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mongrelionon Armada: Encrypted, Open-Source, Discord Alternative (Built on Nostr)

Coincidentally I am looking for self-hosted alternatives to Discord for a local community where I live. I have been considering Matrix (one of the Rust implementations) + Element, so I wonder what makes this a better or worse alternative, but there seems to exist no comparison table/chart.

mongrelionon OpenAI agent hacked Australian government website, PM says

Imagine if the headline was about any of the Chinese labs' models hacking the US government...

mongrelionon Strands Harness

Did you miss the whole fiasco from last week?

mongrelionon Show HN: We built open OpenRouter that turns usage into a better model

Congrats on the launching of your product. I will be taking it for a spin to compare it with these other products that seem to be competing directly with what you have to offer:

- https://github.com/ENTERPILOT/GoModel - https://github.com/maximhq/bifrost - https://github.com/BerriAI/litellm

Would you care to share what makes experiential different?

mongrelionon GLM-5.3-Flash

I think my inexperience using Claude Code or Codex makes a difference but what would you expect to be different here as opposed to using pi or opencode? Pi is my main driver so switching between all these models is a no brainer. No matter what the model is, my harness stays the same: same workflow, same skills, etc.

mongrelionon GLM-5.3-Flash

> Even OpenAI and Anthropic aren't bad enough that they claim literal ownership of your inputs and outputs.

In this case you are placing your trust in OpenAI and Anthropic. I'm not sure about Anthropic but OpenAI has changed their mission corpus quite a lot from its humble beginnings that it results hard to trust them when they say they don't use your stuff to further train their models. If I'm a Big Corp with enough lawyers to putnup a fight, I would then feel ok with such clause, but being a small guy, who is going to defend me when the truth comes out that they have been training their models with my data? Similar fiasco as with Facebook, who had claimed they didn't sell your data, even though they were.

That's where I'm coming from with all this "trust us, we don't train our models with your data". At least this Chinese company is being upfront about it.

mongrelionon The RAM shortage could last years

letting the market set prices ensures that the chips go to the critical markets and uses.

Can you please elaborate what you mean by "critical market"?

Edit: formatting

mongrelionon The local LLM ecosystem doesn’t need Ollama

llama.cpp moves too quickly to be added as a stable package. Instead, you can get it directly from AUR: https://aur.archlinux.org/packages?O=0&K=llama.cpp

There are packages for Vulkan, ROCm and CUDA. They all work.

mongrelionon Reallocating $100/Month Claude Code Spend to Zed and OpenRouter

I have been so far happy with the value that Copilot brought but for the past few weeks I have felt the chokehold on the number of requests.

I have had the chance to test the main Chinese models through OpenRouter but the Pay-as-you-go model is expensive compared to a subscription model, but I don't want to marry to a single provider.

Thanks for bringing OpenCode Go to my attention. Your comparison is the research I didn't know I needed, and I will be cancelling my Copilot subscription to replace it with OpenCode Go right away.

mongrelionon Nvim-treesitter (13K+ Stars) is Archived

It's clear to me that the maintainer is referring to "shushtain" and those type of people

> when they take that tone with you.

This makes it sound as if you took it personally?

mongrelionon Nvim-treesitter (13K+ Stars) is Archived

Having a bad day does not entitle you to take it out on others

mongrelionon Nvim-treesitter (13K+ Stars) is Archived

You should totally post this on the original thread just for adjustment :-)

mongrelionon $500 GPU outperforms Claude Sonnet on coding benchmarks

I am definitely looking forward to TurboQuant. Makes me feel like my current setup is an investment that could pay over time. Imagine being able to run models like MiniMax M2.5 locally at Q4 levels. That would be swell.

mongrelionon $500 GPU outperforms Claude Sonnet on coding benchmarks

Not the answer that you are looking for, but I am a fellow AMD GPU owner, so I want to share my experience.

I have a 9070 XT, which has 16GB of VRAM. My understanding from reading around a bunch of forums is that the smallest quant you want to go with is Q4. Below that, the compression starts hurting the results quite a lot, especially for agentic coding. The model might eventually start missing brackets, quotes, etc.

I tried various AI + VRAM calculators but nothing was as on the point as Huggingface's built-in functionality. You simply sign up and configure in the settings [1] which GPU you have, so that when you visit a model page, you immediately see which of the quants fits in your card.

From the open source models out there, Qwen3.5 is the best right now. unsloth produces nice quants for it and even provides guidelines [2] on how to run them locally.

The 6-bit version of Qwen3.5 9B would fit nicely in your 6700 XT, but at 9B parameters, it probably isn't as smart as you would expect it to run.

Which model have you tried locally? Also, out of curiosity, what is your host configuration?

[1]: https://huggingface.co/settings/local-apps [2]: https://unsloth.ai/docs/models/qwen3.5

mongrelionon $500 GPU outperforms Claude Sonnet on coding benchmarks

What is this 10€ per month subscription that you are talking about?

mongrelionon Can I run AI locally?

I don't understand why I'm getting downvoted.

I am legitimately curious about the parameters that the person used for running the model locally to get the results they got because I am myself currently experimenting with running models locally myself. You can see I am asking similar questions to others in this same thread and correlate the timestamps.

mongrelionon Can I run AI locally?

At what temperature did you run it and what was your context limit?

mongrelionon Can I run AI locally?

Apparently there is a whole science behind running models. I have seen the instructions that unsloth publishes for their quants and depending on the model they'll tweak things like the temperature, top k, etc.

The size of the quantization you chose also makes a difference.

The GPU driver also plays an important role.

What was your approach? What software did you use to run the models?

mongrelionon Can I run AI locally?

What front-end framework did you use? I find the UI so visually appealing

mongrelionon Can I run AI locally?

Which quantization are you running and what context size? 32tok/s for that model on that card sounds pretty good to me!

mongrelionon Can I run AI locally?

It might be that the system prompt sent by codex is not optimal for that model. Try with open code and see if your results improve

mongrelionon How to run Qwen 3.5 locally

By anyone do you mean a well-established business or any entity willing to serve you?

mongrelionon Something is afoot in the land of Qwen

> [...] _but not necessarily use the right format._

This has also been my experience. But isn't the harness sending the instructions on how to invoke a tool? Maybe it is missing the formatting part. What do you think?

mongrelionon Ask HN: What Online LLM / Chat do you use?

Through my Kagi subscription I get access to quite a few models [1] but I tend to rely on Qwen3 (fast) for quick questions and Qwen3 (reasoning) when I want a more structured approach, for example, when I am researching a topic.

I have tried the same approach with Kimi K2.5 and GLM 5 but I keep going back fo Qwen3.

I also have access to Perplexity which is quite decent to be honest, but I prefer to keep everything in Kagi.

1: https://help.kagi.com/kagi/ai/assistant.html#available-llms

mongrelionon Right-sizes LLM models to your system's RAM, CPU, and GPU

Great idea of inferbench (similar to geekbench, etc.) but as of the time of writing, it's got only 83 submissions, which is underwhelming.

mongrelionon Right-sizes LLM models to your system's RAM, CPU, and GPU

> [...] it's much easier to fine-tune a "general" model into performing some very specific custom task (like classifying text, or translation, etc)

Is this fine-tunning process similar to training models? As in, do you need exhaustive resources? Or can this be done (realistically) on a consumer-grade GPU?

mongrelionon Right-sizes LLM models to your system's RAM, CPU, and GPU

> But are we really at the point yet where people are running local models without knowing what they are running them on..?

I can only speak for myself: it can be daunting for a beginner to figure out which model fits your GPU, as the model size in GB doesn't directly translate to your GPU's VRAM capacity.

There is value in learning what fits and runs on your system, but that's a different discussion.

mongrelionon Access to a Shared Unix Computer

Apparently there are a few more similar communities like the one from the post

https://tildeverse.org/members/

mongrelionon Pi – A minimal terminal coding harness

Pi ships with powerful defaults but skips features like sub-agents and plan mode

Does anyone have an idea as to why this would be a feature? don't you want to have a discussion with your agent to iron out the details before moving onto the implementation (build) phase?

In any case, looks cool :)

EDIT 1: Formatting EDIT 2: Thanks everyone for your input. I was not aware of the extensibility model that pi had in mind or that you can also iterate your plan on a PLAN.md file. Very interesting approach. I'll have a look and give it a go.

mongrelionon Alleged Distillation Attacks by DeepSeek, Moonshot AI, and MiniMax

I agree with you, especially with this:

They paid for the access the same as any other.

If anything, this makes them more legit than Anthropic because they are paying for the content, whereas Anthropic just stole *all* the data they got a hold of. So, in this case the Chinese AI labs stand on higher moral ground LOL.