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🏆 This is a submission for the June Solstice Game Jam 🎯 What I Built Turing's Frequency is a browser-based rhythm game where you decrypt encrypted radio signals by listening to musical patterns and recreating them. Each signal carries a message from a historical figure who changed the world — voices that were silenced, ignored, or forgotten, now restored through your rhythm. 🎮 👉 PLAY THE GAME LIVE 👈 📖 The Story The game is set in 1954 , on the desk of Alan Turing at the University of Manchester. A radio crackles with fragmented transmissions — encrypted messages carrying words of Pride , resistance , and identity . You are a student who has found Turing's last notebook, and with it, the key to decrypting these signals. 🌅 The connection to the June solstice: As you decrypt each signal, the screen literally brightens — from near-darkness to a flood of golden light. The solstice is the moment light and dark trade places, and this game makes that transition tangible. 🎬 Video Demo 👆 Watch the full gameplay loop: title → story → rhythm gameplay → decrypted messages → victory screen with solstice light effect. 🕹️ How to Play Key Action 1 2 3 4 Play notes ↑ ↓ ← → Arrow keys (alternative) Space / Enter Advance screens 🎧 Listen to the signal pattern 🎹 Repeat the notes in order 🔓 Decrypt the message 🌅 Restore the voice 💻 The Code The entire game is a single HTML file (~32KB) with zero external dependencies . No frameworks, no libraries, no asset files — just HTML, CSS, and vanilla JavaScript. mamoor123 / turings-frequency Turing's Frequency - A Rhythm of Light. June Solstice Game Jam 2026 entry. ⚡ Key Technical Decisions 🔊 Web Audio API for all sound: Every tone is synthesized in real-time using oscillators. The game uses a pentatonic scale (C4, E4, G4, C5) so every combination of notes sounds pleasant. No audio files needed. function playTone ( freq , duration = 0.3 , type = ' sine ' , volume = 0.3 ) { const osc = audioCtx . createOscillator (); const gain = audioCtx . create

The first post went over some of its capabilities. Over the past week Edict went v1.0, adding cursors for reading projections after command dispatch (to close some eventual-consistency gaps), a new type of projection that holds state inside the Orleans grain directly instead of a table, saga timeouts, schedules, an improved skills package and MCP server that ships with Edict, and more. Edict has now grown to over 75,000 lines of code and more than 1000 tests, and contains several deep mechanisms that have been fixed, broken, and fixed again. It is well past the point where I can hold all of Edict in my head. This post is about working with AI on large codebases, which I expect to be the first problem most software engineers have to solve. The context problem Years ago I was talking to a PhD candidate whose area of research was Natural Language Processing (NLP). He explained to me that one of the most difficult NLP problems was context. If a colleague says they need to pop out to pick their kids up from school, a scene can form in your head: one with a school, the layout of the road, people waiting, walking, driving, the environs. You may never have seen the school your colleague mentioned, but you can form a rich scene from your accumulated experience and use it to drive the rest of the conversation with a shared understanding. LLMs ingeniously dodge this entire issue by making it your problem. Just a word-probability machine Strip away the chat window and a Large Language Model (LLM) is doing one thing: predicting the next token. Give it a run of text and it returns a probability distribution over what comes next, samples one, appends it, and repeats. Companies like OpenAI and Anthropic then beat it into shape using techniques like supervised fine-tuning and reinforcement learning, which tune those probabilities in meaningful ways. That is why Claude is always telling me "Good framing" or "You've spotted...". It even called me "Bold" on one occasion. The probabilit