0x457

> Have you built that one application you always wished existed but it wasn't available?

Yes, more than one and I use them a lot. Not being good at design and not liking front-end, among a few other things, work always stopped from from building them before,

> So you've unlocked tremendous value, but you're not earning any money?

No, I also don't make money from my coffee machine, but it provides value.

Neat, I wasn't aware of this tool. I just either run nixosContainer in systemd-nspawn or OCI image in systemd-nspawn.

I'm confused why VM + systemd-nspawn? From my understaing WSL 2 runs a single VM + something like systemd-nspawn per "linux installation", but it runs a VM because it needs linux kernel. Why not just do systemd-nspawn if you alread on linux?

Globally no, but in the US there probably at most 2 providers covering your area + 5G/LTE providers. Electricity - single provider in the area. So if your job is on-site, then entire company stops working when either goes out. You don't really care that another company on the other side of the globe or street continues working.

We already got used to "internal going down globally" when AWS was the main cloud provider. Now its 3, AWS, GCP, Cloudflare, but "portion of internet that you care about is down for the entire world" is a casual thing to happen now.

Zero-shot just means you give it zero examples. Jev lets you add examples, so it's one-shot/few-shot depending on how many you provide. After playing with it, it seems like once you wonder off their guide examples domains - you have to provide examples to get anything useful from it.

IMO you going to get better results by doing a small tune of a tiny model. Making training dataset for it with LLMs is easy, serving it is going to be cheaper.

The comment I originally replied to said: "IMO, 512 should have never been released for desktop CPUs and restricted to servers."

> Not sure why you keep referring to Skylake since Intel hasn't shipped a Skylake-based CPU in years, and the most problematic CPUs currently shipping that lack AVX-512 support are several architectural generations ahead of Skylake, for both the P-cores and E-cores.

I could have swore Intel's first E-cores were skylake based. My bad. Point is that its Intel issue, not AVX-512 issue.

> They still ship huge volumes of CPUs, especially in laptops where AMD is still underrepresented.

They physically shipped them, yes, but the products were lackluster. I don't recall when Intel had a good desktop CPU last time.

> Targeting a discrete GPU is different than not supporting integrated GPUs at all, which would be analogous.

The analogous case would be targeting a specific graphics API feature level, say, D3D 9.x, because that's what the majority of GPUs on the market support. Except game developers somehow figured out that they can support multiple D3D feature levels instead of being permanently stuck targeting whatever the majority happens to have.

So? AVX-512 shouldn't exist because it can't be retroactively enabled on older hardware? or because Intel can't say goodbye to Skylake? I don't understand the logic.

> AVX-512, on the other hand, has been moving backwards since Intel has not shipped any consumer-level CPUs with it for years now.

Good thing Intel hasn't shipped a viable consumer-level CPU in years either. What should never have happened is Intel removing AVX-512.

> Steam Hardware Survey

While it's a good source, it's not a definitive source. Four out of five amd64 machines at my place support AVX-512F, but none of them participate in that survey. And the one machine that doesn't have it... I wish it did.

At one point, Intel integrated graphics were literally the most common individual GPU in the Steam Hardware Survey - Intel HD 4000 held the #1 spot until the GTX 970 overtook it in late 2015. Does that mean developers shouldn't have targeted discrete GPUs either, just because most survey participants didn't have the hardware they wanted to target?

Neither is AVX or AVX2. AVX512 isn't avaiable on some of the latest intel CPUs is purely because intel can't say goodbye to Skylake for some reason.

It could be harmless, but never fine.

deciduous seems like a great way to ensure there is no space left in context for useful work.

Problem with all these automatic memory managment tools is that they can't reliably classify if something is useful or not. Once it start using some tool it will heavily become biased towards using that tool during that session. First you will have decision to use postgres as a database, by the end of session it will start saving "decided to use SELECT query for this method because that is how you pull data out of database".

I think its a tesla issue, not EV issue.

EREV vs PHEV, in the US, is separated by "do you get more range on battery or gas". That's why US version of i3 got electornically limited gas tank to qualify to be called EREV.

My 35 floors building has exactly 2 chargers. Both are FIFO, max 5 hours or you start parking and both are slow af and pay for charge. Charging M3 on one of those is probably a whole day activity and would cost you probably more than if you get a diesel generator running to power charger.

So I might not know those people, but know of at least 10 teslas that aren't charging at home every day, because usually those spots are taken by polestars.

> what are the alternatives?

HEV, PHEV, ICE. All are valid alternatives. Hydrogen cars are stupid though.

I think you're confusing Matter and Thread. ESP32-H* have Thread radio, but every esp32 that has wifi is able to talk Matter over WiFi.

I have virtual power outlets and fans that actually just some process on linux.

A few notes: if you buy non-module package (i.e MCU itself) then you would need to certify it with FCC (or you local FCC alternative) yourself, so most of vendors buy modules. Those vendors don't go to amazon to buy those because these make sense only if they manufacturing their own boards.

Last time I checked digikey had every module version avaiable for every version. You get hundreds of other versions because there are more variations of those.

It still requires some post-processing to add glitches and metal sound, final result is like this this: https://drive.google.com/file/d/1CJkK9nHQNBPMr6sIxVmlq8gaxDG...

I had a version that on validation dataset nearly 1:1 across entire spectrum, but I ended up linking this revision more.

Also had qwen3-tts version, but no matter what I do it sounded more like brat Cortana. The way piper-tts work under the hood make it sound more machine-like.

> This line of thinking is something you will quickly be disabused of once you try supporting software that hundreds of millions of people use.

> And I say this as someone that loves the Unix philosophy dearly.

Unix way would be just to tell users to `ln -s` and stop bothering with this.

Missing the point that they marketing, but so far I saw it perform good at some tasks, and pretty bad on others. I don't think it's all that general purpose they claim it to be.

Some classifiers are tiny - like 2k params, maybe even less.

This whole thing started because I wanted something to help me play Dune Imperium. Even relatively large models with vision encoders couldn't reliably extract the full state of the board. Now that I have ~2k labeled screenshots, I want to train heads on top of SigLIP2 to extract all of that data in one go.

That's how it started. Now the thing supports multiple kinds of datasets:

  Images - currently the Dune Imperium and Bolatro screenshots, with SigLIP2 heads being the next step.

  STT - my self-hosted Linux dictation tool feeds this dataset. I run Nemotron ASR tuned for my voice.

  TTS - for Piper TTS, trained to speak like SHODAN. Trained from data generated by Qwen3-tts + original video games files.

  Text pairs - for a 1.2B model that converts normal text into "what would SHODAN say?"


  FastApply - a Qwen3.5-4B LoRA adapter for doing fast edits.

  Chat threads - all agent/chat threads get saved too, so eventually I can turn the useful ones into a dataset and train a LoRA for a really good Rust-specialized version of Qwen3.8-27B.

  Tool calls (extracted from chat threads) - this is where I want something Jev-like, mainly to add an auto-approval mode to my agent harness.

A model router isn't planned because I'm trying to gear everything toward self-hosting, and there just isn't that much to route between. I’ll probably build something Jev-like for smart-home control, though.

The FastApply dataset is already ~20k entries, with the majority of outputs being 8k–16k tokens. The STT dataset is roughly 30 hours and growing.

Basically, the whole thing has turned into a Collect -> Distill -> Train pipeline for whatever I happen to need.

I built a whole thing that collects data, trains classifiers, exports models and dataset just for that. Claude writes me a terraform file that contains shape of the classifier and dataset. For images it can create datasets based of another dataset (crop this region from images that have these labels).

Originally it was so I can label data to fine-tune a VLM, but now a few tiny classifiers that run in milliseconds on cpu.

Now its collecting data to make a domain specific BERT and do what Jev does.

I remeber photographers talking about X has "not very nice bokeh" probably since I met a first photographer when phones were monochrome and had no camera. No one beside photographer cared then, no one will care today.

To most people its just blur and they don't care that it is not just blur.

Yeah, its not like companies ever colluded to keep prices high...

https://en.wikipedia.org/wiki/DRAM_industry_price_fixing?use...

It used to be once in awhile someone posts about a neat rust library (that they usually made) plus a bunch of "I did X in rust". Now with AI, its just endless slop that barelly works if it works at all.

When I have to do a temporary solution, I purpusly make it as junky as possible and as PITA to use as possible.

Why? Because dictionary here is smaller than your idea of dictionary for brute force?

Yes, but that won't be a hallucination. Same root cause, but IMO it was pretty clear they meant "given options red, green, blue this will never say purple", doesn't mean it still can't misclassify green as blue.

Why would you think "can't hallucinate" means "can't pick wrong probability of an option" ?

1.76 bpw number is kinda misleading if you compare it directly to IQ2/Q2. The encoding is ternary, but the quantization procedure is way more sophisticated than "round Qwen weights to {-1,0,+1}."

They rotate the weights into a quantization-friendly basis first, then ternarize with per-group scales and error compensation.

It's ARM with extensions.