Scene_Cast2

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.

IIRC the original author of the Lottery Ticket Hypothesis now disavows that idea.

One intuitive way of looking at it is like so - let's say that you have a gaussian-looking plot. You want to fit a gaussian. You have a stupid simple model where you can slide your gaussian left and right.

If your initial starting point happens to be roughly within range, great, your optimizer will take care of it for you and slide it into the correct place. If you're too far, too bad, no meaningful gradient.

Instead, neural nets give you the option to spawn a gaussian anywhere you please. In this case, no sliding is necessary, but it comes at a heavy parametrization cost.

Yes it would. Or, rather, labeling (not extra tokens).

Really neat findings.

I've personally had a line of thought where you bake in the role into the token. Basically have an embedding (same dim as token dim) for each role, add it to each token. This adds an unambiguous, unspoofable tag.

I ran this with a tiny Shakespeare model (not representative) and had a freeform embedding for each speaker. I ended up with a neat similarity map between every character. (I don't think the map was very informative for several reasons, but that's outside the scope of a small HN comment)

I think this also stems from ML being more like biology or alchemy and less like math or programming (where you can get down to the first principles, abstractions are rock solid, and non-determinism is limited in scope).

Krazam already has a video covering this exact idea.

On my (admittedly weird) setup, GPT-5.5 Pro times out.

The reading is off because the thermistor resistance also depends on applied voltage, not just temperature. LLMs couldn't get this even after feeding them multimeter voltage readings, not just ADC readings. They went into guessing much more esoteric things like ADC switched-capacitor input current, burnout-detect current sources or IDACs left enabled, board leakage, leaky cap, etc.

I'm personally heavily testing LLMs on electrical engineering problems. I'm finding that it's not meaningfully better at figuring out what's up than the other models.

To give you an idea - here's a very abridged summary of one sample question (originally a full paragraph): I have a voltage divider with a precision resistor and a thermistor, my voltage reading is off by 17%, where's that coming from. None of the models I tested (including Opus 4.8 and Fable 5) could figure it out.

The dev boards are already up for sale. I'm personally looking forward to the modules being stocked on LCSC, no idea when though.

This is the PCPartPicker chart that I monitor: https://pcpartpicker.com/trends/price/memory/#ram.ddr5.5600.... - $900 for 2x32GB, used to be $200 a year ago.

Just beware that for some people (myself included), it causes stomach issues (quite intense in my case). There are mitigation strategies (slowly build up the dose, use more water, take with food, split up the doses across meals, and consider using the less studied HCL variant).

I was sort of hoping that they were bootstrapped or at least non-VC funded. I'm wary of them introducing consumer-unfriendly revenue-generating schemes.

Exactly the same thoughts here (I've been looking into FCC part 15 myself too). And IIRC nRF has some pre-cert stuff to avoid going through the full gauntlet.

I'm guessing he's using the fact that dev boards are excepted (as opposed to final products). Somewhat unfortunate though, as these do end up in a lot of people's boards.

I get my USB-C connectors at around $0.08 at low volumes (LCSC).

I remember Last.fm's value proposition was 1) discovery and 2) community. (1) is (mostly, for most people) covered by "feed" algorithms of Spotify and YouTube.

I wonder how they're going to position themselves now.

I'm surprised that Perplexity isn't mentioned in the article or on HN. It has replaced Google for all but the most trivial queries. It runs circles around Google for finding anything niche or underspecified.

I use it through OpenRouter - I love how the pricing is per search and isn't a subscription.

There is one very clear example that I ran across due to the reasons outlined in the article. If you have a wavelet and you're trying to slide it around to make it fit, that will fail spectacularly. There are lots of problems that boil down to basically the above.

The neural net answer is being able to spawn a wavelet at any position, as opposed to tweaking the position of an existing one.

Aiming for $350 at launch.

This project doesn't have an online presence yet, just shoot a message to the email in my bio.

If anyone is interested, I'm actually in the process of launching a similar board. Much lower noise (by about 5x, custom AFE), tuned for load cells, 4 simultaneous-sampling channels.

Something that deeply frustrates me, as someone who did R&D on model architectures, is how similar the modern LLM model architectures are to GPT2.

(This is a bit disingenuous, as lots/most of work is spent on the scaling and training side of things.)

What I'm surprised by is that cars are chock-full of ornate, unique parts (cupholders are a good example).

I would have imagined that car infotainment controls would be a small fraction of the BOM, so I've been wondering if it's not really a cost thing. Sort of like small phones or 3D TVs from the early 2000's.