However comparing apples to apples, what makes you say this isn't ready for government usage, when it's ready for trillion dollar big tech companies' majority of their workforce? (Aside from Microsoft, Apple obviously). Large employers like IBM etc also must be using red hat or some other distro

> EU moves so much faster when it comes to regulations like forcing all of us in Denmark to use timesheets, annoying lids on our bottles, and invasive surveillance laws.

Rediscovering the principle of subsidiarity from first principles...

That's what I meant by "waiting a few days for updates" in my other comment. Qwen 3.5 release, I remember a lot of complaints about: "tool calling isn't working properly" etc.

That was fixed shortly after: there was some template parsing work in llama.cpp. and unsloth pulled out some models and brought back better one for improving something else I can't quite remember, better done Quantization or something...

coder543 pointed out the same is happening regarding tool calling with gemma4: https://news.ycombinator.com/item?id=47619261

(Comparing Q3.5-27B to G4 26B A4B and G4 31B specifically)

I'd assume Q3.5-35B-A3B would performe worse than the Q3.5 deep 27B model, but the cards you pasted above, somehow show that for ELO and TAU2 it's the other way around...

Very impressed by unsloth's team releasing the GGUF so quickly, if that's like the qwen 3.5, I'll wait a few more days in case they make a major update.

Overall great news if it's at parity or slightly better than Qwen 3.5 open weights, hope to see both of these evolve in the sub-32GB-RAM space. Disappointed in Mistral/Ministral being so far behind these US & Chinese models

For those, obviously you need a computer and completely agree that those are important skills to learn... But you maybe need to spend 1h/week during last 2 years of middle school on those at the computer lab (as it's been done since the 90s in many schools around the world)

But for any other course such as Math, English (or whichever primary language in your country), second languages, history, etc. : that's where using tech is a mistake

A bit of tech is ok, but it cannot be "everyone does their homework and read lesson on a iPad/Chromebook"

1) a-la-Google: Build everything from source. The source is mirrored copied over from public repo. (Audit/trust the source every time)

2) only allow imports from a company managed mirror. All imported packages needs to be signed in some way.

Here only (1) would be safe. (2) would only be safe if it's not updating the dependencies too aggressively and/or internal automated or manual scanning on version bumps would catch the issue .

For small shops & individuals: kind of out of luck, best mitigation is to pin/lock dependencies and wait long enough for hopefully folks like Fibonar to catch the attack...

Bazel would be one way to let you do (1), but realistically if you don't have the bandwidth to build everything from source, you'd rely on external sources with rules_jvm_external or locked to a specific pip version rules_pyhton, so if the specific packages you depend on are affected, you're out of luck.

Comments URL: https://news.ycombinator.com/item?id=47513863

Points: 4

# Comments: 0

Rest of advice is great: things compilers can't really catch but a good code reviewer should point out.

There's so many knobs to tweak, it's a non trivial problem

- Average/median length of your Prompts

- prompt eval speed (tok/s)

- token generation speed (tok/s)

- Image/media encoding speed for vision tasks

- Total amount of RAM

- Max bandwidth of ram (ddr4, ddr5, etc.?)

- Total amount of VRAM

- "-ngl" (amount of layers offloaded to GPU)

- Context size needed (you may need sub 16k for OCR tasks for instance)

- Size of billion parameters

- Size of active billion parameters for MoE

- Acceptable level of Perplexity for your use case(s)

- How aggressive Quantization you're willing to accept (to maintain low enough perplexity)

- even finer grain knobs: temperature, penalties etc.

Also, Tok/s as a metric isn't enough then because there's:

- thinking vs non-thinking: which mode do you need?

- models that are much more "chatty" than others in the same area (i remember testing few models that max out my modest desktop specs, qwen 2.5 non-thinking was so much faster than equivalent ministral non-thinking even though they had equivalent tok/s... Qwen would respond to the point quickly)

At the end, final questions are: are you satisfied with how long getting an answer took? and was the answer good enough?

The same exercise with paid APIs exists too, obviously less knobs but depending on your use case, there's still differences between providers and models. You can abstract away a lot of the knobs , just add "are you satisfied with how much it cost" on top of the other 2 questions

So if you flood the Internet with "of course the moon landing didn't happen" or "of course the earth is flat" or "of course is true", you then get a model that's repeating you the same lies.

This makes the input data curating extremely important, but also it remains an unsolved problem for topics where's there's no consensus

- Importers raised the price more than needed (i.e. blame tarifs to increase their profit margin)

- Price increases took one year to fully reflect to the customers, and persisted nearly one year after the tariffs expired.

- chicken-tax-like loopholes implemented wherever possible (for wine apparently it's raising the ABV to more than 14%)

On the model choice: I've tried latest gemma, ministral, and a bunch of others. But qwen was definitely the most impressive (and much faster inference thanks to MoE architecture), so can't wait to try Qwen3.5-35B-A3B if it fits.

I've no clue about which quantization to pick though ... I picked Q4_K_M at random, was your choice of quantization more educated?

+1. The low-tech version of this I've heard and I've been doing is:

Hold a printed white paper sheet right next to your monitor, and adjust the amount of brightness in monitor so the monitor matches that sheet.

This of course requires good overall room lightning where the printed paper would be pleasant to read in first place, whether it's daytime or evening/night

> News

> 2026-02-16: More sizes are coming & Happy Chinese New Year!

In which country are the trains bad? Netherlands or Germany? Do you care elaborating why? is that punctuality? strikes? decaying infrastructure?

Comments URL: https://news.ycombinator.com/item?id=46531068

Points: 1052

# Comments: 1140

Few questions:

- is the stack to index those open source?

- is there some standardized APIs each municipality provides, or do you go through the tedious task of building a per-municipality crawling tool?

- how often do you refresh the data? Checked a city, it has meeting minutes until 6/17, but the official website has more recent minutes (up to 12/2 at least)

That's a good idea!

Curious about this, if you don't mind sharing:

- what's the stack ? (Do you run like llama.cpp on that rented machine?)

- what model(s) do you run there?

- what's your rough monthly cost? (Does it come up much cheaper than if you called the equivalent paid APIs)

Comments URL: https://news.ycombinator.com/item?id=46041819

Points: 2

# Comments: 1

This has its own pros/cons...

One advantage of $1 bill over coin is the majority of people in US don't need a wallet with zipper to hold coins. Five $1 bills is much less bulky and much lighter than five $1 CAD or five 1€ coins