flyOS – A fruit fly connectome simulated in real time on an iPhone

Hacker News

Hey HN, I'm Cory. When I saw Google release the fruit fly connectome, I wanted to experiment with it myself and better understand how the fruit fly brain works, so I started hacking on a few different iOS projects. Originally I was going to create an app that let you experience BEING a fruit fly with VR, but then I realized that a fruit fly's main sense is its smell, and an iPhone can't smell. So I ended up building an app based on all of the senses an iPhone has that we could wire to a fruit fly. I came up with flyOS: basically the camera is its eyes, the CPU is its brain, etc. The neuron simulator is written from scratch in Swift and runs in real time on the phone's CPU, with no server involved. Yes, this is partly a joke. But it's also a serious app that lets you view the neurons of a simulated fly in real time, which I think is pretty cool. I wrote a white paper and put it online to share the process of building this, and why I ultimately only ended up using 4,895 neurons out of the total 166,000 in the release. Would love your feedback and thoughts as to how I could make the fly even more realistic and alive.

Visit product View on Hacker NewsSep 21, 2026

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, ios · Missing: supports, reddit linkedin, podcasting

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released

60%60% 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: ios, google · Missing: mobile apps, personal, entrepreneurs

52%52% 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

41%41% 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time · 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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