My point was more of an exercise to point out that finance is about mutating resources. A lot of cash can be a good thing or a bad thing. Same for debt. There’s nothing inherently bad about levels.
Eep, I guess it depends on what kind of learner you are. For me, the notes being in the book, and the book being somewhere on my shelf spatially encodes it into my mind.
Offloading things into a centralized store according to a system not only causes me anxiety but it doesn’t have the same spatial kick.
A corollary to this is, when reading books, mark them up! Don’t be precious with them. Destroy the spine so it’s easier to read, fold pages, write the important stuff inside the front cover of the book. Cross out stuff you disagree with, draw a big circles around the stuff that resonates.
Not only does it make consumption a lot more engaging, it makes a revisit of that book extremely rewarding.
Don’t get caught up trying to figure out the right notation or highlighter color for a line. Just mark them up and later it’ll make sense.
Edit: I think the broader point that some of you may be missing is that I’m not prescribing a particular form of notetaking. I’m saying that reading is made more rewarding with active engagement. How you best engage is up to you.
There are many reasons to use subsidiaries for things like this, like to invite outside investment, ringfence risk, cede operational risk, and many more.
This is all like CFO 101 type stuff, and not nefarious. I find it amusing that people assume the worst for things they understand little about, rather than trying to learn.
Maybe the best way I can explain it to the programming crowd is this: imagine how ridiculous it would sound if outsiders were saying that Google was on the verge of collapse because its codebase has billions of lines of code.
> These companies have valuations reflecting a debt light business.
Sorry, but this doesn’t make sense. The valuations of these companies reflect their growth.
In finance there’s nothing inherently virtuous about a “debt-light business”. It’s all an allocation decision based on how you expect to grow relative the cost of that growth.
Try and reframe it: are cash-heavy businesses given a premium?
I understand why you would see it as excessive brand promotion, but feel free to do the same for yourself.
There are a handful of people who post their own flavor of benchmark on releases and I don't mind them because they open themselves up to feedback about the benchmark, which is valuable meta discussion in and of itself, and they provide continuity and familiarity.
I wonder how big the Pro model is that Google is using behind the scenes to train these smaller ones.
Going on baseless speculation, the lack of accompanying pro models with these flash releases either means: 1) the model is too big to be economical, 2) google doesn't have the compute to serve the big model, 3) their big model has too many alignment issues to serve to the public.
edit: looks like benchmarks are up on https://artificialanalysis.ai/models/gemini-3-6-flash. It's solidly middle-of-pack. However, if you want to be most fair to flash, look at the intelligence vs time per task and intelligence vs outputspeed benchmarks. This is a very fast model.
edit 2: I use antigravity from time to time and in my experience, 3.5 flash is an underrated model, so long as you know what it's good for. It's very good at frontend (much better than gpt 5.5) and it's fast, so it's a great tool for iteration. I expect 3.6 to be no different.
I've been thinking about this too much because it's so ridiculous and funny that I had to at least try wrapping my head around it. My best guess is this:
If you look at past snapshots at archive.org, you notice that the meta keywords are growing like an append-only list, which means it's probably part of some messed up seo pipeline. The other clue is that it's stuffing the meta keywords which apparently only Yandex uses as a search signal[0].
The list has 3882 entries. A lot of them are clustered and look like auto-complete results. But a bunch of them look like hyper-specific, misspelled search results (e.g. "145 gwen rd cheshire ct"). Google's webmaster tools doesn't provide distinct queries like that, but yandex's does[1].
My best guess of what's happening is that Qwen is monitoring it's search queries in Yandex, dumping that list into a serp service that scrapes yandex's autocomplete suggestions, and then taking that list and dumping it into their meta keywords.
It explains the urls in the list (ppls using search engines like address bars), the seeming fixation around certain topics (which usually starts with a misspelling), and the random one-off queries.
I've been thinking about this too much because it's so ridiculous and funny that I had to at least try wrapping my head around it. My best guess is this:
If you look at past snapshots at archive.org, you notice that the meta keywords are growing like an append-only list, which means it's probably part of some messed up seo pipeline. The other clue is that it's stuffing the meta keywords which only Yandex uses as a search signal[0].
The list has 3882 entries. A lot of them are clustered and look like auto complete results. But a bunch of them look like hyper-specific, misspelled search results (e.g. "145 gwen rd cheshire ct"). Google's webmaster tools doesn't provide distinct queries like that, but yandex's does[1].
My best guess of what's happening is that Qwen is monitoring it's search queries in Yandex, dumping that list into a serp service that scrapes yandex's autocomplete suggestions, and then taking that list and dumping it into their meta keywords.
It explains the urls (ppls using search engines like address bars), the seeming fixation around certain topics (which usually starts with a misspelling), and the random one-off queries.
That's hilarious. These keywords are applied globally, even on pages like https://qwen.ai/usagepolicy, where they very kindly ask you not to use their products for sexual content.
I’m grateful for this team. Jellyfin is a great product. I’ve been using llm + Tailscale + Jellyfin to manage my media box and I couldn’t be more happy. I’ve got perfect metadata, perfect organization, and multimodal search on top of it. What a dream.
“How does Kimi Work protect my privacy when accessing local files?”
It doesn’t protect your privacy. It’s like asking, “how do I know you’re not spying on me?” And getting the reply “we cannot physically enter your house.”
side: https://hcker.news
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