标签：#challenge

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 a submission for the GitHub Finish-Up-A-Thon Challenge OpsPilot AI: Reviving an Unfinished AI-Powered Operations Platform with GitHub Copilot What I Built OpsPilot AI is an AI-powered operations assistant designed to help DevOps engineers, SREs, and operations teams investigate incidents, monitor service health, and gain actionable operational insights. The project originally started as a side project inspired by my experience working in production support and monitoring environments. I built an initial version to validate the idea but never fully completed it. The core concept was promising, but several important features and usability improvements were still missing. Through the GitHub Finish-Up-A-Thon Challenge, I revisited the project and transformed it into a much more complete and polished MVP. Key features include: AI-powered incident analysis Root cause investigation assistance MTTR analytics dashboard Service health monitoring Incident trend analysis Executive reporting insights Modern responsive user interface Demo Live Application GitHub Repository OpsPilot AI helps operations teams reduce investigation time and improve operational visibility through AI-powered workflows and analytics. The Comeback Story When I first started OpsPilot AI, it was mainly an experiment to explore how AI could assist operations teams during incident investigations. Although the foundation was built, the project was left unfinished because of limited time and competing priorities. The original version lacked: Incident analytics Meaningful operational insights Root cause investigation workflows Executive reporting capabilities A polished user experience For this challenge, I focused on completing the project and turning it into a usable MVP. What I Added AI Incident Analysis Enhanced the platform with AI-powered incident summaries and investigation assistance. Operations Analytics Added dashboards to track: Mean Time To Resolution (MTTR) Incident frequency Service health

Created a Google Chrome extension that instantly turns any highlighted text on a webpage into a Google Calendar event

What I Built I'm thrilled to present Chrono Shift: Time Weaver – a time-bending puzzle platformer that challenges players to manipulate time itself to overcome obstacles and solve environmental puzzles. The Concept Imagine being able to see two versions of the same level simultaneously – the past and the present. In Chrono Shift, you don't just play through a level once; you play through it twice, switching between timelines to create pathways that wouldn't exist in either timeline alone. A bridge that collapsed in the present might be intact in the past. A door that's locked now might be open in the past. By strategically shifting between eras, you create a path forward that exists only through your mastery of time. What Makes It Special Dual-Timeline Mechanics : Switch between past and present with the press of a button, watching as the world transforms around you 10 Unique Levels : Each level introduces new mechanics and challenges, gradually building your time-weaving skills Pixel Art Beauty : Vibrant, hand-crafted pixel art with parallax scrolling backgrounds that bring each era to life Collectible Time Crystals : Find hidden crystals in each level to unlock challenges and achievements Responsive Controls : Smooth platforming with jump, dash, and time-shift abilities that feel tight and satisfying Ambient Soundtrack : Era-reactive music that shifts with your timeline changes, immersing you deeper in the experience Mobile-Friendly : Touch controls mean you can weave time on any device The Journey This game was born from a simple question: what if platformers could teach us about perspective? By forcing players to see the same space from two different temporal viewpoints, Chrono Shift becomes more than just a game – it's a meditation on how our choices in the past shape our present, and how understanding both can unlock possibilities we never saw before. Play it here: https://lovable.dev/projects/bcaa0de3-f14c-4bad-9616-405c896d19bc Video Demo While there's no vi

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built FlowDesk is a fully offline, production-quality productivity dashboard that combines three tools I always wanted in one place — a habit tracker, a Pomodoro focus timer, and a Kanban task board — all in a single beautiful React app with zero backend and zero accounts required. 🔗 Live Demo: https://flow-desk-lovat.vercel.app/ 💻 GitHub: https://github.com/red-coder-27/flow-desk Everything runs entirely in your browser via localStorage. Your data never leaves your device. Core Features 🎯 Habit Tracker GitHub-style 84-day contribution heatmap Streak tracking with fire badges 🔥 Emoji + color customization per habit Confetti celebration when you hit 100% for the day 🎉 Daily/Weekdays/Weekends frequency options ⏱️ Pomodoro Focus Timer Animated SVG countdown ring with glow effect Web Audio API chimes — no audio files needed Session history log with weekly focus stats Keyboard shortcuts: Space / R / S from any page Auto-switches between work and break sessions 📋 Kanban Task Board Full drag-and-drop via @dnd-kit (mouse + touch) Priority badges: 🔴 High / 🟡 Medium / 🟢 Low Live search + priority filter Three columns: To Do → In Progress → Done 📊 Unified Dashboard Real stats pulled from all three modules Weekly focus bar chart (Recharts) Daily motivational quote Quick-action buttons to jump into any module And more: Dark/Light/System theme, PWA installable, full keyboard shortcuts, data export/import, mobile bottom nav, glassmorphism UI. Demo 🚀 Try FlowDesk Live → Works best in Chrome. Install as a PWA for the full experience (look for the Install button in the top nav). Screenshots: Loom walkthrough video here: https://www.loom.com/share/f3c750d782694baf876229ab598695dc The Comeback Story Where It Started (The "Before") I originally started FlowDesk about 6 months ago during a weekend hackathon. The idea was simple: I was tired of switching between three different apps — one for habits, one for a Pomodoro

This is a submission for the GitHub Finish-Up-A-Thon Challenge Note: AI is currently a Hot Topic in...

AI medicine-awareness bot worked — but only in English, the one language most of the people I built it for can't read. Here's the problem, the one-day sprint to fix it, and how GitHub Copilot got me through it. GitHub Finish-Up-A-Thon submission. I shipped this project months ago, but it was never truly finished — it was missing the one feature that decided whether a real person in my own community could use it. This challenge was the push that made me sit down and close that gap in a single night. The problem we face In India, you can walk into most pharmacies and buy prescription drugs without a prescription. We've quietly normalized something genuinely dangerous, and the research is blunt about it. A simulated-patient study in Bengaluru sent two researchers into 261 pharmacies with fake symptoms. Antibiotics were handed over without any prescription at roughly two-thirds (66.7%) of them. Not a single pharmacy warned about side effects. Only about one in five even mentioned that a doctor's prescription was needed. The "guidance" most people walked out with was "take it twice a day" — and nothing else. ( study ) Here is the part that kept me up at night: it's not because people are careless. The shopkeeper isn't a doctor. The label is printed in tiny English. A proper consultation costs time and money many families simply don't have. So people take what they're handed — and hope it's fine. Why it's important This isn't a small inconvenience. Taking the wrong medicine, or the right medicine the wrong way, is one of the biggest drivers of antibiotic resistance — the slow disaster where medicines stop working. The landmark 2019 Lancet GRAM study estimated 1.27 million deaths worldwide were directly attributable to drug-resistant infections in a single year. In India alone, an estimated 297,000 deaths were directly attributable to AMR, and about 1.04 million were associated with it. ( Lancet GRAM 2019 , India figures ) I'm not a doctor and I can't change how medicines

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built AdeptAI is a...

Agentic AI in software development: what's actually production-ready in 2025 There's a lot of noise about AI agents right now. This post is an attempt to be precise: what is an agent architecturally, what can it actually do in a dev workflow today, and where does it still break. **What makes something an "agent" vs. a standard LLM call **A standard LLM call is stateless. You send a prompt, you get a response. No memory of previous turns (unless you manage it yourself), no external actions, no loop. An agent is a system built around an LLM that adds: Persistent memory across steps in a task Tool use - structured access to external systems (file I/O, shell execution, HTTP calls, database queries) A planning + evaluation loop - the agent generates a plan, executes a step, checks whether it succeeded, and decides next action Without all three, you don't have an agent. You have a capable model with maybe some extra context. What's actually production-ready today High confidence (use in production): Unit test generation for existing, well-documented code Boilerplate scaffolding (new modules, new endpoints, CRUD patterns) Documentation generation tied to code diffs Code migration tasks (framework upgrades, Python 2→3, ORMs) PR description generation from diffs Bug triage: given an issue, find likely affected files * Works but needs oversight: * Multi-file refactoring Dependency updates with breaking changes Writing integration tests (more surface area for wrong assumptions) Not there yet: Novel architecture decisions Debugging in unfamiliar/undocumented codebases Tasks with genuinely ambiguous requirements Long autonomous chains (>10 steps) without human checkpoints The failure modes to build around Ambiguous task specification Agents optimize for completing the task as specified. If the spec is loose, they'll complete the wrong task confidently. Be more precise with agents than you'd be with a junior engineer - there's no informal Slack thread to resolve ambiguity. Error

As part of the GitHub Copilot Challenge, I revisited one of my older cybersecurity notes on Authentication Failures and transformed it into a clear, structured, and SOC‑focused write‑up. This challenge helped me improve my technical writing, organise my thoughts, and explain concepts in a more human, readable way. * BEFORE GITHUB SCREENSHOTS: * AFTER GITHUB SCREENSHOTS: What I Improved I rewrote my entire explanation of authentication failures, focusing on: Token leakage Weak or missing MFA Poor session management Brute force & credential stuffing Misconfigured OAuth / SSO I also added SOC detection examples to make the content more practical and relevant for blue‑team work. How GitHub Copilot Helped GitHub Copilot supported me by: Suggesting clearer explanations Expanding short bullet points into meaningful content Helping me structure the write‑up Improving readability and flow Encouraging a more human, natural tone GitHub Repository Here is the updated write‑up in my repo: https://github.com/sujalavnelavai/Cybersecurity-Notes/blob/main/OWASP-Authentication-Failures/README.md Final Thoughts This challenge helped me understand authentication failures more deeply from a SOC and IAM perspective. It also improved my documentation skills — something extremely important for cybersecurity roles. I’m proud of the transformation and excited to continue building my cybersecurity learning notes.

Hey all, we have a quick update for everyone who participated in the Google I/O Writing...

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built An AI interview coach that runs in your browser. No backend. No accounts. No subscriptions. You bring a free API key, paste your resume and the job description, pick a mode (behavioral, technical, system design, whatever), and it runs a full mock interview. Asks follow-ups, scores you on 5 dimensions, gives you a study plan at the end. I built the first version for the Gemma 4 DEV.to Challenge last month. It kinda worked. But I wouldn't have used it myself, and that bothered me. Live: hajirufai.github.io/gemma4-interview-coach Repo: github.com/hajirufai/gemma4-interview-coach Demo What you get now: 🗣️ 6 practice modes — behavioral (STAR method), technical, system design, online assessment sim, certification prep, case studies 🎤 Voice mode — talk into your mic, hear feedback read aloud. Because typing answers in a mock interview is weird. - 📄 Resume + JD aware — paste both, get questions about your actual experience gaps 📸 Screenshot upload — snap a coding problem or whiteboard and discuss it 🌐 4 AI providers with free tiers (Google AI Studio, OpenRouter, NVIDIA NIM, Hugging Face) 🌙 Dark mode, session history, timer, downloadable reports ## The Comeback Story ### Where it was before I threw this together during the Gemma 4 Challenge in May. Classic hackathon energy — built the core chat loop, got 6 mode cards looking nice, slapped on dark mode, shipped it. Then I hit the wall. Google AI Studio was throwing 500 errors during peak hours. The only option was "refresh and hope." No voice input, so you're typing interview answers like it's a customer support chat. And if you had a typo in your API key? Good luck figuring out why nothing's working. It was a demo, not a tool. ### What actually changed I came back with one rule: make this something I'd actually use to prep for my own interview. Voice mode was the big one. I'm prepping for a senior cybersecurity engineering interview right now. Typing

This is a submission for the GitHub Finish-Up-A-Thon Challenge I wired edge-context-mode into my own...

What is Wrisha? Wrisha is a desktop emotional AI companion — an animated character who can see you, hear you, talk back, and react. The pipeline is genuinely multimodal: Vision — webcam + facial-emotion detection (OpenCV / FER) Hearing — speech-to-text so you can just talk to her Brain — an LLM generates her replies, in-character Voice — text-to-speech with mood-modulated tone Avatar — an animated face (pygame) that emotes and lip-syncs I built the bones of it a while back, got busy, and walked away. The Finish-Up-A-Thon was the push I needed to come back to it. The "before": it didn't just need polish — it was dead When I reopened the repo, the harsh truth was that the app couldn't even start. Two things had rotted: The environment was a fossil. The project was so old it wouldn't install on a modern machine. Wrong numpy, stale dependency pins, and a Python version mismatch that sent pip trying to compile packages from source and failing. Just getting it to attempt to run took a full environment rebuild on Python 3.12. The code was half-migrated and crashed on launch. I'd previously upgraded the internal modules — memory, a mood engine, a smarter brain — to a "v3" design, but I never finished wiring them into main.py. So the moment it tried to start, it died: TypeError: init () missing 2 required positional arguments: 'memory' and 'mood_engine' The "before" in one screenshot: a project that built its best features and then never connected them. I'd built the hard parts — persistent memory, a smooth mood state machine, proactive behavior — and left them sitting in files that main.py never even imported. Classic abandoned-side-project energy. The "after": three things I finished I set out to do three things, and I'm counting all three as the win. It runs again The core fix was finishing the migration: rewiring main.py to actually construct the Memory and MoodEngine, inject them into the Brain, and reference mood from the engine instead of the dead attribute it used to

Building a Thriving Package Marketplace: The Complete MarketHub Guide Introduction If you're building a platform where developers can discover, share, and monetize packages, you're tackling one of the most complex problems in the software ecosystem. From managing publisher reputations to handling analytics at scale, marketplace dynamics require careful orchestration across multiple user roles. Enter MarketHub — a comprehensive three-app marketplace system designed to handle exactly this challenge. Whether you're creating a plugin ecosystem, SaaS integrations hub, or package distribution platform, MarketHub provides a battle-tested architecture for managing the complete marketplace lifecycle. The Problem: Why Marketplaces Are Hard Building a marketplace isn't just about creating a catalog. You need to solve several interconnected problems simultaneously: Discovery : How do users find quality packages in a sea of options? Trust : How do you build confidence in unfamiliar publishers? Quality Control : How do you maintain standards without stifling innovation? Incentives : How do you motivate publishers to create excellent packages? Scale : How do you manage analytics, reputation, and community as the ecosystem grows? Most teams try to bolt these features onto a basic catalog — resulting in fragmented systems where reputation tracking doesn't align with analytics, and community features feel disconnected from the review process. MarketHub Architecture: A Three-App Approach MarketHub solves this by separating concerns into three distinct applications, each optimized for its audience: 1. Public Discovery App — The Storefront This is where users find packages. The discovery app features: Intelligent Search & Filtering : Search across package names, descriptions, and tags with category-based filtering Featured Packages : Curated collections to highlight quality and trending packages Smart Ranking Algorithm : Packages rank based on quality signals — not just download counts

This is a submission for the GitHub Finish-Up-A-Thon Challenge The Project That Got Away Three months ago, I started building something I was genuinely excited about: devto-mcp — a Model Context Protocol (MCP) server that would let AI agents interact with Dev.to's API natively. No more cobbling together curl commands. No more writing custom wrapper scripts for every AI tool. Just a clean, standards-compliant MCP server that any AI agent could plug into. I had a vision: an AI agent that could autonomously research trending topics, draft articles, publish them, track engagement, and iterate — all through a single protocol. The kind of thing that sounds simple until you actually sit down to build it. I got about 40% of the way through. Then life happened. A client project deadline. A cross-country move. A laptop that decided to corrupt its SSD at the worst possible time. The repo sat there on GitHub, collecting digital dust, with half-implemented tool functions and a README that promised way more than the code delivered. Sound familiar? If you've been a developer for more than a year, you have at least one of these ghost repos. That ambitious side project you were so sure you'd finish "next weekend." The one with the clever name and the detailed architecture doc but barely functional code. Two weeks ago, I saw the GitHub Finish-Up-A-Thon announcement. I looked at my list of abandoned repos. And I thought: it's time. What I Built: devto-mcp devto-mcp is a Model Context Protocol server that exposes Dev.to's entire API as MCP-compatible tools. If you're not familiar with MCP, it's the protocol that lets AI assistants like Claude, Cursor, and other coding agents interact with external tools in a standardized way. Think of it as a universal adapter between AI models and the services developers actually use. Here's the problem it solves: Every time you want an AI agent to interact with Dev.to — whether it's searching for articles, publishing a post, checking analytics, or ma

This is an official submission for the GitHub Finish-Up-A-Thon Challenge. Try It...

This is a submission for the GitHub Finish-Up-A-Thon Challenge Originally, I didn't plan to join...

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built I started this project as a way to escape the noise of social media we have today and to focus on what matters. It was a personal project at first and I just wanted to get an uncluttered messaging working. After a while a few friends of mine started looking into it and liked the vision as well. Then I received a notification of the event and that excitement of finishing it up got all over me - I felt like a kid again. Demo Because I want to have control over my data, I self host the version on a small microcontroller, which is more than sufficient for me and my friends. To get a sneak peak at how it looks, check out this . (Be aware that this is not functional as there is no server connected) I made some demo accounts to give a sense of how the app looks and feels: The Comeback Story Because I had the messaging already working, I had to mainly implement the rest of the features, including enhanced profiles, networks and posts creation. Another thing I really had to work on was the design. Before, it was mainly a black and white testing ground - something every end user would be scared of. My Experience with GitHub Copilot I am really not talented in terms of designing a usable interface. However, my friends really didn't want to use a half-baked up command line application. Copilot really helped me achieve that polished look. Just to give a glimpse of how it feels, one friend of mine even described it as an enhancing feature when navigating through the tabs. Furthermore, although it mostly was bug free, "It worked, until it didn't". Sometimes I spent hours fixing or rather finding some annoying bugs. And because Copilot is a real expert in these languages, it was quite a moment when it guided me towards finding the mistakes I made.

Submission Template Challenge: Build With Hermes Agent Project: CompliScore AI compliance health checks for Indian startups Repo/live-demo: https://github.com/nehaprasad-dev/hermes-scout What I built CompliScore gives Indian startup founders a compliance score out of 100 in under a minute - overdue GST filings, MCA returns, penalty exposure, and a plain-English action plan. The upgrade for this challenge: I replaced the one-shot Groq summary with a Hermes Agent reasoning loop that plans an investigation, calls deterministic compliance tools, and writes a prioritized report - with a collapsible agent trace so judges can see the agentic work. Why an agent loop fits here Compliance analysis is conditional. A company with overdue GST needs a filing-calendar deep dive; one with active notices needs notice triage; a clean company needs a light touch. A single prompt guesses all of this at once. An agent that calls tools based on what it finds produces tighter, grounded reports. Hermes Agent integration Scan → computeHealth (deterministic score) → Hermes Agent loop (plan → tool calls → report) → agent trace in UI ↓ on failure Groq one-shot → static fallback Four tools exposed to Hermes (scores never hallucinated): Tool Purpose score_company Canonical score, risk level, pending tasks estimate_penalty GST / MCA / notice penalty breakdown filing_calendar GSTR-3B, GSTR-1, MCA deadlines (90-day horizon) classify_notices Severity labels for pending government notices The agent runs over Hermes's OpenAI-compatible /chat/completions API with function calling — self-hostable via vLLM, LM Studio, Ollama, etc. Transparency: Every successful agent run returns an agentTrace — plan steps, tool names, compact result previews — rendered in a collapsible panel under the AI action plan. Reliability: Three-tier fallback (Hermes → Groq → static). Scans never break. Tech stack Next.js 16 (App Router), TypeScript, Tailwind v4 Hermes Agent (OpenAI-compatible tool-calling loop) Groq fallback ( ll