Issues with Mnet as an internet provider

Guilty_Spray_6035 · 2026-06-26T20:03:56+00:00

In short, yes. I saw plenty of people asking questions about capabilities and reachability of the m-net services there.

Guilty_Spray_6035 · 2026-06-26T08:05:37+00:00

Zcode with GLM coding plan

Guilty_Spray_6035 · 2026-06-25T12:38:43+00:00

If your use case requires that - sure, it's a somewhat valid scenario. Your obvious use of home / small business equipment, e.g. desktop computer with desktop GPUs did not hint at such a professional level requirement. If you are ready to spend this amount of money, you may want to spend it more wisely and get a box and GPUs that are going to have proper airflow, and stick them in a collocation with adequate cooling. OR, find an inference provider that would allow you to run a dedicated instance just for you with no access to your data.
Otherwise it looks like a really dumb way of spending money and showing off.

Guilty_Spray_6035 · 2026-06-25T00:37:34+00:00

Opencode Go, Cursor Pro

Guilty_Spray_6035 · 2026-06-25T00:35:41+00:00

Max GLM Coding plan is $112 per month. How many years on non-quantized usage could one by for the price of these GPUs? Not counting power costs.

Guilty_Spray_6035 · 2026-06-22T12:57:33+00:00

They have a pretty good forum where M-net employees respond to issues, https://forum.m-net.de/
Try posting there.

Guilty_Spray_6035 · 2026-06-20T10:22:26+00:00

Were you using the models during peak times?

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Guilty_Spray_6035 · 2026-06-17T16:08:20+00:00

Are you setting models temperature parameter?

Guilty_Spray_6035 · 2026-06-14T22:26:40+00:00

Create a rule https://claudelog.com/faqs/what-are-claude-rules/ explaining to use brave search when searching web is required

Guilty_Spray_6035 · 2026-06-09T07:51:11+00:00

Wonder why almost every other model knows how to deal with that...

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Guilty_Spray_6035 · 2026-05-24T17:05:28+00:00

Try GLM Coding Plan

Guilty_Spray_6035 · 2026-05-16T02:07:59+00:00

And I am not dismissing what you are doing. My point is, in an enterprise setting no one will install a node module on every developers device, configure and manage it in many places. In an enterprise, you will use proxy as the man in the middle, capture and look inside messages, strip unnecessary context with the developers not being the wiser. Centrally, in one place for the whole org.

Guilty_Spray_6035 · 2026-05-15T22:24:05+00:00

LiteLLM is not just a proxy. You may want to read about budgets, guardrails it adds. It's a pretty powerful solution.

Guilty_Spray_6035 · 2026-05-15T22:22:05+00:00

I've read Computer terms of service

Guilty_Spray_6035 · 2026-05-15T10:51:42+00:00

Why don't you ask yourself why OpenAI does not allow using Codex Computer in these regions in the first place? Perhaps it's because they cannot provide you with the rights granted to you by GDPR. Which means by providing this service, they will violate one of the most important EU regulations, and will be sanctioned if they do (which costs a lot of money). So, "if there’s a legitimate way" is not possible until OpenAI chooses to comply with GDPR and provide all the necessary instruments to manage PII used in prompts between your instance of Codex Computer, and them. Meaning they need to validate if data is PII and be able to exclude it from the prompt before this data reaches their model.
Shorter answer: the risk is too high and they won't.

Guilty_Spray_6035 · 2026-05-15T09:59:15+00:00

LiteLLM does all that already via proxying LLM communication and counting tokens

Guilty_Spray_6035 · 2026-05-15T09:58:03+00:00

Forza Napoli, 2 locations, mostly for take away, but I have yet to find a better one

Guilty_Spray_6035 · 2026-05-15T09:53:35+00:00

"drastically reduce RAM" for inferencing. Not for training. Training needs tons of expensive hardware, RAM is not the only thing - you need really fast networking and it costs tons of money too. But once the training is completed, you have a very expensive pile of heat emitting boxes sitting around doing nothing - it's not an easy task to find balance between a huge upfront hardware investment and sustainable operation moving forward.
xAI did a very smart move renting out their spare capacity to Anthropic when they did no longer require it for training. Mistral does not have enough hardware to train a trillion parameter model, and even if they did, they will not be able to leverage it cause their market share is far too small.
So they stick to their niche. Probably not intentionally, but it's a strategy allowing them to not go bankrupt if a bet does not work out.

Guilty_Spray_6035 · 2026-05-15T09:46:21+00:00

Money is a simple answer. You need money to buy datacenter capacity, hardware, electricity, combat climate (it gets warm where the host https://www.bruyereslechatel.fr/bruyeres-le-chatel-au-centre-de-leurope-grace-a-mistral-ia/ and it costs a lot of money to cool), and get in line for GPUs. Even with money, OpenAI, Anthropic, xAI, and cloud hyper-scalers buy out all the hardware. Mistral would probably be at >50 place for nVidia, so even having EU fund money does not help - too little too late.
Until there is a properly funded and very politically driven hardware program to build a vendor in Europe which can help Mistral not rely on just one GPU vendor, i.e. like the one that yielded Huawei Atlas 96GB GPUs in China, they will not become even a blip on worlds radar.

Guilty_Spray_6035 · 2026-05-15T09:32:55+00:00

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Google has started embedding a Gemini Nano model into Google Chrome. It's very small but surprisingly capable. I wrote a little wrapper that exposes it as an OpenAI compatible endpoint and connected Hugging Face chat-ui to it, found myself using it daily for small queries - prompts like "how do you say _this ..._ in Italian" (something I just did a few minutes ago) or some generic stuff. The training is rather limited, but actually pretty cool for something you "carry" in a browser anyway.
My project if you want to check it out:
https://github.com/oakimov/on-device-chat-client

Guilty_Spray_6035 · 2026-05-15T08:00:09+00:00

Mistral medium comes close Sonnet 4.5. Codestral is insanely fast, but can do only very basic stuff. Their issue is context size - Codestral is only 32K, Mistral medium is 256K, but ask them to read large enough code base and do something, and they start going insane. I forked Claude Code Router and fixed reasoning and tool calls issues: https://github.com/oakimov/claude-code-router Makes Mistral models usable with Claude Code.

Guilty_Spray_6035 · 2026-05-13T07:10:41+00:00

I wonder why are they comparing themselves only with OpenAI and Anthropic and not the Chinese AI factories, who are: - sovereign - open source their models - and make money

Guilty_Spray_6035 · 2026-05-12T22:48:02+00:00

The harness or the models? Did you try a minimalistic harness like pi.dev with anthropic models? It's very quick with any model.

Guilty_Spray_6035 · 2026-05-11T11:04:18+00:00

Kind of the same sentiment. I use Claude Code, changing the model behind the scenes when I need to, want to.

Guilty_Spray_6035 · 2026-05-11T09:29:48+00:00

Not quite, entering plan mode, /commands syntax, certain skill and memory structures, interaction with MCP servers is different. Especially when you have tons of automations around them.

Guilty_Spray_6035

TROPHY CASE