The property has water and electricity. What about a septic system? That’s the most important part that most people don’t realize. A new septic system requires engineering plans, soil testing, and, most important, township approval. Buy this property. Then wait for the township to say ‘nope’ because the water table is too high or a river is too close. And even if everything goes smoothly, it will cost at least $30k to install. So yes, $150k seems cheap but perhaps there’s a reason why it’s cheap.
@YonovFilip Codex can collaborate with other LLMs without any issues. In my setup, I configured Codex to use a local Qwen3.6 endpoint for validation/second opinion. Sometimes Qwen finds flaws in Codex’s approach, so Codex implements Qwen’s proposals instead.
That has nothing to do with grounding. See those thick traces? A lot of current flows through them. Bad or dirty contacts increase resistance, which generates heat and can burn the board. Inspect the mating pins for oxidation or corrosion. Also check that the board is seated firmly - the pins must have a good grip. If anything is loose, it will heat up and eventually burn.
@farlander762@IanCutress I think they use a low boiling point Fluorinert, like FC-72, which has a boiling point of 56°C. That’s why you see the bubbles.
@cjzafir Are you serious? Using a local LLM to search for files - that’s the use case? Instead of find . -name "filename", you’re suggesting prompting a model to run the exact same command?
@georgecursor The first thing I did was vibe-code a service that uses RGB fans as indicators for llama.cpp health and GPU load. Blinking red = model crashed or not loaded, and a blue-to-red gradient indicates GPU load, with red representing 100% utilization.
@realmaximusfps@loyalmoses You’re right! With all the AI breakthroughs, it’s easy to forget how much work it would take to create a realistic animation.
You don’t do much coding, do you? I’m a developer, so for me an LLM’s coding capabilities come first. I’ve tested most of the available models on real coding tasks - for example, giving them a non-obvious bug in a codebase and asking them to fix it.
One example was a lag issue in a Swift app when displaying a modal sheet. Qwen3.6-35B identified the problem and fixed it. Gemma-4-31B found the issue but couldn’t fix it. All the smaller or older models went completely off-topic.
So yes, for tasks like writing emails, summarizing text or booking appointments, a 9B model is probably sufficient. Not for any serious coding work.
In reality, Gemma-4 is absolutely not designed for agentic work that requires a long context. It’s a chat model with broad general knowledge and decent inference speed. All it can really do is talk - and it talks a lot. It’s good for short, one-shot tasks, but once the context grows, it tends to fall into hallucinations and looping behavior.
So, I’m sorry, but unless you can demonstrate an actual application where this model excels, I’ll consider this post clickbait.
@ArchSxlt@LostMemeArchive It’s probably not even the hardware that’s running Doom. While some modern 32-bit microcontrollers can run it, I don’t think that’s the case here. This has to be just a display interface connected to a computer via USB.