ComputerGuru

No, that’s just white labeling. Like you said though, they can switch manufacturers and pull the rug from underneath you at any time.

My example is like if you opened the Amazon Basics box and found Eneloop branded white/black/blue batteries directly.

Amazon Basics is incredible in this regard, they’ve optimized SKU identification down to a pipeline. They’ll essentially randomly pick items off their internal list of highest netting sales and test them to see how dependent they are on brand name recognition and price-quality signalling. To do this as efficiently as possible, they simply purchase a few hundred units of a high quality product in the space, stick it in an Amazon Basics box and list it on their site under their Amazon Basixs brand at a price they feel they can achieve via white labeling, and wait to see how it sells. The use of high quality items (with quality above what can actually be had at the listed price point for the duration of the experiment) means they are really only testing the user base’s willingness to forgo a brand name for the category in exchange for a discount. If it sells well, they then work on sourcing it in bulk as a white labeled item “for real”, while if it sells poorly they simply delist and move on.

I (used to) buy pre-spliced/terminated fiber optic cables with some frequency from Amazon and came to be familiar with the brands and their quality. One time while shopping for some fiber optics, I saw Amazon Basics-labeled OM-3/OM-4 MMF cable at a very tempting price, so I purchased some to see if it was any good.

To my utter shock and surprise, when I received the trademark plain cardboard boxes with the Amazon Basics label on them and proceeded to open them, I found that I was sent boxes of cables still factory wrapped with labels that clearly read “Corning Optical” – which if you know anything about optical fiber, was pretty much the premium brand in the game. I should have stocked up because the next time I went to order I found out their experiment had ended and they no longer sold “Amazon Basics” finer cables.

Yeah, I’m similarly not sure how to square away claims of “trained 100% exclusively on synthetic data” with the fairly obvious fact that it’s derived from an open model, almost all of which are trained on a mix of scraped and data and distilled traces.

Your last bit about it being different is a known issue with models that have been trained on purely synthetic data, no?

Wow, gpt-5.6-Luna was already a fairly unbeatable bargain and now gpt-6-luna is both cheaper and better. And they did a phenomenal job getting gpt-6-sol to max out right where Astra begins; funny how they just so happened to avoid cannibalizing their best model while still being quite cost-competitive near the frontier.

At least it sounds good on paper, the the graphed results do give me pause as it seems the lower cost might come from a slightly nerfed base model combined with more thinking, going by the more erratic scoring curves and the lower no thinking baseline score. I’ll have to try it out but I really hope they haven’t nerfed Luna/Sol to make this price point possible!

Funny. Look up errata AMD-SB-7055: RDSEED Failure on AMD “Zen 5” Processors.

Zen 5 rdrand16/32 return zero with CF=1 on entropy exhaustion and their recommended approach directly leads to the issue you observed: treat all-zero result of rdseed as if cf=0 (failure) and re-roll the dice, effectively recreating the zen 1/zen 2 issue all over again!

They say this might be addressed by a future microcode update… meaning there’s a chance they’ll just patch it to do just that in software. Maybe that’s how they got into this mess in the first place?

Also, am I a complete idiot or is asserting the relative distribution of a mere 64k possible results a rather easy black box validation test that I would’ve assumed they’d be doing? When I used to write cycle-accurate emulators in the past, that would have been an obvious test to include. This isn’t some arcane instruction no one uses or a really complicated case with deep dependency and/or timing issues; it’s like getting rdtsc wrong.

I have been told that ZDR is available upon manual request.

That’s if you have disparate prompts and don’t want to actively select a model. If you’re developing a pipeline, it’s a terrible idea. You want to choose a model, validate it, then stick to it.

Your sensors need calibration, then! This one is as obvious as they come.

How do your path-only rules handle the many approaches for loading a file but making it appear to have a different path, such as bind mounts for one example?

Nice work! I wonder if you could (or did?) choose a more median shade for each color, especially blue? It seems, going by the comparison page, that most flags actually call for a less royal, more sky shade of blue, so you could use the median (on a linear RGB scale, or XYZ) shade instead as the designated representative for that color?

I noticed Rwanda seems to have the wrong aspect ratio, though you are able to encode that arbitrarily, no?

If you're on Windows (where Microsoft famously chose they would not get involved with flags and show country codes instead), execute this in dev tools or save it as a javascript bookmarklet to make the emoji flags show up:

    javascript:(function(){if(!document.getElementById('twemoji-styles')){const s=document.createElement('style');s.id='twemoji-styles';s.textContent='img.emoji{height:1em!important;width:1em!important;margin:0 .05em 0 .1em!important;vertical-align:-0.1em!important;display:inline!important;}';document.head.appendChild(s)}const r=()=>{const o={folder:'svg',ext:'.svg'};twemoji.parse(document.body,o);if(!window.__twemojiObserver){let t;const ob=new MutationObserver(()=>{clearTimeout(t);t=setTimeout(()=>{ob.disconnect();twemoji.parse(document.body,o);ob.observe(document.body,{childList:true,subtree:true});},300);});ob.observe(document.body,{childList:true,subtree:true});window.__twemojiObserver=ob;}};if(window.twemoji){r();}else{const sc=document.createElement('script');sc.src='https://cdn.jsdelivr.net/npm/@twemoji/api@latest/dist/twemoji.min.js';sc.crossOrigin='anonymous';sc.onload=r;document.head.appendChild(sc);}})();

It sounds like you lossily re-encoded lossy JPEGs to AVIF, am I reading that right? If so, you're automatically suffering from generational loss there, it can't be the basis of any coherent argument.

Damn bots copy and pasting a single sentence from the article as a comment.

That mostly doesn’t count, since humans still regularly have kids of their own in their forties (so the direct link to natural selection would overshadow any lower-pressure grandmotherly effects), so you’d have to push the window out to the post-menopausal period, which coincides with the period in which various cancers begin to be an issue.

> I wonder if whales survive to an equivalent of human old age (where we can be pretty fragile and still survive for decades).

They do and this is well-documented. They're one of the few (only?) other species where we can directly see the role matriarchs (grandmothers, either past their reproductive ages or even undergoing menopause) have on the development of the youth, and it's been used to explore the role of living post-reproductive ancestors might have on natural selection (which was previously not thought to be affected by it).

Maternal grandmothers have even been found to ~spontaneously regain their milk to help nurse infant whales, iirc.

Maybe. But whales don’t get cancer either, and whale metabolism is… impressive (well their resting heart rate is lower than ours but they grow orders of magnitude faster, to a point, and sustain themselves gorging on literal tons of food).

So when will we get tier 1 debugging support in Visual Studio?

Already available via the API as deepseek-v4.1-flash-expires-on-0910, with vision.

It’s very much an embodiment of the law that says to monitor a system is to interfere with it; it would be very hard to say they “predicted” general suicide attempts vs they set up a scenario that allowed them to nudge people into such actions given everything.

Instagram is actively hostile to good ui. They constantly go out of their way to break extensions that add a video scrubber.

No easy answer! You’d have to have a myriad of different test suites that as closely resemble your real world usage as possible. Or ship it behind a gate and let your users give you real world feedback if you’re at SQLite (not turso) scale - they’ve done that with a lot of ambiguous optimizations.

> Mostly true but as someone else pointed out it’s still better to issue a single large multi-page contiguous read than the same read broken down into separate requests

That’s the point I was ultimately making.

OpenAI has separate dials for verbosity and reasoning_effort (but could still do a better job).

Interesting article but it gave me a bit of a panic attack. Benchmarking (with TPC or otherwise) is NOT the way to determine the correct approach here; that is strictly only to be used for databases (typically RDBMS) effectively “owning” the complete hardware they are running on. An embedded database might be used in that manner if it’s operating as the backend for a pure crud application that performs ~zero server side rendering, parsing, validation, etc and is essentially just an async http-to-SQLite interface. But more likely than not, an embedded db will be used and deployed on machines (not necessarily even servers) serving many a purpose, and need to perform best both within the confines of the resources available to the machine and in relative terms, necessarily making tradeoffs that might sacrifice performance for “value” in terms of CPU or memory usage.

This isn’t just with regards to benchmarking, it’s an essential consideration *any* time you are taking ownership of the cache away from the kernel, which is the only piece in the stack that has viability into the global state and can be trusted to give back memory under pressure to ensure everything plays nice together. It’s not limited to just databases or even just memory, for example FreeBSD has had greater than its fair share of issues that trace back to the ZFS having a separate cache from the kernel (despite the much tighter integration between the two and presence of various mechanisms to address pathological cases). You can also refer to any comparison or benchmark between the use of spinlocks vs mutexes: spin locks consistently perform better in/on (micro)benchmarks but are almost always actually the worse choice in the grand scheme of things because the benchmarks falsely assume complete and uncontended ownership over system resources.

This isn’t even io_uring specific and I’m hardly the first to bring this up in the context of O_DIRECT.

I think that’s only true on paper; in practice it’ll be true only when you have competing reads (at a thousand-foot view) and it might be possible to algorithmically bundle a portion thereof. It originally let SCSI controllers attached to spinning rust HDDs optimize physical manipulation of the disk heads to optimized queued reads of data in a “traveling salesman” sort of way, but modern nand flash can only internally read a full page at a time (which may be much greater than even the apparent physical sector size) anyway and with a strictly constant cost regardless of the “physical location” of the data on the non-existent platter. Old drives had optimization constraints like higher sequential read speeds at the outside of the platter (more bytes per physical rotation) and extremely pathological cases for data written to the innermost tracks of the platter. Individual requests were much finer-grained and the latency was much more varied, so a request from app/thread X for as little as 512 bytes from one location could be cheaply piggy-backed on an existing request from app/thread Y to read multiple megabytes from a physically proximate source that would otherwise have seriously delayed or starved the queued waiting read while the outstanding request was serviced.

In fact, one consistently sees higher bulk IO numbers when using physical media that has been formatted with a large sector size compared to the old 512 byte fixed emulated size. You’d routinely see lower latency and higher IOPs with 4kn (HDDs or SSDs) than you would with 512e disks, even with SCSI or AHCI controllers that featured similar pipelining support to today’s NVME controllers (or even if you place a spinning rust HDD behind NVMe today!).

FYI/fwiw Fish shell autocompletes branches sorted by last commit date when writing out git commands. It also provides completions for commitish objects grouped by class, giving precedence to the usual targets first, with each group sorted by its own sort key. Handy!

I am merely pointing out additional revenue streams for Apple here. They make money off the hardware and again off the financing. Car manufacturers and have much lower margins and finance via their financial arms with much less predatory terms than your average credit card. The zero percent APR is very attractive, and easy to convince yourself to go with something above what you can afford. Times are good for Apple.

Not even. They log the IP and your ISP (or VPN provider!) knows the name/address of the person using that ip at that time.

The guardrails that prevent generating adult/explicit/whatever content are a completely separate step. This has nothing to do with that.