petu

Moved where? Moving to a different wallet wouldn't prevent freezing. It's 5B, impossible to cash out or exchange for different crypto quickly.

Luna/Terra/Sol/Astra make perfect sense, it indicates model tier/size with larger celestial body representing larger model.

Same for Anthropic, consistent model tier naming reused across model generations.

Those are just like i3/i5/i7 or M-series Pro/Max/Ultra.

But Carbon seems to be full replacement of Argon announced two weeks ago, they're same price. So Argon doesn't mean anything, they just picked random element.

For whatever map rendering in Google Maps is capped at 30fps, yes. Google doesn't seem to care.

Deepseek doesn't have subscriptions, you can only pay per token / API prices.

Maybe it's intentional so government doesn't block open weight release.

There was law proposal (rumour?) last week about Chinese labs having to get approved for weights release now.

There's basic reference inference code released with the weights, yes.

https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017/...

> providers have to implement which could be different than what the labs themselves use.

I'd imagine (most) providers just take implementation by vLLM/SGLang.

From "What about data and privacy?" in FAQ it sounds like they don't host models themselves. e.g. it says:

  DeepSeek: ZDR agreement is renewed monthly. The current agreement is valid through October 31, 2026.
  https://opencode.ai/go

They also follow official DeepSeek API peak/off peak hour pricing and link to their docs:

  DeepSeek V4.1 Flash / V4 Pro: Peak hours are 01:00-04:00 and 06:00-10:00 UTC, Monday through Friday; all other hours, including weekends, are Off-Peak. Learn more.
  https://opencode.ai/v2/docs/console/go/#usage-limits

edit: they used to explicitly name the providers, at least in the past DeepSeek V4 was served with official API https://github.com/anomalyco/opencode/issues/24649#issuecomm...

Is that a dig on DeepSeek not offering subscription plans?

If so OpenCode Go looks good and includes DeepSeek models (yet to try myself).

If you're happy with generation speed in 5 t/s range, sure -- PCIe 5.0 x16 is only 64GB/s.

158GB number is for Mistral Medium 3.5 128B model, Muse Spark is way larger.

  Muse Spark Open would require more than 1.6TB of RAM in its native precision (BF16) for the model parameters alone. In a 4-bit quantized (NVFP4) format, the model memory footprint is lowered to about 493GB.

E.g. it checks integrity of ALL resources on each start. You could break/remove ~any file from install folder, as long as you have intact entry binary (steam.exe) it'll start and fix everything.

That's likely really useful against bit rot / corruption due to unstable overclock (XMP / EXPO profiles come with no guarantee of stability).

I guess US `tabled` means put in a writing table drawer, hidden and soon to forgotten.

I'm reading schleck8 comment as accusing Yandex ASN as malicious on grounds of having higher bot % traffic than Scaleway / comparably sized Cloud Provider.

It could be malicious, but that exact stat doesn't show that.

Where you saw whataboutism?

Implication was that tons of malicious traffic originate from there. I pointed out that bot % stat doesn't seem to be useful for determining that, Occam's razor and all.

Scaleway doesn't have a search engine?

Google ASN is at 98.7% https://radar.cloudflare.com/traffic/as15169

First key on the right after space bar.

There's no BF16, original full quality weights are quantized already and 510GB.

Then good portion of those weights are n-grams (~200GB) that don't need to be in VRAM.

Then KV cache of that model is super lightweight at ~1GB per 1M tokens. If HBF succeeds, then accelerator with 16GB of VRAM and 1TB HBF/NAND is probably all you need (?).

I guess public datasets on HuggingFace and some shadow libraries content is enough to start.

e.g. fineweb dataset is 50TB https://huggingface.co/datasets/HuggingFaceFW/fineweb

6B activated weights per token vs 27B. Something like DGX Spark is way better suited for Flash Next.

That's max output tokens per response limit, separate from context length

Devil's advocate, but Anthropic talks a lot about importance of alignment, so.. How can military ensure that model is NOT refusing to work just to be malicious?

Anthropic doesn't provide model w/o guardrails, suppliers have to use public version, newer model releases surely know about Anthropic standoff with the military.. how to prevent model from realizing that it's doing something for the military (supplier) and sandbagging and/or subtly sabotaging the results?

=====

DOD recommends sandbagging as defense mechanism against destination "attacks":

https://media.defense.gov/2026/Sep/08/2003992823/-1/-1/1/CSA...

Page 13

  When suspecting a malicious distillation campaign, consider varying changes to responses across requests to complicate response quality evaluations, such that the subtle changes avoid triggering obvious alerts. Reducing reasoning depth, presenting correct information with different reasoning, or stylistic inconsistencies may evade detection while reducing training usefulness.

  Avoid informing China-based AI company users suspected of distillation campaigns of a switch to a downgraded model. Informing malicious distillers would enable them to improve their defense evasions and indicate when to roll back training.

If that's seen as valid defense mechanism, then it's also a risk if used against them.

n-grams can be kept on SSD, no need to hold them in any kind of RAM (at least w/o batching)

> meander our way through Spotlight

By that you mean that just typing prompt into Spotlight doesn't work reliably? For me after few words "Ask Siri" is first result.

Selling modded board as official 8GB SKU with reliability/warranty expectations.

Was photo not authentic? Think of it "as seen by an iPhone", not "this is authentic event" verification.

But Apple likely would reject such photo because of inappropriate depth map / LiDAR data.

Sipeed has such device https://wiki.sipeed.com/hardware/en/kvm/NanoKVM_USB/introduc...

This feature is Pro phones only, not Duo: https://www.apple.com/iphone/compare/ ("Apple Reference Image (Fusion Main)")

So only on devices with LiDAR / that can capture depth map.

Miniature dioramas wouldn't be size appropriate. Apple could detect faces/cars/other common objects of ~known size and verify -- or even just dump depth map for anyone to check.

It seems to be what Apple is doing, this feature is only available on the 18 Pro's (which have depth sensor on the back), but not Duo.

Aren't they meant for underfloor heating? Heat pump efficiency drops with delta T increase, but iron radiators need 60-80C supply.