Voice is Underutilized A practitioner reports that speech input and output remain underused with AI systems, citing a personal workflow that pairs NVIDIA's Parakeet STT models with the FluidVoice app and a Claude Opus cleanup pass to make local transcription nearly indistinguishable from 100% accurate. The practitioner also built a daily 10-20 minute text-to-speech briefing podcast, which required months of tuning to add schedule variety and avoid becoming stale, and uses transcribed design syncs and an onsite to preserve context for later agent sessions. Speech is a useful medium for communicating with AI, both as an input/output channel and as back-and-forth. Over 2026 I’ve been increasing the amount of speech I/O and have collected a fair number of use cases. Voice is, of course, great for rambling at coding agents. Oftentimes the transactional “just talking with the agent” convo stuff is easier to ramble out than it is to type. I also occasionally have wrist sensitivity issues that make it difficult to type – now with STT and computer-use, that’s not a hassle. Knock on wood about RSI 😬 Voice is also good for capturing context. Every phone has a voice recorder on it; most meeting software now has transcription support. There’s a lot of ambient context that just exists in conversations that, if you can consentingly capture it, has a ton of downstream value. For example: my team has been transcribing design syncs consistently. A few solo agent sessions interspersed between transcribed design syncs allow engineers to iterate on designs quickly without having to do the “sit down for several hours and write a doc” schtick. Earlier in the year, my team had an onsite where we got a few folks into the room and hammered out long-term plans. Those conversations were fully transcribed and have been instrumental in a bunch of the work I ended up doing later in the year. Having the context in textual form “froze” those ideation sessions, allowing me to rehydrate them months later, when I had cycles to think about the problems discussed again. That being said, there are obviously some things that you don’t want recorded, for one reason or another. In practice, it’s much more common for us to regret not having transcribed a meeting than the other way around. Voice input also allows you to write thoughts or capture things that you wouldn’t otherwise have time to do. For instance, I often now ramble into the Voice Memos app on my phone while commuting home about a problem I’m thinking about, with a few proposed sketches of a solution – just pure stream-of-consciousness. Then when I get home, I transcribe the audio, set an agent up on it with /goal overnight, and come back to find surprisingly often the problem fully solved. Models : Local STT models are excellent. I’ve been using FluidVoice https://github.com/altic-dev/FluidVoice , with NVIDIA’s Parakeet models https://catalog.ngc.nvidia.com/orgs/nvidia/-/collections/parakeet-tdt-0.6b/- , and it’s remarkably accurate. Claude/ChatGPT are good at cleaning up transcription errors. Parakeet + FluidVoice + a Claude Opus cleanup is nearly indistinguishable from 100% accurate for my use cases. Since early this year, I’ve been building and tweaking a personal daily briefing “podcast” which aggregates a bunch of information sources I care about, and uses a text-to-speech model to make a daily audio briefing. This briefing contains things like: It’s generally 10-20 minutes of audio daily. I tweaked it for a couple months to get it exactly how I like it, and now the quality is consistently high. The crucial, and most difficult, part of this was building enough variety into the “schedule” of what segments show up in the briefing every day that it doesn’t end up sounding stale, gimmicky, or formulaic. A simple “Claude, give me a daily briefing” becomes stale after a few days. You really do need entropy and novelty and continuously updating/streaming context for this sort of thing to be consistently interesting. But now it is quite interesting to me It’s made me significantly more aware of local news, and has allowed me to “read” more of the RSS feeds that I subscribe to – especially articles which I likely wouldn’t have read due to the title not being interesting, but which the LLM thought were interesting based on its actual contents and knowledge of my interests. Output is also handy for learning things with LLMs. Earlier this year I set up a system for generating “explainers” as podcasts, and learned quite a bit about aviation, various distributed systems topics, and other areas of interest through passive audio consumption. Models : I had been using GPT-4o mini TTS as my “programmatic output voice model of choice” for quite a while until its deprecation was announced RIP https://developers.openai.com/api/docs/deprecations 2026-10-01-text-to-speech-models . I’ve now switched to Gemini 3.8 TTS https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-8-text-to-speech/ , and it’s fantastic . It features expressive voices, works over OpenRouter, and is cheap. Highly recommended. Claude, ChatGPT, and Gemini all have a “live” interactive voice mode – where you can conversationally talk with the model, and it can respond and take actions using tools, and so on. The primary time I use the interactive mode is during commutes. It’s helpful to use the voice mode to triage tasks, ask research questions, and iterate on ideas. It’s like taking time that would otherwise be dead time and translating it into something that can be productive. Voice is also convenient for walking around the house and capturing things that need to happen. As you remember things, you can have the AI make tasks or calendar entries and burn through a bunch of administrivia without interacting with your phone. It’s like having an actual, literal assistant. My preferred model is Claude for voice interactive mode even though it’s not a “native” voice-to-voice model like ChatGPT . A while back the Claude voice mode added support for using MCPs, and that was a game changer.