Queue the next turn: a tiny Snake game in vanilla canvas JS

Vedant Gajbhiye — Thu, 15 Oct 2026 15:42:59 +0000

I wanted a snake game for GHW and here's what cursor did for me withing 5 min. Cursor is fast, gives great results and kind of COOL I assume.

vee-1745 / snake-game

Snake

Browser Snake game. Move with arrow keys or WASD, eat food, and avoid walls and your own tail.

Play

Open index.html in a browser, or from this folder run:

python -m http.server 8080

Then visit http://localhost:8080

Controls

Arrows or WASD: move
Space: pause / resume
Enter: restart after game over

View on GitHub

Grass Coach: a local Gemma agent that gets you off the screen in 10 seconds

Vedant Gajbhiye — Mon, 12 Oct 2026 05:58:35 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

Grass Coach is a tiny agent whose only job is to make the screen the shortest part of your day.

Most "go outside" apps have the same flaw: they're another app that wants your attention. So I built the opposite. You run one command, spend about ten seconds reading one card, and leave. The card tells you:

When to go: the best free window in the next 24 hours, scored from forecast, air quality, UV, wind and daylight, routed around your calendar, with a bonus for golden hour and sunrise.
Why that window: written by a local open-weight model, in a voice you pick (park ranger, anime training-arc sensei, worried grandma, pirate, or deadpan British naturalist).
A rule to play by, which turns a walk into a side-quest: "Count flower colours; stop at 7." / "No headphones. Name 3 distinct sounds before you turn back." / "Photograph nothing. Memorise one thing and describe it to someone later."
A Touch-Grass Ledger (grass done, grass stats) with streaks and levels, from Seedling up to Ancient Banyan.

If the weather is bad or your calendar is a wall, it doesn't pretend. It prescribes a five-minute "doorstep reset" at an open window instead.

It's for anyone who knows they should go outside and keeps not doing it. Me included.

Demo

This is a real run on my laptop at 4:35 AM in Nagpur, Gemma 3 4B via Ollama, live Open-Meteo data:

🌿 GRASS COACH · Mon 12 Oct · Nagpur
──────────────────────────────────────────────────────────
  CHASE THE DAWN IN NAGPUR!
  The sunrise is painting the sky with beautiful hues, and
  it's the perfect time to witness it before the day gets
  busy. We've got a lovely 30 minutes to enjoy!

  WHEN   6:00 AM – 6:30 AM  (today, in 89 min)  🌅 sunrise
  SCORE  67/100 ███████░░░ good
  SKY    27°C feels-like · rain 0% · UV 0 · wind 5 km/h
  AIR    AQI 105 (unhealthy for sensitive groups)
  RULE   Let's count seven different flower colors – a
         delightful little challenge!
  BRING  A light jacket, A water bottle, A small notebook
  ALSO   today 8:00 AM (52/100)
  ALSO   today 4:30 PM (29/100, golden hour)

  — Get out there and soak it up, friend!
──────────────────────────────────────────────────────────
Screen time for this card: ~10 seconds. Now close the laptop. 🚪
[model: gemma3:4b · forecast: live (Open-Meteo) · calendar: no calendar loaded]

Anyone can try it with no internet and no model:

git clone https://github.com/vee-1745/grass-coach
cd grass-coach
python -m grass --demo --no-llm

Code

vee-1745 / grass-coach

🌿 Grass Coach

A tiny agent that runs on a local open-weight model and has exactly one job: get you off the screen.

It reads the forecast, air quality, sunset time and your free calendar slots, finds the single best window to go outside, and hands you a side-quest card with a little rule to play by You spend ~10 seconds on the screen. Then you leave.

🌿 GRASS COACH · Sat 10 Oct · Nagpur
──────────────────────────────────────────────────────────
  CHASE THE GOLDEN HOUR
  Ranger's orders: The sun sets at 5:43 PM, so 5:00 PM is
  when the light turns soft and everything looks like a
  film. It's 29°C and the air is moderate (AQI 58)
  WHEN   5:00 PM – 5:30 PM  (today, in 80 min)  🌇 golden hour
  SCORE  83/100 ████████░░ prime
  RULE   Photograph nothing. Memorise one thing and
         describe it to someone later.
  ...
Screen time for this card: ~10 seconds.

…

View on GitHub

Python 3.11+, standard library only. No pip install, no API key, no account.

How I Built It

Open pieces: Gemma (via Ollama) as the default writer, Open-Meteo for open weather and air-quality data, and an OpenAI-compatible adapter so llama.cpp, LM Studio or vLLM work too. Swapping the model is one line in grass.toml.

The interesting part isn't the model. It's what I don't let the model do.

1. Code owns the facts, the model owns the flavour. Small models are bad at arithmetic and good at tone. So the Grass Score (0-100), the window times and the quest rule are all computed in plain Python. The model receives them as JSON and writes only a title, a pitch, a re-worded rule, a packing list and a sign-off. (You can see the exact facts it gets with grass --json.)

2. Schema-constrained output. The request carries a JSON schema (Ollama's format field, or response_format on OpenAI-compatible servers), so even a 4B model returns the right shape. Every live Gemma run I did returned a valid card.

3. A number guard. Every number the model writes is checked against the facts it was given. If the model says "it's a lovely 42°C", that's rejected, the model is told exactly why, and it gets one retry. This targets the failure small models actually have: confident, plausible, wrong numbers. It's covered by unit tests that script a fake Ollama server returning hallucinated numbers, malformed JSON and no response at all.

4. It always answers. If the model server isn't running or fails twice, a rule-based writer produces the card. If there's no signal, the last good forecast is cached and reused, labelled as stale. I tested those paths in unit tests; I haven't yet tried it on a trail with no signal.

5. The calendar stays on your machine. A tiny .ics reader finds your busy slots locally. Nothing about your schedule is sent anywhere.

There's also grass compare --models gemma3:4b,qwen3:4b,llama3.2:3b, which runs the same facts through several local models side by side. That only works because the model is a config line, not an API contract.

What the first real run taught me

I wrote the code against a fake model server, so the real Gemma run was the actual test. It worked first time, and it also showed me where a 4B model is weak:

It ignored the voice. My default voice is "park ranger", but Gemma wrote a generic cheerful "Get out there, friend!". Ironically, the dumb rule-based fallback ("Ranger's orders: ... Leave only footprints.") has more character than the model does.
It ignored common sense. It told me to pack "a light jacket" (and on another run "a warm scarf") at 27°C. My number guard checks numbers, not sense. Next step: constrain the packing list by temperature.
It didn't mention the air. AQI was 105 and the card shows it, but the model's pitch never says so. The score and data are honest; the prose isn't always.
The facts never wavered; the prose did. Same inputs, three runs, three different texts. But the time, score and rule were identical every time, which is the whole point of the design.
One thing I'd fix: the "ALSO" list happily showed a 29/100 option. It should hide weak alternatives.

Why Does Open Innovation Matter?

It can work where the grass is. A trail with no signal is exactly where a closed API stops. Local weights plus a cached forecast are designed to keep working there.
Your calendar and location stay yours. An agent that knows when you're free and where you live is the kind of data you don't want on someone else's server. With a local model, the prompt never leaves the laptop.
I could change how it behaves. Constraining output with a schema, adding a verification layer around the model, swapping models and comparing them on identical input are all things I could do in an afternoon because the model is mine to run.
It costs nothing to run. No key, no quota, no bill. A tool meant to be used every day can't have a per-call cost.

My Outing

The card above came out at 4:35 AM and pointed me at a 6:00 AM sunrise window.

I headed out into the cool Nagpur dawn around 6:00 AM for a 30-minute sunrise walk, bringing along a light jacket, my water bottle, and a small notebook to log what I found. I took the challenge seriously (I got to a nice morning walk anyways) and kept my eyes peeled, successfully spotting seven distinct flower colors: bright marigold orange, deep bougainvillea magenta, soft yellow, pure white, crimson red, lavender purple, and tiny wild blue petals nestled along a garden hedge. The AQI sitting at 105 gave the sky a slightly hazy quality, so I adjusted by keeping my pace relaxed and steady rather than turning it into a brisk workout. Stepping away from the laptop after just ten seconds of screen time was a complete breath of fresh air; swapping my usual morning routine (watching reels and youtube on laptop/tab) of mindless scrolling for real sunlight and quiet streets left me feeling remarkably calm, grounded, and present. And also, flowers helped me talk to my crush.

Prize Categories

Gemma: runs Gemma locally by default (gemma3:4b through Ollama), with schema-constrained output and a verification layer around it.

A huge shoutout to shreyashkakad for contributing (https://dev.to/shreyashkakad)

DEV Community: Vedant Gajbhiye