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You use Claude Code, or ChatGPT, or both, every day. Quick question: how many messages did you send last month? Which model ate most of your budget? How much did prompt caching actually save you? You don't know. I didn't either. That's a weird gap. We instrument everything else — git activity, deploy frequency, test coverage — but the tool we now spend the most hours inside is a black box. The vendor dashboard, if it exists, is a billing page, not a mirror. So I built four tiny tools to fix that for myself. They all run 100% locally . No accounts, no API keys, no telemetry, no network calls. They read files that are already on your disk and print something you can look at. All four are open source on github.com/greymoth-jp — and because that's a real claim, the only thing I'll ask is that you grep the source yourself before you trust me. Here's the privacy point up front, because it's the whole design: these read your data, but your data never leaves your machine. That's not a feature I'm bolting on for a marketing line. It's the reason the tools are small enough to audit in one sitting. The one number that changed how I work Before the tools, here's what I assumed: my Claude Code bill is dominated by the prompts I write, so to spend less I should write tighter prompts. Compress the context. Trim the system message. The usual advice. I ran the numbers on my own ~/.claude transcripts and got this: component share of cost cacheRead 72% cacheWrite ~19% output the rest input ~0.3% Input — the thing everyone tells you to compress — was 0.3% of my spend. Compressing my prompts to save money would've been optimizing the rounding error. Worse: compressing a static prompt changes its bytes, which busts the prefix cache, which can make the bill go up . The real cost center was cache reads: long sessions dragging a fat context forward, turn after turn. That points at completely different levers — cache hygiene (milestone /compact , /clear before the context balloons, keeping C

I built a large feature. That's not what this is about. What changed is the baseline — the standards, docs, and automation that exist now and didn't two weeks ago. Everything after this will be built on top of it. Automated tests now ship with new features QA testers were testing. The product was covered. What didn't exist was automation — no E2E suite, no unit tests for new work, no repeatable spec. Now it does. The manual QA cycle stays. The automation catches what humans miss on the tenth pass. Quality leap going forward. Human hours saved. The next feature ships with both. The baseline is set Knowledge lives in the repo. Bug catalog with root causes — so the same thing doesn't get fixed twice. Tech debt inventory with a phased plan. Testing strategy documented, not assumed. GraphQL schema committed and validated against — drift gets caught before it ships. Pre-commit hooks that enforce the standards automatically. The frontend and backend documentation are cross-referenced as single sources of truth. The agent instructions point to the right places. Everything new builds on what's already written. Schema-first development The workflow is now: if the schema accommodates the new field, reuse what exists. If it doesn't, the schema update creates the new structure, the data migrates, and everything stays consistent. No guessing. No drift. One source of truth for what the data looks like. The feature is what you see. The baseline is what you don't — and it matters more.

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Here's a thing that surprises people the first time: an email sent through the API does not carry the signature the user set up in Gmail or Outlook. Provider signatures live in the provider's compose UI, and a programmatic send bypasses that entirely, so a message your app sends goes out with no signature at all unless you add one. The Nylas Signatures API is how you add it: store an HTML signature once, then attach it to a send by ID, and the signature gets appended to the message for you. This post covers signatures from two angles: the HTTP API your backend calls, and the nylas CLI for creating and testing one from the terminal. I work on the CLI, so the terminal commands below are the ones I reach for when I'm setting a signature up. Nylas signatures are separate from provider signatures The first thing to get straight is that these are not the user's existing signature. Nylas doesn't sync the signature configured in Gmail, Outlook, or any other provider, and that provider signature is never applied to mail sent through the API. If a message your app sends needs a sign-off, you create that signature with this API and attach it explicitly; there's no inheriting it from the connected account. That separation is deliberate, because a programmatic send is a different context from a person typing in their webmail. It does mean the responsibility is yours: a user who connects their mailbox expecting their familiar signature to appear on app-sent mail won't get it automatically. Stored signatures are grant-scoped, living at /v3/grants/{grant_id}/signatures , so each connected account has its own set, and they're HTML, so a branded sign-off with a logo and links works the same as a plain one. Create a signature Creating a signature is a POST /v3/grants/{grant_id}/signatures with a name and an HTML body . The name is for you, a label to find it by later; the body is the markup that gets appended to outbound mail. The response returns the signature with its ID, which is w

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Introduction I'm Ahmer. I'm 19, I'm doing a 4-year Software Engineering degree at a fairly...

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Most people lose hours pretending to work or study. I was one of them. I kept setting Pomodoro timers and ending up scrolling Twitter during breaks. That's not rest. That's just a different kind of distraction. So I built Mognota — a free wellness companion for screen workers. The Problem You don't have a discipline problem. You have a system problem. Your brain has a hard biological limit. Sustained attention peaks at 20-45 minutes before quality drops sharply. Long unfocused hours are not work — they are the feeling of work. The solution isn't more willpower. It's intentional recovery. What I Built Mognota pairs a Pomodoro timer with 50+ guided wellness activities: 👁️ 20-20-20 eye break reminders 🫁 Guided breathing & 7 pranayama techniques 🧘 Desk yoga, HIIT, Tai Chi, stretches 🎵 Binaural beats & ambient soundscapes 🧠 Meditation & NSDR protocols 📓 Gratitude journal, mood tracker, brain dump 🎮 Sudoku, sliding puzzle, fractal explorer The Technical Part This is what dev.to might find interesting: Pure HTML, CSS, vanilla JS — zero frameworks Everything in a single HTML file All 8 notification sounds synthesized with Web Audio API localStorage only — nothing leaves your device Works offline once loaded 109+ languages supported No npm. No build step. No dependencies. Just open and use. Try It 👉 https://mognota.com/ Completely free. No account. No ads. Forever. Would love brutal honest feedback from this community.

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I build Voxis, an open-source Windows app that translates whatever your system is playing — a video, a game, the other side of a call — and plays the translation back as spoken voice, a few seconds behind the speaker. No subtitles, no virtual audio cable, no bot joining your meeting. The "no virtual cable" part is the bit worth writing about. Almost every system-audio tool on Windows tells you to install VB-CABLE or VoiceMeeter, or to drop a bot into your call. Voxis doesn't, for incoming audio. This post is how that capture engine works, and the sharp edges I hit building it in Python. I'll be specific about what's hard and honest about what's not mine to fix. The goal Read the exact audio the user is hearing — the post-mix system output — at 16 kHz mono, and do it without installing anything. Then stream it to a translation model and play the result back, all while the original keeps playing underneath. Three constraints fall out of that: Driverless. If it needs a reboot and a driver, it's not zero-setup. No self-feedback. The app plays translated audio into the same system mix it's capturing . Naively, it would capture its own voice and translate the translation. That has to be impossible by construction, not patched with an echo gate. Realtime-safe. Capture can't stall. If the downstream VAD or garbage collector hiccups, the WASAPI ring buffer must not overflow. WASAPI process-loopback: capturing the mix, minus yourself Windows 10 version 2004 added the ApplicationLoopback API — a way to activate an IAudioClient in loopback mode scoped to a process tree, either including only that tree or excluding it. Excluding our own process tree is exactly what constraint #2 needs: the captured mix is everything the user hears, with Voxis's own output removed. You don't get this client from the normal IMMDeviceEnumerator path. You activate it by name through ActivateAudioInterfaceAsync , passing the loopback parameters in a PROPVARIANT carrying a BLOB : params = AUDIOCLIENT_

About a year ago, I turned my gaming PC into a local AI Lab. And yes, the most important word in that sentence is LOCAL . Let me tell you the story of how I sacrificed my gaming hours to build several tools, and now I'm going to tell you about this one that I use every single day. The Problem: Token bankruptcy Day to day, all of us developers who work with Artificial Intelligence share the same headache: tokens and rate limits . We're all victims of the high prices that come with constantly running inference with AI agents like Claude Code, Codex, or Gemini CLI (yeah, I love working from the terminal, I LOVE CLIs). While I was building AI systems (agent orchestration, LLM fine-tuning ), I was burning through way too many tokens. I tried tweaking the prompts and cleaning up the junk in my context, but the real devourer of my quota showed up when I had to learn a new tool. I was implementing solutions in QGIS (QGIS is a free, open-source Geographic Information System (GIS) software that allows users to create, edit, visualize, analyze, and publish geospatial data on maps) for a project and I didn't know the interface 100%. Like any dev facing something new, I leaned on AI agents: I'd take a screenshot, send it over, and ask for explanations. Here's an important fact that hurt my wallet: A screenshot on my MacBook (Full HD resolution of 1920x1080) burns about 258 tokens per tile on models like Claude. That adds up to roughly 1,548 tokens per image (sounds like a lot, and yeah my friend, it is way too much when we're talking about context). Now imagine sending dozens of these images a month trying to understand a complex interface as a 2x dev (99x, I'd say, in this new AI era). I was eating through my hourly Claude allowance just doing visual queries, leaving me with no quota left to generate the actual code I really needed for my development. The Epiphany (and the Hardware) One day, during a forced break thanks to a Claude rate limit , I looked over at my Gaming PC. I

Claude Code is a genuinely powerful CLI coding agent. Its context window handling and multi-file reasoning set a high bar in 2026. But it comes with real constraints - it requires an Anthropic API key, charges per token, locks you into Claude models only, and its source code is closed. For developers running local-first workflows, working in air-gapped environments, or simply preferring auditable tooling, those limitations are dealbreakers. The good news: the open-source ecosystem has matured significantly. Nine production-ready alternatives now cover every major workflow pattern - from terminal-first pair programming to fully autonomous task execution. Why Open Source Matters for AI Coding Agents AI coding agents operate at a high level of system trust. They write files, run commands, and modify your repository. That makes transparency genuinely important - not just philosophically. Open-source licensing lets you read the code, audit its behavior, self-host without sending data to a third party, and customize it for your team's needs. Beyond trust, the practical advantages are real. Open-source agents are model-agnostic by design. They connect to whichever LLM you prefer - Claude, GPT, Gemini, DeepSeek, or a local model via Ollama - letting you optimize for cost and capability on a per-task basis rather than being locked to one pricing tier. OpenCode - The Closest Open-Source Drop-In for Claude Code OpenCode has emerged as the de facto open-source answer to Claude Code in 2026, crossing 161,000 GitHub stars under an MIT license. It connects to over 75 LLM providers via Models.dev - including local Ollama models - and lets you switch providers mid-session. Internally it uses a dual-agent architecture: a Plan agent handles task decomposition while a Build agent executes changes. LSP integration brings symbol resolution into the terminal. Multi-session support lets you run parallel agents on the same project simultaneously. OpenAI Codex CLI - Auditable and Sandbox-Fir