I tested something similar and continous re-transcription was the only way I could get close to batch-level accuracy.
In my current implementation I’m fairly aggressive with it. I don’t rely much on streaming word confidence. Instead I continuously reprocess audio using a sliding window. As new audio comes in, it’s retranscribed together with the previous segment so the model always sees a longer context.
That recovers a lot of the accuracy lost with streaming, but the amount of retranscription makes it hard to justify economically with cloud APIs. That’s why I’m focusing on a local-first approach for now.
I’ve been experimenting with voice-to-code workflows and realized that the benefit isn't actually speed—it’s the reduction of what I call the 'synthesis tax.'
When we type, we are forced to translate raw intent into structured prose in real-time. We self-edit, delete, and reorganize as we go. This 'lossy compression' often causes us to omit the very details (edge cases, the 'why,' uncertainty) that help an LLM produce better code.
I wrote this to explore how pairing high-bandwidth voice (~150 WPM) with 'meta-prompting' (using a secondary prompt to map speech to codebase context/file tags) creates a much higher-signal input than typing ever could.
Curious if others have found that 'rambling' into a model actually yields better architectural results than a carefully typed one-liner.
Right now it doesn't support custom endpoints for local models, but it's high on the roadmap. The plan is to add support for custom endpoints (like LM Studio) and local Apple Intelligence to coincide with the next major macOS release (Tahoe).
Thanks for catching that broken link! It's fixed now.
I got 20k+ downloads for my app by posting on Reddit. It's an app/website blocker for iPhone & Mac, so I focused on relevant communities like r/digitalminimalism and r/dumbphones with genuine, non-spammy posts: https://www.atten.app
I also tried "apps gone free" campaigns by posting on Reddit and using sites like AppRaven. These were very effective for visibility, the launch is currently the #5 all-time post on r/macapps (https://www.reddit.com/r/macapps/top/?t=all). While these campaigns drove a strong spike in downloads, retention was low, so they weren’t as useful for building a long-term user base.
Oxford University just announced "brain rot" as their Word of the Year for 2023. Defined as the cognitive decline caused by excessive exposure to low-quality or mindless digital content, the term is often linked to habits like endless scrolling on social media platforms.
Social media, once celebrated as a means to connect and democratize information, is now being recognized for its unintended consequences:
- Reduced focus and attention spans
- Algorithm-driven echo chambers
- Doomscrolling-induced anxiety
- A decline in creativity and deep work
This raises important questions for our community:
Have we reached a societal tipping point? It seems like there’s growing consensus about the need to rethink how we design and interact with digital products.
What role should creators and engineers play in addressing this?
Should platforms prioritize mental well-being over engagement metrics?
How do we individually combat "brain rot"?
Many of us, despite working in tech, feel the pull of distraction.
Personally, this has pushed me toward digital minimalism. I’ve been experimenting with ways to reduce digital noise, like disabling notifications, setting app limits, and building simple tools to block distractions. (Shameless plug: I recently launched a free app aimed at helping users manage screen time and refocus — happy to share if there’s interest!)
The Word of the Year feels like a cultural wake-up call. While it highlights a grim reality, it also presents an opportunity to build tools, habits, and systems that help us regain control over our attention.
What do you think? Does this mark a broader shift in how society views our digital ecosystem? And what can we, as builders, do to create more intentional tech moving forward?
In my current implementation I’m fairly aggressive with it. I don’t rely much on streaming word confidence. Instead I continuously reprocess audio using a sliding window. As new audio comes in, it’s retranscribed together with the previous segment so the model always sees a longer context.
That recovers a lot of the accuracy lost with streaming, but the amount of retranscription makes it hard to justify economically with cloud APIs. That’s why I’m focusing on a local-first approach for now.