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Comment by cgbur | Hacker News Reader

For essentially infinite and fast dictation I use https://github.com/cjpais/Handy on Parakeet streaming (cohere is far better, but slower and has a token output limit so you cant ramble for many minutes). And then just do a cleanup pass with a cheap LLM, it will in my experience, do far better than trying to voice control to go edit a sentence or change words. I just weave instructions into my writing. I understand this requires technical know-how, but for those with it, this is the best solution I have found to long form writing without my hands.

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I literally today pushed v0.1 of my dictation app that does both in a single package. You can connect either to a cheap LLM but I've set it up out of the box to use Qwen3.5 9b which does the job well for free, and everything stays local, no telemetry. You can also use Claude/OpenAI/Local providers. https://github.com/lnenad/lipwise

Neat, thanks!

Difference with voice ink?

Open source, multi platform

Does the AI processing work fast or consume less resources on 4-5 years old laptops with 8GB RAM?

For slower machines it's much better to use a cloud model, but Qwen3.5 4B can do the job and is fast enough on older machines.

I like the look of this but I may have to do a port if I go ahead with it. Have a primarily Python on Android project that could use STT.

Another plug for Handy, and wanted to share something cool about it.

You can set it "Push to talk" mode (like a walkie-talkie radio), and when you're done talking and release the button, it can paste the text into any text field.

You can even replicate ChatGPT voice conversation mode, by having Handy as your speech input, and then (I forgot the extension) enabling a speech-to-text model for OpenCode. Surprisingly relaxing flow for certain tasks, like tweaking a website's styles.

Whisper flow does a good job of this too, speaking to transcribe directly into a focused text input

+1 for handy and then using LLM's for the cleanup pass, though what are your observations on feeling as if sharing that output though?

Because I have seemingly mixed opinions on it, on one hand, I did put the effort but on the other, the output is AI generated so I am unsure about sharing it with others (because they might think its AI generated)

Do you use it for very small edits (removing just the uhhm's?) or for slightly more edits.

The way that I use it sometimes is that while thinking, I will write something which can sometimes make me feel as if a better re-write can better explain my thoughts or rephrasing it as such. For example. I will think about X topic, connect it to Y, then try to add some more points about X again.

I found LLM's to do a really decent job at generating the final outputs as such, but as I said, I am left sometimes feeling a little confused as to sharing it or not because of it being AI generated and the end user not knowing if I put an actual effort into creation of it or not.

Should I try to share the actual transcript of it as well, I really wish if some good ethics and internet ettiquette could be established about it.

what do you mean:

"what are your observations on feeling as if sharing that output though?"

and "Should I try to share the actual transcript of it as well, I really wish if some good ethics and internet ettiquette could be established about it."

could you rephrase the question

for STT, it's literally you saying it, with a model transcribing, and then another model correcting a little, and you also have control over editing it. I don't think any of the arguments on "etiquette re: sharing AI output" apply here.

> for STT, it's literally you saying it, with a model transcribing, and then another model correcting a little, and you also have control over editing it. I don't think any of the arguments on "etiquette re: sharing AI output" apply here.

Yes, for doing extremely mild edits (like just removing uhh's etc.) this might be true

but I sometimes feel as if writing can allow me to shift paragraphs, so if I am writing para 1, para 2, I can shift back to para 1 and write another sentence in it and edit some parts of para 1 to include that point

but when I am doing STT, although I can move towards the other para, I find myself just speaking in a complete flow and just write in para 2 only.

Thus when I ask AI to write, I would prefer it to move the statements to appropriate paragraphs and in just general, create a more comprehensible viewpoint from all the STT text that I had written.

This does generate AI generated text which can be detected as such. Uploading it on blogs makes me feel as if people might read what they might consider "AI slop" and so the ethics part (as I myself don't wish to read AI slop)

the problem with AI written or edited texts is that I am unsure of how much effort the other person has put in (just a single prompt or a detailed thought was put in), and I feel as if, others feel the same way.

Should one try to show the rough draft as well to try to show that it was an effort which was human generated or that human effort was used, but that means having a proper disclosure that it was AI-generated/AI-assisted, which I feel as if offputs a lot people (including me) as because of the above logic, that there's still friction for the user within testing if real effort was put into place and I am unsure how effective sharing drafts of it could be.

I don't want my blogs to be tainted and treated as AI-slop because I care about them so I am unsure of what to do. I have multiple things that I have written which if I pass through AI can create some meaningful blog piece but as it stands, they are rough drafts and I find myself putting low efforts or being lazy in actually editing them myself as well (and potentially putting in multiple hours) when AI can be used to help create a more polished version just as well and get across my point.

I think you're overthinking it. you clearly care a lot about the output and respecting the reader. you're not the problem lol

Agreed, record and transcribe however you can and then use a LLM to clear out.

I prompt it to:

"Attached (or underneath) is the transcript of a self recording i've done with tons of rambling and some incorrect words transcriptions, please do a pass clearing out and arranging any typos or possible misunderstandings. Keep original in parenthesis when not sure if it's a misunderstanding. Do not summarize or alter the nature of the content, simply tidy the transcript."

Original in parentheses is very interesting.

Love Handy. It has become a core piece of how I use computers.

In case it's helpful to anyone else using it, at first it felt a bit slow to me, because there was a noticeable pause after I finished a message before it would quickly type it all out. I changed the input method from direct to clipboard and it's way faster now, almost instantaneous.

FluidVoice is working on that I believe w/Fluid-1 model https://github.com/altic-dev/FluidVoice

> And then just do a cleanup pass with a cheap LLM

I am especially interested in this part. Could you please share the prompt you are using to instruct the LLM to clean up the dictation? Thanks in advance!

I do this with a summary prompt + a JSON config. The config keys are the full name of the thing that gets commonly mistranscribed, and the values include a phonetic spelling of the name and a n "entity id" which is just the name of a markdown file that provides information about the entity, which the LLM can use to infer what should have been referenced if it's not clear.

My prompt is pretty simple:

    - Intro of the purpose (faithful, high-quality summaries of meeting transcripts useful for readers who did not attend the meeting)
    - Some basic rules
        - Summarize only what is actually said in the transcript.
        - If a speaker's name isn't clear from the transcript, describe them generically (e.g. "a participant") rather than guessing
        - If the transcript is a fragment or cuts off, say so rather than inventing a resolution
        - Only state a causal or explanatory link between two facts (e.g. why someone is absent) if that link is clearly stated in the transcript
        - Preserve relative time references precisely rather than flattening them. If a speaker says something will happen "in two hours" don't reword it as "today" or "same day"
        - Maintain the original voice and framing to keep original intent
        - Attempt to infer meeting attendance based on who actually spoke or was spoken to
        - Prefer plain, direct sentences over compound or clever phrasing
        - Respond directly with the summary. Do not preface it with phrases like "Here is a summary" or "Based on the transcript".
    - A preferred structure for the summary, with examples
    - An instruction to attempt correction of mis-transcribed words based on the included JSON configuration, but only if the certainty is high. If unsure, leave as-is, but add (spelling?) after the word to flag it.
    - The JSON config as a string.

I invoke this with a skill that can also update the JSON config if I identify new mistranscriptions, so it gets updated regularly.

Thank you

Technical knowhow? Handy is a gui right? (Also Parakeet is great I use it everyday)

Thanks for sharing Handy!

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