u/matt8p

Hey guys, I'm Matt, I did my undergrad at Duke and graduated in 2023.

I felt like since graduating, it's been more difficult every year to keep up to date with what's going on with the program like recruiting news, Scheyer press releases, tickets and regular season games.

Maybe things got busier for me, social media has gotten a lot noisier, or maybe it's just me getting older.

Anyways, I'm pretty excited about the upcoming season and stay updated. I've been working on this project where it's a widget on my computer that scans the internet and sends me any updates about Duke Basketball once a week. I've been using it to:

• Monitor for when regular season tickets are coming out. I'm trying to buy the Berkeley v Duke game at Berkeley, since I live in the Bay Area now.
• Any announcements about exhibition games and the upcoming regular season.
• Monitor any updates about injuries, new recruits coming in, or just what people are talking about on social media about Duke.

Under the hood, it's pretty simple. It pretty much just works by asking ChatGPT once a week to surface any news, except it does it automatically and pops up a notification. I tried doing it on ChatGPT, but they don't do notifications, so that's why I decided to build it myself.

I've recently been trying to find new places to work out of. My "side project" became my full time work now, and I was working from home a lot. That got really repetitive, so I wanted to step out more and try out different spots around the city (mine is SF).

Found this cool spot called "The Interval @ Long Now" in Fort Mason, San Francisco.

What's y'alls favorite spot to work out of?

Wanted to shout out Claude Design, it's changed the way that I do work. I am a software engineer, design has always been my weakest when it comes to building products.

I think I heard about Claude Design from a showcase on X and gave it a try, and it's SOO good. I like how everything under the hood is HTML. I think Anthropic realized models are really good at writing and interpreting HTML + CSS as a language for design.

It's interesting because other design platforms like Figma have their own bespoke format for how they render designs. HTML + CSS is the most well documented language for "design" so it makes sense why they chose that.

Anyways, I really like how the claude design agent can also access my github repos and pull designs from there, and export it back to Claude Code for implementation.

Claude design was so clutch in building the onboarding flow for a desktop assistant I've been working on!

I've been exploring different ways I can bring intelligence directly into your mouse cursor. Being able to highlight a piece of text, ask it a question, and the cursor will do web research and rewrite the results directly into the doc. 

I think I nearly achieved that, and that's what I wanted to demonstrate today. A couple of cool things I've been able to do with it: 

• Highlight a certain recipe and talk to Freestyle about it. Brainstorm different alternative ingredients for a recipe. 
• Turn interesting news articles and content on X into summaries that I send to friends. 
• I’m a software engineer by trade, so I was even able to have Freestyle drop me hints and guide me through a Leetcode problem.

 

I just put freestyle out yesterday, so the project is super early. It might be a bit buggy, and the intelligence still feels primitive.  I’ll be working over the next couple of weeks on making it more intelligent. Would love any feedback if you get to try it, let me know what breaks. 

The vast majority of people who use ChatGPT to write will copy text into ChatGPT, wait for it to finish, then copy + paste the results back.

I've been exploring different ways I can bring intelligence directly into your mouse cursor. Being able to highlight a piece of text, ask it a question, and the cursor will do web research and rewrite the results directly into the doc. 

I think I nearly achieved that, and that's what I wanted to demonstrate today. A couple of cool things I've been able to do with it: 

• Highlight a certain recipe and talk to Freestyle about it. Brainstorm different alternative ingredients for a recipe. 
• Turn interesting news articles and content on X into summaries that I send to friends. 
• I’m a software engineer by trade, so I was even able to have Freestyle drop me hints and guide me through a Leetcode problem.

 

The project is live right now and available on Mac and Windows. 

freestylevoice.com 

I just put freestyle out yesterday, so the project is super early. It might be a bit buggy, and the intelligence still feels primitive.  I’ll be working over the next couple of weeks on making it more intelligent. Would love any feedback if you get to try it, let me know what breaks. 

I got a new Fujifilm camera (XS20), went out this afternoon to catch some clips of Fort Mason.

Also found a really cool bar / coffee shop in the Fort Mason shops area too!

Been to hackathons hosted by small startups in SF where they don’t really care about the participants, or the actual hacking. Just using it as a way to push their product somewhere, get GitHub stars etc.

Hey y'all, I'm Matt. My background is in software engineering, I've been working on voice products on the side for the last couple of months. I started working on it full time, and wanted to share my journey so far, how I am thinking about what to build next.

The two months I've been working on it, I shipped Freestyle, it converts what you say into clean polished text at your cursor. Handles filler words, punctuation, formatting to lists and emails. Getting 80% there took a few days. The last 20% was super tough, handling post-processing edge cases, latency e2e down to 600ms.

I put the product out there, it was able to gain some decent traction. Where I'm at right now is at about 1500 dictations / day. However, I felt like voice dictation is a pretty solved space, tons of players out there, and the improvement ceiling is being hit.

I wanted to explore what's next beyond voice dictation. Felt like the natural next step was solving voice-to-intent, having the machine understand what you want done and then doing it for you. I started working on Freestyle remix this week.

The idea is that it will allow you to do things like highlight a piece of text and ask, "Can you convert this to MLA format?". Remix fulfills that command. I'm thinking of phrasing the product like "Writing assistant at your cursor / keyboard". I attached a mockup of what I envision the product to somewhat look like.

I'd love to get some feedback on the idea:

- Have people here used voice dictation before (like Wispr Flow), and what do you mostly use it to write?

- Has anyone here tried out a clipboard / cursor writing assistant? I've been trying to find products like this but it doesn't seem to be heavily explored. Please let me know if you find a really good product like this out there already.

Hey y’all, I’m Matt. My partner is an allergy nurse and at the end of every shift, she spends a ton of time charting (writing patient notes). I’ve been working on a voice dictation app for her that converts what you say into clean polished text. 

I have a strong background in software engineering and I thought it was going to be really easy to build, especially with coding agents too. The dictation part was easy, but nailing medical terms was really hard to pull off. It had a really hard time getting drug and disease names, and units of measurement working.

After working on it for 2 months, I got dictation to handle medical terms pretty well. It's able to nail medical terms pretty accurately, and I got the latency to be under 600ms. So pasting at your cursor feels near instantaneous.

Here’s the progress timeline that I’ve made in the last two months: 

Week 1-3: Start it off trying out basic speech models (ASR), such as Qwen3 ASR, Whisper family models. It was really good for basic dictation, but really struggled to handle complex medical terms. We also start off trying to run models locally on computers, but a lot of computers that people in the medical field use are not powerful enough to run these local ASR models efficiently. 

Week 3-6: Switched over to ASR models that run on the cloud, and introduced post-processing with models on Groq. Post-processing is what cleans up what you say into polished text. This greatly improved the accuracy from just using ASR models. We also prompted the post-processing model to get context clues on what is said and correct medical terms. 

Week 6 - now: We are working on improving latency, and got transcription to work under 600 ms so it feels pretty instantaneous. This was achieved using Soniox streaming ASR, and GPT OSS running on Groq. Medical term accuracy continues to improve as we add more medical vocabulary into the prompt. Dictionary. 

If you're a software developer interested in this kind of stuff, the project is open source and I’m looking to grow a community to work on it. All of the code and system prompts are there for anyone to learn. Please consider joining our Discord community! 

If you work in healthcare, or are just into voice dictation in general, I'd love to have you try out the project and get your feedback on it. I want to know how well voice dictation can work in a real working environment. 

Link: https://freestylevoice.com/healthcare
Discord: https://discord.gg/Fmgt5yZCDu

The PostHog MCP has a very interesting pattern I haven't seen before:

The MCP has a single tool, PostHog__exec, that allows whatever agent connected to it to run CLI commands. So it's an MCP server that wraps a CLI executor.

I think design decision is pretty much "We know coding agents are better at calling CLI commands than tool calls, but we still want the OAuth convenience of MCP".

I've been playing around with the PostHog MCP and works quite well.

Hi y'all, my name's Matt. I've been working on a open source voice dictation app called Freestyle. It works the same way as Wispr Flow: you hold a hotkey, speak, release, and text is pasted where your cursor is. 

When I first started the project, our focus was on local models. Having the ability to pick and choose local voice models and pair them with an optional post-processing step. The customizability is great for those who know what to choose. Local model support is still there.

But we also wanted to provide an out-of-the-box voice dictation pipeline that just works without having to know what the best configuration is. That's why we came up with Freestyle Transcribe. 

The current stack of Freestyle Transcribe is a combination of Whisper Large Turbo V3 with Qwen3-32B as the post-processing model, hosted on Groq and Cloudflare. The latency hovering at around 600 to 800ms which is comparable to what Wispr Flow has. The reliable accuracy is better than Wispr Flow. We've also made it free and open source, so it doesn't come with Wispr Flow's $12/month price tag. 

Looking to get some feedback on the project. For anyone out there that's voice pilled, would love to get your opinion on how it compares to what you currently use.

https://github.com/freestyle-voice/freestyle

Hey y'all, I'm Matt. For the past 5 weeks, I've been working on an open source dictation app like Wispr Flow. Wanted to share some learnings so far on building the dictation pipeline.

I started off with supporting local ASR models that run on-device as the main focus, models like Qwen3 ASR, Whisper.cpp. Quickly noticed they were not enough for practical use.

Local models by themselves had a really hard time with context and terminology, especially for developers. Some examples of testing I did on Qwen3 ASR:

What I Said	What Qwen3 ASR produced
"Upload a file to S3, which fires EventBridge, which triggers Lambda"	"Upload a file to S three, which fires an event bridge, which triggers lambda."
"We're on Postgres 16 with pgvector 0.7"	"We're on Postgres sixteen with PG vector zero point seven."

That's when I started to explore strapping on a post-processing layer, which allows us to make contextual adjustments and cleanup to the transcription. Latency and accuracy was always a fighting tradeoff.

After experimenting with different setups for the dictation pipeline, I landed on this pipeline. Super simple:

1. Hold hotkey, speak, release → audio hits the pipeline, hosted on Cloudflare Workers
2. Transcribe with Whisper Large v3 Turbo (on Groq), biased toward developer vocabulary
3. Post-process with Qwen3-32B. Grammar/punctuation + dev-vocab fixes ("my sequel" → MySQL)
4. Paste at your cursor

So far, I'm happy with the result. It's able to pick up on software dev vocabulary and pretty accurate. The transcription latency end to end is ~600ms which is competitive to Wispr Flow's.

If this project sounds interesting to you, I've released all of my work so far on GitHub. Please consider checking it out and giving it a star! ⭐

https://github.com/freestyle-voice/freestyle

Hey guys, I'm Matt, I'm the maintainer of Freestyle, it's a free open source voice dictation app, alternative to Wispr Flow if you've ever used that.

The inspiration for building Freestyle was that I was voice-pilled and loved using Wispr Flow. To their credit, they've built a great product I was using daily. It's fun speaking to prompt coding agents, as well as do all of my writing like this post.

However, I felt like voice dictation was a commodity, and shouldn't be costing $12 / month, nor understood how Wispr Flow is worth $2B. Voice notation should also be local first. You don't need to send your private thoughts to remote servers.

Here's what I've built so far:

• Basic voice dictation: Hold a hot key, speak, and text will paste at your cursor.
• We provide local models like Qwen3, or cloud models with BYOK.
• Grammar and punctuation cleanup via post-processing. No more "um, oh, but".

If this kind of stuff sounds interesting to you, please consider checking out the project and giving it a star, it would mean a lot! ⭐

https://github.com/freestyle-voice/freestyle

I was able to ride a Lime scooter through the Presidio a couple of days ago. Did they just put out this ban? Couldn't find anything on their website about it.

I've been spending the last couple of weeks building a Wispr Flow clone as an open source project. For context, it is a voice dictation app that lets you type faster, by speaking instead of actually typing.

I spent the first week building the basic STT capabilities. One of the coolest features that Wispr Flow has is ASR biasing. Wispr Flow calls it its dictionary. I was able to figure out how to implement that for my project and wanted to share how it was done.

What is ASR biasing?

ASR biasing is a transcription technique that guides the model with hints on how words are spelled, or what phrases are common. In my example in the video, I gave guidance that I wanted to talk about the “Knicks” and “OG Anunoby”.

When you have biasing set up, the words that you have set up are more likely to show up when you say phrases that sound similar.

How it's implemented in code

Implementing ASR biasing is actually incredibly easy. Each model provider handles it differently, and they call it different things. For example, OpenAI and Groq set a prompt as its bias mechanism, similar to an LLM system prompt. Local models like whisper.cpp and local Mac models from MLX also run the same prompt system.

In other providers like Deepgram and Eleven Labs, they call them key terms and are configured by search parameters.

This is what it looks like to implement in Groq. It's as simple as injecting the dictionary words into the model's “system prompt”.

 const transcription = await groq.audio.transcriptions.create({
    file: fs.createReadStream("YOUR_AUDIO.wav"),
    model: "whisper-large-v3-turbo", 
    prompt: "vocabulary: Knicks, OG Anonuby", // Optional
    response_format: "verbose_json",
    timestamp_granularities: ["word", "segment"], 
    language: "en", 
    temperature: 0.0, 
  });

In Freestyle, we've implemented ASR biasing and call it our “Vocabulary” feature. When you create a vocabulary, it is saved locally within Freestyle. Every time you run inference, your saved vocabulary is freshly injected into models’ system prompt or keyterms.

Freestyle oss project

All of the work that we've done around ASR biasing is open source and available in our GitHub repo. If this project sounds interesting to you, consider giving it a star!

We're also looking to build a community of people interested in working on open source voice dictation.

https://github.com/freestyle-voice/freestyle