We just had a successful Alpenswitch on Testnet. In case you missed it. 👀

The time is now Degens do your thing 🫡

I spent $4,699 on a DGX Spark and used it as a chatbot for six months. Not proud of it. But I think most Spark owners are doing the same thing, so let me tell you what changed. The moment was dumb. I had a recording of a client call, needed notes, and my first instinct was to open a browser tab and upload it to some transcription SaaS. Then I looked at the box on my desk. 128 GB of unified memory. Sitting there. Running a chat model I'd asked maybe 30 questions that day. And I thought: I have a supercomputer and I'm about to pay a stranger to listen to my client call. So I tried to do it locally. And here's where it got annoying: - my LLM can't hear audio - the ASR model I found is great but it's a CLI script - the transcript came out in two languages, need translation - summary needs the LLM again - now I want a mind map, that's another tool - oh and here's a scanned PDF from the same meeting, OCR, another tool Copy paste copy paste. Output saved to ~/Downloads/final_v2_REAL.txt. That's when it clicked: tokens per second was never the point of this box. A 24 GB gaming card forces you to pick ONE model, so everyone picks the biggest generalist and asks it to do everything. But 128 GB doesn't force that. You can keep six specialists loaded at once. A 1.7B speech model transcribes better than a 35B chat model ever will. A tiny translation model just translates and doesn't get creative. The big LLM does the thinking, and only the thinking. The hardware was never the problem. The missing piece was the thing that ties them together. So I built it. SparklingKit. (the name is a tribute to the Spark. also: a spark is one flash, sparkling is when it keeps going) What it actually is: → transcription, OCR, translation, image grounding, text-to-image, mind maps, chat. one panel. → six models hand-picked to fit together in 128 GB with room left over → queue-first: drop a 3-hour recording, go get coffee, check from your phone → nothing is a dead end: finish a transcript and "translate / map / chat with it" are already waiting → visual workflows: chain the steps once, run them forever → everything is a file. one folder. no database. copy it to back up. point Obsidian at it. → nothing leaves your machine unless you send it. The thing I care about most: if you stop using SparklingKit tomorrow, nothing happens to your data. It was already a folder of markdown and json. There's no lock-in to escape. Spark owners: you bought room, not a faster chatbot. This is how I'm using the room. No Spark? The panel runs anywhere Docker runs. Point it at your own endpoints. It's open source, and it's version one. Rough edges exist. Issues and PRs very welcome. Full details and a one-line install at sparklingkit.com If you've ever uploaded something private to a cloud AI and felt weird about it, this is for you.

9/ The other thing worth watching is who hasn't weighed in yet. @coinbase @binance @krakenfx @Official_Upbit @okx @Gemini hold 70.8M SOL between them. None of them has cast a ballot on either proposal. That's a lot of SOL belonging to a lot of people, and the exchange is the one who decides whether it gets voted. There's still a day for that to change.