their recent changes to the privacy policy broke their promise of zero data retention. specifically, they offered chatgpt luna under a zero data retention privacy policy. luna was later shown to be 30 days retention.
their privacy policy has never guaranteed your prompts will not be logged and when asked they have failed to revise it.
when challenged about sending data to openrouter without listing it as a 3rd party processor they offered a dismissive response. the same with running prompts through cloudflare. seems trivial, but signifies general disinterest in security.
by default if you run the harness outside your config file by accident, it will automatically run silently with a free model that sends your prompts and local data to an endpoint with training enabled. on top of that it used to dump all the prompts sent to free models into an s3 bucket, the feature was literally called 'datadumper' in the source.
For instance, this "marketing" claim that it'll take 24y to break even if a user uses 100m tokens/day (~$1.14 in DeepSeek v4 Flash usage) ignores the fact that OpenCode Go has 5h & weekly throttles. If you're running automated jobs [0], those throttles make it utterly useless. Besides, I think the local GPU setup in question could serve 10+ "users" at the same time, bringing down the break even by 22y (10x).
[0] For comparision, we routinely do 200m to 500m tokens on 3 to 8 automated code reviews per day with DeepSeek v4 Flash on max.
For example, I needed to write an invitation letter for immigration control for a relative visiting me. Previously I would have used a search engine for a template. Today I fire up my local qwen 3.5-9b for this kind of stuff and feed it all the private data I need.
Unfortunately it is unlikely the average user will known how to avoid this data collection. Even if the LLM is local you are likely feeding the prompts to remote servers if you harness/chat-interface is not properly vetted.
It isn't even really worth the (minimal) ops to stand up rented MI300Xs to sell excess capacity to them even if it was minimally profitable, when I tried I was content to give API keys to friends to beat on it.
> With Go, you pay $10/month and we aim to give you 6x that in usage.
For most models, we make this work through bulk discounts and reserved GPU capacity. We then pass those savings on to you through the 6x multiplier.
https://opencode.ai/docs/go/#why-some-models-have-lower-usag...
I think it's back to 2X usage, meaning it's cheaper token-burn than usual to use. Which is lovely.
OpenCode Go has been so nice to have. I love having access to DeepSeek, Qwen and MiniMax M3 when doing design work, to see what different models cook up. I've been very surprised with MiniMax M3, not as a particularly good architect, but at it's very good ability to state the problem elegantly & to frame the different decision points very well. That's been a fun ongoing surprise.
Maybe hallucination is good for prototyping and creative work, but maintaining and debugging code might be better done by a boring model?
I have a stack of ten or so 3090s sitting in boxes, but it's not worth the hassle to use them. You can easily run models as cheap as water in the cloud.
Sitting around 15 minutes for local Minimax is stupid when you're trying to be productive. You can spin up parallel job instances and multitask in the cloud.
If you want freedom, build open source cloud infra.
You rent your ISP line. Why isn't renting GPU compute seen the same way? You still have compete ownership over your stack, you're just letting someone else deal with the capital outlay and headache.
If you're just vibe coding with giant frontier models, yes, the value will be worse. Especially now, where GPU prices have spiked another 20% last month.
For some tasks where owning the setup and full kv cache matters, the payoff calculation is ridiculously in favor of running your own deployment.
For instance for some batch classifications jobs where the prefix cache hit rate will be >95%.
The calculus also changes if you just use AI as a light tool while coding and don't need the giant models; qwen3 27B runs at 80TPS on a 5090 properly deployed.
I mean obviously it's worth it just so you can flex on HN. But curious whether there was any other reason? Retired scalper?
Is it mostly seen the same way. What is unacceptable is removing the freedom (which you mentioned) of people who prefer to run models locally.
Just as people have the right to tinker at home with DIY and electronics, fully knowing they won't compete with the latest ASML machine, people are free to use open-weight models at home.
BTW Minimax H3 running local can generate amazing short vids quite fast: about 50 seconds to generate a 7 seconds vids on a 4090 (depending on the settings). I've got a friend who spams my Telegram daily with such (no censorship and NSFW btw) vids.
I don't run local but I'll defend the rights of people who want the freedom to do so and I won't look down at them from my high-horse talking about "electricity" and "productivity".
I eventually switched to LM studio and the same model runs much better, like 70tk/s.
Not sure if it was because I was running llama.cpp inside podman or badly tuned LLM arguments. But LM studio is unfortunately much more practical.
Although I agree with you. I do not really know what kind of telemetry LM studio is running and I would rather not be using it.