mtone

Firefox's AI Chatbot feature only proposes one open model provider (Mistral) and zero local option. They're not walking the talk.

It was trained from scratch by Cohere. They're the only Canadian AI lab - I'm glad they're releasing open weights and I wish them luck catching up!

I am using the `voipmonitor/vllm:lucifer` docker from the RTX6K discord community discussed at the same link the other commenter posted. It is based around this PR https://github.com/vllm-project/vllm/pull/43477

Here's a DeepSeek-V4-Flash benchmark on 2X RTX Pro 6000:

  - Prefill: ~10K tok/s
  - Decode: 190 | 375 | 980 tok/s (for 1 | 4 | 16 concurrent requests)
  - GPU power draw during benchmark: Average: 585W | Max: 849W | Limit: 1200W with undervolt. Idle PC is 125W.

Electricity-only (@ USD $0.08/kWh)

  Usage          | IN price  | OUT price | Monthly cost
  Concurrency=1  | $0.040/M  | $0.080/M  | $8.65 to $38.88 (5% to 100% active)
  Concurrency=4  | $0.024/M  | $0.044/M  | up to $48.67 (cheaper per token but higher power draw)

A) Breakeven API pricing (est. 2B IN + 1B OUT throughput/month):

                        IN price    OUT price
  Self-hosted           $0.121/M    $0.363/M
  OpenRouter (budget)   $0.098/M    $0.196/M
  OpenRouter (DeepSeek) $0.140/M    $0.280/M

    1 user: $563/mo (oh, hai)
    25 users: $23/mo
    100 users: $6/mo

You're forgetting a critical factor: concurrency. If a given hardware serves a single request at 150 tokens/s, it can also serve 20-30 requests at 100 tokens/s. Suddenly your $5K becomes $100K/month, enough to recoup the cost of the hardware in a year or so.

The reason it works: each time you read the model (memory bound) to calculate the next token, you can also update multiple requests (compute bound) while at it. It's also much more energy-efficient per token.

[1] https://aimultiple.com/gpu-benchmark

Thanks!

There's a heading control to include rotation in link: https://earth.google.com/web/@3.63731074,-23.1618975,-2690.7...

Does privacy of Netflix ratings matter? The issue is not “Does the average Netflix subscriber care about the privacy of his movie viewing history?,” but “Are there any Netflix subscribers whose privacy can be compromised by analyzing the Netflix Prize dataset?”

Well said.

For this type of repetitive application I think it's common to "fine-tune" a model trained on your business problem to reach higher quality/reliability metrics. That might not be possible with this chip.

Good question. That was for my MS account/licenses and some Azure stuff. I use Google Authenticator for most things.

Thanks for the link, I'll take a look. I might just move it to a secondary device first.

Just looked - Microsoft Authenticator doesn't appear to work. I might be able to get off of it but it will take some prep. My banks are supported so that's good.

if you could exclude all of the R&D and training costs

LLMs have a short shelf-life. They don't know anything past the day they're trained. It's possible to feed or fine-tune them a bit of updated data but its world knowledge and views are firmly stuck in the past. It's not just news - they'll also trip up on new syntax introduced in the latest version of a programming language.

They could save on R&D but I expect training costs will be recurring regardless of advancements in capability.

Recently llama.cpp made a few common parameters default (-ngl 999, -fa on) so it got simpler: --model and --context-size and --jinja generally does it to start.

We end up fiddling with other parameters because it provides better performance for a particular setup so it's well worth it. One example is the recent --n-cpu-moe switch to offload experts to CPU while filling all available VRAM that can give a 50% boost on models like gpt-oss-120b.

After tasting this, not using it is a no-go. Meanwhile on Ollama there's an open issue asking for this: https://github.com/ollama/ollama/issues/11772

Finally, llama-swap separately provides the auto-loading/unloading feature for multiple models.

Do you really need a H200 for this? Seems like something a consumer GPU could do. Smaller models might be ideal [0] as they don't require extensive world knowledge and are much more cost efficient/faster.

Why can't you build this today?

[0] https://arxiv.org/pdf/2506.02153 Small Language Models are the Future of Agentic AI (Nvidia)

Related discussion on Anubis: https://news.ycombinator.com/item?id=43427679

I use Readdle Documents to sync PDF folders with my server PC via FTP. Free version supports PDF highlighting & simple annotations, basic file management, and automatically syncs back everything.

Assuming it's correct, I think this answer explains it well: https://stackoverflow.com/a/31996121/283879

Basically, yes it may write snapshots per-file (never per-commit) locally but there is a separate routine to transparently repack the whole thing with deltas.

There was talk about introducing type syntax as valid but ignored in the JS language, making TS valid JS.

It would take forever to become mainstream but if node and major browsers started to support this tomorrow, along with ESM modules we could drop TS compilation and bundling entirely during development, safely publish npm packages as TS (even a bundled TS) and simplify tooling for monorepos, IDEs, etc.

Unfortunately that wouldn't solve dealing with templates like JSX/TSX or future language syntax/features.

https://devblogs.microsoft.com/typescript/a-proposal-for-typ...

They're turing-complete and modular so it's not really about what they can or cannot do.

Testability, tooling and the open-source ecosystem and either bad or non-existent. Writing PL/SQL is the worst environment I've worked in. That database sent emails, processed CSVs scheduled jobs, etc. yet there was still a web app to maintain next to it.

They're OK for certain things like essential triggers or performance-sensitive functions, but I would never deliberately put app logic in there. Major red flag.

Just upgraded to Windows 11 (for its HDR features) and weather is now part of the "Widgets" bombarding me with ads and poor news sources. I genuinely tried to customize my "feed" but it's all junk -no reputable sources whatsoever- and I didn't find a way to remove them.

So the only sane course of action was to disable widgets altogether while I still can. And now I don't have the weather anymore.

In a monorepo with project references where each symlinked (not paths) package has a src/ and output lib/, I've always had issues with VsCode incorrectly proposing to import from src/ over lib/ from other packages. Never got it to work just right.

Does anyone know if the new package.json resolution settings can help resolve these issues?

We're not on 18 yet but things are clear, thanks!

I notice that ReactDOM.render is also deprecated.

In our embedded/plugin component scenario where we are given a <div> to load in, it appears we should replace our current pattern ReactDOM.render(React.createElement(... with createRoot(_jsx(....

it has by far the best vertical tab implementation of all browsers (someone please copy this)

TIL. I think it's still far behind TreeStyleTabs (TST) on Firefox. Missing for me:

- No nested groups. I know it's called vertical and not tree, but branches offer more context when navigating deep.

- Groups are standalone containers, while in TST the group is the root tab. This saves space but also allows to close/CTRL-F4 a tab to close the group/branch.

- Open in a new tab adds the tab at the end of the group, loosing context. There might be an extension for that.

- Closing a group and all its tabs requires to open a context menu.

- No automatic collapse behavior when selecting other groups and there are lots of tabs open.

- Open in a new tab doesn't create a group automatically, they must be manually initiated. Killer feature for me: by the time I realize I should have created a group it would be too late to bother assembling one.

- Scroll-wheel up/down doesn't quick switch tabs. Killer feature for me.

- Drag'n'drop zone to create a group VS reorder tab action is very small and hard to hit.

Nice feature though. I'm kind of amazed they bothered to implement this yet flat out refuse to re-enable the vertical taskbar lost with Windows 11.

I close a door or put a box in its way. No app.

When I check my recommended feed it's all stuff very closely related stuff to my subscriptions and recent views, and some useless shorts. It's just an echo chamber at this point.

If I'm looking for potentially interesting content outside my core subscription views, I'm better served by turning on FM radio.

Both bigger than bog-standard CRUD apps, but there is:

https://github.com/dotnet-architecture/eShopOnWeb - Demo app from MS

https://github.com/jellyfin - A plex alternative

Recently Akka [0], a 12K star project.

[0] https://github.com/akka/akka/pull/31561

Thanks. I really like the presentation/animation style.

I add stuff in FF Group Speed Dial [0] under a variety of headings. I try to stick to ~100-150 links in there, deleting entire headings once past their usefulness.

They're no good as a long term knowledge base however -- search is too limited.

[0] https://addons.mozilla.org/en-US/firefox/addon/groupspeeddia...

and probably seems [..] foreign

Yes, it's French for "free" after all.