~ / cmdr2

• #findstarlink
• #ai
• #llm

I spent some time today doing support for Freebird, Puppetry and Easy Diffusion. Identified a bug in Freebird (bone axis gizmos aren’t scaling correctly in VR), got annoyed by how little documentation I’ve written for Puppetry’s scripting API, and got reminded about how annoying it is for Easy Diffusion to force-download the poor quality starter model (stock SD 1.4) during installation. The majority of the day was spent in using a local LLM for classifying emails. I get a lot of repetitive emails for FindStarlink - people telling me whether they saw Starlink or not (using the predictions on the website). The first part of my reply is always a boilerplate “Glad you saw it” or “Sorry about that”, followed by email-specific replies. I’d really like the system to auto-fill the first part of the email, if it’s a report about Starlink sighting.

• #ai
• #ml
• #llm

Built two experiments using locally-hosted LLMs. One is a script that lets two bots chat with each other endlessly. The other is a browser bookmarklet that summarizes the selected text in 300 words or less. Both use an OpenAI-compatible API, so they can be pointed at regular OpenAI-compatible remote servers, or your own locally-hosted servers (like LMStudio). Bot Chat Summarize Bookmarklet The bot chat script is interesting, but the conversation starts stagnating/repeating after 20-30 messages. The conversation is definitely very interesting initially. The script lets you define the names and descriptions of the two bots, the scene description, and the first message by the first bot. After that, it lets the two bots talk to each other endlessly.

• #ai
• #learning
• #self-awareness

Today I explored an idea for what might happen if an AI model runs continuously, processing inputs, acting and receiving sensory inputs without interruption. Maybe in a text-adventure game. Instead of responding to isolated prompts, the AI would live in a simulated environment, interacting with its world in real time. The experiment is about observing whether behaviors like an understanding of time, awareness, or even a sense of self could emerge naturally through sustained operation.

• #easydiffusion
• #ai
• #lab
• #performance
• #featured

// Cross-posted from Easy Diffusion’s blog. tl;dr: Explored a possible optimization for Flux with diffusers when using enable_sequential_cpu_offload(). It did not work. While trying to use Flux (nearly 22 GB of weights) with diffusers on a 12 GB graphics card, I noticed that it barely used any GPU memory when using enable_sequential_cpu_offload(). And it was super slow. It turns out that the largest module in Flux’s transformer model is around 108 MB, so because diffusers streams modules one-at-a-time, the peak VRAM usage never crossed above a few hundred MBs.