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Thisten, search and discover podcast transcripts in one place

Hacker News

Thisten, search and discover podcast transcripts in one place

Hey HN Community! Ben Grynol here – I’m one of the co-founders of Thisten (thisten.co) Thisten is an audio-to-text platform where transcripts are created and aggregated from podcasts, for people to search, reference and discover. When we first started building Thisten, our product was much different – it was a platform where we transcribed speaker sessions at conferences to make them more accessible. We joined Startup School this past winter, and were getting relatively good growth with the product that we had built. Over the course of 2 months, we went from $0 to $10,125 in revenue, and gained ~4,000 users, with ~1,000 of them being MAUs. After conferences started getting cancelled recently, the writing was on the wall – our revenue stream was no longer viable. We did a micro-pivot last week, and rebuilt our product to become better for searching and consuming podcast transcripts. For our launch, we've transcribed more than 2,000 episodes of 160 podcasts, and added them to the network – from True Crime to Tech, and Comedy to Culture. The transcripts aren’t perfect and are ~95% accurate, since our tech is built using AI and ML models for Speech to Text. Our team learned the ropes of developing tech by helping to build one of the most impactful on-demand food delivery companies in the world – SkipTheDishes – into a multi-billion dollar company from its early days. Now we're taking everything we learned about scaling startups, and are building the world's largest database of transcripts. Thisten will be updated daily with new transcripts, as podcast episodes are released. You might not find what you're looking for, but you'll be sure to find something. We’d love your feedback <3

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting
93%93% 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.
Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released
75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month, users · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue, growth · Missing: arr, mrr, profit
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · 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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