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Hello world!
As a video editor, researcher, digital media enthusiast, and lover of all things FLOSS, I've been on the hunt for an open source alternative to proprietary services like Otter.ai, Sonix, and Descript. I've pitched my idead on open-source-ideas, but I wanted to create a dedicated post for it so that it can reach as many people as possible.
A simple, easy-to-use application where users can dictate or upload audio or video files, and an automated transcript is generated. This transcript is synced to the audio track, clickable, and editable, so that users can skip to certain passages and refine the transcript accordingly.
The revised transcript can then be exported as plain text, .srt caption file (and other subtitle formats), .pdf, shareable web page, etc. for further processing.
Pode parecer intimidador mas te garanto que se seguir o passo a passo que preparei vai conseguir tranquilo E lembra, só precisa fazer esses passos uma vez e vai estar seguro contra imprevistos com seu computador.
| Export VM as OVF | |
| /Applications/VMware\ Fusion.app/Contents/Library/VMware\ OVF\ Tool/ovftool VM.vmwarevm/VM.vmx . | |
| Convert .vmdk to .qcow2 | |
| qemu-img convert -p -f vmdk -O qcow2 VM-disk1.vmdk VM-disk1.qcow2 | |
| Create custom VM in UTM matching VM hardware | |
| Remove default disk | |
| Add drive / import QCOW2 file (file is copied to default UTM VM folder) | |
| Add CD/DVD drive |
| :root { | |
| --min-item-width: 28ch; | |
| --max-item-width: .5fr; | |
| --grid-spacing: .25rem; | |
| --item-padding: .25rem; | |
| } | |
| /* Let items expand on small screens */ | |
| @media (max-width: 600px) { | |
| :root { |
| #version 460 | |
| /******************************************************************************************************************** | |
| Speculative Raymarching | |
| Shaders run code in "waves" or "subgroups" of 32 or 64 threads (or 4 in WebGPU on my box, which is why there's no | |
| web demo for this). The idea here is that instead of 32 pixels running the same (expensive) sdf function which is | |
| likely to return similar results to advance their respective rays - they all band together and cooperatively march | |
| a cone that is large enough to hold all of them. |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
This guide will show you how to compile and install the legacy Nvidia driver on Debian. The steps bellow are compatible with both nvidia-legacy-340xx-driver and nvidia-legacy-390xx-driver and can be installed on Debian 13 (Trixie) as well as Debian 12 (Bookworm) or Debian 11 (Bullseye) in both 64-bit and 32-bit version. Execute the lines one by one and wait for every line to finish before proceeding to next. Only the lines starting with sudo should be executed as root. The lines with sudo pbuilder will take a lot of time.
To install the nvidia-legacy-390xx-driver version replace 340xx with 390xx everywhere bellow.
To install both 64-bit and 32-bit libraries for wine/steam you should run Step 2. twice.
It was confirmed that this method also works with the nvidia-tesla-470-driver version.
Update, upgrade and install the required packages.
These files are configured in a way so that an Ubuntu cloud-image is modified by downloading the RKE2 install script from upstream as well as installing the qemu-guest-agent. This is done so the Ubuntu image can now function in an airgapped environment as an RKE2 node. Previous methods I've done involved using libguestfs tools and it was a bit clunky due to how it needed to be managed. Packer's QEMU provider fixes that for me.
Unfortunately, Packer's QEMU provider must run locally as there is no Harvester provider that would run these commands on a remote Harvester cluster to save us the dependency problem. Perhaps in the future we can explore that level of sophistication, but for now this works great.
There is a post-install provisioner that uploads the resulting image to Harvester using a VirtualMachineImage CR template. If you do not wish to upload to Harvester, feel free to comment out that section in spec.pkr.hcl, it is located towards the bottom and starts with the lines `post-p
A starter prompt you can drop into any AI platform to get going. Originally created in Feb 2026, updated in June 2026 to address latest changes to AI capabilities.
This was generated by my second brain by asking it to generate a starter prompt that others could use to build a similar system. Fork / branch / modify / share / try it!
See my related LinkedIn article which explains more about my experience using this, and the implications to knowledge work once everyone is working this way.
