yalok

Could star link add some cameras on the back of those satellites and make the detection actually much better?

is ultrasonic scanning completely harmless for developing baby? when my wife was pregnant, I remember they wouldn't recommend too frequent ultrasonic scans...

Impressive speed up at the cost of quality - a bit lower quality than 12B model, but multiple times faster…

if there're some specific tests/evals to satisfy that an agent can test by itself, it can easily iterate for hours. And this time also includes running those tests/evals, which may not be small.

0.8GB is for text only. It's more like ~1.1GB if you include video/audio encoder

Could just be more tests? :) Which is good for code quality in general and reduces support burden, but doesn’t lead directly to more features

imo, it really depends on what you enjoy doing. Regardless of AI, choose software development if you like to build complex systems that no-one has built before, and have enough patience to dig deep / debug things to make them work exactly as you expect.

For some of us here, it's just what we love to do, no matter what tooling is available. When I first started building my own software long time ago, it was a very slow Basic and fast raw machine codes (in octal system, PDP-11 like CPU). I enjoyed it not because of tooling, but despite of it.

Over the years, the tooling was getting better in general, which allowed us to build increasingly more complex systems.

With AI, we will still be creating & debugging. It's just that before AI, I had to spend 90% of my work on mechanical not-so-fun things to get things to work, and only 10% on fun algorithmic-intensive parts. But with AI tools, this ratio seems to change, and all kind of boilerplate code & algorithms can be written much faster by AI, hopefully leaving more time for us to work on creative part of the work.

just saw a relevant HN post about this very matter in Berkley - https://news.ycombinator.com/item?id=48392004

I heard they do CS exams on air-gapped machines at UC Berkley. Use of AI to do CS homework is strongly discouraged, and if someone cheated, it shows up at the exam...

but ASML is in Europe - so they hold at least some critical part of the stack.

First git itself is distributed and built for scale.

there're different dimensions for "scale" - like handling large monorepos, orders of magnitude more commits, tighter requirements for latencies (for agentic use, e.g. for agentic history navigation)...

were you in that room where Adam was making that call? No? didn't think so...

Just give people some benefit of doubt. There're much simpler ways to explain certain things that suspecting some universal evil in every move...

There're tons of ways to fine-tune WebRTC that it wouldn't corrupt audio in poor network - it has all of the controls to smoothly trade-off latency vs quality. Not just NACKs - FEC, disable PLC/Acceleration/Deceleration, larger JB (tons of parameters) etc.

Most of the glitches I heard with OpenAI's Voice were not WebRTC related - but rather, to my ear, they sounded more like realtime issues with their inference - which is a very different component to optimize.

it doesn't necessarily have to be tied to monetization & privacy directly.

It may just be that ROI doesn't make sense: very few user out there truly care about (or even understand) E2EE, for quite some users it creates an inconvenience & support incidents (harder to move from device to device, forgot your passphrase - lost your history, new joiners to a group chat don't see previous history, etc), it requires a significant additional engineering effort to just maintain it, many new features get shipped much slower because of it...

building new features on top of E2EE is genuinely hard, and I've seen many companies struggle to keep innovating while staying strictly E2EE.

Having seen multiple leading messaging/VoIP stacks from inside, the amount of engineering spent to work around various limitations of E2EE in real prod scenarios is insane, and even for simple every-day-use features metrics don't compare to the metrics of the same feature running without E2EE.

amazing to see Claude Code top models still way above all other models for C++ & Java, while GPT 5.5 is higher in Python & JS and others. Shows the skew in the training data sets, and maybe the go-to-market focus - with Anthropic focusing on enterprise customers much more than OpenAI?

Matches with my experience with Opus for C++.

C# results are empty - @gertlabs - any ETA for those?

Does this include repos content in BitBucket?

For those in the USA - if you never tried brunost (brown cheese) - look at specialty cheeses of a larger grocery chain (Whole Foods, Safeway, …) - it’s called “Ski Queen” here, and is sold as a perfect cube in red/brown plastic pack.

It’s very delicious.

I was ecstatic when I found it quite a few years ago in a regular store. A Norwegian friend of mine used to send me a brick of this cheese once a year for Christmas, when I was a student, and I treasured it as one of the most valuable possessions :)

Also, fun fact - the reason this cheese tastes sweet is due to caramelization - the milk gets boiled for a long time (hours) to get the brown color and sweetness. So it’s completely natural, zero added sugar ;)

He may just want to buy them, to accelerate things, once SpaceX IPOs

vibe-coded all the way through

Fwiw, this sounds like a healthy discourse - you don’t have to agree on everything, every approach has its merits, code that ends up shipping and supporting production wins the argument in some sense…

This is not special to Meta in any way, I observed it in any team which has more than 1 strong senior engineer.

Tangentially, on this CD policy - it leads to really high p99s for a long tail of rare requests which don’t get reliable prewarming due to these frequent HHVM restarts…

very cool idea. But, time savings are not true for every tool call, and it's not clear to me yet whether this is batch-able; also, intuitively, for most of the models that run on GPU, you'd still want to offload tool exec part to CPU since it's much cheaper...

I'm not fully on board with his world model strategy as the path forward

can you please elaborate on your strategy as the path forward?

is Google using LLM-guided fuzzers that can inspect the code first?

I just had a problem installing iMovie on a MacOS 14 - 11-year old MBP13, perfectly functional otherwise (my 10-year old kid uses it), the original iMovie that used to work earlier, just stopped launching (maybe I need to change some xattrs for it?), and the new iMovie from the App Store can't be installed on such an old OS (why not show the older version there, like iOS AppStore does on older OSes?)...

for Google AI Overview (not sure which Gemini model is used for it, must be something smaller than regular model), looks like search/RAG helps it get it right - since it relies on LinkedIn and Hacker News (!) posts to respond correctly...

as of Feb 16, 2026:

====

Drive the car. While 50 meters is a very short distance, the car must be present at the car wash to be cleaned, according to LinkedIn users [1]. Walking would leave your car at home, defeating the purpose of the trip, notes another user.

Why Drive: The car needs to be at the location to be cleaned. It's only a few seconds away, and you can simply drive it there and back, says a Hacker News user. [2]

Why Not to Walk: Walking there means the car stays home, as noted in a post. [3]

The best option is to start the engine, drive the 50 meters, and let the car get washed.

[1] https://www.linkedin.com/posts/ramar_i-saw-this-llm-failure-... [2] https://news.ycombinator.com/item?id=47034546 [3] https://x.com/anirudhamudan/status/2022152959073956050/photo...

But the regular Gemini reasons correctly by itself, without any references:

==== Unless you have a very long hose and a very patient neighbor, you should definitely drive. Washing a car usually requires, well, the car to be at the wash. Walking 50 meters—about half a New York City block—is great for your step count, but it won't get your vehicle any cleaner! Are you headed to a self-service bay or an automatic tunnel wash?

in ~30 years of my work in DSP domain, I've seen insane amount of ways to do signal processing wrong even for simplest things like passing a buffer and doing resampling.

The last example I've seen in one large company, done by a developer lacking audio/DSP experience: they used ffmpeg's resampling lib, but, after every 10ms audio frame processed by resampler, they'd invoke flush(), just for the sake of convenience of having the same number of input and output buffers ... :)

this. Deep understanding of physics involves building a mental model & intuition how things work, and the process of building is what gives the skill to deduce & predict. Using AI to just get to the answers directly prevents building that "muscle" strength...

their Play store review practices are such a joke. Apps review is a completely obscure process, no clear way to see that the app is in review state, if they reject - amount of information why it was rejected is minimal and you have to second-guess; appealing is not trivial; most of the reviews are done by AI which gets triggered in totally random places from time to time (e.g., in my case, some pictures which looked fine for kids for years and went through many previous reviewed, suddenly seem too violent).