Anthropic Education the AI Fluency Index
https://www.anthropic.com/research/AI-fluency-index
#HackerNews #Anthropic #Education #AI #Fluency #Index #AI #Literacy #Machine #Learning #Future #of #AI
Index, Count, Offset, Size
https://tigerbeetle.com/blog/2026-02-16-index-count-offset-size/
#HackerNews #Index #Count #Offset #Size #DataStructure #Programming
@mrdk Old terminology dies slowly, doesn't it?
Yesterday I was wondering: codices have the index at the back. For a scroll, it seems to me that wouldn't work, you would need to put it at the front where the table of contents is. Was the alphabetic index invented before the scroll was superseded by the codex? And if so, did they put it at the front?
Then again, scrolls are much shorter, so maybe you don[t need a per-scroll index. Instead, I suppose you would have one index scroll, separate from the others, that would tell you which scroll of a multi-volume work had what you were looking for.
@mjd “Was the alphabetic index invented before the scroll was superseded by the codex?” — Not really. Plinius' “Natural History“ has an index and he tells in his preface about another work with an index, but that seem to be the only cases. The problem is that with a scroll, you cannot actually use the index, because “leafing” through the pages (or better, “scrolling”?) would be too much effort.
It is also said that the introduction of Christianity was the reason why in later times, codices were much more popular than scrolls. The problem was the Bible: To work as a theologian, you have jump backward and forward between gospel and prophet books, between different gospels, and so on — much too much work if you have scrolls. The Bible was the first hypertext!
For more about indices, see “Index, A History of the” by David Duncan (https://en.wikipedia.org/wiki/Index,_A_History_of_the).
Anthropic Economic Index economic primitives
https://www.anthropic.com/research/anthropic-economic-index-january-2026-report
#HackerNews #Anthropic #Economic #Index #economic #primitives #economics #research #AI #insights
Copenhagenize Index 2025: The Global Ranking of Bicycle-Friendly Cities
https://copenhagenizeindex.eu/
#HackerNews #Copenhagenize #Index #BicycleCities #UrbanMobility #Cycling2025 #SustainableTransport
Yogthos
boosted
[#HK] A wave of renewed #optimism is sweeping through #HongKong’s #export #sector, fueled by a significant de-escalation in #international #trade #friction. The latest #HKTDC Export #Confidence #Index for the third quarter of 2025 reveals a dramatic upswing in #sentiment, leading to a substantial #upward revision of the city’s full-year export #growth #forecast. https://cnbusinessforum.com/hong-kong-exports-surge-in-2025-hktdc-lifts-growth-forecast-amid-trade-optimism/
[#HK] A wave of renewed #optimism is sweeping through #HongKong’s #export #sector, fueled by a significant de-escalation in #international #trade #friction. The latest #HKTDC Export #Confidence #Index for the third quarter of 2025 reveals a dramatic upswing in #sentiment, leading to a substantial #upward revision of the city’s full-year export #growth #forecast. https://cnbusinessforum.com/hong-kong-exports-surge-in-2025-hktdc-lifts-growth-forecast-amid-trade-optimism/
Okay, Back of the napkin math:
- There are probably 100 million sites and 1.5 billion pages worth indexing in a #search engine
- It takes about 1TB to #index 30 million pages.
- We only care about text on a page.
I define a page as worth indexing if:
- It is not a FAANG site
- It has at least one referrer (no DD Web)
- It's active
So, this means we need 40TB of fast data to make a good index for the internet. That's not "runs locally" sized, but it is nonprofit sized.
My size assumptions are basically as follows:
- #URL
- #TFIDF information
- Text #Embeddings
- Snippet
We can store an index for 30kb. So, for 40TB we can store an full internet index. That's about $500 in storage.
Access time becomes a problem. TFIDF for the whole internet can easily fit in ram. Even with #quantized embeddings, you can only fit 2 million per GB in ram.
Assuming you had enough RAM it could be fast: TF-IDF to get 100 million candidated, #FAISS to sort those, load snippets dynamically, potentially modify rank by referers etc.
6 128 MG #Framework #desktops each with 5tb HDs (plus one raspberry pi to sort the final condidates from the six machines) is enough to replace #Google. That's about $15k.
In two to three years this will be doable on a single machine for around $3k.
By the end of the decade it should be able to be run as an app on a powerful desktop
Three years after that it can run on a #laptop.
Three years after that it can run on a #cellphone.
By #2040 it's a background process on your cellphone.