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LambdaWiz, a mystical GPT adventure based on SICP

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LambdaWiz, a mystical GPT adventure based on SICP

I've been working on an OpenAI "Assistant" based on MIT's canonical Structure and Interpretation of Computing Programs (SICP). [1] The idea is basically a text-adventure vibe, set in a magical realm, to play on the books esoteric / wizard-y vibes. I initially built this via the Assistant API, and then ported over to the new GPTs creator once that became widely available today. In addition to the source material, I created the outline of a curriculum in collaboration with the new, giant-context GPT4 beta, as well as a set of gameplay mechanics. As you progress, you get awarded things like honorary titles, coins, etc. At first these were Emoji, but then I realized that with DALL-E enabled in the bot, I could have it generate the artwork on-the-fly in the 16bit RPG aesthetic I was after. It works in a kind of pseudo-Scheme language, and seems able to 'evaluate' your answers. Anyway, this is just for fun and I'd love to hear what you think. Given the standard limitations of LLMs, there's sure to be the odd hallucination here and there – but, I've found it to be pretty fun. It can adapt to your skill level, and at the base-level I was aiming for something that even a 9 year old would have fun playing. 1: SICP is licensed under Creative Commons Share Alike.

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, context, openai · Missing: mac, agents, macos
85%85% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting
65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, io · Missing: https docs, excited, just released
50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: answers, way · Missing: mobile apps, ios, personal
48%48% 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
47%47% 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
13%13% 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.

Incorrect prediction on native model

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