标签：#EV

I've noticed more and more people, usually developers and tech nerds, adding a guestbook to their personal websites. First, if building a personal website is becoming more common, that would be amazing. I love that "small web" vibe. Second, the guestbook idea is awesome. I really hope it's a thing. submitted by /u/kixxauth [link] [留言]

Had a lot of fun building this one. The kitty is Sphinx; he's a feral that likes to hang out on our porch 😃 Stack is boring next.js, typescript and tailwinds. Been running the core stack for a good while now. I just build locally and upload though, because fuck Vercel 🤣 submitted by /u/classicwfl [link] [留言]

Built a small API that turns any URL into structured brand data (logos, colors, fonts, screenshots, company info, etc). Originally made it for onboarding + AI workflows, but wondering if it’s actually useful beyond my own use cases or just “cool but unnecessary”. Curious what you think: Would you use this in anything real? If yes, where? submitted by /u/Quiet-Ad2219 [link] [留言]

If you've ordered glasses from them, you can go to: https://www.zennioptical.com/myAccount/myOrders My page just spins. I was curious where my glasses order was after a couple weeks of not receiving them. The network tab shows 30+ css files being downloaded for a very simple website, 10+ trackers and advertising js scripts, and the page still won't load. How about this: SELECT * FROM orders WHERE customer_id = :id ORDER BY order_date Then you can render my most recent orders on the server and at least render some HTML with relevant data. Ok, at scale that might now work. I understand that. Zenni isn't Amazon or Google, but they probably get many requests. In that case we could set up 1+ load balancers that simply forward the request to a sharded server based on userid to balance the load. It's absolutely crazy that we could handle thousands of requests per second in the early 2000s with a few servers in a colo facility and these days everyone has to pretend their facebook or myspace that needs to analyze complex graph connections between people. I just want to see what the status of my order is. It's not that hard. submitted by /u/DrAwesomeClaws [link] [留言]

This is a submission for the Hermes Agent Challenge TL;DR — Type a one-line bill topic. Twenty-five Hermes agents (1 Speaker, 19 ministries, 5 parties) run a full Polish legislative session in 2 minutes. Vote tally, social impact, party tweets — and a side-by-side "current law vs proposed amendment" with every clause cited to a real statute. Built on delegate_task for parallel ministry consultation. 🌐 Live: https://web-production-53027.up.railway.app/ 🎥 Walkthrough: https://www.loom.com/share/92cdac7da31c471088a4e569b0cfe1ed 📦 Repo: https://github.com/monsad/ai-politics (MIT) What I Built Watch a politician debate a new tax law on TV. They argue whether it's fair, whether it'll work, whether the other side is lying. Nobody ever shows you the diff — which paragraph of which statute actually changes, and from what to what. The conversation is theatre on top of an invisible legal document. So I built the theatre AND the legal document. Virtual Parliament is a multi-agent simulation of the Polish Sejm. You type something like "four-day work week" or "flat income tax" , and 25 Hermes agents run a full legislative session: 🎯 Marszałek (Speaker) — the orchestrator. Classifies the topic. Picks 2–3 ministries via delegate_task in parallel . Reads their findings. Routes the bill to a party debate. 🏛️ 19 ministry experts — Finance, Climate, Labour & Social Policy, Justice, … Each returns a structured analysis: legal finding · budget impact · top 3 risks · recommendation . Every claim cites a real statute via PageIndex RAG. 🗳️ 5 party agents — KO, PiS, TD, Konfederacja, Lewica. Each one carries the real party's seat count (157, 194, 65, 18, 26 — totalling 460), policy positions and rhetorical style. First reading. Second reading with rebuttals. 📊 Vote — weighted by seats. >230 passes. 📜 Draft bill — produced with explicit "Article 129 §1 of the Labour Code **is amended to read …" diffs against current law. The frontend surfaces the diff as a Current law vs proposed change panel

Here is what a real solo founder orders. $ git worktree list ~/app a1b2c3d [ main] ~/app-review e4f5g6h [ review-branch] ~/app-content i7j8k9l [ draft-post] Three checkouts. One machine. Each one runs its own Claude Code session that cannot touch the others. That is a normal workday for me. I run a one person shop. Content and code, same desk, same hour. Anthropic's common workflows page lists about a dozen recipes for everyday work, and the docs are strong. What they do not tell you is which recipes survive contact with a real workday and which ones stay theory. After running Claude Code as my whole operation, five workflows carry the load. Here is the honest split. https://code.claude.com/docs/en/common-workflows 1. Worktrees changed how I work The problem worktrees solve is collision. You ask Claude to fix a bug. While it edits, you want to keep building a feature. Same repo, two streams of edits, and now your working tree is a fight nobody wins. A git worktree is a second checkout of the same repo on its own branch. Claude runs inside it and never sees the other windows. claude --worktree feature-auth Real scenario from this week. The post you are reading was drafted in one worktree while a separate Claude session reviewed an open pull request in another. Neither touched the other's files. When the review finished I merged, came back to the draft, and never lost my place. If you take one workflow from the docs, take this one. The setup cost is close to nothing and parallel agents stop stepping on each other. 2. Subagents protect the one resource you cannot buy more of The model's working memory is your budget. Every file Claude reads to answer a question spends it. Ask "how does our auth refresh work" in a large repo and Claude reads a pile of files to answer. Those files now sit in the window for the rest of the session, crowding out the work you care about. Delegate that to a subagent. use a subagent to investigate how our auth system handles token refresh The

A survival guide for when everything goes wrong in production. Your Redis dashboard looks perfect. Hit ratio: 99.2%. Latency: sub-millisecond. Memory usage: 60% of available. Every metric says healthy. Then at 2:47 PM, your API starts returning 500s. Response times spike to 30 seconds. Users can't log in. The dashboard still shows 99% hit ratio because the cache is working — it's serving cached errors to everyone equally fast. Redis is doing exactly what you told it to do. The problem is what you told it to do. Why Single-Threaded Is Fast (Until It Isn't) Redis processes commands on a single thread. No locks. No context switching. No synchronization overhead. One CPU core, fully utilized, can handle 100K+ operations per second because it never waits for another thread to release a lock. The event loop model (similar to Node.js) multiplexes thousands of client connections on a single thread using non-blocking I/O. Read a request, process it, write the response, move to the next. When your commands are simple — GET, SET, INCR — each one takes microseconds. The trap: slow commands block everything. KEYS * on a million-key database? That's a full keyspace scan on the main thread. While it runs, every other client waits. SORT on a large set? Same. LRANGE on a list with 10 million elements? Same. Redis 6.0 introduced I/O threading ( io-threads config) for reading and writing network data on multiple threads, but command execution is still single-threaded. Redis 7.0 improved this further, but the fundamental model hasn't changed. Long-running commands on the main thread stall everything. Rules: Never use KEYS in production. Use SCAN instead — it's cursor-based and returns results incrementally. Watch out for O(N) commands on large data structures: LRANGE , SMEMBERS , HGETALL on million-element structures. Use SLOWLOG to find commands that are blocking the event loop. Pipelining: The Easiest 10x You'll Ever Get Every Redis command involves a network round trip: send request

A survival guide for when everything goes wrong in production. There's a moment every engineer who works with Kafka experiences. You check the producer. Messages are sending. You check the consumer. Nothing. The consumer group shows zero lag because there's nothing to lag behind — as far as the consumer knows, the topic is empty. But it's not empty. The messages are there. Somewhere. In some partition, at some offset, behind some configuration you set six months ago and forgot about. Kafka doesn't lose messages. But it's very good at hiding them from you. Consumer Lag: The Number Everyone Watches Wrong Consumer lag is the difference between the latest offset in a partition and the offset your consumer group has committed. Simple concept. Dangerous in practice. The mistake: treating lag as a single number. Lag is per-partition. If you have 30 partitions and one consumer is stuck on partition 17 while the others are healthy, the total lag looks manageable. But partition 17's data is hours behind, and whatever downstream system depends on that data is serving stale results. Monitor lag per partition. Tools like Burrow, Kafka Exporter for Prometheus, or even kafka-consumer-groups.sh --describe break it down. If one partition's lag is growing while others are stable, you have a stuck consumer, a hot partition, or a poison message. A poison message is a record your consumer can't process — malformed data, unexpected schema, null where it shouldn't be null. The consumer throws an exception, the offset doesn't commit, and it retries the same message forever. Lag grows. The consumer looks "alive" because it's processing — just not making progress. The fix: dead letter queues. After N retries, move the message to a separate topic, commit the offset, and move on. Alert on the dead letter topic. Investigate later. Don't let one bad record block millions of good ones. Rebalance Storms: The Silent Killer Consumer rebalancing is Kafka's mechanism for redistributing partitions acro

Every "what should I build" thread on here is full of AI wrappers fighting over the same five SaaS founders. Meanwhile there's a guy sitting at a loading dock right now, doing arithmetic in his head, who is about to undercharge his broker by a few hundred bucks because nobody built him a 30-second tool. I built that tool. It's a free detention-pay calculator for truck drivers. This is the build log — the niche-selection, the single-file stack, and two decisions (an SVG gauge and a no-mail-service auth scheme) that were more interesting than the app deserves. I'm not a trucker. I build small free web tools for industries other may find unglamourous or not enticing enough. That honesty matters later. The problem (worth $2–6k/yr to one user) Truckers get a "free time" window at a dock — usually 2 hours. Past that, the broker owes detention pay (~$50–100/hr). Drivers leave an estimated $2,000–6,000/year of it unclaimed, mostly because the math + the paperwork is annoying enough to skip. So the spec wrote itself: In/out times + free hours + rate → dollars owed. Export a dispute-ready PDF they can email the broker. Work on a phone, no login, instant. Validating before writing a line The mistake I almost made: assume the niche is empty because I'd never heard of it. I checked. It is not empty — DockClaim ($49/mo, GPS tracking), Detention Buddy, a couple of $9.99/mo App Store apps, even a free email-gated web calculator or two. That killed my first instinct ("be the only one") but clarified the real wedge: everything is a paid app download or email-gated. The opening was a genuinely free, no-signup, instant web version that also generates the claim PDF. Not "the only detention tool" — the one with the least friction. I'll say more on why I'm careful about that claim at the end. Lesson: validate to find your angle , not just a go/no-go. "Crowded but all friction-heavy" is a fine market. The stack: one HTML file No framework. The whole app is a single self-contained .html — m

SQL-like Queries in FSRS Plugin for Obsidian Spaced repetition in Obsidian usually works as "show all cards with due earlier than today." That's enough for simple cases, but once you have hundreds of notes, you want to filter, sort, and select. My FSRS plugin now has a query language resembling SQL. It turns a markdown block into a live table that updates with every review. ``` fsrs-table SELECT file as "Note", r as "Retrievability", date_format(due, '%d.%m.%Y') as "Due" WHERE r < 0.7 ORDER BY r ASC LIMIT 20 ``` → the table shows the 20 most "forgotten" cards, sorted by retrieval probability. From Simple Settings to an Embedded DB Initially I planned to offer table settings using standard SQL syntax. But pretty quickly the syntax became a real query language, and the implementation itself — an embedded lightweight DB. High-level test coverage in TypeScript made it easy to iterate on functionality located in the WASM module via an AI agent. When faced with dual-language testing (TypeScript + Rust), the artificial intelligence prefers to do the job properly rather than fake it. After implementing the lexer → parser → AST → evaluator pipeline for numeric values, I extended it to strings, added filtering via WHERE, then functions. Extending the syntax or adding a function came down to a single request to the agent — and a feasibility check. What's Inside fsrs-table Supported Features SELECT — choose fields, rename via AS . WHERE — conditions with = , != , < , > , <= , >= , AND , OR . ORDER BY — sort ascending ( ASC ) or descending ( DESC ). LIMIT — cap the number of rows. date_format() — convert the due date to any text format. Available fields: Field (alias) Type Description file string path to the note due date next review date stability (s) number stability in days difficulty (d) number difficulty retrievability (r) number probability of recall (0…1) reps number total number of reviews state string New, Learning, Review, or Relearning elapsed number days since last r

I’ve been writing my project - Python port scanner for 9 months now. You might be wondering, “Why is it taking so long?” Most of the time was spent figuring out how raw sockets work, how to write a function for manually assembling a packet, calculating the checksum, packing the IP packet bytes, the TCP header, the pseudo-header using struct.pack, sending the packet, and how SYN scanning works. Why did I decide to take such a complicated route instead of just using Scapy? I’m a principled person and have a very exhausting yet useful skill—understanding everything. That’s how I got acquainted with big-endian, or “network byte order.” I won’t go into the details of big-endian logic, to be honest, I’m already mentally exhausted It took several evenings and nights to analyze and understand the principles—watching videos, reading RFCs, and looking at GitHub code (which I didn’t understand)—but what bothered me most was that I had to ask gemini for an explanation. As I mentioned above, I’m very principled; I can’t just copy code without understanding it, so I ask gemini for a prompt like this: “Don’t write the code for me. If I end up asking you for an example because I’m tired, explain it line by line.” Yesterday I realized I don’t fully understand Python (basics)—I don’t remember REPL—so I went to ask Gemini for advice; I don’t have anyone competent who could help me with advice. I’m not very sociable, and the only thing that’s interested me for the last four years is IT. I used to make music. Lately, something strange has been going on with my health, the day before yesterday I woke up because of a nosebleed; this has happened before, but on a larger scale. I stopped working on the scanner yesterday and decided to try writing a backup script in Python. I found an article and jotted down in Obsidian what the project should and shouldn’t do. Previously, the project used Docker, Prometheus, and Grafana. My questions: Am I a good developer, and am I even one at all? Should

I have been using PandaCSS + Ark UI to build out my components on my app. I kind of like it, I mean really its an alternative to Tailwind basically using CSS in JS, but with full types so you don't need an extension to get auto-complete. Where it gets clunky for me though is the build tooling having to build the styled-system directory with everything...but I mean I guess Tailwind has its own building configuration. I'm just curious cause I've kind of liked it, but really see it used much. Most I can find on info is like videos and stuff when it first came out. submitted by /u/k032 [link] [留言]

Hey everyone, I’m the author of prose, an MIT-licensed SDK/engine for building reading apps: https://prose-reader.com/ It’s a TypeScript, web-based reader engine that can be used to build anything from a simple image/archive-based comic or manga reader to a more complex EPUB reader with navigation, search, annotations, gestures, zoom, pagination, and custom reading behavior. It supports EPUB and archive-style formats like ZIP/CBZ/CBR, and can run in the browser or inside mobile apps through WebView/React Native. One of the main ideas behind Prose is its enhancer system , which works like a plugin system. Enhancers let you add or modify reader capabilities without changing the core engine, so apps can build their own UX and features on top. The project has been around for about 6 years, but I haven’t promoted it much. It’s also used in Oboku , an open-source reading app I’ve been building: https://oboku.me/ AI disclaimer: I have about 15 years of experience as a developer, and I use AI as a tool to help me iterate faster and improve quality, not as “vibe coding” or a replacement for my own judgment. It has helped me deal with many small problems and move the project forward faster. I’m looking for developers who might be interested in trying it, giving ideas, or providing feedback to help improve it. GitHub: https://github.com/mbret/prose-reader Docs: https://doc.prose-reader.com/ Demo: https://demo.prose-reader.com/ Discord: https://discord.com/invite/dffDEgwNc5 Would love to hear what you think. submitted by /u/yabai90 [link] [留言]

Link: https://nova3d.xyz It's free but you'll need to bring your own API Key (BYOK). For open source enthusiasts, check out the github repo at: https://github.com/RareSense/Nova3D This project is a big deal if you see limitations of current AI 3D generators (like Meshy or Tripo). They produce solid, monolithic 3D objects that look good but are practically useless, because: - Want to change the arm of a robot you generated? Regenerate the entire asset. - Want to edit something manually? The whole thing collapses because it's not actually structured. - Want to rig or animate it for a game? Can't easily do that, because it’s a dead, monolithic blob instead of a functional, modular asset. This is a flutter front-end with a python, fastAPI and headless blender back-end. Would love to get community feedback! submitted by /u/mhb-11 [link] [留言]

The same issues keep showing up — the kind that look fine but quietly cost signups or search ranking. Sharing in case it helps your own site: Forms with placeholder text instead of real labels. "Email"/"Password" shown inside the box looks labeled, but it isn't — the text vanishes when you type, and screen readers + browser autofill don't read it. Quiet signup killer. Heading levels that skip. Jumping from an H1 straight to an H3 with no H2. Invisible to the eye (it's in the markup), but it confuses screen readers and how Google reads your page. A main CTA that doesn't actually go anywhere. More common than you'd think — the button looks fine but the click loops or dead-ends. Worth literally clicking your own "Get started" and following it through. Missing title / meta description. Google then writes its own (usually worse) snippet for you, costing click-throughs. No social preview tags (Open Graph). Share your link on X or Reddit and it shows a blank, ugly preview instead of a title + image. Founders share links constantly, so this one stings. Most take 5 minutes to fix once you know they're there. Full disclosure: I built a tool that checks for this ([Sweep]( http://www.usesweep.app) ) — happy to run yours and send the report if you drop a URL below. But honestly, just checking these five by hand is worth an afternoon. submitted by /u/Hairy_Wash_9086 [link] [留言]

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

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. Star git-lrc to help devs discover the project. Do give it a try and share your feedback for improving the project. You might not have noticed, but Chrome quietly started shipping a local AI model called Gemini Nano bundled right into the browser. No API keys. No cloud round-trips. No per-token cost. It just runs on your machine. The interface to talk to it is called the Prompt API , and it landed in Chrome 138. I spent some time going through the full API surface and built a playground that lets you experiment with every feature session management, streaming, structured output, multimodal input, response prefixing, and more in one page. This post walks you through all of it. Why does this matter? On-device AI flips the usual tradeoffs: Free at runtime — the model runs on the user's hardware, not your servers Private by default — no data leaves the device once the model is downloaded Works offline — after the initial download, no network required Low latency — no round-trip to a data centre The catch is that Gemini Nano is a small model. It's great for classification, summarization, Q&A on focused content, and structured extraction. It won't replace GPT-4 for complex reasoning. Think of it as a smart, free, always-available layer you can add on top of your existing product. Enabling the API The Prompt API isn't on by default in all Chrome builds. Enable two flags: Step 1 — Go to chrome://flags/#optimization-guide-on-device-model and set it to Enabled BypassPerfRequirement . Step 2 — Go to chrome://flags/#prompt-api-for-gemini-nano and enable both the base API and the multimodal option. Relaunch Chrome. Then visit chrome://on-device-internals to check the model download status. First use will trigger a download — Gemini Nano is a few gigabytes. The Playground I put together a single-file HTML playground that covers the entire API

Inspired by a similar project called GenosDB and Cloudflare’s initiative to rebuild Next.js, I decided to rebuild GunDB with a modern coding style, incorporating improvements and addressing shortcomings in the original technology. I used the OpenCode tool with the Big Pickle model to rewrite the project in a new graph database called Garfo (the Portuguese word for “fork”), and I was impressed with the results and its practical applications. In this article, I’ll explain the technology and its improvements over GunDB. Introduction Garfo is a modern, browser-first fork of GUN.js — the decentralized, offline-first graph database. A fork of the original project that keeps the familiar GUN graph API while bringing meaningful improvements to the modern JavaScript ecosystem. Why a Fork? GUN.js is a revolutionary technology — a graph database that syncs in real time, works peer-to-peer, resolves conflicts automatically, and runs in the browser. However, the JavaScript ecosystem has evolved. TypeScript has become the standard, ES modules are the norm, and new transport layers like Nostr have emerged as promising decentralized protocols. Garfo was born to fill these gaps: a GUN rewritten with modern typing, designed with the browser as a first-class citizen, and with native support for the Nostr protocol. Key Features Familiar Graph API If you've used GUN before, you'll feel right at home. Garfo exposes the same chainable API: import Garfo from ' garfo ' ; const db = new Garfo ({ localStorage : true }); db . get ( ' users ' ). get ( ' alice ' ). put ({ name : ' Alice ' , status : ' online ' }); db . get ( ' users ' ). get ( ' alice ' ). on ( profile => { console . log ( ' Update: ' , profile ); }); All the classic methods are there: get() , put() , set() , on() , once() , map() . Optional Nostr Transport This is one of the most exciting additions. Garfo can use Nostr relays as a transport layer, allowing peers to exchange graph messages through public or private relays: const

1. Stale closure & primitive capture What is the output of the following code? function createIncrement () { let count = 0 ; const message = `Count is ${ count } ` ; function increment () { count ++ ; } function log () { console . log ( message ); } return { increment , log }; } const { increment , log } = createIncrement (); increment (); increment (); log (); Test your understanding of closures, lexical scope, and primitive value capture. ✅ Output Count is 0 🧠 Explanation This is a classic stale closure trap — but not in the way most developers expect. Step-by-step execution: createIncrement() is invoked → new lexical environment created: count = 0 (mutable binding) message = "Count is 0" (primitive string, interpolated immediately at assignment) Inner functions increment and log are defined. Both close over the same lexical environment. increment() is called twice: count mutates: 0 → 1 → 2 ✓ This works as expected. log() is called: It references the variable message message still holds the original string value "Count is 0" The template literal was evaluated once, at the moment of assignment — not re-evaluated when log() runs. 🔑 Core Concept > Closures capture variables , not expressions . > But if a variable holds a primitive value (string, number, boolean), that value is fixed at assignment time. message is not a live reference to count . It is a snapshot . 🛠 How to fix it (if dynamic output is desired) Re-evaluate the template literal inside log() : function log () { console . log ( `Count is ${ count } ` ); } 🎯 What this question tests Concept Why it matters Template literal evaluation timing They run at assignment, not at access Primitive vs reference types Primitives are copied by value; objects/arrays are referenced Closure capture semantics Closures close over bindings, but the value of a primitive is immutable once assigned Mental model of "live" variables Not all variables in a closure are "live views" — only the bindings themselves are 2. JavaScript co

How to build your professional network as a developer — authentic strategies Building a Genuine Professional Network in Tech: A Practical Guide for Introverts and Extroverts Networking in tech isn’t about collecting business cards or forcing yourself to “work the room.” It’s about building real relationships with people you can learn from, collaborate with, and support over the long term. Whether you’re an introvert who prefers deep one-on-one conversations or an extrovert who thrives in crowds, you can build an authentic network that opens doors to mentorship,Jobs, collaborations, and career growth. Redefine Networking: It’s About Relationships, Not Transactions Forget the image of awkward name tags and empty promises to “grab coffee sometime.” Real networking is: Swapping war stories about debugging nightmares Sharing a job posting with someone who’d be a great fit DMing a speaker to say their talk inspired you Helping someone solve a problem without expecting anything back Quality over quantity isn’t just a buzzword-it’s your career strategy. You need 5-10 real connections, not hundreds of superficial contacts. Leverage Twitter (X), LinkedIn, and Dev Communities Effectively Twitter/X for Developer Networking Dev Twitter is alive and vibrant. Use it to: Share what you’re learning (builds credibility) Comment thoughtfully on others’ posts (starts conversations) DM speakers after webinars to say you enjoyed their talk Join tech conversations using relevant hashtags (#100DaysOfCode, #BuildInPublic) LinkedIn Profile Optimization Write a clear headline that explains what you do and what you’re curious about Share project updates, lessons learned, or thoughtful commentary on industry trends Join niche developer groups related to your tech stack Send personalized connection requests mentioning something specific you admired about their work Developer Communities (Discord, GitHub, Open Source) Join Discord servers for your favorite languages/frameworks Contribute to open