Seventy-five years of game AI, and every system plays a handful of games
Jul 15, 2026 One Machine, Any Game Part 1 Part 2 Here’s a question that’s been bugging me for a long time. If you handed a machine a board game, the instruction manual plus what’s in the box (the cards, the board, the dice, the little wooden pieces), could it teach itself to play well? No programmer wiring it up for that specific game.
- ▪Jul 15, 2026 One Machine, Any Game Part 1 Part 2 Here’s a question that’s been bugging me for a long time.
- ▪If you handed a machine a board game, the instruction manual plus what’s in the box (the cards, the board, the dice, the little wooden pieces), could it teach itself to play well?
- ▪No programmer wiring it up for that specific game.
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| Original publisher | Github |
| Canonical URL | https://kallin.github.io/blog/game-ai-one-machine-any-game/ |
| Publication time | Wed, 29 Jul 2026 13:47:41 +0000 |
| Retrieval time | 2026-07-29T14:13:06.765Z |
| Last seen | 2026-07-29T14:13:06.765Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | C8NvGLvbtMak · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Jul 15, 2026 One Machine, Any Game Part 1 Part 2 Here’s a question that’s been bugging me for a long time. If you handed a machine a board game, the instruction manual plus what’s in the box (the cards, the board, the dice, the little wooden pieces), could it teach itself to play well? No programmer wiring it up for that specific game. Just the rules, and the machine. Not one game. Any game. The same system that teaches itself chess should handle Monopoly, Ticket to Ride, and the half-finished prototype a designer scribbled last week, because it never knew which one it was playing in the first place. I think the answer is yes, and I’ve started building the tool to find out. This series is the working log of that attempt, from the very beginning.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
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