], "checks" : [ "pnpm --filter @example/auth test" ] } Later, token validation moves to packages/security . A maintainer updates the source documentation but forgets to regenerate the handoff index. There are now two internally consistent answers in the same repository: the source documentation says packages/security ; the generated agent context still says packages/auth . The old answer is not malformed. That is precisely why it is risky. I reproduced the drift with one edit I tested this against the public fixture in Doc Bridge , using version 1.2.6. The first index and freshness check passed: Index is fresh expected: 359355e5... actual: 359355e5... Then I changed one agent-facing source document: - Package: packages/os-core - Layer: L1 + +Token validation now belongs to packages/security. I did not touch the generated index. The next check returned exit code 1: ak-docs gate run index-freshness Index is stale. Run: ak-docs index expected: b099695d... actual: 359355e5... After I ran ak-docs index , reviewed the generated change, and ran the gate again, both hashes matched and the check passed. The hashes are not trying to prove that the documentation is true. No checksum can do that. They prove a na

🦄 Thanks @francistrdev for starting the conversation that really got me to thinking about this idea in the first place. I started truly working with AI shortly before I started writing these posts a little over a year ago. My thesis was simple at the time: prove that AI was far more capable a tool than what I had seen anyone using it for so far. My proof was strictly gut instinct and I spent a lot of time fighting with Copilot to prove I was right. Not all of those experiments went according to plan exactly, but I'm still convinced I'm right. That particular ADHD spiral has came and went, and most of it is ingrained as habit. I don't use Spec Kit because by the time it showed up I already had my own version running. I also need to get back to sharing what really works for me. So here we are again. Back to writing (with AI) and the proof to back it all up. One thing up front, because somebody is going to ask: everything here is personal projects and my portfolio . There's no critical prod system anywhere in this post, and if there were, a few of these answers would shift. Not all of them — I'd still let AI run a lot further off-leash than most of my enterprise counterparts would. 🐒 The Org Chart Has One Employee 🪧 I don't just use AI as a tool. I design it as a living system, and I grow the tech as the tech grows. I ran one prompt across Codex, ChatGPT, Claude Code, Cowork, and Gemini, all separately, and asked every one of them what was actually different about the way I work. Five different systems, each one with its own long history of putting up with me, and not one of them could see what the others said. One came back with this: I use AI to write code, review the AI-written code, review the review against the live branch, test the corrections, and then record whatever went wrong as a rule for the next AI. Apparently I recreated management. Most of the private exchanges quoted in this post came out of that same pile, whether they were my own prompts, my memory fi