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Academa – Long-form STEM lecture videos generated by LLMs

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Academa – Long-form STEM lecture videos generated by LLMs

Hi HN, we are Sina Atalay and Abdullah Geduk, co-founders of Academa. We are both PhD students. We thought: what if lecture videos were written as code and compiled into video using computer graphics and TTS? Then LLMs could write them, and lectures could be fixed with code edits instead of video production. On correctness: LLMs may make mistakes. But these videos are code sitting in our repository, and we can maintain them. Every report and review becomes a fix in the source, and everyone who watches after that gets the corrected lecture. Every recorded lecture on the internet is stuck with its mistakes. Ours will continuously improve. Each video also comes with an AI chat that understands everything said and shown in the lecture. There is a longer write-up at the bottom of the page. Happy to answer any questions.

Visit productView on Hacker NewsAug 30, 2026
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Product HuntOn track for Day 1 leaderboard · Strong signals: computer, using, code · Missing: mac, agents, macos
94%94% 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: mistakes · Missing: supports, reddit linkedin, podcasting
78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal
61%61% predicted probability of success on TrustMRR, 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
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% 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
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

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