Scene_Cast2

I ended up going with a Synthetic subscription (they host some open-weight models including K3 and GLM 5.3). They're the only subscription that I found that's simultaneously cheaper than token rates, and is extremely transparent about usage and pricing.

The libraries, yes, but the code you write is portable (at least until you get into squeezing the last few percent and switch to Triton / Helion in case of GPU, and even those are decently portable).

There's also Halide, where you write the algo but the framework gets you the scheduling and SIMD.

What about numpy, numba, and torch.compile?

The official page still downloads 1.23.2. I wonder when this will go live, and if the old app will auto-update.

How? I don't see it. (I'm familiar with the ML side, not the combinatorics side.)

How many tokens are you getting, roughly?

I found LLMs circa 4 months ago to be decent at textbook-style electronics, but they choke at anything nontrivial. They approach things like someone that knows the undergrad and grad textbooks, but has absolutely zero experience. No ability to answer questions such as "is this adequate", "do I need a LPF here", "how do I kill resonance without increasing impedance", etc.

I'm guessing that this is due to a lack of RL and data.

Why does it work for Red Bull?

It worsens the supply crunch, no? A unit you use sparingly vs that memory going into a GPU that serves many more people.

If you need that precision, especially for position and not values, why use floats at all?

Explicitly trying to re-identify data that has been de-identified is typically a fireable offence at FAANG.

Accidentally making a machine learning system that happens to (potentially) do it is a different matter.

Oh hey, that's cheaper than Kimi K3! Amusing to see a SOTA OpenAI model be cheaper than a Chinese open weight model.

Fwiw I love K3 and use it as a daily driver. I haven't tried Sol, as I dislike OpenAI.

I made the move some time ago. There are some unpolished parts here and there, but I'm glad I made the move.

Somewhat unrelated criticism of Gmail - during the migration, I found out that there is no way to search for starred archived emails in Gmail. They don't show up in Starred, nor in any search with is:starred. Their star status also doesn't carry over when migrating.

Awesome. I'd love if Flutter could also finally get Impeller on Web (I'm tracking the GitHub issues, seems like there's progress at least).

I personally would like for it to pick up some ideas from Svelte.

In one of their dev surveys, they did float the idea of divesting Flutter.

There's a trade-off between how long you wait and the skill spread of the team. IIRC at the very top, for DOTA2, the pros complained enough for the devs to make a "wait longer" matchmaking mode.

I got a similar looking Stax amp (the SRM series). Also quite satisfying. It has a split volume dial so that you can adjust the volume of each channel separately.

I wonder how much effect the CPU's register remapping has on these results. Also, they ran it on an ancient CPU, but I'm not sure how much that matters.

I love the hackability of Thinkpads circa that generation.

I'm using Kimi K3 + OpenCode. I pay their API pricing, costs about $5 / hour (and chews through ~10 million tokens / hour) during continuous use when I have one or two sessions running and doing their thing.

Can't comment on how it compares to plans (I really don't like the limitations and general shenanigans I see around plans, so I've never tried them).

It is notably slower than Fable / Opus / Gemini, but also vastly cheaper than their API pricing.

Very much agreed. Even more basic than that - memory access patterns are important. The amusing thing is that you end up writing GPU-style code even for CPU. For example - instead of an array of objects, using parquet-style object of arrays is one such trick.

What are some good tools for _authoring_ HDR photography? Lightroom still seems to be mostly SDR. The new Resolve will probably have the tooling, but I haven't tried it.

I meant that they're increasingly converging to be Oppo phones (now running the same OS, hardware is a slightly tweaked Oppo phone variant, etc).

They were one of the brands with unlockable bootloaders and slide switches for mute. Unfortunately the Oppo takeover didn't preserve either.

Written on a OnePlus 8 Pro.

I wonder if this tool can help with EMC compliance testing. My TinySA needs an LNA, so I wonder if this has the required noise floor.

The FIRE net worth seems unrealistically low.

Huh. When I rented a Z7 ii about 5 years ago, I found their Android app to be pretty great. (My next big camera is likely to be a Nikon, in part due to the nice app)

I've spent the last several years of building a very, very fancy scale (well, more of an industrial and R&D lab tool that can be used as a scale).

The reason is that most people use a dirt cheap HX711 or cheaper. A fancier microcontroller doesn't help all that much.

I got to 100dB of dynamic range at 1ksps (1 gram of noise at 100kg max load), so it's very much doable.

I remember a story on HN from a while back. The idea is that the larger the org, the simpler the message and the tool has to be to reach everyone. The comment author was saying that as a junior, his company implemented a "tokenmaxxing" scheme for A/B testing - more tests, better for performance review. He, back then, thought it was stupid. However, it got the desired outcome of everyone being familiar with what experiments are and how to run them.

Not necessarily. I'm a proponent of (admittedly not very popular) methodology of "train, do interpretability analysis, adjust model architecture".

It's not more popular for a few reasons: 1) you first need to train a full general model anyhow 2) interpretability is nontrivial and not guaranteed 3) once you make the architectural changes, you can't commit to that architecture as you might miss out in the future with more advancements 4) with modern transformers, there is limited amount of architectural "play" happening.