Loqua is LIVE on Product Hunt today, and welcome to the family!
AI keeps getting smarter yet using it feels more tiring: prompting, switching apps, screenshotting, explaining context again and again. Often the problem isn't the AI; it's that we still have to learn how to talk to it.
So we built more than better voice typing. Just say what you want, and Loqua understands your app, screen, language, and tone dictate in Gmail, ask about a chart you're looking at, tell Codex or Claude Code what to change, set reminders, open apps, or have a page read aloud.
We believe voice agents are a necessary path to AGI and a core part of physical AI. As a model-first team, Loqua is our first step: our own multimodal model for better transcription, translation, and multimodal Q&A, with deeper agentic, long-horizon abilities rolling out.
If this resonates, we'd love your support
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Upvote & try Loqua:
[Website link]: https://theloqua.ai/
[Product Hunt link]: https://www.producthunt.com/prod...

Voice agents should become more than transcription tools. Instead of simply turning speech into text, they should understand what users are trying to achieve. The same spoken idea may need to become a short Slack message, a detailed email, a technical issue, or a personal note. A useful voice agent should adapt automatically.
This is the direction Loqua is exploring: voice as an intelligent layer across the apps people already use. It can understand the current application, nearby content, personal vocabulary, and writing style, then shape spoken thoughts into something ready to use. Users should be able to speak naturally without organizing every sentence beforehand.

The next step is real-time duplex conversation. A good agent should know when someone has finished speaking, when they are only pausing, and when they want to interrupt. It should also recognize signals beyond words, such as emotion, hesitation, emphasis, and tone. Its own voice should feel equally natural, expressive, and appropriate to the situation.

Behind this conversation, the agent needs deeper capabilities. It should remember relevant context, think through difficult requests, use tools, and continue working after the conversation moves on. The voice experience remains fast and fluid, while more complex reasoning happens quietly in the background.
We all know that long-horizon coding tasks are difficult, but understanding long, multi-turn conversations is even harder. The agent must remain consistent, respond with the right emotion, use tools correctly, and remember what matters.