jomohke
The publishers have been saying that their ability to promote books has drastically reduced with the internet, along with changes in reading and information habits.
It seems like a book needs a far bigger push today to rise above the noise of the internet (and people's over-abundance of content to consume), and this unfortunately meant that small publishers struggled unless they "joined together" to make a bigger push.
There's extremely small (self published) books and extremely large hits, but the middle is increasingly less viable, it seems. Similar to films.
The hype people are excited because they're guessing where it's going.
This is notable because it's a milestone that was not previously possible: a driver that works, from someone who spent ~zero effort learning the hardware or driver programming themselves.
It's not production ready, but neither is the first working version of anything. Do you see any reason that progress will stop abruptly here?
https://cloudinary.com/blog/jpeg-xl-and-the-pareto-front
If decode speed is an issue, it's notable that avif varied a lot depending on encode settings in their test:
> Interestingly, the decode speed of AVIF depends on how the image was encoded: it is faster when using the faster-but-slightly-worse multi-tile encoding, slower when using the default single-tile encoding.
I'm quite surprised these are still around as I hadn't seen them mentioned in so long.
I always assumed the phase out of screensavers (and introduction of CPU low power modes) were terminal for them.
I think the high distractability is a trifecta of volume, non-naturallness of the sound (compression etc: feeling out of place in the space) and this point.
> ... the answers are a statistical synthesis of all of the knowledge the model makers can get their hands on, and are completely unique to every individual; at the same time, every individual user’s usage should, at least in theory, make the model better over time.
> It follows, then, that ChatGPT should obviously have an advertising model. This isn’t just a function of needing to make money: advertising would make ChatGPT a better product. It would have more users using it more, providing more feedback; capturing purchase signals — not from affiliate links, but from personalized ads — would create a richer understanding of individual users, enabling better responses.
But there is a more trivial way that it could be better with ads: they could give more quota (and/or better models) to free users, since there's some income from them.
The idea of ChatGPT's own output being modified to sell products sounds awful to me, but placing ads alongside that are not relevant to the current chat sounds like an Ok compromise to me for free users. That's what Gmail does and most people here on HN seem to use it.
I posted a link and transcription of the rest of his "three to six months" quote here: https://news.ycombinator.com/item?id=46126784
> .... and in 12 months, we might be in a situation where the ai writes essentially all of the code. But the programmer still needs to specify what are the conditions of what you're doing. What is the overall design decision. How we collaborate with other code that has been written. How do we have some common sense with whether this is a secure design or an insecure design. so as long as there are these small pieces that a programmer has to do, then a human will be enhanced by AI.
(He then said "and we will eventually reach a place where..." , but this was not in the 12 month prediction.)
Photomator has shown that you can add a lot of professional-level editing control to an Apple-Photos-like interface without making it difficult to use.
Their ML team also seems quite good — for instance, their spot/object removal tool was often more reliable for me than the one in Lightroom, despite being from a far smaller team than Adobe.
(I also feel that Photoshop has reduced in cultural significance in recent years, and that Lightroom is the more significant tool going forward, but that could reflect my own bubble)
It does 200 simple selects, after inserting random data, to match the article's "200 queries to build one web page".
(We're mostly worrying about latency here, so I don't think the exact details of the simple queries matter. I ran it a few times to see that the values don't change drastically between runs.)
SQLite:
Insert Latency: 3.456 ms
Query Latencies:
Min: 0.007 ms
Max: 0.031 ms
Mean: 0.007 ms
Median: 0.007 ms
Total for 200 queries: 1.088 ms
PostgreSQL:
Insert Latency: 26.797 ms
Query Latencies:
Min: 0.023 ms
Max: 0.170 ms
Mean: 0.028 ms
Median: 0.026 ms
Total for 200 queries: 4.361 ms
I've seen latency of 4ms for a single query be normal in the past when running DBs on separate machines, hence 200 queries would add almost an entire second to the page load. But this is 4.3ms total, which sounds reasonable to me.
SQLite is impressively fast here, though as mentioned, this comparison is not optimised in any way. The difference may also narrow if complex queries are needed.
But if we're considering running SQLite, the apt comparison would be with other DBs on the same machine, because we've already decided local data storage is acceptable.
I assume Postgres/MySQL/etc still have higher latency due to IPC overhead, which SQLite wouldn't have — is it significant?
> AlphaProof is a system that trains itself to prove mathematical statements in the formal language Lean. It couples a pre-trained language model with the AlphaZero reinforcement learning algorithm, which previously taught itself how to master the games of chess, shogi and Go
https://deepmind.google/discover/blog/ai-solves-imo-problems...
Github Actions is pay-per-minute-used, unfortunately, so if anything they have a negative incentive to speed things up. Unless people become frustrated enough to switch to a non-bundled provider.
The iPhone 15 Pros were the first iPhones with 8GB. All M1+ Macs/iPads have at least 8GB of ram.
LLMs are very memory hungry, so frankly I'm a little surprised they support such low memory requirements (especially knowing that the system is running other tasks, not just ML). Microsoft's Copilot+ program has a 16GB minimum.
> The wavelength of our orange LEDs is 610nm, putting them outside of the spectrum visible to must bugs, but still well inside the boundaries of human vision. As a result, most bugs will not congregate around our orange light because they simply don’t know it is there.
That colour also feels reminiscent of a camp fire, which I've often thought could be related — a bug wouldn't survive long if it flew into fires.
Famously they divested their ARM-based mobile processor division just before smartphones took off.
The new CEO, as the article mentions, seems to have a lot more of a clue. We just hope he hasn't arrived too late.
I had assumed most of their web content was from Common Crawl, and the older pre-ChatGPT Common Crawl datasets used would still be available. But it looks like Twitter, for one, was not in Common Crawl.
Their ladder (using public data, and hiring humans to classify to taste) is still available I believe.
What's the advantage of re-using a format?
Ecosystem? That won't help SQLite here, who don't have dependencies.
Keep in mind that anyone trying to write a parser for this is also writing a parser for the entire SQLite file format (the only way to access bytes). And it's a spec simple enough to fit on one monitor.
Design?
BSON (and others?) seem to have different goals.
SQLite's format seems to minimise conversion/parsing (eg. it has multiple TEXT types depending on how much escaping is needed; BSON has one fully-parsed UTF-8 string type). BSON is more complex: includes many types not supported by json (dates, regexs, uuids...) and has a bunch of deprecated features already.
SQLite's on disk-format is something they intend to support "forever", and as with the rest of SQLite, they enjoy pragmatic simplicity.