I’ve worked in e-commerce for years and the thing that always slows down the sites the most is 3rd party scripts. Are you addressing this? I couldn’t find anything in the repo.
Ive had websites slow down 10x just by introducing the Facebook re-targeting script for instance
Yes, we've had similar experiences and plan to address them to some extent. In certain cases, like with Facebook, it might not be straightforward, but we want to provide built-in alternatives e.g. analytics specifically tailored for e-commerce that cover ~80% of what you need, with the rest being a trade-off.
I also think Facebook, Google, Hotjar et al. will eventually get better with those scripts.
Fyi regarding cover images: I have built and run a handful of book related websites and Amazon is the easiest place I found to get book covers. You just need the Amazon id and every image is a standard url.
This resonates a lot. For years, I would dive in for a week or two, sometimes I would make it to a few months. Lately, I've just been telling myself no, to keep focus on my start up, but it sometimes feels like I'm squandering this spark of energy you described. I absolutely love your approach of doing the planning in that initial burst rather than just getting some part of the idea working in a day or two.
I think those ultra-productive days earlier in my career account for some of the best learning, but now, over a decade into software engineering, focusing on the planning aspect seems much more prudent.
I've recently come to believe the differences in hypertrophy are negligible between eccentric and concentric focused movements, but can't find any recent, compelling research that says so. There was a 2017 meta analysis by Brad Schoenfeld (basically THE hypertrophy researcher) that showed a pretty significant different in hypertrophy: an average of 10% vs 6.8%.
I know Greg Nuckols from StrongerByScience believes this is mostly caused by lifters, especially untrained lifters which most of the research is on, having spent less time in eccentric phases so there is more opportunity for growth there, but it will eventually plateau.
I'm trying to understand this approach. Maybe I am expecting too much out of this basic approach, but how does this create a similarity between words with indices close to each other? Wouldn't it just be a popularity contest - the more common words have higher indices and vice versa? For instance, "king" and "prince" wouldn't necessarily have similar indices, but they are semantically very similar.
You are expecting too much out of this basic approach. The "simple" similarity search in word2vec (used in https://semantle.com/ if you haven't seen it) is based on _multiple_ embeddings like this one (it's a simple neural network not a simple embedding).
This is a simple example where it scores their frequency. If you scored every word by their frequency only you might have embeddings like this:
act: [0.1]
as: [0.4]
at: [0.3]
...
That's a very simple 1D embedding, and like you said would only give you popularity. But say you wanted other stuff like its: Vulgarity, prevalence over time, whether its slang or not, how likely it is to start or end a sentence, etc. you would need more than 1 number. In text-embedding-ada-002 there are 1536 numbers in the array (vector), so it's like:
The numbers don't mean anything in-and-of-themselves. The values don't represent qualities of the words, they're just numbers in relation to others in the training data. They're different numbers in different training data because all the words are scored in relation to each other, like a graph. So when you compute them you arrive at words and meanings in the training data as you would arrive at a point in a coordinate space if you subtracted one [x,y,z] from another [x,y,z] in 3D.
So the rage about a vector db is that it's a database for arrays of numbers (vectors) designed for computing them against each other, optimized for that instead of say a SQL or NoSQL which are all about retrieval etc.
So king vs prince etc. - When you take into account the 1536 numbers, you can imagine how compared to other words in training data they would actually be similar, always used in the same kinds of ways, and are indeed semantically similar - you'd be able to "arrive" at that fact, and arrive at antonyms, synonyms, their French alternatives, etc. but the system doesn't "know" that stuff. Throw in Burger King training data and talk about French fries a lot though, and you'd mess up the embeddings when it comes arriving at the French version of a king! You might get "pomme de terre".
King doesn’t need to appear commonly with prince. It just needs to appear in the same context as prince.
It also leaves out the old “tf idf” normalization of considering how common a word is broadly (less interesting) vs in that particular document. Kind of like a shittier attention. Used to make a big difference.
Yeah, that is a poorly written description. I think they meant that each word gets a unique index location into an array, and the value at that word's index location is incremented whenever the word occurs.
Came here to say this exact thing. I have the exact modem/router mentioned in the article - my reliability, speed, and coverage all improved when I switched to my own $100 router.
I looked into this to see where it was getting new information, and as far as I can tell, it is searching wikipedia exclusively. Useful for sure, but not exactly what I was expecting based on the title.
There are wikipedias in other languages - Maybe this framework could be adapted to translate the search terms, fetch mulitlingual sources, translate them back, and use those as comparisons.
I've found a lot of stuff out through similar by-hand techniques that would be difficult to discover on english search. I'd be curious to see how much differential there is between accounts across language barriers.
As a base for researching the idea, Wikipedia seems like a decent data source.
For broader implementation you would want to develop the approach further. The idea of sampling other-language Wikipedia mentioned in a sibling comment seems to be a decent next step.
Extending it to bringing in from wider sources would be another step. I doubt it would be infallible but it would be really interesting to see how it compares to humans performing the same task. Especially if there were a additional ability to verify written articles and make corrections.
> As a base for researching the idea, Wikipedia seems like a decent data source.
If your goal is to generate a wiki article, you can't assume one already exists. That's begging the question. If you could just search wikipedia for the answer, you wouldn't need to generate an article.
I’ve never really thought about this before, but it is obvious in hindsight. When I finally made enough money to move into a newer apartment, the noise from neighbors and outside was almost nonexistent. It was a stark contrast, but I never thought about how that affected my sleep/productivity, let alone how it would affect all apartment dwellers.
Its not even a sure bet new ones are like that. I’d guess you have cement walls. A lot of the new 5 story apartments are timber framed with cheap sheetrock.
While I haven't read 3BP yet, you might be interested in the Zones of Thought series by Vernor Vinge. Extremely grand and unique universe, some politics, diverse cast, lots of interesting tech. I use it as a benchmark for grand space operas.
Ive had websites slow down 10x just by introducing the Facebook re-targeting script for instance
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