1/ The Fairness of High-Skilled Immigrants Act, 2019, or #HR1044/#S386, which would've removed country caps on green cards in the US for Indian and Chinese nationals, particularly bringing the wait time for Indians from 150yrs to ~10...
2/ ... was blocked in the Senate by Sen. Dave Perdue after bipartisan support in the House. If you were Indian and moved to the US for an undergraduate degree in 2001, you'd be 36, have spent half your life in the country and not have a green card.
3/ You might be married with kids but if you lose your job, you might have to leave your family after paying for a college degree and 14yrs worth of usually fairly high taxes. Isn't that absurd?
4/ Despite being Indian, and a beneficiary of this bill, there are problems with this bill. One, most Indians in the backlog are not high skilled tech workers, but cheap outsourced labour from IT consultancies like Wipro and Infosys.
5/ Two, without a smoother cap removal transition plan, this would essentially flood the green card quota with Indians for the next ~10yrs, throttling competent candidates of other nationalities.
6/ If those two issues are fully addressed, I this bill will be unanimously favored and @sendavidperdue will let it pass and hopefully Trump will sign it!
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We just dropped a 12 page AI report on how ~500 execs at US enterprises use generative AI.
I read it all so you don't have to. Top 8 takeaways:
Anthropic is the #1 model provider in the enterprise, with 40% of ~$37B spend, with OpenAI dropping to #2.
1/8
On overall AI spend.
Generative AI has captured ~6% of software spend at $37B, growing ~3.2x YoY. Investments are coming to fruition and buyers are seeing results.
2/8
On where the spend goes.
Companies are using off-the-shelf models more than they're training their own. Horizontal AI tools like ChatGPT Enterprise, Claude for Work, Msft Copilot and Glean have exploded. "Departmental" AI like Cursor and Github Copilot also sees a huge boost.
This new DeepMind research shows just how broken vector search is.
Turns out some docs in your index are theoretically incapable of being retrieved by vector search, given a certain dimension count of the embedding.
Plain old BM25 from 1994 outperforms it on recall.
1/4
This result gives me a lot of joy as a search nerd for more than a decade.
Haters will say that the dataset the authors created, LIMIT, is synthetic and unrealistic, but this has been my observation building search systems at Google / Glean.
Vector Search was popularized as an approachable drop-in search since OpenAI embeddings grew in popularity, but has clear limitations in production settings.
Even aside from this result, showing it just misses certain docs constantly, it
– doesn't search for concepts well
– often retrieves similar but unrelated results
– doesn't account for non-content signals of similarity (recency, popularity)
3/4
I'm using GPT5 Pro to find me the best stocks and startup investments.
Asked it to use modern portfolio theory and size investments.
—Top Privates [+9.7%]: Databricks, Stripe, Anthropic, SpaceX
—Top Publics [+14.2%]: Nvidia, TSMC, Microsoft, Meta
Just put $1000 into the stocks!
Prompt: "Check all public / private stock market companies and tell me what I should invest in from first principles reasoning. You have $1000.
Please do deep research and present rationale for each investment. Each one should have a target price and expected value. Use advanced math for trading. Draw research from authoritative sources like research and unbiased pundits. Size my bets properly and use everything you know about portfolio theory. Corroborate each decision with a list of predictions about those companies.
Your goal is to maximize expected value. Make minimum 5 investments. Write it in a table."
This follows my previous experiment on Polymarket, which seemingly had ~2-4x the expected returns!
And yes, I know they’ve always reported on the 477 denominator, but that’s NOT “SWE-Bench verified”, that’s an entirely different metric, it’s “OpenAI’s subset of SWE Bench Verified” and that number can’t be compared
Microsoft just leaked their official compensation bands for engineers.
We often forget that you can be a stable high-performing engineer with
great work-life balance, be a BigTech lifer and comfortably retire with a net worth of ~$15M!