That doesn't work, if you do that it will mark DeepSeek's models with a warning symbol along with the error "paid model training violation".

I wonder why the question about data security and training comes often with DeepSeek, Kimi, Glm and never with Anthropic, OpenAI, and Google models.

Why is that?

IIRC, USA data protection protects data of US citizens only, foreigners data is not protected, and the companies are not even allowed to disclose when they collect those data.

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This is correct. Sorry I was using my phone to post. Here's what my bash alias verbatim looks like (.bashrc / .zshrc). The DEEPSEEK_API_KEY var is setup separately (so claude doesn't see it):

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alias clauded='ANTHROPIC_BASE_URL=https://api.deepseek.com/anthropic ANTHROPIC_AUTH_TOKEN=$DEEPSEEK_API_KEY ANTHROPIC_MODEL=deepseek-v4-pro[1m] ANTHROPIC_DEFAULT_OPUS_MODEL=deepseek-v4-pro[1m] ANTHROPIC_DEFAULT_SONNET_MODEL=deepseek-v4-pro[1m] ANTHROPIC_DEFAULT_HAIKU_MODEL=deepseek-v4-flash CLAUDE_CODE_SUBAGENT_MODEL=deepseek-v4-flash CLAUDE_CODE_EFFORT_LEVEL=max claude'

----

I doubt that the opus, sonnet, and haiku model args actually matter if you want to omit them.

I run this on a VPS that has no other credentials or project access so I can give it the skip permissions arg.

At several times the price of DeepSeek, though, so it's a tradeoff... Even then Pro is still cheaper than Haiku.

yes, this is pretty much just rerouting Claude to call Deepseek's Anthropic-style-compatible endpoints instead of its own defaults Once removed, it'll work just like before

Correct.

Seriously. When I first looked this project had been pushed the first commit two hours prior. Projects should be at least 3 months old or automatically removed.

Convenience? Am I supposed to take the prompt and use my own tokens on it? Why should I have to do that?

For the same reason that GitHub has a releases page for uploading binaries.

is the value the working outputs or the inputs? A prompt alone would not let you recreate this project.

Recruiters used to use the candidate's Github "sources" page for evaluating candidates as a kind of proof-of-work.

Ah, for aws bedrock, just use `aivo keys add` add baseurl and apikey, everything is ready, `aivo models` to see models

Currently no, but it can be added

> But my impression so far is even when model subsidization is done, those open source models are quite viable alternatives.

My understanding is that DeepSeek V4 Pro is going to be uniquely good at working on consumer platforms with SSD offload, due to its extremely lean KV cache. Even if you only have a slow consumer platform, you should be able to just let it grind on a huge batch of tasks in parallel entirely unattended, and wake up later to a finished job.

AIUI, people are even experimenting with offloading the KV cache itself to storage, which may unlock this batching capability even beyond physical RAM limits as contexts grow. (This used to be considered a bad idea with bulky KV caches, due to concerns about wearout and performance, but the much leaner KV cache of DeepSeek V4 changes the picture quite radically.)

have you used it for non coding tasks via MCP, like Figma/Paper for design or Ableton MVP for sound design?

The token cost makes it tempting to use for token-heavy tasks like this

> even when model subsidization is done, those open source models are quite viable alternatives.

Model inference was never subsidized. Inference is highly profitable with today's prices. That's why you have many inference providers. My guess, the prices for inference will go down, as more competition starts cutting the margin.

It's model training, development and R&D that cost a lot, and companies creating closed models don't have any business model except astroturfing and trying to recover training costs through overpriced inference.