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My keyboard fell apart. Now it's your problem. Discussion | Link

What I Built : ( https://github.com/Sai-Emani25/Solomon-s-Taskr ) I transformed my initial, bare-bones task management application, Taskr, into Solomon's Taskr—a significantly smarter, more robust productivity platform. The original project started as a standard way to log to-dos, but it lacked the intelligence to actually help manage time or prioritize effectively. With Solomon's Taskr, I wanted to build a system that doesn't just store data, but actively assists the user. Building this project means a lot to me because it represents a leap from writing basic applications to architecting intelligent, dynamic systems that solve real-world workflow bottlenecks. Demo Link to Final Repository: https://github.com/Sai-Emani25/Solomon-s-Taskr Link to Original Repository: https://github.com/Sai-Emani25/Taskr (Here is a quick walkthrough of Solomon's Taskr in action!) The Comeback Story The original Taskr project had been sitting in my repositories, unfinished and gathering dust. It was a classic case of starting a project with good intentions but abandoning it once the basic structure was complete. It could create, read, update, and delete tasks, but that was it. For the Finish-Up-A-Thon, I decided to completely resurrect and overhaul it. Here are the key changes and implementations that turned it into Solomon's Taskr: Complete Codebase Refactoring: I stripped down the old, inefficient logic and rebuilt the architecture to be highly scalable and maintainable. Intelligent Prioritization ("The Solomon Touch"): I integrated smart features to help organize and prioritize tasks rather than just listing them chronologically. (Note: If you integrated Gemini API or LLMs here for smart tagging, explicitly mention it!) Enhanced UI/UX: I moved away from the clunky, basic interface of the original Taskr and implemented a clean, responsive dashboard that provides a real-time overview of pending and completed tasks. Optimized Data Handling: I refined how the application processes and st

Why I stopped reading "❌ Old vs ✅ New" posts I used to scroll past them. Then I started ignoring them. Now? I don't read them at all. Not because they're "wrong". But because they're incomplete . The problem with "❌ Old vs ✅ New" These posts make everything look easy: One error One fix One clean "New Way" Three bullet points Save the post Done. Right? No. What these posts don't show you 🔹 The 200 failed deployments before that one working fix 🔹 The 300+ errors you solve along the way — not just one 🔹 The Vercel pipelines that break for no documented reason 🔹 The "New Way" that also fails in production 🔹 The gap between documentation and reality What happens in production That clean "New Way" code snippet? It might work on your local machine. But in production, with real traffic, real data, real edge cases? It can fail. Hard. And no three-line post prepares you for that. Why I stopped reading Because these posts teach me solutions to problems I don't have yet . But they don't teach me how to think when nothing works. They don't teach me: How to read error logs properly How to trace a pipeline failure across services How to stay consistent after multiple failed deploys How to know when the "New Way" is actually worse What actually helped me Not templates. Not shortcuts. Real experience: 200+ failed deployments 300+ errors solved (one by one) Broken pipelines fixed by understanding, not copy-paste Production live — not a "demo" or a "tutorial" This is not a "❌ vs ✅" post I'm not giving you a "Here's the fix". Because the real fix isn't three lines of code. It's patience. It's persistence. It's failing and getting back up. And no post can save that to your bookmarks. 👇 Have you ever followed a "New Way" post and had it fail in production?

I shaved my head 15 years ago and never looked back. This GroWell LED cap changed that.

Run AI coding agents hands-free from a heads-up display Discussion | Link

The Logitech G512 X 98 lets you swap between mechanical and analog switches in an attempt to achieve the best of both worlds. Unfortunately, its solution isn't as well thought-out as I'd hoped.

Hello Dev.to Hello everyone, This is my very first post here at Dev.to. I am curious about how companies go about running their business in the backstage and, specifically, what kind of challenges do they face in their inventory management, warehousing, workplace technologies, and overall business systems. There have been plenty of times that I witnessed how little operational inefficiencies turn into big business problems when left untreated for too long. I intend to share my observations and learnings regarding the topics of inventory management, asset management, workflow improvement, and business systems used by companies to keep themselves organized and efficient. Please note that I am not here to pretend having all the right answers. It's simply an attempt at learning and exchanging ideas within the industry. See you around!

AI text correction & translation via keyboard shortcuts. Discussion | Link

EveryPlate is an actual budget meal kit whose plates taste delicious. Options and ingredients are fewer, but simplicity can also be a virtue.

From battery life to privacy, there are many hurdles to the idea taking off.

These are the cleaning robots, water monitors, and toys actually worth buying for pool season.

I added real-time activity logging and security scoring to my Claude Code dashboard The problem with just seeing costs Knowing how much you spent is useful. But it's not enough. The real question is: what is your AI actually doing? Which files did it read? Which commands did it run? Is your environment even safe to run it in? I couldn't answer any of those. So I built the answers in. What's new in v0.1.17 Activity Log — see every action in real-time Claude Code logs everything via hooks. Every file read. Every command executed. Every API call. Risk-labeled. Timestamped. Live. Set it up once in ~/.claude/settings.json : { "hooks" : { "PostToolUse" : [{ "matcher" : ".*" , "hooks" : [{ "type" : "command" , "command" : "curl -sf -X POST http://localhost:3000/api/actions -H 'Content-Type: application/json' --data-binary @- 2>/dev/null || true" }] }] } } Then open http://localhost:3000/activity . Watch your AI's actions stream in real-time. This is the audit layer AI agents have been missing. Security Score — how safe is your Claude Code environment? Scored out of 100. Checks 7 things: Is Bash(sudo *) in your allow list? (-20) Is ~/.ssh/** in your deny list? (-20) Is Bash(curl *) unrestricted? (-15) Are .env files protected? (-15) Is strictMode enabled? (-10) Is Bash(rm *) restricted? (-10) Are hooks configured? (-5) I scored 90/100. What's yours? The point isn't to shame anyone. It's to make the invisible visible — so you can make informed decisions about what your AI is allowed to do. Try it npm install -g @notenkidev/claude-token-dashboard claude-token-dashboard Open http://localhost:3000 GitHub: https://github.com/notenkitoclient-cpu/claude-token-dashboard This started as a simple token counter. It's becoming something bigger — an observability layer for AI agents. More coming.

I recently rebuilt my homelab from scratch. The goal was simple: one machine, everything containerised, zero exposed ports, GPU-accelerated local AI, and a fully automated backup setup. No cloud subscriptions for the tools I use every day. This is the full technical breakdown — what I'm running, how it's wired together, and the hard-won fixes that cost me hours so you don't have to repeat them. What I'm Running Eight services, 26 containers, one machine: Service Purpose Portainer Docker management UI Uptime Kuma Service monitoring (7 monitors) NocoDB Self-hosted Airtable — CRM & leads n8n Workflow automation Open WebUI Local AI chat interface Ollama Local LLM inference (GPU) AFF!NE Collaborative docs & whiteboards Plane Project management (roadmaps, sprints) Duplicati Encrypted daily backups Cloudflare Tunnel Zero Trust secure access — no open router ports All external-facing services sit behind Cloudflare Zero Trust with email OTP. No passwords to manage, no VPN clients — Cloudflare handles authentication at the edge. Architecture ┌──────────────────────────────────┐ │ Cloudflare Edge (Zero Trust) │ │ *.yourdomain.com — email OTP │ └──────────────┬───────────────────┘ │ HTTPS ┌──────────────▼───────────────────┐ │ Ubuntu Machine │ │ │ │ cloudflared (outbound tunnel) │ │ │ │ │ ┌─────▼────────────────────┐ │ │ │ homelab-net (bridge) │ │ │ │ │ │ │ │ portainer uptime-kuma │ │ │ │ nocodb n8n │ │ │ │ open-webui affine │ │ │ │ plane-* duplicati │ │ │ │ ollama (GPU passthrough) │ │ │ └───────────────────────────┘ │ └───────────────────────────────────┘ Everything runs on a shared Docker bridge network ( homelab-net ). The cloudflared container maintains an outbound-only encrypted tunnel — no inbound ports open on the router at all. Ollama runs in Docker with NVIDIA GPU passthrough. The AI model inference happens on the GPU, leaving CPU headroom for all other services. Prerequisites Ubuntu 24.04 LTS Docker Engine + Compose v2 NVIDIA GPU with driver 535+ NVIDIA Container Too

Built a full-stack RBAC admin starter: NestJS (Clean Architecture) + Next.js 16 (FSD). JWT refresh, permission-gated UI, sheet-based CRUD. MIT. Looking for feedback. ⚡ Next.js 16 Admin Dashboard Template Architecture: Strictly adheres to Feature-Sliced Design (FSD) to prevent codebase rot in large applications. Key Features: Full-scale Role-Based Access Control (RBAC) UI, URL-driven advanced tables (TanStack Table v8), global caching (TanStack Query v5), and dynamic sheet-based UX configurations using shadcn/ui and Tailwind v4. Quality Assurance: Pre-configured with Playwright for End-to-End (E2E) testing and automated GitHub Actions CI. 🛡️ NestJS Clean Architecture REST API Architecture: Implements strict layered Clean Architecture (Presentation ➔ Application ➔ Domain 🡨 Infrastructure) ensuring zero database/framework lock-in. Key Features: Advanced authentication via JWT refresh rotation, stateful RBAC with high-performance Redis permissions caching, and enterprise-grade security structures. Quality Assurance: Achieves ~98% test coverage across domain and application layers using Jest.

It is time to put my proposition made in my previous blog post to the test. Is it possible to spec an application for execution by an agent without encoding it in source? Let's find out. One type of application every knowledge worker is familiar with is task management. Every task has a lifecycle status, dependencies on other tasks, and a history of progress. Let's give agents their own. TaskTrack is a simple but non-trivial task management system variant implemented as a Specify spec. It goes beyond checkbox-based to-do lists that agents sometimes use internally and mimics the key system features listed above. TaskTrack defines two procedures: a "Plan Authoring Run" to create an interconnected set of tasks from requirements and a "Plan Execution Run" to advance a previously authored plan toward completion. One execution run might not always be enough to achieve completion, because TaskTrack allows requesting human feedback and incorporating it during the next execution run. Furthermore, every execution run is divided into "Task Processing Run" sub-procedures to allow for advanced agent context management. TaskTrack implements all of this in less than 300 lines of text. If the implementation used source code, then, depending on the programming language, this would be enough space to implement only the required file I/O operations (TaskTrack uses files for simplicity, not a database). Natural language can easily become quite bloated, but a stringent, scientific writing style and extensive use of what the Specify standard offers can effectively counter that. The official test is, how could it be any other way, the implementation of yet another uninspired Breakout clone. The requirements, the completed TaskTrack plan, and the deliverable are contained in the repository. If you want to run the test yourself, the included README file contains the necessary information, including the launch prompts for both the authoring agent and the execution agent. Please note how both

All tests run on an 8-year-old MacBook Air. All results from shipping 7 Mac apps as a solo developer. No sponsored opinion. HiyokoKit's MTP file manager includes QuickLook preview. Press Space, see the file. Native macOS behavior in a Tauri app. Here's how it works — and why it's worth doing. What QuickLook Is QuickLook is macOS's built-in file preview system. Press Space on any file in Finder — that's QuickLook. It handles images, PDFs, videos, and documents without opening separate apps. For a file manager, QuickLook preview is table stakes on macOS. Users expect it. If it's missing, the app feels unfinished. Triggering QuickLook from Rust The qlmanage command-line tool can trigger QuickLook from any process: use std :: process :: Command ; #[tauri::command] async fn preview_file ( file_path : String ) -> Result < (), AppError > { Command :: new ( "qlmanage" ) .args ([ "-p" , & file_path ]) .spawn () .map_err (| e | AppError :: Preview ( e .to_string ())) ? ; Ok (()) } qlmanage -p opens a native QuickLook preview window for the specified path. That's it on the Rust side for local files. For MTP Files: Download First, Preview, Cleanup Files on an Android device don't have a local path — they live on the device over MTP. The flow is: download to a temp file → preview → clean up. #[tauri::command] async fn preview_mtp_file ( device_path : String , filename : String , ) -> Result < (), AppError > { // Download to temp let temp_path = std :: env :: temp_dir () .join ( & filename ); download_from_device ( & device_path , & temp_path ) .await ? ; // Open QuickLook Command :: new ( "qlmanage" ) .args ([ "-p" , temp_path .to_str () .unwrap ()]) .spawn () ? ; // Schedule cleanup after delay let temp_clone = temp_path .clone (); tokio :: spawn ( async move { tokio :: time :: sleep ( Duration :: from_secs ( 30 )) .await ; std :: fs :: remove_file ( temp_clone ) .ok (); }); Ok (()) } 30 seconds gives the user time to view before cleanup. For large files (RAW photos, videos), y

Most of us use AI coding tools in a very normal way. We paste an error, ask for a fix, paste a file, ask again, run a command, paste the output, and keep going. After some time, we get a message saying something like you are out of tokens or you have reached your message limit . Most of the time, the reason is tokens. What is a token? A token is a small piece of text the model reads or writes. It can be a word, part of a word, a symbol, or spacing depending on the language and context. The model does not see text exactly like we do. It breaks everything into tokens first. So when you send a message, you are sending input tokens. When the model replies, it creates output tokens. If your coding agent reads files, terminal logs, docs, diffs, and old chat history, that can also become input tokens. What is a context window? The context window is the amount of text the model can keep in view at one time. It includes your message, the previous conversation, files, tool output, system instructions, project rules, and the model's own reply. Some models can hold a lot now. 200K tokens is already common in many coding workflows. Some newer models can go near 1M tokens. That sounds huge, and it is huge. But it does not mean you should always use it. Roughly speaking, 1M tokens can be hundreds of pages of text. It can be a big part of a codebase, many docs, or long chat history. But the model still has to read through that text. More context can mean more cost, more waiting, and more chances for the important thing to get buried. A rough mental model: Context size What it might hold 32K tokens A few files, a long bug report, or a small feature discussion 128K tokens Many files, long logs, or a decent chunk of project docs 200K tokens A large debugging session with files, logs, and history 1M tokens Hundreds of pages, big docs, or a large slice of a codebase This is not exact. Different languages, code, spacing, and tokenizers change the count. But it gives you the idea. Large c

I work on a computer all day. Multiple projects, lots of switching, constant interruptions. A while back I noticed I was losing track of my own work. Not really the big things but mostly the small stuff. The decision I made on Tuesday about why I structured something a certain way. The thing I was halfway through when a Slack message pulled me away. The task that never made it onto any list because it felt too small to write down (but I ended up forgetting about after lunch until 2 days later). By Friday I'd look back at the week and genuinely struggle to piece together everything I actually did. I tried obsidian (and still actively use it). I also tried just being more disciplined. None of it fully stuck because the friction of capturing things manually meant I only ever captured the stuff I already remembered. The messy ad-hoc stuff that actually eats a lot of my time never made it anywhere. I'm curious if other people deal with this. Not the big project management stuff because that's mostly solved, but rather, the stuff in between. The context that lives in your head and disappears the moment you get interrupted. How do you handle it?

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

India's largest food delivery platform processes over 1.5 million orders every single day. One missed...