Jokes: fine-tuning GPT to make AI funnier

Hacker News

"Jokes" uses ML to come up with punchlines that will hopefully make you laugh. We created it to test and showcase our ML-focused developer platform "Cloudburst" (links are on the site). Last year we got some good feedback on burst, our open-source project that facilitates on-demand GPU-enabled cloud compute for developers ( https://news.ycombinator.com/item?id=28191450 ) Over the last 6 months we have taken that feedback and worked to build the tech into an easy-to-use, free-tier + pay-as-you-go development platform focused (though not exclusively) on Machine Learning, especially exploratory research and model training. Our beta version builds on Jupyterlab, providing IPython notebooks, python-aware editing, and remote terminal access. We include all the source code for the "Jokes" project, as well as tutorials for basic Pytorch and Tensorflow projects. We're hoping to land a few users to kick the tires and let us know if we are on to something. Links are on the "Jokes" page. If for some reason that page has problems (presumably because it went viral!) try http://cloudburst.host/jokes .

Visit product View on Hacker NewsFeb 20, 2022

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

90%90% 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: mac, model, user · Missing: agents, macos, agent

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, exclusive · Missing: plus, intuitive, reviews

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

15%15% 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

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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