标签：#dev

Hey everyone. I have created a fully free PDF editor that doesn't require accounts. The files stay on your device, they are not uploaded anywhere. I have tried to add as many features as I could think. I hope someone will find it useful. Let me know what you think about it! The website is http://katanapdf.com submitted by /u/Standard_Ad_6045 [link] [留言]

Been working a lot on this on the side for a few months, it's stable enough now that I wanted to put it in front of people who actually run OpenTelemetry. So it reads the OTel traces your services already emit and flags I/O anti-patterns: N+1 SQL and HTTP, redundant calls, slow queries, excessive fanout, chatty services between services and pool saturation. It works at the protocol/span level, so it doesn't need to know whether you're on Hibernate, EF Core, SQLAlchemy or a bare driver, it just sees the queries they end up sending. Two ways to run it: as a one-shot CI gate on captured traces (exits non-zero past a threshold, emits SARIF so findings land in GitHub/GitLab code scanning), or as a long-running daemon that ingests OTLP and exposes Prometheus metrics, a query API and a self-contained HTML dashboard. Single static binary, no agent to attach to your runtime. It idles around 17 MB and in daemon mode it sustains roughly 1M events/sec at ~190 MB (peaks around 1.8M on the pipeline, benchmarked it on an M4 Pro in Docker). The piece I put the most care into is the energy/carbon scoring partly because while I'm a developer, I came with a personal background in environmental science and didn't want to just slap a green badge on it. Each finding gets an I/O intensity and waste ratio score, and it estimates CO2 per request with the SCI v1.0 / ISO 21031 formula. The default model is directional and I say so plainly: it ships a 2x uncertainty bracket and isn't a wattmeter replacement, so you can wire in measured energy ( Scaphandre RAPL , Kepler eBPF or Redfish ) and live grid intensity from Electricity Maps to tighten it. The methodology doc lists the actual sources instead of waving hands. To be clear about what it isn't: not a full APM, not a profiler, not (yet) an "official" regulatory carbon accounting tool (kinda already in a process with INR these days). If you want a full SaaS experience, Datadog or Sentry already do that. This is the narrow, lightweight bit the

Finally got around making an portfolio site how is it ? (dev) submitted by /u/Miserable_Advice1986 [link] [留言]

I’ve added an Easter egg to my portfolio. I wanted to run "Bad Apple" I started experimenting with ASCII art, but then I remembered that you can also print pic in console in the latest versions of Chrome that bad apple didn’t exist before, so I went ahead and did it: NPM : https://www.npmjs.com/package/bad-apple-console Github : https://github.com/alienpingu/bad-apple-console full video : https://www.youtube.com/watch?v=lDpTDnPwZhk let me know if it is usable, thanks! submitted by /u/alienpingu [link] [留言]

I’ve added an Easter egg to my portfolio. I wanted to run "Bad Apple" I started experimenting with ASCII art, but then I remembered that you can also print pic in console in the latest versions of Chrome that bad apple didn’t exist before, so I went ahead and did it: NPM: https://www.npmjs.com/package/bad-apple-console Github: https://github.com/alienpingu/bad-apple-console full video: https://www.youtube.com/watch?v=lDpTDnPwZhk let me know if it is usable, thanks! submitted by /u/alienpingu [link] [留言]

Just to showoff one little project of my own. I've been building a lots of SaaS (5k euros mrr currently) and I finally wrote the whole recipe in a tool. Write your idea in plain language, it will evaluate who your competitors could and if your project could generate money. With that it will build a full roadmap over 30 days, from the landing page, to the marketing and billing. https://letmecookit.app Happy to get your feedback! submitted by /u/InnerPhilosophy4897 [link] [留言]

22 Astro Best Practices: The Bookmark-Worthy Tips At QuotyAI I'm using Astro to build landing pages and blog posts, so I have hands-on experience how to use it properly and how to vibe-code without headache. Astro is the best framework for content sites right now - #1 in developer satisfaction in the State of JS 2025 survey, with Cloudflare backing it since January 2026. But like any tool, it rewards people who use it the way it was designed. This is the reference I wish I had when I started. Whether you're building your first Astro project or vibe-coding a blog at 2am, these are the habits worth forming from day one. Heads up on versions: This article covers Astro 6.x (released March 2026) and Astro 6.4 (released May 2026). Some APIs from older tutorials are now deprecated - those are called out explicitly below. Always check the upgrade guide when moving between majors. 🖼️ Assets & Media 1. Use <Image /> instead of <img /> Astro's built-in <Image /> component does a lot of work at build time that plain <img> tags leave on the table: it converts images to WebP, generates the right width and height attributes to prevent layout shift, and compresses everything without you touching a single config file. --- import { Image } from 'astro:assets'; import hero from '../assets/hero.png'; --- <!-- ✅ Optimized: converted to WebP, compressed, no layout shift --> <Image src={hero} alt="Hero image" /> <!-- ❌ Skips all of that --> <img src="/hero.png" alt="Hero image" /> For art-direction scenarios (different images at different breakpoints), reach for <Picture /> instead. 2. Use the Astro 6 Built-in Fonts API Almost every website uses custom fonts, but getting them right is surprisingly complicated - performance tradeoffs, privacy concerns, self-hosting, fallback generation, and preload hints. Astro 6 added a built-in Fonts API that handles all of it for you. Configure your fonts in astro.config.mjs : // astro.config.mjs import { defineConfig , fontProviders } from ' astro/conf

The Problem We Were Actually Solving We were not building a demo. We needed to let Veltrix operators run A/B experiments on synthetic user journeys without melting the underlying SQL warehouse. The real question was: how close could we push the warehouse to the AI inference layer before the planner started dropping predicates and the warehouse returned rows that made no sense for the user journey. The warehouse in question was a Snowflake XL on AWS, billed by the second. Our synthetic user model generated 250 k journeys per minute during peak. The AI layer had to annotate each journey with intent tags (shopping, support, fraud) within 200 ms to stay ahead of the next batch. That was the operating envelope, not the sales slide. What We Tried First (And Why It Failed) First cut: put the intent model in a sidecar container next to the Spark cluster that generated the journeys. We picked ONNX Runtime v1.14 with a DistilBERT fine-tuned on our own corpus because the latency slide said 30 ms. Reality: ONNX packaged the tokenizer as a separate DLL. Tokenization alone took 85–110 ms on c6i.large instances, pushing the total inference time to 190 ms when the warehouse was cold and 280 ms when Snowflake decided to spike the warehouse cluster. The operator dashboards immediately showed orange pings; the business called it a red fire drill. Worse, the tokenizer DLL leaked memory. After two hours on a 64-core cluster, each pods RSS climbed to 2.4 GB, and the Kubernetes scheduler evicted five pods in a row. The warehouse downstream received duplicate rows with NULL intents, so every metric we exported was off by 7–12 %. The Architecture Decision We ripped out the sidecar entirely. Instead, the Spark jobs write raw event JSON to an S3 bucket every 60 seconds. A Lambda function (Python 3.12 runtime) picks up the bucket, tokenizes offline, and stores the tokenized blobs back in S3. A nightly Kubernetes job then loads the tokenized chunks into Snowflake as temporary tables. The AI inf

GitHub: github.com/vivek1504/forge Live Demo: forge.vivekjadhav.xyz i built this project that runs full IDE entirely client-side. I've attached a quick demo video showing it in action. It uses WebContainers under the hood. You can pick a framework (React, Vue, Svelte, or plain Node), write code in a Monaco editor, and get a live preview with HMR. It includes a functional file explorer and a real integrated terminal without spinning up any cloud VMs or Docker containers. submitted by /u/viks98 [link] [留言]

How a shared filesystem became the cheapest, fastest outbox I've ever built — and why FSx for OpenZFS is the version of that idea that finally scales I was staring at an AWS bill last quarter where a single Kinesis Data Streams line item was costing more than the entire S3 footprint sitting behind it. The events on that stream had a useful lifetime of about ninety seconds. They were written by one service, read by another, processed, and dropped. We were paying full streaming-bus price for bytes that barely outlived a TCP timeout. That bill is what got me thinking about transitional data as a category that deserves its own architecture, and about why every "use the right tool" instinct I had — Kinesis, Kafka, MSK — was the wrong tool for this particular shape of work. The right tool, it turns out, is a filesystem. Specifically, AWS FSx for OpenZFS, used as an outbox between producers and consumers, with only a tiny pointer message traveling through whatever messaging bus you already have. This article is the case for that pattern. It's also the design, the failure modes, the code, the cost math, and the honest list of when not to do it. I'll walk you through the architecture from first principles, show you the safe-write protocol that makes it correct under crashes and concurrent retries, compare the cost against Kinesis, MSK and EFS at a realistic petabyte-class workload, and explain why the recent addition of FSx Intelligent-Tiering changes the cost story in a way that makes the pattern attractive even for teams that don't ingest petabytes. If you've ever felt the queasy sensation of paying twice for the same bytes — once to land on a stream, again to land in storage — this is for you. What "transitional data" actually means Most data falls into one of two cleanly shaped buckets. Durable data is the stuff you keep — user records, orders, financial events, audit trails. It needs to live for years; you pay storage costs for those years and you get value over those y

You enter a domain and it runs a full security stack analysis in one shot: Redirect chain - traces every hop from HTTP to HTTPS, flags mixed content and redirect loops SSL certificate - expiry date, issuer, SANs, grade (A–F), protocol versions (TLS 1.0/1.1/1.2/1.3) Security headers - HSTS, CSP, X-Frame-Options, Permissions-Policy, Referrer-Policy, and more, each rated HTTP/2 & HTTP/3 - detects ALPN negotiation and confirms QUIC via actual UDP connection DNSSEC & CAA - checks if the zone is signed and if CAA records restrict which CAs can issue certs HSTS preload status - tells you if you're on Chrome's preload list (or why you're not) Mixed content scanner - crawls the page and flags any HTTP resources loaded over HTTPS Beyond the one-off check there's also: Bulk check - up to 10 domains at once Cert expiry reminders - email alert before your cert expires Domain monitoring - periodic re-checks with email diff when something changes No account required. API available for automation. Do you find this useful? Looking for feedback. submitted by /u/EveningRegion3373 [link] [留言]

A few weeks ago, I was updating some marketing assets for one of my projects. Everything looked good until I noticed a small problem. The image contained an old QR code. The QR code was pointing to an outdated page, and I needed to remove it before publishing the image again. My first thought was, "This should be easy." I opened a few image editing tools and quickly realized it wasn't as simple as I expected. Most solutions required installing software, learning editing techniques, or manually covering the QR code with another object. Some AI tools could do it, but they were either paid or required creating an account. For a task that should take a few seconds, I was spending far too much time. That's when I started wondering: "Why isn't there a simple tool that only removes QR codes?" The Problem With QR Codes QR codes are everywhere. They're on flyers, product images, posters, presentations, screenshots, and social media graphics. The problem is that QR codes don't always stay relevant. Businesses change landing pages. Campaigns expire. Links break. Sometimes you simply want to reuse an image without the QR code. Yet removing one often requires using software designed for professional designers. Building a Simpler Solution Instead of continuing to search for a solution, I decided to build one. The goal was simple: Upload an image Detect QR codes automatically Remove them Download the cleaned result No accounts. No complicated editing. No learning curve. Just a tool that solves one problem well. After several iterations, the result became the Remove QR Code tool on ConvertKR. What I Learned One thing I've learned from building developer tools is that users don't always need more features. Sometimes they just need fewer steps. The best tools are often the ones that remove friction from a small but frustrating task. Removing a QR code is not something people do every day. But when they need it, they want the process to be fast. Try It Yourself If you've ever found yo

This article will teach you how to export any list into Excel in C# using the ClosedXML library. Steps to complete Create the data model with dummy data that we'll export into Excel. Create ExportExcel interface methods that accept any type of List (using IEnumerable<T> ) and a Dictionary List and export a byte array. Create extension methods and convert the provided data into rows and columns (using DataTable ). Create a service class that implements the interface methods and export the data table into a Memory Stream (byte array) using ClosedXML . Create one API endpoint that exports data in memory into Excel. Create another endpoint that exports incoming (custom) request data into Excel. Wire up dependencies. Project structure ├── Program.cs ← Project startup & dependency injection │ ├── controllers / │ └── ExportToExcelController.cs ← API entry point ├── services / │ ├── IExportToExcelService.cs ← Export Excel interface │ └── ExportToExcelService.cs ← Export Excel concrete class │ ├── models / │ ├── Car.cs ← Car class definition & dummy data │ ├── ExcelResponse.cs ← Wrapper class for excel file name and data │ └── ExportExcelRequest.cs ← Request class for that accepts any kind of list that will be exported │ └── extensions / └── IEnumerableExtensions.cs ← Extension methods for List<T> and List<Dictionary> 1️⃣ Data model I've created the dummy data model to demonstrate the dynamic implementation. public enum FuelType { Petrol, Diesel, Electric, Hybrid } public class Car { public Guid Id { get; set; } public string Name { get; set; } public string Manufacturer { get; set; } public int YearProduced { get; set; } public string Color { get; set; } public FuelType FuelType { get; set; } public int HorsePower { get; set; } public int NumberOfDoors { get; set; } public bool AutomaticTransmission { get; set; } public double AverageFuelConsumption { get; set; } public int MaxSpeed { get; set; } public decimal Price { get; set; } public static List<Car> GetCars() { ... } }

It is straightforward to configure a minimal API to produce an OpenAPI document at build time . This runs the API during build, requests the OpenAPI document from it, and saves it to disk. The slightly trickier part is to put checks in Program.cs to exclude any startup code that cannot run at build time. This is typically done because configuration key/value pairs are not available at that time. For example: if (! isBuildTime ) { connString = builder . Configuration . GetConnectionString ( "AppDB" ) ?? throw new InvalidOperationException ( "Connection string 'AppDB' is not configured." ); builder . Services . AddDbContext < AppDbContext >( options => { options . UseNpgsql ( connString ); } ); } The question is how to deduce that the API has been launched at build time, i.e. isBuildTime should be true? The official way of doing this is to check that the assembly that invoked the API is "GetDocument.Insider" : var isBuildTime = Assembly . GetEntryAssembly ()?. GetName (). Name == "GetDocument.Insider" ; GetDocument.Insider.dll is the command line tool that automatically runs during build of the API if the .csproj includes the following reference: <PackageReference Include= "Microsoft.Extensions.ApiDescription.Server" Version= "10.0.7" > ... </PackageReference> This package is a shim. It only provides build targets and props and hooks into the build of the API to run the command-line tool dotnet-getdocument . This tool in turn runs the command line tool GetDocument.Insider that we check for. This is a pretty convoluted sequence: Microsoft.Extensions.ApiDescription.Server provides targets that run during build of the API. One of those targets runs the command line tool dotnet-getdocument That in turn runs the command line tool GetDocument.Insider That in turn runs the API and fetches the /openapi/v1/json (or other configured endpoint) to get the OpenAPI document and saves it to disk. Checking in Program.cs if the API was invoked by the assembly GetDocument.Insider.dll t

The problem I often switch between Codex, OpenCode, Cline, Claude Desktop, scripts, and terminals. The annoying part is not starting a new tool. The annoying part is explaining the same workspace state again: what changed what is still pending what should not be touched what tests passed what the next agent should read before editing What I built AgentContextBus (acb) is a local-first CLI for handing off workspace context between coding agents. It saves a local handoff packet, then lets the next agent read it through: paste-ready prompts brief prompts a local dashboard JSON output explicit MCP tools First run npx @xiaoshuo1988/acb verify first-run For Chinese output: npx @xiaoshuo1988/acb verify first-run --lang zh-CN A normal handoff From the agent that has context: acb handoff --from codex --summary "Ready for the next agent" --git From the receiving side: acb receive --latest After the receiving agent summarizes the packet: acb ack --latest --by opencode What ACB intentionally does not do no hidden prompt injection no traffic interception no third-party client config mutation no cloud sync no background daemon Why local-first I want the user to be able to inspect the packet store, copy text manually, and decide exactly when context crosses from one agent to another. What I want feedback on Is the handoff packet concept clear? Is verify first-run enough to understand the tool? Is receive --latest the right receiving-side command? Which client path needs the most work? Would you trust this workflow in a real project? Repo: https://github.com/xiaoshuo1988130/acb Feedback discussion: https://github.com/xiaoshuo1988130/acb/discussions/1

Google AI Studio Mobile + Gemini Managed Agents: Build and Deploy AI Agents Without Infrastructure in 2026 TL;DR Summary Google AI Studio is now a standalone mobile app on iOS and Android — speak an idea, and a working app builds in the background Gemini Managed Agents deploy reasoning agents with one API call — code execution, Google Search, URL reading, file management, and web browsing included Agents are configured via markdown skill files (SKILL.md), not complex orchestration code — no server setup, no sandbox management State persists between sessions — files and context survive, no re-uploading Prototype on mobile , refine on desktop , share live deployment via URL — continuous workflow across devices Direct Answer Block Google has launched two new agent surfaces: AI Studio Mobile (a standalone iOS/Android app where you prototype with voice or text and see generated apps on your phone) and Gemini Managed Agents (serverless reasoning agents deployed with one API call, including code execution sandboxes, web search, browsing, and file management, all configured via markdown skill files instead of orchestration code). Introduction The gap between "I have an idea" and "I have a working AI agent" is mostly infrastructure. You need a server, a sandbox, tool integrations, state management, deployment pipelines. Google's two new releases collapse that gap from both ends: AI Studio Mobile removes the need for a desk, and Gemini Managed Agents remove the need for infrastructure. Together, they let you go from voice note to deployed agent without touching a server config. How does Google AI Studio Mobile let you build and preview apps entirely from your phone? AI Studio Mobile is a standalone app (iOS and Android) that brings Google's AI development environment to a phone. The workflow described in the AlphaSignal newsletter: Speak or type an idea — "Build me a weather dashboard with 5-day forecast and location search" App builds in the background — AI Studio's agent in

You Accumulate Technical Debt When You Skip Code Review. Here's What You Accumulate When You Skip the Human. There's a concept in software engineering called Technical Debt. You skip the right abstraction, move fast, ship. Someday you pay it back in refactoring hours. I've been thinking about a different kind of debt. One that doesn't show up in your codebase. Human Debt: When you build with AI as your only collaborator, you remove the one thing that makes you feel obligated to show up. Not accountability in the corporate sense — the simpler thing. Someone is reading your work. You don't want to waste their time. That's not a productivity hack. It's closer to a structural property of how humans behave when observed. The Research Didn't Start With AI In 2015, Gail Matthews ran a study on 267 professionals tracking goal completion. One group wrote their goals. Another group wrote their goals and sent weekly progress reports to a real person. The second group completed 76% more of their goals . Not 10% more. Not "statistically significant at p<0.05." Seventy-six percent. The mechanism is what Gouldner called reciprocity norm in 1960 (doi: 10.2307/2092623): when someone gives you their attention, you owe them something back. Not contractually. Biologically. You don't want to disappoint someone who showed up for you. Harkin et al. confirmed this across 138 studies, 19,951 participants — the effect holds across cultures, domains, and formats. None of this was discovered because of AI. It was hiding in plain sight for 65 years. AI Has No Concept of Day 14 Here's what changed. For most of the history of side projects, your "collaborator" was a rubber duck or Stack Overflow. Those tools don't simulate accountability. Nobody was surprised. Then came AI pair programming. Which is genuinely useful. But it introduced a specific failure mode: you now have a collaborator that responds, scaffolds, and generates — but doesn't notice when you stopped. AI has no concept of Day 14. It

I often record my screen for clients to show what I’ve built or how something works. But almost always I end up writing detailed comments and explanations anyway, because they just blink and miss things in the video. So I thought it’d be cool to add freeze-frames with annotations right inside the video, to pause their attention and explain what's happening at that exact moment. I tried doing this in video editors, but it takes way too much time, so I hated the process. But the videos with freeze-frames actually worked great for my clients. I got way fewer questions from them. That’s why I decided to build a tool to add freeze-frames and annotations quickly and easily. I wanted it to feel as simple and natural as drawing in Excalidraw. Let me know if you find this useful, or if it feels unnecessary. You can try it here: https://framepin.com . There is a quick demo to save you time. No registration, everything works locally in the browser. submitted by /u/aksuta [link] [留言]

So for about 10 years I've been wanting this every time I'm on a scam website or news article that has no comment section or locked reddit thread. I've seen a couple similar attempts, but most of them focus on personal notes or having drawing features and things I'm not really interested in. This is just about letting users, customers and the general public communicate about URLs and sub-urls the websites themselves don't provide or won't provide the public the ability to comment on. In the extension you can see how many comments are on a URL and your notifications if anyone replied to you. There are some rules like everyone is only allowed one top level comment to reduce spam or potential bots. You can replay as many times as you want. You can also edit your top level comment, which will show last when it was edited. Here is the main list of reasons I created it: Warn people on scam websites when they go there they can just read the comments Comment on locked social media posts, x profiles, youtube videos, reddit threads etc.. Comment on news articles that have no comment section Comment on top of social media that would otherwise require an invasive account to comment. Almost all social media also has a url these days. This also works with any string so technically you can comment on anything including, addresses, businesses, cities. If you paste in a URL it will normalize it so you won't accidentally create a duplicate forum. On the PublicNotes.xyz homepage you can just create a forum based on anything. Feedback and bug notes welcome. Website: https://publicnotes.xyz Chrome extension: https://chromewebstore.google.com/detail/public-notes/keaopkaplnpjhccneageakejbbbeoncp?pli=1 submitted by /u/PandorasBucket [link] [留言]

I've been working on a side project called WEBDEVPACK - a collection of free web-based tools for developers, designers, and generally anyone working online. The idea is simple: lightweight tools that help us solve everyday problems. Check it out at webdevpack.com submitted by /u/ivopetkov [link] [留言]