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# Candy Crush, Doom, and a 1977 Atari: the unexpected gaming side of Codemotion Milan 2026

**[Codemotion](https://daily.dev/sources/codemotion)** · 5 min read · 0 upvotes · 0 comments

## Summary

Three talks from Codemotion Milan 2026 explore the intersection of gaming and modern tech. Carolina Pinto (King/Candy Crush) argues that game design principles around onboarding, trust, and retention can teach AI product teams how to build better user experiences. Enzo Lombardi demonstrates connecting Claude to a 1977 Atari VCS via MCP and a Go-based emulator, reverse-engineering assembly code to build an AI opponent. Stefano Fiorucci (deepset/Haystack) challenges the 'bigger is better' AI narrative by training a 1-million-parameter ModernBERT model to play Doom on a CPU in milliseconds, using supervised fine-tuning and reinforcement learning.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.codemotion.com/magazine/dev-life/events/candy-crush-doom-and-a-1977-atari-the-unexpected-gaming-side-of-codemotion-milan-2026>

## Questions this post answers

### How did someone connect Claude to an old Atari 2600 emulator?

A Cloud Software Architect at Cleafy built a Model Context Protocol (MCP) server linking Claude to a Go-based emulator of the Atari VCS 2600, a 1977 console with just 128 bytes of RAM and no frame buffer. He manually reverse-engineered the assembly of an obscure 1980s game, then had Claude build an AI opponent capable of putting up a real fight.

_Developers exploring MCP integrations with unconventional systems can follow similar experiments on daily.dev._

### Can a small 1-million-parameter model play Doom effectively?

Yes, a ModernBERT model with only 1 million parameters was trained to play Doom on a CPU in milliseconds, using supervised fine-tuning followed by reinforcement learning. The project, built by an engineer at deepset (makers of the Haystack framework), was designed to test when a small model can actually outperform a frontier LLM for a narrow task.

_Anyone weighing small models against frontier LLMs for specific tasks can track these tradeoffs on daily.dev._

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---

Tags: [#llm](https://daily.dev/tags/llm), [#game-development](https://daily.dev/tags/game-development), [#mcp](https://daily.dev/tags/mcp), [#reinforcement-learning](https://daily.dev/tags/reinforcement-learning)

[View this post on daily.dev](https://daily.dev/posts/candy-crush-doom-and-a-1977-atari-the-unexpected-gaming-side-of-codemotion-milan-2026-zzu62zvhl)

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