Letterflow: GPT4 Powered Newsletter Generator

Hacker News

Letterflow is a GPT-4 powered copywriting tool made specifically for newsletter generation from direct sources. I think Letterflow can help newsletter editors save time on the writing by generating an initial draft that they can easily build from. Letterflow allows you to spend more time on finding relevant, exciting new sources of information for your newsletter subscribers. Here's how I built Letterflow: I am using the Diffbot api to fetch the content from sources that users input in the sources textarea. I then feed this into the GPT4-api with a prompt based on the specifics that the user specified. It is very slow right now, but I plan to change that very soon by: - Migrating to LangChain - Parallelizing the GPT-4 api requests - Realtime streaming GPT-4 api response If there is interest, I would be more than happy to opensource the project. Many new things coming soon! I would extremely appreciate it if anyone could try Letterflow out. There is NO SIGNUP at all, and it is completely free of course. If you have any feedback, feel free to reach out at jmiran15@jhu.edu or leave it here. Thanks!!

Visit product View on Hacker NewsApr 13, 2023

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

76%76% 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: para · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: soon, users · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 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.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

11%11% 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.

Correct prediction on native model

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