codebyaditya

Hey, I didn't generate everything by AI but only to frame the words since i was busy with something else. I'll make sure from now on such mistakes will never happen again.

sincerely.

Hey, lemme apologize first. I indeed generated this above comment by AI because I was extremely busy with something else and didn't have time to respond but i'll make sure this will not happen again.

sincerely.

Excellent point. My analysis calculated Level I concentration within each employer type but not absolute volumes.

You're correct that if staffing firms file significantly more total applications, their absolute number of affected Level I positions could exceed product companies despite lower concentration.

To properly claim "3x harder hit," I'd need to show either: 1. Total Level I applications by employer type, or 2. Total estimated selection losses by employer type

Without those absolute numbers, the "3x harder" claim overstates what the data shows. The accurate claim is that product companies have 3x higher Level I concentration - but that's not the same as 3x more impact.

This is a good catch. The proportions tell us about hiring patterns but not total system impact.

Author here.

Quick summary of probability shifts under the weighted system:

  Level I ($80K median):  -57% selection probability
  Level II ($103K median): -14%
  Level III ($135K median): +29%
  Level IV ($158K median): +73%

The counterintuitive finding: product companies (direct employers) have 22% Level I concentration vs 7% for staffing firms. The rule designed to stop "lottery abuse" hits direct employers 3x harder.

Data source: DOL LCA disclosure data. Happy to discuss methodology.

(Methodology): Author here. Technical methodology: The counterintuitive finding: DHS justified the weighted lottery by citing "employers submitting large volumes of low-wage registrations"—understood to mean staffing firms. But our data shows:

Product companies (direct employers): 22.0% Level I concentration Staffing/consulting firms: 7.1% Level I concentration Ratio: 3.1× Tech companies hiring junior engineers get hit 3x harder than the outsourcing firms the rule supposedly targets.

Data: DOL H-1B LCA Disclosure Data FY2024, 517,874 certified applications. Stack: Python, pandas 2.1.0.

Fair point. To be clear we aren't saying wage should be the only rule, just that it's weird to see such a huge disconnect. Usually money talks.

Your "gateway" hunch is likely spot on. Most rural docs start on J-1 waivers (mandatory 3 years). If they actually stayed after that, we'd see way more H-1B conversions filed by those rural hospitals to keep them. Since the volume is so low, it suggests once the 3 years are up, they bail for the city. The wage premium just isn't enough to anchor them.

Author here. Quick methodology notes:

Data: 20,225 H-1B LCA disclosures from DOL, FY2024, healthcare occupations only Analysis: Python (pandas), mapped ZIP → RUCC codes, median wage by volume quintile Key limitation: This is LCA data (intent to hire), not final USCIS approvals

Interesting rabbit holes:

Urban/rural split isn't binary—codes 4-6 show gradient effects

Wage level inversions strongest in codes 7-9 (most rural)

Happy to answer methodology questions.

We analyzed the FY2024 H-1B LCA dataset to look for geographic inefficiencies.

Methodology: We joined DOL disclosure data with USDA Rural-Urban Continuum Codes (RUCC 2013) using a ZIP-to-County crosswalk.

The Signal: We found a clear inversion of the standard supply curve. Rural areas offered a 21.4% wage premium ($250k median vs $206k) yet achieved 10.2x lower placement volume.

Systemic Friction: The data suggests that for high-skill labor (physicians), geographic friction and regulatory overhead (including the new $100k fee) outweigh significant monetary incentives. The market is not clearing.

What I appreciated here is how calmly Tao separates useful pattern matching from actual mathematical understanding. There’s no AI hype or dismissal but just a reminder that proof, verification, and intuition are different things. It made me rethink where LLMs genuinely help vs where they just feel convincing. Thank you for sharing!

The disturbing part isn’t that bad encounters happen — it’s that these techniques are officially banned, yet keep showing up across unrelated cases. When policy and on-the-ground behavior diverge this consistently, it stops looking like individual misconduct and starts looking like a systems problem.

I read it less as obliviousness and more as internal language leaking into marketing. What’s “Liquid Glass” to Apple reads like an aesthetic system though but to outsiders it sounds like jargon inflation. I feel the gap between internal coherence and external clarity shows up in these releases a lot.

You’re right on the numbers....Firefox never had majority share. The stronger claim is causal influence, not dominance. I recently read somewhere that the Firefox (and later Chrome) forced standards compliance and broke IE’s de-facto monopoly mindset. IE’s decline was gradual and multi-factor, but Firefox clearly shifted developer and user expectations.

What’s unsettling here isn’t any single policy, but the convergence: predictive policing, protest restrictions, and administrative punishments all justified as “risk management.” Even if each tool seems narrow, together they normalize acting on suspicion rather than action, which quietly lowers the bar for dissent.

What struck me is that Vision Pro’s problem isn’t price or hardware, but mental models. Apple keeps framing spatial computing through TV/movie conventions, when the real power is presence with minimal mediation. At least for me, long takes, fixed viewpoints, fewer edits feel “boring” on TV but transformative here.