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https://archive.ph/y4Egm

https://en.wikipedia.org/wiki/Patrick_Stewart

This popup has been a constant gripe of mine for years. I almost NEVER want to allow this permission and there is no system setting to disable it globally and prevent the popup from interrupting your typing, etc. This tiny swift app I built will autoclose it in 1/4th of a second and leave behind a little notification that it did so. Would love to hear feedback!

Video captions cost us manual work twice: fixing misheard words, then fixing layout, like lines ending on “the”.

Overtone uses multiple models to listen to the audio: gpt-transcribe, Azure STT, and Elevenlabs Scribe. When they return conflicting results, Overtone sends small clips to the models again, and then lets an LLM decide what was actually said.

The LLM is like a human doing a sanity check on the transcription model. Humans would easily recognize a word to be different based on context. The LLM basically does the same, using an optional context prompt + its world knowledge. Because of this the Word Error Rate (WER) on many test files is lower when using Overtone, compared to just using a model without it.

Sure, the cost is higher because we use up to 3 different STT models + an LLM, but it still removes mistakes, reducing the time needed to fix captions to a minimum, and reducing the chance you'll miss something. For us it's worth it. For ultra high volume use cases it may not be.

The rules-based cutter nicely cuts the transcript into captions that look good, depending on screen size: 16:9 is different from 9:16, and the Overtone Format we created supports any type really. Pauses become breaks, captions are grouped per speaker, and names/numbers are never spread out over multiple captions.

How to add these captions to your videos? A videojs plugin can do this automatically, so you don't burn in your captions. The plugin lets you cycle between styled captions, plain captions, and off. You can burn them into an MP4 file with overtone burn.

Quickly try it out just the cutter? Try:

  npx overtone-captions recut yoursub.srt

Transcribing audio with API keys works too. Use --dry-run to show a cost estimate before you send.

We open-sourced this first version (MIT) because we believe many people can benefit from this, especially if your captions just need to be good. There's plenty of room to improve though, so we've made it easy to help contribute to add style packs, languages, and providers/models.

You can find a demo at: https://overtone.lont.ai/

Find the code and benchmarks here: https://github.com/lonthq/overtone-captions

https://www.nytimes.com/2026/10/29/us/politics/nvidia-china-..., https://archive.ph/K7bih

https://simantic.com). We are building the simulation layer that lets engineers and AI agents test and validate firmware without hardware.

Before Simantic, we were ex-firmware engineers at companies like Neuralink and Tesla. We’ve worked on some of the coolest projects, like the Cybertruck and Roadster, back when they were still early prototypes.

We started working on firmware back when ChatGPT didn’t exist yet, but over the years, we saw how much of the engineering and development work has been automated as the frontier models improved to this point. In software, AI can run and test code inside your computer in a matter of seconds. But as firmware engineers, we didn’t have that luxury. An AI agent can write firmware in seconds, but in order to test it, we had to flash a physical board, then manually run tests using expensive and clunky tools with software made over a decade ago.

To speed up the validation loop, we built the simulation layer that lets AI agents test the firmware and make decisions based on evidence, instead of waiting for the engineer to run tests for them. We used to spend hours testing our firmware manually on devboards, but now we use our own simulation to validate firmware in seconds.

What we offer: - Virtual devboards you can program and run with a single API call, without leaving your terminal. - An AI code reviewer for firmware, that makes informed decisions based on evidence, using our simulation as a verification engine. - A CI/CD pipeline for your hardware projects that validates your firmware against a simulated version of the microcontroller on every commit.

Here is a demo of our simulation used in an AI code review session: https://www.youtube.com/watch?v=kBK1ZjGWWbQ

Our users are using Simantic for bare-metal and RTOS development for applications like flight controllers, electric vehicles, robotics, and satellites. For safety critical systems like avionics and automotives, a robust simulation system is incredibly important, because every change in the firmware puts a human life at stake, especially for machines that cost hundreds of thousands to millions of dollars.

We recently helped an early stage space startup building satellites, to test their firmware on hardware that they don't yet have. Even the development modules for those satellites costs over $100k, and we helped them do that at just a hundredth of the cost and time.

Early on in our journey, we established our idea to make better simulation but didn’t have a clear idea of how we would do it, we considered developing our software around open source emulation frameworks like Renode (https://github.com/renode/renode) and QEMU (https://www.qemu.org/), because they were tools already used within the firmware industry. We built our MVP that was essentially a wrapper around Renode, but we quickly realized there were a lot of limitations to doing this.

1. We were bottlenecked by the development speed of Renode themselves. We tried making contributions on Github to fix issues we came across, but the speed in which we could merge our features in was not fast enough for a startup like us trying to move fast.

2. Renode is built using Dotnet, and it was proving itself to be slower than we would have liked. Renode was released in 2017, which was when all code was still being written by hand, so I think it made sense looking back, but there were limitations because of the language itself.

3. The open source tools were bloated, with features that we would never use. Yes, there were a breadth of features, but we had a lot of things we had to work around just because we couldn’t remove or change them. Users were complaining about simulation accuracy. Renode is an emulator at the core, but our users, especially those who are building systems where timing accuracy is crucial did not find the accuracy good enough to validate their firmware.

Because of these issues, we made our own simulation engine from scratch, fully written in Rust.

- Our simulation is focused on accuracy. Open source frameworks often focus on expandability and developer experience, which means there were a lot of tradeoffs made to prioritize a breadth of models rather than going deep into a set of select models.

- We have our own hardware validation rigs running 24/7 to ensure our simulation models are as close to real world hardware as possible, and any gaps are detected and fixed as soon as we can. It currently looks like devboards connected to a Saleae Logic MSO and a SWD bus, controlled by a Linux PC, running Claude 24/7 just finding these gaps.

Our cloud-hosted simulator is available to use right now! We also provide a native binary with our developer plan licenses that let you run your firmware on your machines locally without any limitations.

Check it out at https://simantic.com/cloud-sim, and you can start using our AI firmware reviewer bot in your own Github repos here: https://simantic.com/dashboard/simantic-bot.

We currently have support for some models of the STM32F0, F4, H7 series MCUs, ESP32-S3/C3, and RP2040, but we’re constantly working to release new models every week, and improve our existing ones.

Thank you, and if you have any questions, feel free to send us an email at founders@simantic.dev

- Most tax software derives trust from the party who maintains it (e.g. Intuit, the IRS, etc.)

- Vibe coded software lacks this trustworthiness.

- Therefore any formalization should build in verifiability and mechanisms that make it possible for users/maintainers to trace claims/interpretations back to authoritative source documents.

It's very much a back-burner passive project right now (do not use this for anything real!), but I feel strongly that something like this ought to exist for the public good. I built it to get a sense of what the shape of what such a thing might be.