Co

Code Podcast: Type Systems

Hacker News

Code Podcast: Type Systems

Share card

Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
64%64% 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.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto
59%59% predicted probability of success on BetaList, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · 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
24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
21%21% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Ty
Type Systems FAQ45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Type Systems FAQ

Hacker News1
Vi
Visions – User defined data type systems71%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Visions – User defined data type systems

Hacker News45
Re
Recommender Systems in Keras48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Recommender Systems in Keras

Hacker News14
Fe
Fern – L-systems in Go48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Fern – L-systems in Go

Hacker News3
PV
PVBenchmark – UserBenchmark for PV Systems34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PVBenchmark – UserBenchmark for PV Systems

Hacker News1
PV
PVBenchmark – UserBenchmark for PV Systems34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PVBenchmark – UserBenchmark for PV Systems

Hacker News1
Tr
Troubleshoot distributed systems51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Troubleshoot distributed systems

Hacker News6
Aventis Systems
Aventis Systems27%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

“Get IT Done”

Indie Hackerscommitment-full-time
Co
Code && Beyond – a podcast about software developement and more49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Code && Beyond – a podcast about software developement and more

Hacker News1
We
Well Made podcast45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Well Made podcast

Hacker News2