标签：#tooling

You know the moment. You push the branch, open the PR, and immediately see it — the undefined return on the refund path, the token logged to the console, the TODO that was supposed to be temporary six weeks ago. The reviewer catches it four hours later and you reply "good catch, fixing now" as if someone else wrote that line. The first reviewer on most pull requests should have been the author. Half the comments you will receive — the missing null check, the untested error branch, the duplicate logic that could be extracted, the import that now goes nowhere — are things you would have caught with one more careful read-through. You skip that read because you have been in the code for two days and your brain completes the sentences for you. You see what you meant to write, not what is on the page. This post is about closing that gap with a structured AI-assisted self-review before the PR opens. Not to skip the human reviewer — to walk into the review with the obvious problems already gone, the test gaps already filled, and the PR description already written. So the reviewer's attention can land on what actually needs a second pair of eyes. The tool is branchdiff : a local browser app that runs your diff on localhost , stores everything in ~/.branchdiff/ , and keeps the AI surface controlled through an explicit branchdiff agent command API. Nothing leaves your machine until you decide to push it. Why "before the PR" is the right moment If you review after opening the PR, every AI fix becomes noise: a force-push, a re-read for your reviewer, another commit in the audit trail. If a teammate is already mid-review when you discover the bug, you look careless. The patch that should have been in the original push becomes a distraction for everyone downstream. If you review before opening the PR, the AI's output is a private workspace. You act on what matters, commit the fixes into your own history (often as fixup! commits you squash before pushing), and the PR that goes up i

There is a fascinating psychological phenomenon in modern software engineering: the relentless pursuit of the upgrade. As frontend developers, we are conditioned to believe that speed and efficiency come from adopting the newest technologies. We migrate from Webpack to Vite to shave seconds off our build times. We transition between UI libraries in search of better reconciliation algorithms. We constantly audit our CI/CD pipelines. We treat performance as a destination we must reach by continuously adding or swapping out the moving parts of our toolchain. Yet, amidst this endless cycle of optimization, we consistently overlook the most sophisticated, highly optimized piece of software in our entire stack. It is the software you are using to read this article right now: the web browser. The Underappreciated Engine The modern browser is an absolute marvel of engineering. Over the past decade, teams of the world's most talented systems engineers have engaged in a fierce arms race to optimize browser engines like V8, SpiderMonkey, and JavaScriptCore. Today’s browsers feature Just-In-Time (JIT) compilation, sophisticated garbage collection, and massively parallelized rendering pipelines. They are capable of executing highly complex, interactive applications with a level of fluidity that was unimaginable a few years ago. However, when we evaluate developer tools—specifically the online IDE or the browser-based code editor—there is a stark contrast. The environments we use to write and test our code rarely reflect the speed of the engine they run inside. Why the Standard Web IDE Misses the Mark If you want to quickly prototype a component or isolate a bug, you will likely reach for a frontend playground or a popular Replit alternative. What happens next is often a masterclass in friction. The environment feels heavy. The interface is cluttered with features you didn't ask for. As you type, the live code editor experiences micro-stutters. The instant live preview isn't actu

GitHub - nikhilt101/email-blast-tool: Open source HTML email sender tool using CSV/XLSX + Gmail SMTP · GitHub Open source HTML email sender tool using CSV/XLSX + Gmail SMTP - nikhilt101/email-blast-tool github.com

Hi! I want to share a project that I work for a while. It started from idea to get rid off manual copying data from game design documents to game engine. Here you can define your game objects, their props, relations and everything will be stored in structural JSON format that can be read by Unity, Godot, Unreal and other engines. What we have now? construct **wiki-like documents **using a block editor and template system (markdown is supported too) design dialogues of your game in special graph editor create maps and prototype levels on canvas store and manage database of game objects use created objects inside engine directly or export data to customizable data formats (arbitrary JSON, CSV) Made it free and open source. Please try (have Windows and Mac builds) and give your feedback Source code: https://github.com/ImStocker/ims-creators Itch.io: https://nordth.itch.io/imsc-desktop

What is nbwipers? nbwipers is a CLI tool that strips outputs and metadata from Jupyter notebooks before git commit. Written in Rust - faster than nbstripout Supports git clean filter Works with .ipynb files Why use it? Jupyter notebooks store cell outputs inside the .ipynb file (JSON). This causes problems: Noisy diffs - output changes pollute every commit Repo size - images and large outputs bloat the repo Security - sensitive data can leak in outputs (API keys, query results) The solution: strip outputs automatically on git add via a clean filter. Why not nbstripout? nbstripout is written in Python. It is slow - git status , git diff , and git add all became noticeably slow on this repo because nbstripout was invoked for every .ipynb file. The main cause is Python startup time. With 100+ notebooks, nbstripout can take 40+ seconds where a Rust-based tool takes ~1 second. Faster alternatives: Tool Language Notes nbstripout-fast Rust Up to 200x faster; no git filter install support nbwipers Rust Inspired by nbstripout-fast; adds git filter + pyproject.toml config nbwipers is essentially nbstripout-fast with better git integration. Switching to nbwipers fixed the slowness. Setup 1. Install felixgwilliams/nbwipers is now in the aqua registry as of v4.517.0 . Using aqua , add to aqua.yaml : packages : - name : felixgwilliams/nbwipers@v0.6.2 Then run: aqua install 2. Configure git filter Run once per repo (writes to .git/config ): git config filter.nbwipers.clean "nbwipers clean -" git config filter.nbwipers.smudge cat git config filter.nbwipers.required true Or edit .git/config directly: [filter "nbwipers"] clean = nbwipers clean - smudge = cat required = true required = true makes the commit fail if nbwipers is not installed. This prevents accidentally committing outputs. 3. Add .gitattributes In the repo root, add .gitattributes : *.ipynb filter=nbwipers **/.ipynb_checkpoints/*.ipynb !filter **/.virtual_documents/*.ipynb !filter The !filter lines exclude checkpoint an

Over the last few weeks I built a website audit tool and ran it on 200+ small business and service websites — dental practices, plumbing companies, landscapers, law firms, real estate agents. Not Fortune 500 pages optimized by dedicated teams. The sites that actually serve local customers. I expected some issues. I did not expect this. Here's the raw data, the patterns I found, and what you can actually do about it. The Scorecard I grade sites across five dimensions on a 0–100 scale. Here are the averages from 200+ audits: Dimension Average Score Worst Score Speed 56 11 SEO 68 29 Mobile usability 61 18 Accessibility 52 8 Security 70 17 SEO and security tend to be passable (automated checks from Google Search Console and automatic SSL help). Speed and accessibility are consistently neglected — probably because the feedback loop is invisible. A slow or inaccessible site loses visitors silently, and the owner never knows why. Finding #1: 67% of Sites Ship >50% Unused CSS This was the single most surprising data point by far. When a browser loads a page, it downloads every byte of CSS, parses it, builds style rules for every selector, and only then paints. If 60% of those rules never get applied (because they target a contact form behind a click, or a mobile menu at 768px+), the browser still processed them. Worst case: a dental practice shipped 287 KB of CSS. Only 31 KB was used on first paint. That's 256 KB of unnecessary render-blocking weight that delayed First Contentful Paint by roughly 1.4 seconds. The fix: If you're using Tailwind, make sure tree-shaking is enabled. If you're writing vanilla CSS, open DevTools > Coverage tab > Reload. Anything over 40% unused is worth addressing. Most bundlers handle this — you just need to turn it on. Finding #2: Average Image Payload Is 1.8 MB — Way Too High Average image payload across all scanned sites: 1.8 MB per page. Only 34% serve WebP or AVIF (modern formats that cut file size by 30-50%). Only 28% serve responsive sizes

I recently launched LuaPlay, a free browser-based Lua editor. No setup, no install — just open the site and write Lua. Why I built it: Every time I wanted to test a quick Lua snippet, I had to either open a local environment or use tools that weren't built for Lua specifically. So I built my own. What it does: Run Lua scripts directly in the browser Clean, minimal editor interface Free to use Would love feedback from the dev community. What features would make you actually use it day-to-day? 👉 https://luaplay.online

TRIESTE, Italy – For developers, system administrators, and digital hoarders alike, the daily struggle of locating a specific snippet of text buried deep inside hundreds of nested project files is a universal headache. While heavy-handed IDEs and clunky terminal commands exist, they often feel like using a sledgehammer to crack a nut. Enter FiXiY, a lightweight, blazing-fast utility designed to do exactly one thing flawlessly: scan a folder and find precisely what you’re looking for inside the files. Created by software engineer Lorenzo Battilocchi (known online as XeroHero), FiXiY has officially launched as a free, open-source project on GitHub. Simplicity Meets Speed Unlike built-in operating system searches that are notorious for missing code snippets or taking ages to index, FiXiY bypasses the bloat. It provides a localized, no-nonsense approach to file-content searching. Users simply point the tool to a folder, type in the phrase, string, or code block they need, and FiXiY maps out every instance across all supported file types within seconds. "As developers and creators, we waste an incredible amount of cumulative time just navigating our own file structures looking for a variable, a configuration line, or a specific piece of text," says creator Lorenzo Battilocchi. "FiXiY was built out of necessity. It’s a nimble, friction-free alternative for anyone who wants instant answers without waiting for a massive IDE to load or fighting with complex regex syntax in a terminal." Key Features of FiXiY: Deep Folder Scanning: Recursively searches through complex directory trees and nested folders seamlessly. Intelligent Text Matching: Pinpoints exact strings of text, code, or data buried within plain text, source code, scripts, and logs. Lightweight Footprint: Operates with zero background bloat, making it perfect for rapid-fire asset hunting on any machine. 100% Open Source: Built transparently for the community, ensuring full privacy with no data leaving your local mac

AI workflow automation looks simple in demos. A form submission comes in. An AI model reads it. The CRM gets updated. A Slack message goes out. An email is sent. But once you move from demo to production, the workflow becomes more sensitive. What happens if the AI summary is wrong? What happens if the CRM is updated with incomplete data? What happens if the customer request needs human approval before the next step? What happens when a workflow fails halfway? That is where AI workflow automation needs better architecture. In one recent project, we designed an AI workflow automation system using: Make.com for workflow orchestration FastAPI for custom backend logic OpenAI/GPT APIs for summarization and structured output Monday.com CRM for record management Slack for internal notifications Gmail for email-based communication Human review steps for approval and control The goal was not to build a chatbot. The goal was to reduce repetitive manual review work while keeping the workflow controlled, traceable, and practical for daily business use. The workflow problem The original workflow had several manual steps: A new request came in. Someone reviewed the request manually. Important information was extracted. A CRM record was created or updated. The internal team was notified. A follow-up email was prepared or sent. The team tracked the workflow manually. This kind of workflow is common in service businesses, operations teams, sales teams, and CRM-heavy processes. The pain was not that any one step was too difficult. The pain was that the same steps repeated again and again. That makes the workflow slow, inconsistent, and dependent on manual copy-paste work. Why not fully automate everything? The obvious idea is: Let AI read the request and update everything automatically. But that can be risky. AI-generated output can be incomplete, overconfident, or slightly wrong. That may be acceptable if the output is only a draft. It is not acceptable if the output directly updates

Every Linux PHP developer knows the dance. You need to switch from PHP 8.1 to 8.3. You run your sudo commands, update your global symlinks, and then realize your local development server in the other window just crashed because it was running on the old version. Why should managing PHP versions be a system-wide struggle? The Solution: Per-Shell Version Isolation phpvm brings the seamless developer experience of tools like pyenv , rbenv , or nvm to the PHP ecosystem on Linux. Instead of changing /usr/bin/php globally, it uses a lightweight shim directory prepended to your PATH . When you call php , the shim inspects your environment variables and forwards the execution to the correct binary. It supports three layers of resolution, falling back gracefully: Shell pin : Pinned manually via phpvm shell <version> Project default : Resolved from .php-version or composer.json requirements when you cd into a directory Global default : The system fallback managed by update-alternatives Effortless Provisioning No need to look up repository installation guides. The built-in installer automatically detects your distribution (Ubuntu or Debian) and configures the appropriate upstream repositories (Ond?ej Sur�'s PPA or deb.sury.org ) to fetch the exact CLI and FPM packages you need. Polish in v2.5.1 Our latest release focuses on making the environment rock-solid: Tray App Auto-Start : Spawns the GTK desktop tray app immediately after installation by resolving the graphical session environment from active processes. PATH Priority : Actively prevents IDEs, login shells, or snap profiles from overriding the shim's position in PATH . Clean Cleanup : Ensures all background processes are terminated during uninstallation. Getting Started You can install or upgrade using the interactive script: curl -fsSL https://raw.githubusercontent.com/rijverse/phpvm/main/install.sh | sudo bash If you are already running v2.5.0, simply run: phpvm --self-update Check out the project website, or find the