If you use Purchasing Power Parity to compare income between US and India, $100k/yr = ₹22 lakh.
But using PPP-based comparisons to make real world decisions on where to live is fallacious — it's closer to ₹30-37 lakh, 1.5x that!
🧵 Thread and post!
1/7
Assumptions:
- Expenses for single taxpayers from age 22–30 living in the Bay Area vs Bangalore
- Lifestyle comparisons beyond goods you can buy—such as proximity to family and pollution—also aren't accounted for.
- Savings are treated as absolute, not relative
2/7
Why isn't PPP good enough?
PPP isn't used to compare incomes — its calculated from a weighted basket of goods for GDP comparison.
The weighting and even the goods don't apply to most — iPhones and equivalent cars are still going to be far more expensive in India!
3/7
Many other factors matter for "effective" PPP:
—Stage of life
—Absolute wealth
—Country-specific spending differences
—Similar quality services are priced differently
—Savings should be considered absolute
—Lifestyle differences
Simply dropping a PPP of 22 doesn't work well
4/7
In the US, according to my spending estimates, if you make $100k, you'll save ~$22k a year.
To save an equivalent amount in India, you'd need ₹37 lakh, for an effective PPP of 37.
Why should you equate savings if spending habits are different?
For big-ticket long term spending like ssupporting parents, a house, a US-based college education and international travel, money can be treated as an absolute.
Even if you relax that, I'd say ~₹30 lakh.
6/7
Its worth mentioning that jobs in India also:
- Require longer hours
- Don't increase in salary as much
- Are fewer than the US
This comparison doesn't account for this.
I don't make a recommendation on where you should live—that's more complex and personal.
7/7
Okay I knew there were going to be disagreements with the assumptions: it would help if instead of just pointing out an expense you disagree with, point out an alternate figure.
I cant edit the tweet but I'll update the post with the new assumptions!
• • •
Missing some Tweet in this thread? You can try to
force a refresh
Extending H-1B visa grace period from 60 to 180 days was approved at the @WHIAANHPI meeting today and will be announced by USCIS in ~3mos!!
For the 100k+ workers affected by layoffs, this is a huge sigh of relief. Thanks @ajainb!
🧵
1/3
The second recommendation was to grant an EAD and travel documents to all approved I-140 applicants after 5yrs.
If you're confused by this, so was the committee. There was a long discussion and the vote was postponed to the next meeting in June/July. There is hope!
2/3
Recorded livestream of the @WHIAANHPI meeting is here, with important timestamps:
5:57:53—6:06:00: H-1B grace period recommendation
6:06:02—6:28:47: EAD+AP for approved I-140 in 5yrs recommendation
6:59:27—7:14:56: Voting on both policies
Caveats:
- Assumed no other optimizations
- Inference efficiency may be < 50% peak
- Hardware cost may be further subsidized by Microsoft (I used $12/hr for 8 A100 80GB from LambdaLabs)
Even with no MSFT discount, >11.7% efficiency on public cloud hardware is unit profitable!
This biggest issue with search with LLMs is that
generative performance != embedding quality
GPT-3 / ChatGPT is impressive for pure generative apps, but OpenAI's embeddings are worse quality + more expensive for use in vector search than
...plain ole info retrieval!
1/8
Traditional search apps use "lexical" methods to retrieve docs — they use keywords and traverse posting lists in an inverted index using simple, basic arithmetic scoring methods.
In literature, BM25 is the most commonly used ranking method.
2/8
In Vector /semantic/neural search, you "encode" documents into long sequences of numbers that represent its meaning, do the same for the query and find the k nearest neighbors.
Modern vector DBs include Pinecone, Weaviate, Redis, Qdrant and Vectara.
3/8
Ever find yourself on a SaaS website with the "Products" and "Solutions" layout.
You scroll through it.
But you still know nothing about the company.
Here's 6 reasons why that happens 🧵
1/7
1. The PR firm making the website knows nothing about the product.
In a game of broken telephone, the Founder may tell marketing to take care of the website, who in turn tells a PR firm who in turn tells their team. The last 2-3 layers may not have even tried the product!
2/7
2. The company is trying to pitch itself as a "category creator" and trying to sell a "vision".
If they simply describe what they do *now*, the total market (TAM) might be un-investable by VC. Therefore, they claim things they don't do while not "really" claiming them.
3/7
This story spans 70 years and is actually ridiculous.
The Salman Khan starring Bollywood movie Hum Dil Chuke Sanam (1999) was based on one of the first documented inter-racial Indian romances between a Romanian and a Bengali in the 1930s Calcutta!
Here's what happened 🧵
1/10
Mircea Eliade was a Romanian author born in 1907 with vivid memories of the WWI as an adolescent. He started writing books in his teens, eventually becoming a professor in Philosophy and Religion at UChicago.
When he was 21, in 1928, he set sail to Calcutta..
2/10
In Calcutta, Mircea studied under Surendranath Dasgupta, a Bengali scholar from Cambridge. In 1930, he fell in love with his then 16yro daughter, Maitreyi Devi—reprehensible at the time—whilst barely speaking the same language.