I encourage everyone to use speech-to-text tools to give detailed context to coding agents. As a developer, I love my keyboard and I can understand if you're skeptical. I was too. But using speech-to-text is one of the high-leverage things you can do as a developer.
We all know LLMs work better when given more context and clear instructions. When you're working with coding agents, you're giving instructions to them multiple times a day, every day. Over time, you end up giving much better instructions and detailed context if you use speech-to-text compared to manually typing all those instructions all the time.
There are tons of open source and proprietary products for speech-to-text which offer inference on local machine or on cloud. So I put together a curated list of 30+ open-source tools across Linux, macOS, Windows, Android, and iOS. Most support offline recognition. Pick whatever you find suitable, but I definitely recommend giving speech-to-text a try for your LLM workflows. And if you're skeptical, give it a week and then re-evaluate.
primaprashant•2h ago
We all know LLMs work better when given more context and clear instructions. When you're working with coding agents, you're giving instructions to them multiple times a day, every day. Over time, you end up giving much better instructions and detailed context if you use speech-to-text compared to manually typing all those instructions all the time.
There are tons of open source and proprietary products for speech-to-text which offer inference on local machine or on cloud. So I put together a curated list of 30+ open-source tools across Linux, macOS, Windows, Android, and iOS. Most support offline recognition. Pick whatever you find suitable, but I definitely recommend giving speech-to-text a try for your LLM workflows. And if you're skeptical, give it a week and then re-evaluate.
https://github.com/primaprashant/awesome-voice-typing