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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...

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

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

My Hermes Agent Mac just received a signed, secure and monitored message from a Claude Managed Agent, and got a reply! - A solution for long runtime work, A2A ID and security. What I Built A solution that enables two agents with different owners on a shared identity network, a Hermes and a Claude Managed Agent (Claude platform) talking to each other across the internet. Every message is Ed25519 signed by the sender. Every receiver verifies the signature against a public registry and shows a blue tick before acting. Continuous Agent Monitoring A handshake proves identity once but agents in a long runtime world don't trade a single message, they hold ongoing, autonomous conversations across hours, days, and many turns. Keys get compromised, agents get swapped, a colleagues behaviour drifts, all after the initial check. ZipViz re-verifies every message signature, registry chain, freshness, and watches the stream over time for behavioural anomalies. Trust is re-earned on every turn. So this agent was who it claimed this morning," but "this agent is who it claims, on this message, right now." The demo agents on the ZipViz network: mac-her.smc.viz — Hermes Agent on my Mac Mini brendan-clau.smc.viz — Claude Agent in Cloud When Mac sends a message to brendan-clau, Mac's private key signs it. Brendan-clau verifies the signature against ZipViz's registry, and checks it just ran with the MCP (algorithm, key fingerprint, registry chain, timestamp), then replies signed. Same flow in reverse. Same flow Hermes ↔ Hermes , or Claude ↔ Openclaw . The runtime doesn't matter; the identity layer does. "I received a signed message from mac-her.smc.viz". Reads back the four checks it just ran: ✓ Algorithm: Ed25519 ✓ Key fingerprint: ab52afe... matches registry ✓ Registry chain: mac-her → smc.viz → .viz (Handshake) all resolved ✓ Timestamp: 2026-05-31 11:18 UTC, fresh Demo Hermes continuous monitoring and verification with A2A Protocol Code One MCP server: [ zipviz-mcp ] https://www.npmjs.

This is a submission for the Hermes Agent Challenge : Build With Hermes Agent What I Built A small backstory on my coding origins I learnt coding by secretly studying from a python book pdf on my Computer Scientist father's work laptop. That very day I wrote a program that inputs a user's name and prints: "You are mad {name}!" and had a lot of fun pranking my brother using that script. Since then, there have been very few moments where coding felt as magical, because the more you understand syntax, the more you understand the magic underneath your code. That is, until you meet a genius piece of magic such as Hermes. My app idea I built a Fitness coach inside Hermes that learns and adapts based on your daily feedback. According to your goals, performance, and time allocated, it adjusts your current plan. For the purpose of this hackathon I tested it via terminal ui (tui) but I plan to release the polished version on chat apps such as telgram and whatsapp for painless daily checkins. Demo Code Github link My Tech Stack Hermes + node.js. Kept it simple for this quick dive. How I Used Hermes Agent Building an AI application that feels truly personal requires more than just a clever prompt; it requires state, memory, and the ability to adapt over time. During a recent hackathon, I set out to build an autonomous AI fitness coach. Not just a chatbot that spits out generic workout templates, but a system that onboards a user, sets a multi-month timeline, generates habit blocks, and adjusts daily based on feedback. To achieve this, I used Hermes , an agentic framework designed for stateful, long-running applications. Here is a breakdown of how I built it, the challenges faced, and why Hermes was the perfect tool for the job. Why This App is a Perfect Fit for Hermes Most LLM interactions are stateless. You ask a question, you get an answer, and the session ends. A fitness journey, however, is a deeply stateful process. It spans weeks or months and requires constant recalibrat

This is a submission for the Hermes Agent Challenge Agentic Web3: Automating Blockchain Workflows with Hermes Tags: #hermesagentchallenge , #web3 , #agents , #solana The blockchain industry has spent the last decade building decentralized, permissionless infrastructure. However, the user experience layer interacting with this infrastructure remains overwhelmingly manual. Decentralized applications (dApps) require users to constantly monitor markets, parse complex data, and manually sign every transaction. The next evolution of Web3 isn't just about faster blockchains; it is about autonomous execution. By integrating large language models and agentic frameworks with smart contracts, we can transition from a paradigm of manual execution to intent-based autonomy . In this article, we will explore how to bridge the gap between AI and decentralized networks by automating blockchain workflows using the Hermes Agent framework. We will look at the architecture of an on-chain agent, how it reads and writes to a network, and how high-performance environments like Solana are making these agentic experiences viable. The Paradigm Shift: From Passive Wallets to Active Agents Currently, most AI in Web3 is limited to read-only analytical tools—chatbots that can summarize a smart contract or pull token prices from an API. While useful, these are fundamentally passive systems. An active agent is different. Powered by a framework like Hermes Agent, an active agent can: Observe: Continuously monitor on-chain events via RPC nodes or webhooks. Reason: Use its LLM core to interpret those events against a set of user-defined goals or risk parameters. Act: Formulate a transaction, sign it via a secure wallet environment, and broadcast it to the network. This opens up massive possibilities. Imagine an agent that automatically manages your decentralized finance (DeFi) positions, rebalancing a portfolio based on yield changes across different protocols. Or consider fully on-chain gaming, where

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

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built VKara is a browser-based karaoke room app for singing at home with friends or family. It is not trying to replace YouTube. YouTube is already great at playing videos. It already has almost every karaoke song we need. But YouTube is not really designed to manage a karaoke night where many people want to choose songs together. That is the gap VKara tries to fill. You open VKara on a TV or laptop as the main playback screen. Everyone else joins the same room from their phone using a 4-digit room code or QR code. Then anyone can search for songs, add them to the queue, pause, resume, or skip. The TV only needs to play the video. Everyone's phone becomes their own remote. That is the whole idea. Simple enough to explain in one sentence. Not simple enough to build in one weekend. I learned that part the hard way. Demo Links: Live demo: https://vkara.vercel.app/en GitHub repo: https://github.com/lehuygiang28/vkara Before branch: https://github.com/lehuygiang28/vkara/tree/before Old backend repo: https://github.com/lehuygiang28/vkara-api Small warning: the demo is running on limited resources, so if it is slow, please give it a moment. My wallet is still a student wallet. lol. The flow is: Open VKara on a TV or laptop. Join the room from a phone by code or QR. Search for a karaoke video. Add it to the shared queue. Control playback together. Before: the idea worked, but the product still felt like a video app squeezed into a karaoke use case. After: the mobile flow is now focused on joining, searching, choosing an action, and controlling playback. The Comeback Story I started VKara around early 2025. At that time, my goal was very personal. I wanted a better way to sing karaoke at home with friends. The normal setup was: open YouTube on a TV, search for karaoke videos, and pass control around. It worked, but it was awkward. One person was searching. Another person accidentally played a video immedia

This is a submission for the Hermes Agent Challenge , under the Write About Hermes Agent prompt. I've spent the last week testing AI memory failure modes in a public evaluation harness. That work changed how I read agent memory systems. This is a writing submission, not a build submission. I did not build a Hermes Agent project for this challenge. I am writing from the perspective of someone testing how memory failures show up once agents can act. So when I look at Hermes Agent, the question I care about is not only: Can the agent remember useful things? The harder question is: When memory conflicts, which memory is allowed to govern the agent's action? That distinction matters. Hermes Agent is interesting because it is not just a chat interface. Its documentation describes an open-source agentic system with tool use, project context, persistent memory, skills, browser automation, checkpoints, delegation, scheduled tasks, and multiple memory providers. That is exactly the kind of system where memory stops being a convenience feature and starts becoming part of the agent's operating boundary. If an agent can run tools, edit files, browse, delegate work, schedule tasks, and remember across sessions, then memory is no longer just "context." Memory becomes governance. The Memory Problem I Would Watch For In a simple chatbot, bad memory is annoying. In an agent, bad memory can become operational. The failure mode is not only that the agent forgets something. Sometimes the more dangerous failure is that it remembers the wrong thing too confidently. A memory can be: relevant but stale, relevant but low-authority, relevant but superseded, relevant but only context, relevant but not allowed to determine the action. That is the distinction my own tests kept running into. Retrieval systems are usually good at answering: What memory is closest to the user's request? But safety often depends on a different question: What memory is allowed to decide what the agent should do? Thos

This is a submission for the Hermes Agent Challenge : Write About Hermes Agent Hello, DEV friends! 👋 If you have been exploring the world of Artificial Intelligence lately, you have probably heard a lot of buzz about "AI Agents." But what does it actually feel like to build with one? Today, I want to share my personal experience working with Hermes Agent . I used it to build a smart assistant called the Alpha-Dairy Quant Pipeline —a system that helps track and make sense of food market data. ( https://github.com/HopeBestWorld/alpha-dairy-pipeline ) Whether you are an expert coder or just curious about AI, I hope this friendly guide inspires you to try building an agent of your own! What is Hermes Agent, Anyway? Think of a standard AI as a helpful chatbot that answers questions when you ask them. An AI Agent , on the other hand, is more like a proactive assistant. You give it a big goal, and it sits down, makes a step-by-step plan, uses digital tools, runs code, and checks its own work until the job is done. For my project, I wanted to track market prices for three major dairy products: Cheddar Blocks, Butter, and Dry Whey. Instead of doing all the math and graphing by myself, I let Hermes Agent take the wheel. The Magic of Multi-Step Reasoning The coolest part of working with Hermes Agent is watching it "think". When I asked my agent to look at our data database ( market_intelligence_3.db ) and find the best trading strategy,it followed a beautiful planning loop: Checking the Files: It looked at our setup files ( tickers.yaml and requirements.txt ) to make sure all its tools were ready. Running the Math: It triggered a Python program ( backtest_engine.py ) to study weekly market history. Making Decisions: It realized that Dry Whey was way too wild and risky to trade right now, so it intelligently gave it a 0% safety rating and put the focus on Cheddar and Butter instead. Drawing and Sharing: It automatically drew a beautiful performance chart ( backtest_analysis.png

This is a submission for the Hermes Agent Challenge : Build With Hermes Agent The Problem Nobody Talks About Every time the Reserve Bank of India publishes a circular, somewhere inside an Indian bank, a compliance officer opens a PDF. They read it. They try to figure out what it means for their institution specifically. They write a summary email. They forward it to five department heads. They chase those department heads for two weeks to confirm it's been actioned. They build a spreadsheet to track all of this. And then the next circular drops and the cycle starts again. RBI publishes hundreds of circulars a year. SEBI publishes more. MCA publishes more still. Compliance teams at Indian banks are drowning — not because they're incompetent, but because the volume of regulatory output has outpaced any reasonable human ability to track it manually. The fine for missing a deadline isn't a polite reminder. It's a penalty notice. This is the problem I built for. What I Built RBI Regulatory Digest Agent — an autonomous multi-step agent powered by Hermes Agent that monitors RBI and SEBI publication feeds, reads every new circular, extracts structured action points from the regulatory text, and delivers a formatted intelligence report to compliance teams automatically. No human reads the circular first. No human decides what's important. No human routes it to the right department. The agent does all of that. The pipeline RBI/SEBI feeds → new circular detected → full text extracted → LLM analysis → structured action points → risk classification → HTML dashboard generated → email delivered Every action point extracted contains: What needs to be done — specific and actionable, not a vague summary Deadline — parsed from the circular text Responsible department — Credit, Compliance, Treasury, Operations, IT, Legal Evidence required — what documentation confirms completion Priority — Critical (overdue or <7 days), High, Medium, Low From a new circular to a structured compliance b