Wheen

It's just slop. Tried to install it via flatpak. It has flatpak instructions in the readme. It does not ship the flatpak files it references in the readme.

It's a dependency of OpenClaw. Everything else aside, that's ~390k likely users minimum due to the auto-starring.

Though, I realize now the "hundreds of thousands" claim I was responding to is per week, while my guess is all time.

I think you're right, but in the wrong direction. I'd imagine it's closer to a million.

- It has 3.6 million weekly NPM downloads.

- 110k GH stars.

- It's 5th (and its fork is 6th and a dependent is 8th) in monthly Openrouter tokens. Add them all together and they get close to Claude Code numbers.

Not the person you're replying to, but judging by the emphasis on the cost of cached input tokens in the OP article, I'd guess it has to do with DeepSeek v4.1's KV cache efficiency. It uses <1000 bytes per token, so they're able to get 1M token context in under a GB.

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

> but where's the outcome dataset justifying this? how did they get that probability? what was the specific methodology of the tests? what variables did they account for?

It's just high school statistics: https://en.wikipedia.org/wiki/Confidence_interval

It's not a probability. It's essentially an assertion that if the test were run 100 times, the result would be within the interval 95 times.

Between all the posts fabricating scenarios to justify the AA score and the others trying to undermine AA, I'm getting strong astroturf vibes.

Either that, or the average poster on HN isn't nearly as critical as I had thought.

Have you posted the code to this proxy somewhere? I'm dabbling in custom harnesses and would love to learn from it.

Just a hunch, but it might be because of the 51B parameter n-gram embedding. At 125B, you'd expect ~16gigs for a 1-bit quant. Add 51gigs for the n-grams and you're not far off the actual size.

If that's true, it'd scale linearly with number of bits in the quant with an offset of about 51gigs. So Q4 should be a bit bigger than 82gigs, I'd guess in the 90s (as opposed to a ~280gig q4 if the whole 70gigs of the 1-bit quant scaled linearly).

Sorta, but you're off by one layer. You can store the 18B in VRAM and stream the rest from RAM. There's still a performance hit relative to storing it all in VRAM, but it's tolerable.

Generally, for local consumer use, these large MOE models are best for unified RAM systems like DGX Spark or Mac Studio.

> will rely heavily on public chargers

Public chargers =/= DC fast chargers (like Tesla superchargers).

I've already seen level 2 chargers that drop down from light posts in my city. Roll those out to every light post near apartment buildings and throw in some curb-side stalls if you need more capacity. Most people won't need to charge every night with 200/300mi+ range, so you won't need a 1:1 mapping of chargers to cars. This way, people charge passively overnight at stations that are cheaper to build than DC fast chargers.

As a former HBO dev, can confirm that the majority of test cases or random debugging messages are Game of Thrones quotes.

Sorry for the late reply.

Recently, mostly Python and Javascript

For me personally, just scrobbling.

At this point, that's the only thing stopping me from using it. Looks great, but I'm a stat freak and I need my scrobbling.

As someone who is (Probably a bit more than) a junior dev with no formal education or experience in a team, do you have any suggestions to write better code?

I feel like my code would be considered bad, but I have no idea what good or bad code looks like. Does anyone have some examples and comparisons between good and bad code?

>Technically adept attacker

It was a 15 year old kid http://krebsonsecurity.com/2014/02/the-new-normal-200-400-gb...

I have a friend who claims his aunt (Who works of MS) said that it would cost $1k (This was literally the day of the announcement, too). He has been rather reliable in the past, but it should still be taken with a grain of salt, since I was given no context.

Airs shouldn't be that bad. IIRC, Linus runs one.