yalok

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).

In my experience, anything related to Google Ads - they never reacts to any claims of scam…

Their incentives contradict healthy behavior… :(

is there any good comparison of Hybrid vs EREV efficiency (when main battery is depleted), even with Atkinson cycle ICE for EREV? my understanding was that the main reason for all this complexity in Hybrids was due direct-to-wheel power transfer efficiency, while in EREV there's efficiency loss when converting ICE output to electric current...

thanks! are any of them multi-lingual? what languages may be supported?

how large are the STT and LLM models that you use on-device? are they part of the OS or downloaded with the app?

extremely telling - lot's of tech mentioned there either never reached expected potential or completely obsolete...

Prompts to be much more cautious with the current tech, and invest more time in fundamental stuff (like closer to science).

Not really, in my personal experience and per my friends, most of big companies are pretty lenient about it, except for Apple.

moonlighting is permitted by law in California (companies legally can't prevent you from doing it, iiuc), as long as there's no conflict of interest with your main job...

add to it multiple iterations of having to restart pretraining from some earlier checkpoint when loss plateaus too early or starts increasing due to some bugs…

:) this is exactly how we used to do it even on iOS, back in the days before camera APIs were not made public, but Steve Jobs personally allowed such apps to be published in the iOS App Store (end of 2009) ...

Amazon support family sharing for the same home address - 2 adults and 4 children I think can share the same Prime

My wife and I used to share 1 account, but then I wanted to buy her a gift that had to be a surprise - so had to create a new account and add it as part of the family to the original one…. Then kids grew up and wanted to make small orders themselves, and I didn’t want them to see our order history…

Classic problem of non-atomic stack pointer modification.

Used to have a lot of fun with those 3 decades ago.

The paper only mentions evals for Ruler 4K and 16K - I wish they’d go further and measure for longer context windows. I was wondering if there would be some gain as compared to baseline (no compression) for this method - their results for Qwen with Ruler 16K seem to allude to that - at small compression ratios the evals look better than baseline - which means they are not just improving inference speed/memory, but addressing attenuation dilution problem…

Summary from GPT (wow, not sure about avoiding cheese & milk - sounds risky for calcium/bones health):

Omega-3 fats seem to protect against myopia, while saturated fats seem to increase the risk. Other nutrients didn’t show clear effects.

Foods rich in omega-3 (protective foods) • Fatty fish: salmon, mackerel, sardines, tuna, herring, anchovies • Seafood: oysters, mussels • Plant sources: flaxseeds, chia seeds, walnuts, hemp seeds • Oils: flaxseed oil, canola oil, soybean oil • Fortified foods: some eggs, dairy, or juices enriched with omega-3

Foods high in saturated fats (risk foods) • Fatty meats: beef, lamb, pork, processed meats (sausages, bacon) • Dairy: butter, cheese, cream, whole milk, ice cream • Baked goods: pastries, cookies, cakes made with butter or shortening • Fast foods: fried chicken, burgers, pizza • Coconut and palm oil products (though plant-based, they’re high in saturated fats)

So the takeaway is: More fish, nuts, and seeds may help protect children’s eyes, while too many fatty meats, butter, and fried foods may raise the risk of myopia.