Launch Intel
I

I wrapped the Zorks with an LLM

Hacker News

I wrapped the Zorks with an LLM

I grew up on the Infocom games and when microsoft actually open-sourced Zork 1/2/3 I really wanted to figure out how to use LLMs to let you type whatever you want, I always found the amount language that the games "understood" to be so limiting - even if it was pretty state of the art at the time. So I figured out how to wrap it with Tambo.. (and run the game engine in the browser) basically whatever you type gets "translated" into zork-speak and passed to the game - and then the LLM takes the game's output and optionally adds flavor. (the little ">_" button at the top exposes the actual game input) What was a big surprise to me is multi-turn instructions - you can ask it to "Explore all the rooms in the house until you can't find any more" and it will plug away at the game for 10+ "turns" at a time... like Claude Code for Zork or something

Visit productView on Hacker NewsJan 27, 2026
Share card

Actual performance

111points
58comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: claude, code, open · Missing: mac, agents, macos
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: game engine, io · Missing: https docs, excited, just released
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

In
Integer Factors Wrapped on a Sphere46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Integer Factors Wrapped on a Sphere

Hacker News19
Ne
Netflix Wrapped60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Netflix Wrapped

Hacker News1
Ov
Overcast Wrapped 202354%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Overcast Wrapped 2023

Hacker News9
Wrapped.li
Wrapped.li39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Create your LinkedIn Wrapped Carousel

TrustMRRContent Creation
Re
Redis-LLM – Redis module integrates LLM with Redis45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Redis-LLM – Redis module integrates LLM with Redis

Hacker News2
Li
LitLLM the Spiciest LLM Wrapper34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

LitLLM the Spiciest LLM Wrapper

Hacker News1
LL
LLM Reasonsers46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

LLM Reasonsers

Hacker News2
Re
Resilient LLM23%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Resilient LLM

Hacker News1
He
Hegelion – Force your LLM to argue with itself before answering58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Hegelion – Force your LLM to argue with itself before answering

Hacker News1
LLM Hotkey
LLM Hotkey39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
TrustMRROther
Browse all productsAnalyze your launch