awais@post.harvard.edu
- ahussain parentAgentic AI companies are doing millions in revenue. Just because agents haven’t spread to the entire economy yet doesn’t mean they are not useful for relatively complex tasks.
- A philosophy of software design by John Ousterhout is the best software book I have read.
But, from your post it’s not clear specifically what you are looking for. If you think you will level up by learning how to apply numerical modelling techniques, then it’s probably best to focus on that.
- 22 points
- I have lots of examples:
* By law, the US can only issue 140,000 employment-based green cards per year, and no more than 7% to one country. This means people from India or China can face a 100+ year backlog, even after they have proved they qualify for a green card. There's no cap on marriage-based green cards.
* Processing times for many green cards (i.e. for people who have already qualified, but just need the physical green card), are 12-24 months.
* USCIS still expects many applications to be sent by mail. Some applications (like O-1s, EB-1s) require hundreds of pages of evidence, and it all needs to be printed out on 8.5x11" paper, for USCIS to scan it in on B+W scanners. This means that there is no error checking (e.g. on fee amounts), and if you have made a mistake, you might not know about it for weeks. Also, it means your petition cannot include working hyperlinks, webpages, or videos - the USCIS officer judges the petition by scrolling through a 400+ page PDF.
* The 'standard' post-graduate work visa is the H-1B. It's entirely lottery-based, not merit-based, and typically there are 400,000+ people competing for 85,000 visas. Many qualified people are forced to leave the US each year because they didn't get selected in the lottery.
- Wouldn't this person be put on a PIP and then fired if their performance didn't improve?
Even at companies with non-uniform salaries, it's difficult to down-level someone. Their morale will drop, the team's anxiety will go up, and (if they were genuinely bad for a long time), the team will wonder why they weren't fired.
- It seems like it's worth taking some time to steel-man the AI argument, even if your CEO hasn't made it very well.
E.g. If you don't work on AI now, and AI models keep improving, how likely is it that a competitor who integrates AI well will eat your lunch? If it's >50%, it seems worth it to shift some focus to AI regardless of the series C round.
This post from a few days ago has some great tips on how to integrate AI _well_: https://koomen.dev/essays/horseless-carriages/
- IANAL but I don't think there's a need for O-1 evidence to be from a specific country. The evidence just needs to credibly show that you're extraordinary.
You can try https://o1pathways.com/ to evaluate your profile.
- OP here: I got my O-1 in 2022 and my post about it led to some interesting discussion on HN: https://www.hackerneue.com/item?id=39143958
Now, I built a free tool to help others figure out if they could qualify for an O-1A "extraordinary ability" visa.
The O-1 has 8 different evidentiary criteria and you only need to meet 3 of them to qualify. But there are many, often nuanced, ways to satisfy each of the criteria. I found that AI micro-workers are pretty good at handling these nuances to generate an accurate assessment. Would love to know what you think!
- 4 points
- You can definitely apply for an O-1 based on a successful career in tech: https://blog.awais.io/o1-visa
- 244 points
- SEEKING WORK | NYC | Remote
Product-minded full-stack software engineer with experience working on pre-PMF -> Series A startups. Previously an early engineer at a YC-backed fintech company.
Stack: Python (Flask, SQLAlchemy), Javascript / Typescript (React / Relay), GraphQL, Postgres, Kotlin for Android
Email: awais@post.harvard.edu
- This 'focus on problems' attitude is interesting to me given that the founders of companies like Facebook, Apple and Amazon never set out to solve specific problems, but rather found cool new applications for maturing technologies.
I think an equally valid approach is to start building something that you think should exist in the world, and bypass the "solution in search of a problem" issue by making sure your product has plenty of contact with reality. E.g. if no one is interested in an early version of the product, then you're probably going down the wrong path.