I'm planning my Product Hunt launch and getting conflicting advice. Some people say to do a soft launch first to test the waters, get initial feedback, and learn the platform. Others say you only get one shot at the spotlight, so you should wait until everything is perfect and go all-in.
The soft launch camp argues you can iterate based on feedback and launch again later with a better strategy. The all-in camp says featured products get 90% of the attention, and if you don't get featured on your main launch, you've basically wasted your shot.
Over the last few months more and more companies have shared that ChatGPT has become one of their top acquisition channel
Some examples:
Guillermo Rauch, CEO of Vercel, explained that they went from getting 1% to 10% of their sign-ups coming from ChatGPT within 6 months
Marie Martens, Co-founder of Tally, said that they scaled from 2 to $3M ARR in the span of 4 months thank to a major influx of customers coming from ChatGPT & Perplexity
Just a month ago, we were nervously prepping for Lifetoon s MVP launch here on Product Hunt. Now, over 700 users have jumped in and turned their ideas into comics, and we re incredibly grateful for all the support and love from this community. Thank you!
I just saw the Aqua Voice changelog and thought it was a great example of a product that's focused on the right things. It's no secret that a lot of us on the Product Hunt team are fans of Aqua Voice, especially @jakecrump ! Here's their latest changelog:
0.4.0
This is a "Snow Leopard" release focused on making the basics faster and more reliable.
Hello I just built an API that allows users to create custom text classification models with their own data. I built this API because I saw that many people have specific datasets that they want to do perform text classification tasks with and usually for those people classification with LLMs is just too inaccurate and don t cut it for them.
I am at the MVP stage and I am launching it on RapidAPI for now: https://rapidapi.com/textclf-tex... I built the classifier in this API to be accurate, lightweight and fast to train. Basically it gets the job done at much lower cost and overhead than fine-tuning an LLM and with higher accuracy.
What do you think about this API? would you use it or do you think there is a market for it? If you use similar product what pain point you hope an API like this alleviate? Are you happy with speed and accuracy of the API? Would you prefer a pre-trained model instead that you can just call for prediction?
