Apollo Lunar Module landing simulation

Hacker News

I have always had deep fascination for Apollo spacecraft, especially the Lunar Module (LM). The technical ingenuity of its design, reliability during the missions, and the quirky form make it my favorite machine of all time. I have often dreamed about what the Apollo astronauts (particularly Armstrong and Aldrin) experienced, as they guided the vehicle towards the unknown lunar surface. This is my attempt to simulate the last 3 stages of the descent sequence: Braking (P63), Approach (P64) and Final Landing (P66). The last one requires manual attitude and thrust control to achieve a soft touchdown. I've tried to maintain balance between historical accuracy with simplification, so that an average person like me can manage to land safely with a bit of training. I hope fellow space enthusiasts will enjoy learning its nuances, and experience the challenge & thrill of achieving a soft lunar landing. Note that this requires a keyboard, and is not mobile friendly yet.

Visit product View on Hacker NewsSep 9, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac · Missing: agents, macos, agent

68%68% 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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

12%12% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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