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Jev Plays Pokémon Red

Details

External ID
49845172
Source
HN
Company
—
Product
Jev Plays Pokémon Red
Website domain
vercel.app
Launched
Sept. 25, 2026
Cohort
—
Upvotes
280
Upvotes percentile
0.9824561403508771
Tags
—
Fetched at
Sept. 29, 2026, 5:01 p.m.
Updated at
Sept. 29, 2026, 5:01 p.m.

Description

Hey HN! Wanted to share a fun project I've been hacking on. Given Jev can make decisions really fast (but not fast enough to play Doom yet sadly), I wanted to try and push it to play a more complex game than Tetris. So I went with Pokémon.I've spent endless hours playing this game as a child so building this was a ton of fun.I open sourced everything in case you want to hack on it yourself here: https://github.com/christianmat/jev-pokemonThe game is being streamed live including the tokens and cost - hopefully we get all the badges and don't get stuck in a cave :)

Enrichment

Theme
Vertical
Media & entertainment
Function
Agent / copilot
Audience
B2C
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
autonomous agent playing pokemon red
Manually corrected
False

Could you build this?

Partial Streaming an emulator to a web page is straightforward, but engineering an AI agent capable of fast, cohesive decision-making over long-horizon RPG game states requires specialized RL or agent-architecture design.

What it would actually take: The stack uses a Game Boy emulator core (like PyBoy) wrapped in Python, interacting with an agent via memory-mapped state observation (reading RAM addresses for coordinates, battle states, and menu selections). The hard part is the decision policy: designing a low-latency hierarchical agent with spatial memory, pathfinding heuristics, and contextual state caching rather than relying on naive frame-by-frame VLM prompting.

Discussion

20 comments analyzed.

Competitors mentioned: OpenAI

Concerns raised: Excessive harness guidance and railroady choices, Model acts mostly as an RNG without state memory, Single-shot classifier ill-suited for multi-step RL tasks, Lack of intelligence compared to leading LLMs

Feature requests: Feed previous decisions back into context state, Integrate with a vLLM to view reasoning logs, Test on models with Pokemon training data abliterated

Competitors

Other products that read as similar to this one — 304 launches clear the similarity bar, closest 8 shown.

Attention rank: #6 of 305 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 331 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a agent / copilot tool for Agriculture yet.