AgentGate

**AgentGate** is a safety loop for code that agents write. A builder opens a pull request, CodeRabbit reviews it, and Jev is the only thing allowed to merge. If CodeRabbit still has a critical or security finding, or the tests are failing, the pull request stays blocked and goes back to be fixed. On a live GitHub pull request, CodeRabbit flagged a refund endpoint that dropped the payment lookup, so any payment id could receive a completed refund, and that never checked the amount. Jev’s decision on that evidence is fix, at risk 9.9 out of 10. After a clean review the same gate can say merge, at risk 2.7. Nothing ships while a blocking finding is open. Built for JEVATHON with CodeRabbit and Jev. Repo: https://github.com/manvendersingh21/PR-slayer

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HackerSquad project

Painting Band

A live band you conduct with paintings: arrange artworks on a musical staff, and every bar an AI conductor (TypeSafe Jev) decides how the band plays while Google's Lyria RealTime generates the music. # Painting Band 🎨🎶 **Arrange paintings on a staff. A playhead sweeps across. Every bar, an AI conductor decides how the band plays, and an AI music model plays it live.** Nothing is pre-recorded. The music is generated live from what's on the canvas. ## How it works 1. **See:** Gemini looks at each painting and writes how it should sound: mood words, sound ideas (e.g. "cascading koto arpeggios"), instruments, and the painting's musical tradition (Japanese, Hindustani, Western…). Upload your own image and it's described in about 2–3 s. 2. **Arrange:** Drag paintings onto the staff. - **Left → right** = when a painting plays - **Height** = register (high or low) - **Size** = how strongly its mood and sounds lead 3. **Conduct:** Every bar (~2.7 s), **TypeSafe Jev** answers up to 12 typed questions in parallel in ~150–300 ms: - how loud each painting should be, and the mood and energy - drums, bass and the lead instrument - two extra sounds and the transition - which culture leads when traditions mix Jev never plays notes; it only decides. 4. **Play:** Jev's choices become weighted text prompts plus density, brightness and mute settings for **Google Lyria RealTime**, which streams AI-generated 48 kHz audio and blends toward each new decision. The music changes when you move, resize, add or remove a painting. ## Architecture ``` Canvas → scene as words → Jev (decides) → translator → Lyria RealTime (plays) ↘ Strudel (backup engine) Images → Gemini vision → mood, sound ideas, instruments, culture ``` - **Deciders never make sound; engines never make decisions.** Code sits in between. - **Deadline-safe:** if Jev is later than 150 ms before the next bar, the band reuses the last bar's choices, so the music never stops. - **Always playing:** if Lyria stalls, the app switches to **Strudel** (a browser-based live-coding music engine) mid-song, and you can switch back with one click. - **Explainable:** the `</>` panel shows each bar's questions and Jev's odds for every option, the exact prompts sent to Lyria, what Gemini saw in each painting, and a live log of every model call. ## Features - Drag, resize and delete paintings; draggable playhead (Home = start) - Silence where there's no painting under the playhead - Upload any image; Gemini describes it (with automatic model fallback when busy) - Cultural awareness: each painting's musical tradition shapes the sound, and Jev picks which tradition leads - Jev vs. Rules toggle (keys J/R) to hear the difference judgement makes - Live stats: Jev latency, missed bars, and cost (~$0.00001 per bar) ## Tech stack - **Vite + React + TypeScript** - **TypeSafe Jev:** per-bar decisions (`/v1/systemone`, typed choice answers with confidence) - **Google Gemini:** image understanding (structured JSON output) - **Google Lyria RealTime:** streaming music generation (`@google/genai`) - **Strudel:** fallback engine (`@strudel/web`, General MIDI soundfonts) - API keys stay server-side behind a Vite proxy (except Lyria, which streams from the browser in this local demo) ## Run it ```bash npm install cp .env.example .env.local # add JEV_KEY, JEV_BASE_URL, VITE_GEMINI_KEY npm run dev ``` Open http://localhost:5173 and drag a painting onto the staff. ## Paintings Seven public-domain works from Wikimedia Commons: Hokusai, Monet, Van Gogh, Bruegel, Seurat, Rembrandt and Raja Ravi Varma.

TypeSafe AIGMI

HackerSquad project

Reinforcement Learning for Enemy ai

a 2D platformer where every decision and every physics tick goes through jev, a physics/AI backend (currently a local mock, swappable for the real service). You have 30 seconds to reach the flag as many times as possible. Deaths respawn you — the clock doesn't stop. High score persists across sessions. Everything runs through jev: - step — all physics: player, bullets, monsters (gravity, velocity, AABB collision) - aim — two turrets that study your recent movement history and adapt after misses (leading runners, ambushing campers, shooting landing spots of jumpers) - decide — two flying monsters that dash every second in 2D, intercepting where your velocity says you'll be - reset — wipes all learned state on a new game (N); respawns keep the memory — the enemies keep learning mid-run Controls: ←→/A/D move · Space jump · R respawn · N new game How the game talks to jev The context stream — history Every frame, game.js appends one sample: ```js { t: 12.483, x: 340.2, y: 464, vx: 320, vy: 0 } ``` It's a rolling 12-second ring buffer (HISTORY_SECS), ~720 samples of your position and velocity — the raw material for every enemy decision. Nothing else about you goes over the wire; habits are computed from this stream, on jev's side. The three decision channels (all async, all JSON request/response): ``` game.js jev ───────────────────────────────────────────────────────── step({ body, solids, dt }) → { x, y, vx, vy, onGround, touchingWall, stats } aim({ id, muzzle, history, now, { fire, dir, target, mode } report }) decide({ id, offset, pos, → { action:'dash'|'idle', history, now }) dir:{x,y}, vx, vy } reset({}) → {} ``` step — physics. The game never moves anything itself. Player, both monsters, and every bullet are each submitted as {x, y, w, h, vx, vy, noGravity?} plus the level's solid rects and dt. jev returns resolved positions and contact flags; the game applies them verbatim. noGravity: true on a body = flight (monsters, bullets share the same solver, gravity skipped). aim — the shooters. Per frame, per turret: the request carries the turret's id and muzzle, the full history buffer, now, and report — the outcome of its last shot ({hit: true|false}). That's the feedback loop: jev answers fire: false while its 2 s cooldown runs, or {fire: true, dir, target, mode} where mode names the tactic it picked (lead runner-prediction, zone bombardment of your favorite territory, anti-jump at your landing spot). Each turret has an independent cooldown by id, but hit/miss learning is shared — what one learns, both use. decide — the dashers. Per frame, per monster: id + offset give each its own 1-second cadence, phase-staggered (m1 is offset 0.5 s so they don't dash in lockstep). The request has the monster's pos and the same history. jev reads the last ~1 s of samples, averages your vx/vy, and returns a normalized 2D dir plus the impulse — it intercepts where you're heading, including vertically (swoops up at jumpers, dives at fallers). idle between pulses; the game dampens velocity 0.94×/frame. The loop in one breath: the game is a sensor array and a renderer — it records what you do, asks jev what everything should do next, and draws the verdict. The mock computes the brains locally; set endpoint in jev.config.js and the identical JSON POSTs to {endpoint}/step|aim|decide|reset. The tactic word in the HUD — zone, lead, or anti-jump changes based on what you just did (standing still → zone, running → lead, jumping → anti-jump) - Shots get more accurate over a run — every miss feeds back to jev, which adjusts where the next bullet aims; the target coordinates in the HUD shift after each miss - N resets it — new game wipes the memory and the enemies go back to firing dumb straight shots That's the whole story: the turret's aim line in the HUD is literally jev telling you what it learned.

The AI Collective

HackerSquad project

Sreak

Real-time persuasion training game — talk your way past an AI guard, investor, or friend, and every sentence is instantly scored and reacted to.

TypeSafe AI