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This is a submission for the June Solstice Game Jam What I Built Solstice Cipher: Enigma of the Longest Day is a browser-based puzzle game built around the Caesar cipher — the same substitution cipher technique used in ancient cryptography. On the theme of the June Solstice, I tied the longest day of the year to an Enigma Machine-inspired challenge: decode encrypted messages before time runs out, with the difficulty scaling as the sun climbs higher. The game features a real-time animated sky that shifts through dawn, noon, and dusk to reflect the solstice theme. Players are given a cipher shift key and must decode encrypted phrases by working through the Caesar cipher manually or by reasoning out the pattern — no brute-force tools allowed in-game. This connects to the June Solstice theme because the game is literally set on the longest day: the puzzles grow harder as the day progresses, and the sky animation mirrors real solstice light from sunrise to sunset. Video Demo Play it here: gtxpoffic-developer.github.io Code GTXPOFFIC-developer / Solstice-Chiper-Enigma-of-the-Longest-Day This is a Enigma based June Solstice game feel free to include your own code or tinker this project just mention the orignal developers name pls Solstice Cipher — Enigma of the Longest Day A browser-based Enigma machine puzzle game set on the June solstice. Decode (or encode) encrypted transmissions before the daylight runs out. Built By Sudipto — Original developer Feel free to fork, tinker, and include this in your own projects. Just mention the original developer's name. How to Play Objective Configure the Enigma machine correctly to decode each level's ciphertext (or encode the plaintext) before the sun sets. Each wrong guess costs 45 minutes of daylight; correct guesses pause the timer for 30 seconds. Controls Control What it does Rotor dropdowns Select which 3 rotors (I–V) are used ▲ / ▼ buttons Adjust each rotor's starting position Plugboard Drag from one letter to another to connec

I wanted to do something entirely different than my normal, meet overwatch.earth Explore the world through a fully interactive 3D globe with real-time feeds from over 150,000 sources. Track live events as they happen—from earthquakes and satellite movements to live webcams, global transportation networks, and digital infrastructure. submitted by /u/tuxxin [link] [留言]

Hi all, About three years ago I built a small library called Trig.js to expose scroll data to CSS via data‑attributes. It recently got highlighted as one of the “Enterprise Heavyweights” of scroll animation libraries by CSSAuthor, which made me revisit the idea. I’d always planned to make a Cursor.js, so I built it and then I started wondering, what else could be exposed to CSS variables? That question spiralled into something bigger, and I’ve now ended up creating a full stack of small, browser‑native libraries that all share the same philosophy: Once I reached Keys.js, something clicked. Keys aren’t animation, they’re input. That led to the bigger question, could you build full applications or even games this way? The answer turned out to be yes, and that’s when I came up with State.js. For the first time, here’s the full stack together: Trig-js - exposes scroll data to CSS Cursor.js - exposes mouse/touch position Motion.js - a global clock for CSS‑driven animation Keys.js - exposes keyboard input State.js - a reactive state layer for HTML Gravity.js - a DOM‑element physics engine rendered in CSS Together, these for a declarative application/game engine using the native browser without webGL, webGPU or canvas. Your HTML is your state graph, the CSS is your rendering engine and JS becomes the wiring that connects everything up. These libraries all work independently or together. As every one of these open up capabilities that wasn't possible before that's why they are all individual so you can pick or choose or use them altogether for a complete stack. A few months ago I wouldn’t have believed half of this was possible in the browser without heavy abstractions. It’s made me realise how much capability we’ve historically hidden behind frameworks instead of exposing directly. I’m excited to share this approach and would love to hear your thoughts, ideas, or critiques. If you’re curious about browser‑native reactivity or CSS‑driven rendering, I’m happy to dive deeper.

I built AIPDFKit because I kept running into the same friction: needing to do something simple with a PDF -- redact some sensitive info, pull out a table, or convert a document to Markdown -- and every tool either required an account, put the good stuff behind a paywall, or made me wonder what was happening to my files afterward. PDFKit is my answer to that. PDFKit -- Free AI-Powered PDF Tools PDFKit is a free, browser-based PDF utility suite powered by AI, built for developers and technical professionals who need fast, reliable document processing without the friction of paid plans or mandatory accounts. Whether you're parsing data out of PDFs, sanitizing sensitive information, or converting documents into developer-friendly formats, PDFKit gets the job done in seconds. What it does AI-assisted PII redaction -- automatically detect and mask emails, phone numbers, names, and more Table extraction to Excel -- pull structured data out of PDFs without copying and pasting PDF to Markdown conversion -- especially useful for feeding document content into LLMs or RAG pipelines These aren't just format converters. The AI layer means the output is clean, structured, and actually ready to use. Privacy first No account creation required. PDFKit stores no user data and automatically deletes all uploaded files after one hour. For developers handling client documents or sensitive data pipelines, this is a meaningful differentiator over SaaS tools that retain files indefinitely. Who it's for Developers preprocessing PDFs before feeding them into RAG pipelines Anyone automating document workflows People who need to quickly extract structured data without spinning up a Python script Anyone dealing with sensitive documents who can't afford to have files sitting on someone else's servers It's the kind of utility you bookmark and reach for constantly. Built to be fast, free, and frictionless. Check it out: https://www.aipdfkit.com/ Would love to hear what features you'd find most usefu

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built After being abandoned for several months, I have come back to build and complete Claude with LeetCode, which is a DSA learning system that automates daily algorithm education with Claude code directly inside GitHub repo. Every time I submit an accepted solution on Leetcode, the Github workflow fetches my Leetcode account data and commit the problem with the solution to the repo. Claude will then run on a fixed schedule and automatically generates a full structured lecture, covering the DSA topic, brute force through optimal solutions in Python, complexity analysis, and a YouTube video packaged in a GitHub Issue. This project means a lot to me because it merges two things I care about daily: now not only can I solve Leetcode problem, my solution is automatically analyzed by a powerful AI agent mentor. Demo Link to my project: https://github.com/Stewie-pixel/claude-with-leetcode.git Link to my application walkthrough: https://youtu.be/ClWdW3v9JJ0 The Comeback Story At first this was only a project to store the Leetcode questions I have solved. The process required manual pushing the problem to the repo and nothing special. Later I have added the automation workflow to fetch data from my Leetcode account, Claude will be prompted like an experienced dsa mentor from Claude and skill.md file to give a thorough analysis on that problem. And at the end of the day, Github Copilot workflow will give a daily summary report to cover my daily progress. My Experience with GitHub Copilot I built a DSA Mentor skill that gives Copilot the full context of what a lecture should contain: topic identification, the brute force to optimal approach structure, complexity analysis requirements, and the YouTube search step. Without Copilot, writing the dsaMentor.js orchestration logic and getting the agent to consistently produce structured markdown output would have taken significantly longer. I then use Copilot cli

This is the eighth and final post in a series on the false assumptions teams make when building with generative AI. The series began with the observation that the trough of disillusionment for AI-assisted development has arrived — not because AI is useless, but because eight false assumptions made the trough inevitable. This post covers the last assumption and closes the series. The Fallacy "If one AI agent gives us a 10x boost, ten agents will give us 100x." Why it's tempting The arithmetic feels irresistible. One agent generates code for the backend. Another generates the frontend. A third writes tests. A fourth handles database migrations. A fifth generates documentation. Each agent works in parallel. No meetings, waiting or coordination overhead. Pure throughput. Leadership sees the potential: a five-person team with fifty agents has the output of a fifty-person team at the cost of a five-person team plus API credits. The scaling is linear. The economics are transformational. And the early results confirm it. Each agent, working on its own, produces impressive output. The backend agent generates Go code. The frontend agent generates React components. The test agent generates test suites. Each agent, in isolation, looks like a 10x developer. Why it's wrong You've seen this problem before. It has a name. It's called distributed systems. A distributed system is a collection of independent actors that must coordinate to produce a coherent result. Each actor makes decisions locally. The system's correctness depends on those local decisions being compatible globally. When they aren't, you get inconsistency, conflicts, data corruption, and cascading failures. AI agents working on the same codebase are a distributed system. Each agent makes decisions — variable names, error handling strategies, retry policies, data formats, abstraction levels, dependency choices. Each decision is made locally, in the context of one prompt, one file, one task. No agent sees the full pict

We are exploring an experimental blockchain mechanism called "Proof of Weather" In the world of blockchain, various methods are used to achieve network consensus. The most well-known is Bitcoin’s Proof of Work (PoW). While PoW is an excellent mechanism, it has one major drawback. It consumes an enormous amount of electricity. At one point, I found myself wondering: Does blockchain really require such vast computational resources? Isn’t there something else that’s needed? This led to the creation of Dawn, the experimental cryptocurrency project I am developing, and an experimental blockchain mechanism called Proof of Weather. In this article, I will discuss: Why I decided to use weather How Proof of Weather works Security considerations Implementation in Rust How Does Proof of Work Work? Proof of Work is often explained as a mechanism where computers compete against each other in computational tasks. However, one important property of PoW is that it produces outcomes that are difficult to predict in advance. Miners repeatedly perform massive amounts of hash calculations, and only those who happen to meet the conditions can generate a block. This unpredictability plays a role in determining who can produce the next block. However, this process consumes enormous amounts of electricity worldwide. So I wondered: Aren’t there already phenomena in nature that are difficult to predict? Why Weather? Proof of Weather utilizes weather data as that unpredictable element. Of course, weather forecasts exist. However, Temperatures several days in the future Atmospheric pressure at specific locations Precipitation Wind speed and other factors cannot be predicted with absolute certainty. In particular, when combining observations from multiple locations, it becomes even more difficult to accurately calculate future values in advance. In other words, meteorological observations have the potential to be used as A real-world information source where future values cannot be fully predic

Spent the weekend building a local tokenizer to stop leaking DB passwords and API keys to ChatGPT, literally can't stop testing edge cases. Written in pure TypeScript. Uses greedy reverse anchoring to mask credentials locally in the browser. Provided the core sanitizer logic here: https://github.com/abests/ghost-sanitizer-js submitted by /u/zero_backend_bro [link] [留言]

Hi everyone, I’ve always felt frustrated by how many ads are crammed into most tier list websites. That’s why I created a brand new site with absolutely no ads. I also made sure it's highly mobile-optimized and offers multiple downloading options for your tier lists! I'm always looking to improve, so please feel free to share any feedback or features you'd like to see added. ps. As a thank you for checking it out early, if you create 1 tier list template now, I will upgrade you to a lifetime Premium account. Even if ads are introduced later down the road, your account will remain completely ad-free. submitted by /u/Mountain-Shoe-6070 [link] [留言]

Hello reddit! I am P2P engineer so in my free time was working one little side project I'm excited to share, it's called AlterSend. AlterSend is a free, open-source app for sending files directly between your devices, no cloud, no uploads, no size limits. Files transfer peer-to-peer and are end-to-end encrypted, so nothing is ever stored on a server. GitHub: https://github.com/denislupookov/altersend Features: No accounts No servers storing your files End-to-end encrypted No file size limit Cross-platform (desktop + mobile) Open source The idea was to build a good alternative to the established cloud file-transfer apps, without the cloud. How it works, roughly: AlterSend is built on Hyperswarm, which underneath is a Kademlia DHT. For every transfer we generate a random key that acts as a discovery topic, you share that with whoever should receive the files. Each peer announces itself on the DHT under its own node ID, so peers can find each other directly. A handful of public bootstrap nodes serve as the initial entry point and after that peers discover one another through the DHT without relying on any central server. Once two peers connect, the transfer is direct and encrypted end-to-end. Would love to hear your feedback! submitted by /u/AlgoAstronaut [link] [留言]

Just something for your bookmarks and also a little bit of a learning resource. For those of you who are using PageGym, I also (very) discretely integrated it into the request view dialog. https://pagegym.com/tools/cache-control Cheers! submitted by /u/svvnguy [link] [留言]

I was working with websockets and wanted some lightweight solution which was easy to work with and I never liked working with plain websockets and as an experiment I started building a typesafe solution and which is how I came up with @frsty/wsrpc . You can define procedures on the server (like trpc) , get typed send and on on the client. No codegen, no event codes objects to share between client and server, 0 runtime dependencies. Handlers are generator functions, yield events, return the RPC response: https://preview.redd.it/0igrus569n5h1.png?width=3680&format=png&auto=webp&s=880c6b2fd90a04866058bc91d703995c14482b07 All the event codes, returns and callback functions are typesafe. Works with zod, valibot, arktype, anything that implements Standard Schema. framework-agnostic. Still early so would appreciate some feedback. For detailed example see github.com/frstycodes/wsrpc submitted by /u/frstyyy [link] [留言]

The shadcn registry directory is pretty stacked, but there isn't currently any depth in the gamification space. So I decided to build a library of 17 components across the major features you see in most consumer platforms. Things like streaks, achievements, leaderboards, points etc. Trophy UI is fully open-source, and while it seamlessly integrates with Trophy itself, the UI components just accept regular props and so can be used with any backend. Most interesting components: Streak calendar - weekly, monthly or yearly (git-style) view of streak history, with support for streak freezes which are pretty common in consumer apps like Duolingo. Leaderboard rankings - flat list of rankings each with support for avatars, bylines and change indicators. Also supports pagination and collapsed rows to focus rankings around a particular position i.e. show users to top three users above and below them. Achievement badge - a simple badge with support for locked/unlocked states plus features like percentage completion and rarity (the share of users who have unlocked the badge). Points levels timeline - progression path for points levels with support for sub-levels (Bronze I, II, III, Silver I, II, III etc) plus anchoring to a particular users current progress. Every component is installable via shadcn CLI: npx shadcn@latest add https://ui.trophy.so/<component> Once installed you own the code, customize and modify as you see fit. Also very happy to accept contributions for new components or features for existing components. Would love to hear what people think, and would very much appreciate a star on GH if you think its valuable! submitted by /u/CBRIN13 [link] [留言]

I was working with being tortured by Instagram API for a client project. Eventually I delivered something that barely worked with duct tapes and prayers. Never again I'll take on any instagram/meta related project. And after that horrible experience github's rate limits, API coverage and documentation, everything about it felt like a breath of fresh air. Whereas instagram was like; "I'll give you one API call per sometimes and you better cache it" I thought I'd add some pictures from the app I'm working on since it's showoff saturday. Though there's still a lot I need to work on and I'm not sure if I'll even finish it. I just found a way to build desktop apps in python using html/css and I'm going nuts with it lol Edit : If I ever finish this app, it'll be free and open source. Basically you generate an API key, put it into the app and it lets you view, filter, and update your issues. Can also click on repo name or issue title to view in your default browser. I recently downloaded github desktop and it didn't have this feature, so I thought I'd do it myself and learn more python in the meantime. I have a few more ideas for it but I'm trying not to scope creep myself submitted by /u/mekmookbro [link] [留言]

I am a senior full stack dev with 7+ YOE and I think we can all agree the market sucks right now! Primarily I have been applying to full stack roles but I am backend leaning (PHP/Laravel) I seem to be seeing a lot of .NET/C# roles for backend-only roles. Is the market for those devs less chaotic? I'm considering learning .NET anyway, but would like to know if it's worth fully investing my time into it if things are better. submitted by /u/EthanCPP [link] [留言]

I've been working on an open-source project called Canary. It reads your code diffs, understands which UI flows are likely affected, and lets Claude Code tests those flows in a real browser. Under the hood, Canary ships with a QuickJS WASM sandbox that exposes the full Playwright API, allowing Claude to perform long-running browser workflows such as authentication, onboarding flows, form submissions, and navigation across complex applications. Each run captures: Screen recordings Playwright traces HAR files Network requests Console logs Screenshots Unlike most agent runs, every Canary session also generates a reusable Playwright script that can be cleaned up and replayed locally or in CI with zero inference cost. Canary UI submitted by /u/wixenheimer [link] [留言]

You pick a tool on the canvas, and in real-time you get suggestions on what you can add next, how your tools pair together, or what can be missing. There are also recommended tech stacks available for different project types. Web apps are one of the primary types, but the tooling also covers data projects, automation and so on. In total there are over 150 tools present. Everything in the app is rule-based, there’s no AI there yet whatsoever. I do plan to add an AI chat later, but it will be built on the same deterministic engine. It’s a beta for now. The builder and the tools catalogue are fully functional, user accounts are also planned for later. But this core part will stay free to use without accounts. Paid plans, if introduced, would cover extras like AI features. For my own tech stack, it’s Vue.js for frontend, FastAPI for the backend and PostgreSQL for the database. https://tekyous.dev submitted by /u/sartek1 [link] [留言]

5 Principles of Survival for Software Engineers Adapted from Leon Business School's "5 Principles of Survival" Your stack won’t save you. Your principles will. In the wild, survival isn’t about having the best gear. In software, survival isn’t about having the absolute best framework. It’s about how you operate when production is on fire, the roadmap shifts overnight, and AI just turned your "moat" into a weekend hobby project. Here are 5 core principles that keep you alive in modern software engineering. 1. 🔥 Adapt or Perish Change is not optional; it is the price of survival. In the wild: The species that cannot adapt to winter dies. In software: The team that cannot adapt to change dies slowly at first, then all at once. "Localhost is for amateurs" used to be a strongly held belief. Now, Claude writes a full CRUD API in 30 seconds on localhost . "We’re a React shop" was a proud identity. Now, HTMX ships the same feature before your Webpack build even finishes. Your identity as an engineer cannot be tied to a specific tool. Your identity is solving problems . The syntax is temporary. Agreement on what to build is what actually matters. 🛠️ Survival Action Every quarter, deliberately kill one "we’ve always done it this way" rule in your workflow. 2. 🧭 Stay Calm Under Pressure Panic is the first casualty of poor preparation. In the wild: Panic burns critical calories and gets you lost. In software: Panic causes a git push --force to main on a Friday at 4:59 PM. Outages don’t kill companies. Panicked responses do. The team that has clear runbooks, relies on feature flags, and can execute a rollback in under 90 seconds stays calm. Why? Because they prepared when it was quiet. If your first step in incident response is opening X (Twitter) or complaining in a public Slack channel, you have already lost. 🛠️ Survival Action If you don't have a tested rollback plan, you don't have a deployment plan. Write it down before your next release. 3. 💡 Resourcefulness Over Resources

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

If you've ever hit Unexpected token in JSON at position 42 or Unterminated string , there's a good chance an unescaped character broke your payload. JSON is strict about what's allowed inside a string, and the fix is almost always escaping . Here's the practical version. What does escaping a JSON string mean? A JSON string is wrapped in double quotes. Any character that would confuse the parser must be replaced with a backslash escape sequence. Escaping doesn't change the meaning of your text — it just makes the string valid JSON so parsers can read it. Unescaping is the reverse: turning those sequences back into readable characters (handy when you copy a value out of logs or an API response). The characters you must escape JSON defines exactly seven characters that must be escaped inside a string: Character Escaped as Double quote " \" Backslash \ \\ Newline \n Carriage return \r Tab \t Backspace \b Form feed \f The forward slash / may optionally be escaped as \/ , but it isn't required. Unicode can be written as \uXXXX (four hex digits). JSON escape examples Double quotes — He said "hello" becomes: "He said \" hello \" " Backslashes (Windows paths) — C:\temp\file.txt becomes: "C: \\ temp \\ file.txt" Newlines and tabs — a two-line, tabbed string becomes: "Line 1 \n Line 2 \t Tabbed" How to escape and unescape JSON in code In production you rarely escape by hand — every language has it built in. JavaScript const escaped = JSON . stringify ( text ); // escape const back = JSON . parse ( escaped ); // unescape Python import json escaped = json . dumps ( text ) # escape back = json . loads ( escaped ) # unescape Java (Jackson) ObjectMapper mapper = new ObjectMapper (); String escaped = mapper . writeValueAsString ( text ); String back = mapper . readValue ( escaped , String . class ); C# (.NET) using System.Text.Json ; string escaped = JsonSerializer . Serialize ( text ); string back = JsonSerializer . Deserialize < string >( escaped ); Common escaping mistakes (and f