Loqua - Speak naturally and move from thoughts to being done
Your thoughts shouldn’t have to slow down for a keyboard. With Loqua, you can speak naturally to turn rough ideas into ready-to-use writing, understand what’s on your screen, listen when you’d rather not read, and move work forward by voice - from rewriting and translation to scheduling and coding workflows. Less typing, less context switching, more time in flow.
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vibe coding❌
voice coding✅
Congrats on the launch!
@ll_wen 😂 Exactly. Hands on the code, voice on everything else. Thanks for the support! 🚀
This is probably perfect for those “wait, I just had an idea” moments
@eren_mindful Exactly! Our team member and many users use it as notebooks for random thoughts, especially for young parents who work and attend baby at the same time.
Screenshot to ask is such a simple idea but removes so much friction. No more explaining screenshots manually.
@zaniaz Yep. Just capture what you're looking at and ask about it directly.
Dictation tools always mangle product names and internal jargon, which is half of what I'd be dictating. Is there a way to teach Loqua custom words, or does it pick them up from whatever's on screen? That part would decide it for me.
@protsenkoalexandra Totally get that, product names, acronyms, and internal jargon are exactly where generic dictation tends to get frustrating.
Loqua supports a personal dictionary, so you can add names and domain-specific terms you use a lot instead of hoping the model guesses them correctly every time. Screen context can also help when a term is visible, but we don’t rely on that alone.
If that’s the deciding factor for you, I’d definitely stress-test it with your real vocabulary, that’s exactly the kind of use case we want to get right.
@protsenkoalexandra We designed customized dictionary and a set of algorithms to auto pick your terms on screen for context. The more you use it, the better it works!
Great Product! But I have a question does Capture to Ask hold up on all screens like a spreadsheet or code editor, or is it mainly reliable on simpler layouts like the calendar and chat view shown here?
@sidraarifali It’s not limited to simple layouts, we’ve designed Capture to Ask for things like spreadsheets, dashboards, code editors, docs, and other dense screens too.
That said, the more complex the screen, the more important it is to capture the relevant area rather than everything at once. For a spreadsheet or code editor, that usually gives Loqua much cleaner context and a better answer.
So yes, those are definitely target use cases for us, not just calendar/chat-style screens.
@sidraarifali It works on all conditions, complex tasks such as spreadsheets, formulas, candlestick charts or picture caption, you name it!
The screen context part is what caught my attention. Being able to just point at something and ask instead of explaining what I’m looking at every time sounds genuinely useful. Curious to try this in my daily workflow!
@mi_leeli Exactly, that’s one of the interactions we really wanted to make feel effortless. Sometimes explaining what you’re looking at takes longer than the actual question 😅
Really curious to hear how it fits into your day-to-day once you’ve tried it!
How long does setup actually take? I don't want to spend 20 minutes configuring another AI tool.
@renchu_song It's designed to be pretty lightweight: install, sign in, grant the required permissions, and you're ready to go.
The omni model choice is the part that stands out to me, since most stacks still drop the screen context somewhere between ASR and the LLM. I work on voice AI for daily check-in calls with older adults, and the piece that never gets easier is endpointing: telling a real end of thought from a mid-sentence pause, which gets harder the more rambling the speech is. How does Loqua decide you are finished talking during a long dictation? And does the screen context feed into the rewrite step, or only into command routing?
@igorgurovich Great question! Endpointing is one of the areas where we really didn’t want to rely on a fixed silence threshold.
Loqua uses semantic VAD and semantic tokens alongside acoustic tokens, so the model is looking at cadence and intent, not just “how long has the user been quiet?” The goal is to distinguish a genuine end of thought from a mid-sentence pause, especially in longer or more meandering speech.
And yes, screen context can feed into the rewrite/understanding step too, not just command routing. We want the model to use what you’re looking at as part of understanding what you mean, rather than treating screen context as a separate routing signal.
Your use case with older adults sounds especially challenging btw: slow cadence, long pauses, background audio, and barge-in are exactly the kinds of cases where naive endpointing falls apart.