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I record a meeting on my phone and send the audio file to my Telegram chat with Hermes, my local AI agent. That is the only trigger—there is no app, upload page, or extra step. Hermes runs on my PC through WSL and handles the rest locally, except for the LLM analysis. Step-by-step: 1. Send the audio via Telegram. I drop the meeting recording into my Telegram chat with Hermes. 2. Create the meeting record. When Hermes receives the audio message, it calls a FastAPI endpoint at `localhost:8200`. The endpoint creates a meeting record and uploads the file. It is a thin Python server backed by SQLite, using async SQLAlchemy. 3. Transcribe the meeting locally. The backend runs faster-whisper on my PC, so there is no cloud transcription API or transcription cost. It handles large files and produces the full text with timestamped segments. 4. Identify the speakers. A diarization pass determines who spoke when. I can optionally upload a 10-second voice sample for each person; the system stores MFCC features and can automatically label speakers such as “Steve” and “Andrew” in future meetings. Diarization is best-effort, so transcription still works if it fails. 5. Analyze the transcript with an LLM. The transcript and timestamped segments are sent to DeepSeek through the OpenRouter API. A single structured prompt extracts: - A TL;DR - Thematic topic groups - Decisions made - Open questions - Risks flagged - A participant list - Tags - Proposed action items, each with an owner, priority, due date, confidence score, source timestamp, and the exact transcript quote it came from 6. Send action items to the todo app. Each proposed task has a Create in Todo button. With one click, I send it to my separate local `agent-native-todo` service on port `8100`, along with the meeting title, evidence quote, and timestamp as context. The todo app runs independently; the meeting app only calls its REST API. 7. Review everything in the web miniapp. The results appear in a React SPA at `/miniapp/`, which includes a meeting list, FTS5 full-text search, transcripts with clickable timestamps that jump to the corresponding point in the audio, color-coded tasks, speaker name editing, and audio playback with seek. The stack is: - Backend: Python FastAPI, async SQLAlchemy, and SQLite - Transcription: faster-whisper, installed with `pip install faster-whisper` - Diarization: pyannote.audio or a similar tool; optional because transcription works without it - LLM: OpenRouter API with DeepSeek, using one structured prompt and JSON mode - Frontend: React, Vite, and plain CSS without a framework - Agent glue: Hermes Agent receives Telegram messages and orchestrates the API calls - Todo integration: Any task app with a REST API; the meeting app only needs to POST to it The only part that costs money is the LLM analysis step, which costs about $0.01 per meeting through DeepSeek. Everything else runs on my PC.

I built Shots2Points, a golf scoring app for iPhone and Android, using AI as my development partner. The idea came from organising and playing in golf society events. Stableford scoring itself isn’t particularly complicated, but running an event can be. Organisers have to prepare groups, handicaps, and courses; collect scores from different groups; calculate results; manage withdrawals and ties; and eventually produce a leaderboard. I wanted to simplify that process while also providing an easy scoring app for ordinary casual rounds. The unusual part is how I built it. I’m not a professional software developer, and I don’t have a development team. I started by describing what I wanted the app to do to AI and gradually turned the idea into a working product. My workflow evolved into this cycle: idea → discussion → specification → implementation → real-world test → refinement. I repeat it for each feature. Step-by-step: 1. I define the problem and user experience with ChatGPT. I discuss ideas, challenge assumptions, work through workflows, and decide how a feature should behave before changing the code. 2. Once the behaviour is clear, I turn the idea into an implementation task. I use AI to specify exactly what needs to change, including edge cases and how the new feature should interact with existing functionality. 3. I build and inspect the code with Cursor. Cursor works directly with the project codebase, allowing AI to investigate existing code, implement changes, and report exactly what it changed. I test the result rather than simply accepting AI-generated code. 4. I test development versions on real iOS and Android devices. I follow the actual user journey, take screenshots or capture errors when something isn’t right, and bring those results back into the AI workflow. 5. I use AI to diagnose problems, make another targeted change, and test again. The result is a real cross-platform application rather than a prototype. Shots2Points includes free casual Stableford scoring and an Event Mode designed for golf societies and groups. Organisers can create events, import players, allocate groups, and allow each group to enter scores while everyone follows a live leaderboard. Building the app has required much more than generating code. AI has helped me work through database design, APIs, authentication, in-app purchases, App Store and Google Play requirements, debugging, user-interface decisions, testing, and release management. The biggest lesson for me has been that AI doesn’t remove the need to understand the problem or make decisions. It gives one person access to capabilities that would traditionally have required several different specialists. I provide the product knowledge, requirements, judgement, and testing; AI provides much of the technical capability and an extraordinarily fast feedback loop. That combination allowed me to take a personal idea for improving golf scoring and event management and turn it into a functioning iOS and Android product.

I built Bubble Grotto, a skill-based arcade game for mobile devices, using AI as a development partner. The concept is deliberately simple: start with a small bubble, grow it, navigate through a cave filled with hazards, and decide when to escape. The larger the bubble becomes, the greater the potential reward—but the harder it becomes to manoeuvre safely. The aim was to create the classic “one more go” experience: controls that can be understood almost immediately, with gameplay that becomes progressively harder to master. The interesting challenge was that building a game is very different from implementing a list of features. The code can work perfectly and the game can still be no fun. Timing, movement, difficulty, visual feedback, and risk versus reward all have to feel right when somebody actually plays it. Step-by-step: 1. I used AI to discuss how the core concept should work, including bubble growth, movement, hazards, progression, scoring and rewards, and the escape mechanic. 2. Rather than designing the entire game upfront, I used AI-assisted development to turn each mechanic into working code and get it onto a real device as quickly as possible. 3. Once a mechanic existed, I tested it myself. I checked whether movement was responsive, whether the bubble grew too quickly, whether obstacles were fair, whether escaping was too easy, and whether failure made me want another attempt or simply became frustrating. 4. I brought those observations back into the AI workflow, identified the relevant behavior or code, made targeted changes, and tested again. 5. Once the core loop felt enjoyable, I refined the interface, visual feedback, progression, and presentation instead of allowing cosmetic work to hide weak gameplay. This produced a development loop of: idea → mechanic → playable build → play-test → adjust → repeat The final result is Bubble Grotto, an arcade game with simple controls but increasingly demanding skill-based gameplay. Players grow their bubble while navigating hazards and must balance risk against reward by choosing the right moment to escape. One of the most useful things I learned is that AI can dramatically accelerate game development, but it cannot replace judgment about whether something is enjoyable. AI can help create a mechanic, investigate why it behaves incorrectly, and implement changes extremely quickly. The human still has to play the game and decide: is this actually fun? That combination allowed me to move rapidly from a simple game idea to a functioning mobile game while spending far more of my time experimenting with gameplay than wrestling with implementation.