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Hey DEV community! 👋 How many times a day do you need to format a messy JSON string, convert a CSV file, or parse a timestamp? And how many times do you find yourself pasting that data—which might contain API keys, user emails, or proprietary code—into a random website you found on Google? We’ve all done it, but in an era of constant data leaks, it’s a massive security risk. That’s exactly why I built Parsify . What is Parsify? Parsify is an all-in-one data converter and developer toolset designed to handle your daily formatting, parsing, and data manipulation tasks completely offline and client-side. No servers, no tracking, and absolutely no data leaks. Everything happens right inside your browser sandbox. 🚀 Key Features 100% Secure & Offline: Your data never leaves your local machine. Once the page loads, you can literally pull your internet plug and it will still work perfectly. All-in-One Toolkit: No more bookmarking ten different sites for ten different tasks. From JSON formatting and base64 encoding to data conversions, it’s all under one roof. Built for Speed: A clean, lightning-fast UI with batch-processing support to keep your workflow uninterrupted. Privacy by Design: Zero tracking scripts, zero ads, and zero database logging. Why I Built It Most online utilities are bloated with tracking pixels, pop-up ads, and cookies. Worse, you have no idea what happens to the data you paste into their input fields. As a developer, I wanted a tool that felt like a local desktop app but possessed the accessibility of a web app. Parsify is the bridge. It gives you the convenience of a web utility with the strict security boundaries of local execution. Check It Out (It's Free!) If you’re tired of compromising on data privacy for quick utilities, give it a spin: 👉 parsify.tools I’m actively working on adding more tools and converters to the suite. I would absolutely love to get the DEV community's feedback! What converters or formatting utilities do you use daily that you

Half a year ago I asked a simple question: during an online call, could a short, to-the-point hint appear on my screen in a second or two — while the other person is still talking? Not an after-the-fact transcript, but help in the moment. The result is a desktop assistant (macOS + Windows). Below is an honest breakdown of what turned out to be hard, and which solutions worked. Engineering only, no marketing. Architecture in one paragraph On the device there are only two things: audio capture and a thin UI overlay. All the "brains" (provider keys, prompts, model selection) live on the server. The client gets a short-lived per-session token and streams audio; the server returns the transcript and the generated answer. I picked this split not for "security theater" but because otherwise keys and prompts would have to be baked into the binary — and both leak instantly. Hard part #1: system audio, not the microphone The mic only captures you. You need the other party's audio — i.e. the system output. And that's where the platform pain starts: macOS. For a long time there was no native "give me system audio" API; the classic path was a virtual audio device (BlackHole/Soundflower-style) or, in recent versions, ScreenCaptureKit, which can hand you a process's audio. ScreenCaptureKit turned out to be the best option: no kernel extensions for the user to install. Windows. WASAPI loopback saves you — you can grab whatever is going to the output device, without virtual cables. Takeaway: "system audio capture" is not one feature but two different subsystems for two OSes, and most of the early bugs were about permissions and device selection, not about audio itself. Hard part #2: latency is everything A hint that arrives 6 seconds late is useless — the conversation has already moved on. The latency budget has three parts: STT (speech → text). Streaming only. Batch "recognize after the phrase ends" immediately adds 1–2 seconds. The key metrics weren't "overall accuracy on a benchm

I built a Chrome extension called Margin — a news reader that lives in the browser's side panel and shows one bite-sized story at a time, instead of an infinite-scroll feed. This is a build log: the decisions, the constraints that pushed back, and a couple of things I had to solve in slightly unusual ways. Why the side panel Chrome shipped chrome.sidePanel in MV3 a while back and most uses I saw were utility tools — note-taking, translation helpers. Nobody was using it for content consumption. News felt like a good fit: a side panel that stays open next to whatever you're working on, where you tap through headlines in a couple of minutes without leaving the page. The reading model is intentionally narrow: one card, one headline, one short summary, tap to read the full article at the source . No infinite scroll, no algorithmic feed. If you've used InShorts, the shape will be familiar. The stack Preact + Vite + @crxjs/vite-plugin . Preact because the side panel is a small UI surface and I didn't want React's weight for what's essentially a card stack and a settings screen. @crxjs/vite-plugin handles the MV3-specific build wiring (manifest generation, service worker loader, HMR for the extension context) that would otherwise be a lot of manual plumbing. The constraint that shaped onboarding chrome.sidePanel.open() requires a user gesture . You cannot call it from a background service worker on install — Chrome will throw. That one constraint shaped the whole first-run experience. My first instinct was "just auto-open the panel on install so people see it immediately." Doesn't work. The fix ended up being two-pronged: On chrome.runtime.onInstalled with reason === 'install' , open a real browser tab with a short walkthrough (find the icon → pin it → open the panel). The button on that page calls sidePanel.open() — valid, because the click is the gesture. The first time the panel itself is opened, show an in-panel welcome screen before onboarding, nudging the user to pin

I build algorithmic trading bots as a side project. Nothing fancy — just small strategies that trade US equity options automatically. The problem I kept running into wasn't the strategy logic. It was the data. Every time I wanted to pull real-time options chains, Greeks, or IV, I had two options: Pay $99+/mo to a data provider Scrape something I probably shouldn't be scraping Neither felt right for a hobbyist project. So I built Market-Options — a simple REST API for US equity options data at $20/mo. What It Does It's a plain REST API. No SDK, no special client library — just HTTP requests and JSON responses. It covers four endpoints: chain — full options chain for a given underlying contract — data for a single contract contracts — batch lookup across multiple contracts contract-overview — Greeks, IV, expiration details Coverage is the top 100 US equity underlyings, which accounts for roughly 95% of actual US options volume. If you're building a bot that trades SPY, QQQ, AAPL, TSLA, or anything in that tier — it's covered. Why Only 100 Underlyings? Because that's what most people actually trade. When I looked at my own bots, and at what most retail algo traders focus on, the top 100 covers everything practical. Exotic underlyings with low volume are also harder to get real data on reliably — so rather than promise coverage I can't deliver, I focused on doing the core well. A Simple Example curl "https://api.market-option.com/chain?symbol=SPY&expiration=2025-01-17" \ -H "Authorization: Bearer YOUR_API_KEY" Response is clean JSON: { "symbol" : "SPY" , "expiration" : "2025-01-17" , "options" : [ { "strike" : 480 , "type" : "call" , "bid" : 3.45 , "ask" : 3.50 , "iv" : 0.182 , "delta" : 0.42 , "gamma" : 0.031 , "theta" : -0.18 , "vega" : 0.29 } ] } No parsing headaches, no weird date formats. Pricing Free tier : 1,000 credits/day — enough to test and build Pro : $20/mo, unlimited within fair use Trial : New accounts get 7 days of Pro, no credit card required Who It's F

I rebuilt StrictBlock (my app) from the ground up. StrictBlock is an iPhone app blocker and focus app designed to help people stop procrastinating, protect deep work, and build better focus habits. For this relaunch, I did not want to just “refresh the UI.” I wanted to redesign the full product experience around one question: How can I make starting a focus session feel simple, strict, and useful? The new version focuses on reducing friction. Users can create focus profiles for study, work, sleep, deep work, or Pomodoro sessions, then start blocking distracting apps and websites with less setup. I also redesigned the app around accountability. StrictBlock now includes streaks, trophies, weekly reports, widgets, session journaling, and consequences for ending sessions early. As a developer, this redesign was a good reminder that productivity apps are not only about features. They are about behavior. The UI, the flow, the defaults, and the friction all shape whether someone actually stays focused. StrictBlock is now live with a complete redesign. Would love feedback from other builders, iOS devs, and product engineers. Try it here: Download strictblock on appstore

This was originally posted on my blog . I was not planning to build a rate limiting library. I had a FastAPI project, I needed rate limiting, I reached for SlowAPI, which is the most popular choice for FastAPI, and wired it up. Took maybe 20 minutes. Then I started actually using it. The request: Request problem The first thing that got to me was the request: Request requirement. SlowAPI's decorator needs access to the request to extract the client IP or user ID, and the only way it can get that is if your route function accepts it as a parameter. So every rate-limited route ends up looking like this: @app.get ( " /items " ) @limiter.limit ( " 10/minute " ) async def get_items ( request : Request ): return await fetch_items () That request: Request is not doing anything. fetch_items() does not use it. It's there purely because SlowAPI needs it. Small thing, but it bothered me — I like function signatures that say exactly what they need, and this one was lying. The header injection problem I could live with that. The thing I could not live with was the header injection. Rate limit headers are genuinely useful — X-RateLimit-Remaining tells clients when to back off, Retry-After tells them how long to wait. Standard stuff. So I enabled SlowAPI's header injection and immediately hit a wall: every rate-limited route now had to return a Response object directly. Not a dict. Not a Pydantic model. A Response . The moment you enable header injection, SlowAPI expects to work with an actual response object, and if your route returns anything else it just breaks. So I was suddenly doing this: @app.get ( " /items " ) @limiter.limit ( " 10/minute " ) async def get_items ( request : Request , response : Response ): items = await fetch_items () return JSONResponse ( content = jsonable_encoder ( items )) Which is annoying because one of the things I actually like about FastAPI is that you declare a return type, FastAPI handles the serialization, and your OpenAPI schema just works. Sl

Hey everyone, I wanted to share an open-source library I’ve been developing to solve a persistent issue in frontend file ingestion: handling large-file uploads efficiently without blocking the main thread, consuming excessive client-side memory, or introducing heavy npm dependencies. The core architecture leverages Fetch Duplex streams combined with Web Streams API to achieve constant memory usage during large file transfers. For browsers lacking full duplex stream support (such as Safari), it seamlessly switches to an automated chunked XHR fallback at runtime. ⚙️ Core Architecture & Features Constant Memory Footprint: Streams large chunks sequentially using Fetch duplex streaming where supported. Intelligent Runtime Fallback: Detects capabilities instantly and falls back to a robust, chunked XMLHttpRequest pipeline to ensure cross-browser compatibility (including Safari). Resilient Lifecycle Management: Built-in hooks for pause, resume, manual abort, and automated chunk-level retries with a configurable exponential backoff algorithm. Zero Dependencies & Tree-shakeable: Written entirely in vanilla TypeScript with no external runtime dependencies (npm install u/universal-uploader/core). The architecture is highly modular, ensuring that unused upload strategies are completely tree-shaken during compilation. React Primitive Included: Ships with a declarative React hook that maps the entire upload lifecycle to state primitives without causing redundant re-renders. 🛠️ Why Existing Solutions Didn't Fit Most mainstream uploading libraries either rely on heavy multi-part form encodings that require buffering files entirely into browser memory, or pull in heavy polyfill architectures that bloating the initial bundle size. I designed this to isolate the transport layer logic via a composition-based approach, separating the stream controller from the network client. To ensure deterministic behavior, the codebase is fully covered by 127 integration/unit tests validating network

The obvious counterargument to everything I'm building is this: Google already does it. You type "best AI tools for video editing" into Google and an AI Overview surfaces a curated list, synthesized from the same kind of data I maintain, without requiring a click. My three directory sites — Top AI Tools , Find Games Like , and Open Alternative To — are competing with a feature baked into the world's dominant search engine. I launched these sites on 2026-04-23, built on an architecture that runs at about $25/month . Traffic is essentially zero — the sites have been indexed for three weeks and organic crawling takes time. The question I keep returning to isn't whether Google will eventually index my pages. It's whether anyone will prefer clicking through to my site over reading the AI Overview box that already answered the same question. Here's my honest, falsifiable position. The bet, stated plainly By October 2026 — six months post-launch — at least one of the three sites will show organic click trends in Google Search Console indicating real query traffic to specific comparison or filtered-browse pages. I define that as: at least 200 non-homepage organic clicks per month, sustained for two consecutive months, from queries I didn't directly drive through social or newsletter posts. If that doesn't happen, I'll publish the Search Console screenshots and write a post explaining what I got wrong. I'm committing to that here. The counterargument I take seriously AI Overviews have gotten genuinely good at list-and-compare synthesis. If you search "open source alternative to Notion" today, Google often returns a four-item structured list with one-sentence descriptions directly in the Overview box. My Open Alternative To site covers that territory. The AI Overview absorbs the zero-click version of that query. The optimistic response is: "my site appears as a citation source." The pessimistic response is: "Google consumes your signal and stops sending clicks." The pessimist

Teachers and parents lose a surprising amount of time hunting for printable practice sheets, then hitting a sign-up wall or a paywall. So I built a small set of free tools that make the sheet you need in a couple of clicks, with no account and no email. They run entirely client-side in the browser, so nothing is uploaded or stored, and every sheet prints straight to paper or saves as a PDF. What is in the set so far: A maths worksheet generator (addition, subtraction, multiplication, division, mixed) with an answer key A name-tracing sheet generator for early writers A spelling worksheet generator A word search maker Routine and chore chart makers The hub is here: Free printable tools A few build notes for anyone making something similar: Keeping it fully client-side meant zero backend cost and instant load, which matters when a teacher opens it on a school tablet on a slow connection. The fiddly part was the print layout. A dedicated print stylesheet with CSS page breaks gave a much cleaner result than forcing a PDF library. Removing the sign-up step takes out all the friction, which is the whole point for a busy classroom. I run a small Brisbane children's book imprint, Lantern Path Books , and these started as a side project to help the parents and teachers who read our picture books. They are free to use and share. Happy to talk through the print-layout approach if it is useful to anyone.

Whitespace problems are the ones you can't see until they bite. A pull request where half the "changes" are trailing-space diffs. A shell script that breaks in CI because someone's editor saved it CRLF. A .env with a UTF-8 BOM that makes the first variable name mysteriously not match. A file with no final newline that turns one-line changes into two-line diffs forever. None of it shows up on screen. All of it shows up in git blame . Today, catching this takes three or four tools stitched together — and I got tired of that, so I built wssweep : one zero-config command that finds all the common whitespace smells and, with --fix , cleans them in place. $ npx wssweep src/app.js (2) 14: trailing-whitespace trailing whitespace - missing-final-newline no newline at end of file config.yml (1) - mixed-eol mixed line endings (CRLF×3, LF×1) ✖ 3 whitespace issues in 2 files (mixed-eol=1, missing-final-newline=1, trailing-whitespace=1) $ npx wssweep --fix # clean them It checks seven things: trailing whitespace, mixed CRLF/LF line endings, lone CRs, a missing final newline, extra trailing blank lines, a UTF-8 BOM, and tabs mixed with spaces in one indent. Non-zero exit on findings, so it's a CI gate. pip install wssweep gets the same tool in Python — byte-for-byte identical output and fixes. Why not editorconfig-checker / pre-commit / prettier? Because each does part of it: editorconfig-checker reports — but you have to author an .editorconfig first, and it can't fix anything. pre-commit 's trailing-whitespace / end-of-file-fixer / mixed-line-ending hooks do fix, but only inside the pre-commit framework, and they're three separate hooks. Nobody runs them ad-hoc on a fresh checkout. prettier fixes whitespace only as a side effect of reformatting all your code, and won't touch files it can't parse. dos2unix does line endings and nothing else. wssweep is the one npx / pip command, no config and no framework, that does the whole set at once and drops into any CI regardless of toolch

Body: Hey everyone 👋 I've been building hermes-agent-skills — a production-grade skill collection for Hermes Agent that does three things no other skill pack does: 1. Self-Evolving Skills Skills aren't static YAML. The built-in EvolutionEngine tracks 5 health dimensions (usage frequency, success rate, user corrections, freshness, command validity), assigns a health score, and tells you which skills are rotting. Think of it as npm audit for your AI assistant's capabilities. 2. SOUL.md Persona Awareness Drop a SOUL.md in your Hermes config — naming conventions, comment density, architecture preferences, commit style — and every skill that touches code output adapts to it. hermes-skill soul generate bootstraps one in one command. The persona-aware-coding skill reads it at runtime so your agent writes code that actually looks like you wrote it. 3. CLI Toolchain hermes-skill create my-workflow # scaffold a standards-compliant SKILL.md hermes-skill validate skills/ # validate against the Agent Skills Standard hermes-skill list skills/ -f json # enumerate with health metadata hermes-skill soul generate # bootstrap a persona file What's in the box (v1.1.0): | Skill | Phase | Hermes-only Feature | |---|---|---| | requirement-analyzer | Define | Persistent memory across sessions | | spec-driven-dev | Spec | /skills chain forming workflows | | test-driven-dev | Build | delegate_task parallel test execution | | debugger-coordinator | Verify | browser + terminal + vision tri-tool | | code-quality-guardian | Review | patch auto-fix + /curator tracking | | cicd-orchestrator | Ship | cronjob scheduling + webhook triggers | | skill-curator | Evolve | Direct /curator integration | | persona-aware-coding | Identity | Native SOUL.md persona system | Why this is different: Most agent skill collections are portable but shallow — they can't use any platform's unique superpowers. These skills go deep on Hermes specifically: slash commands, delegate_task, persistent memory, vision+browser+t

Written by Stephanie Dover, Software Engineer 10+ YOE, ex GitHub, Twitch, Microsoft. Creator of Klaussy. LinkedIn · GitHub · Klaussy Desktop · Klaussy Agents TL;DR klaussy-agents is a free, MIT-licensed CLI ( pip install klaussy-agents ) that makes the prose an AI coding agent writes, PR comments, review notes, commit messages, read like a person wrote them. It works in two layers: a humanization spec baked into the agent's skills so it writes clean prose up front, and a deterministic klaussy humanize pass that scrubs the output afterward. The scrubber is rule-based regex, not an LLM, and it never touches code. There's also a part I didn't expect going in: once the AI tells are gone, what's left can read curt and run long, so the spec also handles tone (don't be rude) and length (one sentence for a reply, one to five for a review comment). Repo: github.com/steph-dove/klaussy-agents. The problem You can spot AI-written text now. Everyone can. And the place it grates most is a code review comment or a commit message, where the prose sits next to your name in a thread your teammates read. The tells are consistent. The em-dash is the biggest one. Right behind it: filler openers like "It's worth noting that…" and "I wanted to point out that…", chatbot scaffolding like "Hope this helps!" and "Let me know if you have questions!", and stacked hedges like could potentially . An agent that leaves those in your PR reads like a bot, and people notice. The obvious fix is to tell the model not to do it. Add "don't sound like AI" to the prompt and move on. That helps, inconsistently, and it regresses silently the moment you change the model or the prompt drifts. Editing every comment by hand works too, but hand-editing every comment defeats the point of having an agent write them. I wanted something I could trust without rereading. Why "just tell the model" wasn't enough The honest answer to "why not just prompt for it" is: a prompt asks, it doesn't enforce. The model tries to com

Hey everyone, I've been deep in prediction market infrastructure for a while and just open-sourced a market maker bot designed for CLOB-based prediction markets like Polymarket. What it does: Quotes both sides of a binary market automatically Adjusts spreads based on order book depth and volatility Manages inventory risk to avoid getting stuck on the wrong side of a resolved market Built on top of Polymarket's CLOB API with Gnosis Safe / EOA wallet support on Polygon The core challenge with prediction markets vs. regular markets: Normal market making is about capturing spread. Prediction markets add a brutal edge case — resolution risk. If you're holding YES at 0.6 and the market resolves NO, you're not just down on the spread, you're down the full position. So the bot has to: Track time-to-resolution and widen spreads as resolution approaches Reduce inventory exposure on markets with high directional momentum Use FAK orders to avoid resting limit orders too long near resolution Stack: Rust Polymarket CLOB API Polygon (USDC settlement) SQLite for order state tracking What's next: Dynamic spread model based on implied volatility Multi-market portfolio rebalancing Better signal integration (news feeds, oracle data) GitHub: https://github.com/HarrierOnChain/Prediction-Markets-Trading-Bot-Toolkits Happy to answer questions on the architecture, risk model, or anything CLOB-related. Always looking for feedback from others building in this space.

As a solo developer I wanted a fast, mobile-friendly interactive map for Palworld that didn't bury me in ads. The result is Pindrop , and here are a few of the technical decisions behind it. Rendering 1000+ markers without jank The interactive map uses Leaflet with a custom marker-clustering layer. Markers are served as static JSON from the edge and hydrated client-side, so the first paint is server-rendered and the heavy marker work happens after. A breeding calculator as a pure function Palworld's breeding combos are deterministic, so the breeding calculator is just a lookup over a precomputed table rather than a backend call. That keeps it instant and fully cacheable. Stack Next.js (App Router) for SSR + static generation Leaflet for the map layer Supabase for the small amount of dynamic data Vercel for hosting and edge caching If you play Palworld, the guides section collects the breeding, location and boss notes I kept losing track of. Feedback from other devs welcome — especially on the clustering approach.

🌿 Introduction As climate awareness grows, individuals are looking for actionable ways to reduce their personal carbon footprints. However, most carbon calculators are either too complex or offer generic, unhelpful advice. To solve this, I built CarbonWise —a production-ready Carbon Footprint Awareness Platform. It combines deterministic scientific carbon calculations with real-time, personalized AI reduction strategies using the Groq LLM API. Here is a technical deep-dive into how I built, secured, and optimized this application for the PromptWars: Virtual challenge. 🏗️ Architecture & System Design The application is designed to be lightweight, secure, and highly performant, avoiding heavy framework overhead. System Data Flow ┌──────────────────────────────────────────────────────────┐ │ User Browser │ └─────────────┬──────────────────────────────▲─────────────┘ │ HTTPS (POST / GET) │ Rendered HTML/CSS ┌─────────────▼──────────────────────────────┴─────────────┐ │ Flask App (app.py) │ └─────────────┬──────────────┬───────────────▲─────────────┘ │ │ │ ┌─────────────▼──────────┐ ┌─▼─────────────┐ │ │ SQLite DB (carbon.db) │ │Secure Session │ │ │ - Users & Logs │ │ Cookies │ │ │ - WAL Mode Enabled │ └───────────────┘ │ └────────────────────────┘ │ Structured JSON Insights ┌────────────────────────────────────────────┴─────────────┐ │ Groq API (Llama 3.1) │ │ - Model: llama-3.1-8b-instant │ └──────────────────────────────────────────────────────────┘ Backend : Flask (Python) handles routing, user session state, and database operations. Database : SQLite manages users and logs. We activated WAL (Write-Ahead Logging) mode to enable concurrent reads and writes. AI Engine : Connects to the Groq API using the ultra-fast Meta Llama 3.1 8B model ( llama-3.1-8b-instant ). Frontend : Rendered server-side with Jinja2 templates and styled with a custom dark-mode glassmorphism design system in Vanilla CSS. ⚙️ Feature Deep-Dive 1. Deterministic Carbon Calculations ( carbon_engine.p

You added .env to .gitignore . You felt responsible. But three weeks later it's still in the repo, still pushed to GitHub, still in every clone — because adding a path to .gitignore does nothing to a file git already tracks. That's not a bug. It's documented behavior: .gitignore only stops untracked files from being added. Anything already committed keeps getting tracked, ignore rule or not. So the secrets, build artifacts, and 40 MB log files that were committed before someone wrote the rule just... stay. The fix is one command — git rm --cached — but only once someone notices . And nobody notices, because git status is clean and the file looks ignored. So I built gitslip : a zero-dependency CLI that finds every tracked file your own ignore rules say should be gone, and hands you the exact fix. $ npx gitslip 2 tracked files are ignored by your rules but still committed: config/secrets.env ↳ .gitignore:7 *.env logs/app.log ↳ .gitignore:2 *.log Fix — stop tracking them (keeps your local copy): git rm --cached -- config/secrets.env git rm --cached -- logs/app.log or let gitslip do it: gitslip --apply It tells you which rule caught each file ( .gitignore:7 *.env ), so there's no guessing. And --apply runs the git rm --cached for you — it only un-tracks, it never deletes your working copy. Why not just grep? You can grep your .gitignore patterns against git ls-files . But: A raw grep '\.env' can't tell a still-tracked leftover from a file that's correctly excluded, and it has no idea about !negation rules, build/ directory rules, nested .gitignore files, .git/info/exclude , or your global core.excludesFile . Reimplementing gitignore's matching semantics to get this right is exactly the kind of subtly-wrong code you don't want guarding your secrets. gitslip doesn't reimplement anything. It asks git. How it works (the fun part) Detection is a single git incantation: git ls-files -i -c --exclude-standard -c = tracked (cached), -i = ignored, --exclude-standard = use all the

Build-in-public. This is the real architecture behind running ~50 local AI agents on 6GB of VRAM — one GPU lock, an eviction watchdog, a resource governor, and a model router. Originally posted on my blog. The question I get most often is some version of "there's no way you run that many agents on a 6GB laptop GPU." The honest answer: not the way you're picturing it. I don't run 50 models at once. I run one model at a time, very deliberately — and most of the engineering is about scheduling, not inference. Here's the actual architecture. The hard constraint: 6GB of VRAM A single consumer GPU with 6GB of VRAM holds roughly one 7B-parameter model at a usable quantization. Two at once? It thrashes — the GPU starts swapping, latency explodes, and eventually a driver out-of-memory can take the whole machine down. I've had the desktop freeze from exactly that. So the first design rule wrote itself: only one heavy model is allowed on the GPU at any moment. That sounds limiting. It isn't — because almost nothing I run is latency-sensitive. A blog post that publishes at 7am doesn't care if it was generated at 6:52 or 6:58. Once you accept that your AI workforce is a batch system, not a chat window, the whole problem changes shape. A lock, not a crowd Every agent that needs the GPU has to take a lock first. It's a simple file-based queue with: FIFO ordering PID-based ownership Stale-lock detection, so a crashed job can't wedge the line forever If an agent can't get the lock within its timeout, it skips gracefully and tries again on its next scheduled run instead of piling up. So at 50 agents, what's really happening is: dozens of cron-scheduled Python workers wake up throughout the day, and the ones that need the model form an orderly line for it. The fleet is huge; the GPU contention is always exactly one. That's the trick. It's less "50 models" and more "50 employees sharing one very busy workstation, politely." Eviction and a VRAM watchdog Even with the lock, idle models l

You know that feeling when you start a new project and spend the first 20 minutes doing nothing productive? Hunting for the Android keystore. Finding the right .env file. Copying VS Code settings. Again. And again. Every. Single. Project. I got tired of it. So I tried building something to fix it — coffee-installer. How it works Create a collection folder and point coffee-installer to it: mkdir ~/.coffee-collection echo '{ "baseSource": "~/.coffee-collection" }' > ~/.coffee.config.json Add your reusable files to the collection: mkdir -p ~/.coffee-collection/my-app/android/app cp android/app/keystore.jks ~/.coffee-collection/my-app/android/app/ cp android/key.properties ~/.coffee-collection/my-app/android/ Preview before installing: $ coffee diff my-app Diff — my-app ( config ) + add android/key.properties + add android/app/keystore.jks + add frontend/.env.development.local 3 to add, 0 to overwrite, 0 to skip Then install with one command: $ coffee install my-app 📦 Installing my-app... ✅ copied android/key.properties ✅ copied android/app/keystore.jks ✅ copied frontend/.env.development.local ✅ my-app installed. All commands coffee list # see everything in your collection coffee diff my-app # preview before installing coffee install my-app # install into current project coffee pull my-app # sync changes back to collection Why I built this I work across multiple projects — mobile apps, web backends, Flutter apps. Every project needs the same credentials, the same IDE config, the same environment files. The alternative was a folder of files I'd manually copy every time, or worse — storing credentials in a repo (never do this). coffee-installer keeps everything in one local folder that never touches version control. It's not perfect yet, but it already saves me a lot of setup time. Zero dependencies The entire thing runs on Node.js stdlib only — no external packages, nothing to audit, nothing that breaks when a dependency changes. Try it ihdatech / coffee-installer CLI fo

HiveTalk.space is a privacy focused chat app. HiveTalk.space should not be confused with hivetalk.org. While both platforms focus on communication, they are separate projects with different goals and feature sets. HiveTalk is closed source and cloud hosted, making it easy to start chatting without setting up your own server. Despite not being self-hosted, privacy remains a core focus. Private conversations are designed with privacy in mind, allowing users to communicate without unnecessary tracking or intrusive data collection. Every account includes generous free limits. Users can upload files and videos up to 1 GB each, send unlimited messages , and sign in using supported social login providers or a traditional account. Creating communities is simple, with the ability to make your own chat rooms for friends, gaming groups, project teams, schools, or fanbases in just a few clicks. HiveTalk also aims to provide a modern messaging experience with features such as polls, rich text formatting, media sharing, and room management tools, while keeping the interface simple and easy to use. Whether you want a private conversation, a small group chat, or a larger community, HiveTalk is designed to scale without placing artificial limits on everyday usage. Unlike many messaging platforms that reserve key features for paid subscriptions, HiveTalk offers its core functionality for free. The goal is to make private, feature-rich communication accessible without requiring users to pay just to unlock basic messaging features. As the platform continues to develop, new features and improvements are regularly added, with a focus on privacy, usability, and giving communities more control over how they communicate.

Disclosure up front: I'm affiliated with diagram.now — I'm connected to the product. I'm posting this to get developer feedback on diagram import/export interoperability, not to pitch an install. Most teams I've worked with don't have one source of truth for their diagrams. They have: a few Mermaid blocks living in READMEs and Markdown docs, an old Visio ( .vsdx ) or Lucidchart file someone made two reorgs ago, a SQL schema that is secretly the "real" ERD, and a pile of screenshots pasted into docs and tickets. The diagram is rarely the hard part. The hard part is that the same diagram lives in five formats and none of them stay in sync with the docs they're supposed to explain. I've been working on diagram.now , a browser-based editor for technical diagrams — flowcharts, UML, ERD, BPMN, cloud/network architecture, mind maps, wireframes. It's a free browser editor with no signup to start. There's an optional Confluence app for teams that want diagrams editable inside Confluence pages, but that's intentionally not what I want to talk about here. I want feedback on the editor itself, and specifically on the interoperability story. What it does today Import/insert from Mermaid and SQL — paste a Mermaid graph or a CREATE TABLE block to start an editable diagram instead of a static render. Import Lucidchart and Visio .vsdx files — this is migration-oriented, and honestly the part I most want real-world files to stress-test. Export to PNG, SVG, PDF, or a URL. Templates/shapes for the diagram categories above. I'm deliberately keeping the Confluence side secondary. The thing I actually want to learn is whether the browser editor plus import/export is useful on its own. Where I'd love feedback Imports: Which format matters most to you — Mermaid, SQL→ERD, .vsdx , Lucidchart, or something else (PlantUML, draw.io XML, Graphviz)? If you've ever tried to migrate diagrams between tools, where did it break? URL export: Is a shareable diagram URL genuinely useful in your workflow (