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Running a full desktop environment in a microVM, and accessing it remotely over LAN (Omarchy, ping DHH and vaxry 😊) QVM now supports ultra low latency desktop streaming, for purposes such as VDI / Virtual Desktop Infrastructure, RBI / Remote Browser Isolation and agent sandboxes Supported host-OS:es and hypervisors... show more

Joel Eriksson

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23,583 Aufrufe • vor 1 Monat •via X (Twitter)

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This is a very nice description of the problem. I think the first part of the solution is to not give into the hyper productivity hype: running a gazillion things in parallel. There's likely only a handful of humans that have the mental make-up to survive the permanent context switches. You can train your brain to become better at it, but there's a limit to that, and I found that it will lead to much worse outcomes in terms of output quality of both you and your clanker. I'm no neuroscientist, but I would assume there's a hard limit on how many things your brain can juggle at a time, probably made worse by the complexity that is code. I generally don't do more than 2-3 things in parallel. I use virtual desktops as "bundle of things that belong to a task", so terminals + editor + browser. Works fine, doesn't need any special OS support, expose on the virtual desktop let's me quickly switch to the task specific app I need. I also have a little pi extension that shows me what GH issue/PR the clanker session is about, with a link I can click to open the issue/PR in the browser. Plus 1 virtual desktop for entertainment, so the brain can get some rest. (better alternative: get up and walk a bit every 45 minutes) Video: - First desktop: pi session for a PR, extension shows me what PR I work on, can click to open in browser, aux terminal for random BS, editor, browser. Yes, I could split instead of having two terminal windows. I'm old. - Second desktop: clawdbot pi session, aux terminal for testing, editor, browser - Third desktop: entertainment booth

This is a very nice description of the problem. I think the first part of the solution is to not give into the hyper productivity hype: running a gazillion things in parallel. There's likely only a handful of humans that have the mental make-up to survive the permanent context switches. You can train your brain to become better at it, but there's a limit to that, and I found that it will lead to much worse outcomes in terms of output quality of both you and your clanker. I'm no neuroscientist, but I would assume there's a hard limit on how many things your brain can juggle at a time, probably made worse by the complexity that is code. I generally don't do more than 2-3 things in parallel. I use virtual desktops as "bundle of things that belong to a task", so terminals + editor + browser. Works fine, doesn't need any special OS support, expose on the virtual desktop let's me quickly switch to the task specific app I need. I also have a little pi extension that shows me what GH issue/PR the clanker session is about, with a link I can click to open the issue/PR in the browser. Plus 1 virtual desktop for entertainment, so the brain can get some rest. (better alternative: get up and walk a bit every 45 minutes) Video: - First desktop: pi session for a PR, extension shows me what PR I work on, can click to open in browser, aux terminal for random BS, editor, browser. Yes, I could split instead of having two terminal windows. I'm old. - Second desktop: clawdbot pi session, aux terminal for testing, editor, browser - Third desktop: entertainment booth

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Schmidhuber: "Well, large language models are far from AGI. LLMs large language models such as ChatGPT. They are just a clever way of indexing the world's existing human generated knowledge, such that it can easily be addressed through in a way that humans are familiar with, which is natural language. That's good enough to facilitate many desktop jobs, for example, writing summaries of existing documents in a particular style, or creating illustrations or an article. And and so on. However true AGI goes far beyond that. It is much harder, for example, to replace craftsmen such as plumbers or electricians, because the real world, the physical world, is much more challenging than the world behind the screen. At the moment, the only Al that works well is behind the screen, and it's good for desktop to desktop workers. But but not really for for people working in the physical world."

Schmidhuber: "Well, large language models are far from AGI. LLMs large language models such as ChatGPT. They are just a clever way of indexing the world's existing human generated knowledge, such that it can easily be addressed through in a way that humans are familiar with, which is natural language. That's good enough to facilitate many desktop jobs, for example, writing summaries of existing documents in a particular style, or creating illustrations or an article. And and so on. However true AGI goes far beyond that. It is much harder, for example, to replace craftsmen such as plumbers or electricians, because the real world, the physical world, is much more challenging than the world behind the screen. At the moment, the only Al that works well is behind the screen, and it's good for desktop to desktop workers. But but not really for for people working in the physical world."

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This is Scene Editor. Running on Flutter 3.47 stable. Flutter is excellent for building serious 3D desktop apps. Powered entirely by Flutter's built-in graphics stack, Flutter GPU and Impeller. No forks, no platform views, no external rendering contexts. Scene Editor is just a vanilla Flutter application. Build 3D apps for macOS, Linux, Windows, iOS, Android, Web, and custom embedders. Non-destructive importing, overrides on nested prefabs, live material and shader editing, custom gizmos, and component inspectors you define with property annotations. It watches the filesystem, and custom components you write become instantly available in the editor upon saving. Configure, build, and run your live app without leaving it. Flutter is now 3D.

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ANTHROPIC 🔥: Early look at Claude Science, a new app designed for scientific research. > "Artifacts traced to their code, environments managed on demand, and 60+ optional scientific databases that you can connect." Claude Science was named "Operon" during development and is now being distributed as a desktop app. Interestingly, the app acts as a local web server, and its UI runs in your browser. Some unique features include the ability to create dedicated "Categories" in Memory and custom agents called "Specialists". We need this on consumer version too 👀

ANTHROPIC 🔥: Early look at Claude Science, a new app designed for scientific research. > "Artifacts traced to their code, environments managed on demand, and 60+ optional scientific databases that you can connect." Claude Science was named "Operon" during development and is now being distributed as a desktop app. Interestingly, the app acts as a local web server, and its UI runs in your browser. Some unique features include the ability to create dedicated "Categories" in Memory and custom agents called "Specialists". We need this on consumer version too 👀

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QVAC SDK 0.13.0 is live, and this version brings a lot of exciting updates! Local AI now plugs into your coding agent, ships as a desktop app in one command, and runs even more models. Highlights: NEW INTEGRATIONS - OpenCode and coding agents: the new @qvac/ai-sdk-provider makes QVAC a local provider. Less setup, same-model requests queue cleanly, and managed mode starts and supervises qvac serve for you. - Broader OpenAI-compatible API, validated across supported flows so covered capabilities stay consistent and testable. - Turn your QVAC project into a real desktop app for Mac, Windows, or Linux with a single command. The new Electron plugin handles the packaging and keeps the app small by including only what it needs. NEW MODELS - New pi0.5 model support - run a vision-language "robot brain" on a single ordinary graphics card, at full accuracy. - Image-to-video, fully local, via the Wan2.1 model in the Diffusion addon. - New BCI add-on: brain-computer interface transcription, fully local. Decode recorded neural signals into text on-device via the Whisper.cpp-based BCI model. IMPROVEMENTS - Whisper GPU transcription on Android, auto-picking the best backend (OpenCL on Adreno 700+, Vulkan elsewhere), unified on one ggml engine. - Parakeet steadier on mobile, with real end-of-utterance detection for streaming. - Supertonic TTS now runs full GPU across Metal, Vulkan, and OpenCL, with native streaming.

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