u/iknownothingabtland

Newsom signed AB 956, SB 1117 and AB 1621 on Sept 29, and all three take effect January 1, 2027. AB 956 lets you build two detached ADUs on a single-family lot instead of one, but the city no longer has to approve a JADU once both are built, so you still top out at four homes total. SB 1117 means an ADU over 750 sq ft only pays impact fees on the square footage above 750, which matters a lot if you're building a 1,000 to 1,200 sq ft unit. The catch is that the fee break only applies to lots with two ADUs or fewer, so a garage conversion plus two backyard units puts you back on the old formula unless each one stays at 750 or under. AB 1621 isn't an ADU bill, but it caps building permit plan check at two rounds and cuts appeal deadlines to 30 business days, which should help anyone stuck in endless correction cycles. I wrote up the details, including what's still unclear, here: https://www.adupilot.com/blog/ab-956-sb-1117-ab-1621-california-2027

Governor Newsom signed AB 956 on September 29, 2026. Starting January 1, 2027, California cities must approve two detached, new-construction ADUs on a single-family lot through ministerial review, up from one.

Quick backstory for those who missed the earlier posts:

About a year ago I got laid off from Microsoft and went all-in on applying to AI companies. I noticed something weird: while Big Tech was freezing headcount, AI startups were burning VC money and hiring like crazy. The problem was that these companies were scattered across a dozen ATS platforms, and half of them had 20-30 employees with maybe 4-5 open roles — small enough that you'd never stumble on them through LinkedIn or Indeed.

So I wrote a scraper, dumped everything into a Notion page, and shared it on Reddit. Didn't expect much. That post blew up way more than I thought it would.

Fast forward to today: that Notion page is now fastaijobs.com, the scraper has been replaced by a multi-agent AI pipeline that runs daily, and the database has grown from ~100 companies to 1,500+ — net of removals. That last part matters, and I'll explain why.

First, the positioning — why no Google, no Amazon, no Microsoft.

You don't need me for that. Those jobs are on LinkedIn, on their careers page, everywhere. What you do need help finding are the 25-person startup that just closed a Series A and is hiring 6 engineers, or the company that's been around for 18 months and just got their first enterprise customer. These companies have tiny web footprints. Their job postings don't circulate. And because they're small, roles fill fast — by the time word gets around, the seat is taken.

I think in this market, the "off-meta" opportunities are where the edge is. Everyone is fighting over the same FAANG openings. Meanwhile the AI startup world is quietly hiring nonstop, especially right after a funding round — first thing a founder does with new capital is grow the team.

Aggregating these opportunities in one place is an information arbitrage play. That's the whole thesis.

1,500+ companies (and counting down)

The database started at ~100 and has grown to over 1,500, but it's a living list. Companies shut down, get acquired, or just go silent. All of those get cleaned out. Over the past few months, several previously listed companies were acquired (Stainless, Cal AI, among others) — those get flagged and removed or updated accordingly.

I've actually accumulated a decent-sized list of dead startups at this point. Thinking about publishing a "Startup Graveyard" section — would anyone actually find that interesting? (...The acquisition tracker is live now, though. Go see what you missed lol.)

New filters — all based on user requests

Almost none of these were my idea. People kept asking, so I built them.

Salary & Benefits. You can set a minimum salary floor and sort by base comp. You can also filter for companies that offer equity (surprisingly, not all startups do) and 401(k) — which is actually a fascinating data point. You'd be shocked how many Series A, even Series B companies don't offer it. When you're negotiating an offer, everyone fixates on base and equity. But 401(k) match compounding over years is a variable that most people completely overlook. It's one of those "boring" benefits that quietly adds up to a lot of money.

Oh, and there's a $500K+ salary tag. For the confident ones in the audience. You're welcome.

Immigration sponsorship. This was requested multiple times, so it was non-negotiable. You can now filter for companies whose JDs explicitly mention H-1B / PERM sponsorship, as well as those that require Green Card or US Citizenship. Here's a flip-side tip: if you already have a GC or citizenship, filtering for "GC/Citizen only" roles is actually a cheat code — you instantly cut the applicant pool by a huge margin.

Work intensity. 124 companies are tagged with "Long Hours / 6-7 Days." Some people want that grind-it-out environment. Others want to avoid it. Now you can self-select. (This one was my call, not a user request lol.)

Funding stage. Series A/B is the sweet spot for most people — that's when companies hire the most aggressively and have the most open roles. Companies that just raised are in maximum expansion mode. Getting in at that stage means more opportunities, more title flexibility, and frankly a more interesting ride than joining a late-stage company where everything is already set in stone.

"Why don't I just build my own scraper?"

I've asked myself this question many times. And the answer is: you absolutely can.

Technically, it's not hard. Write a scraper, run it once, get a snapshot. If all you want is a point-in-time list, you could knock it out in a weekend. You don't even need to write code — one well-configured AI agent with the right skill file could do it.

But the value was never in the snapshot. It's in the maintenance.

1,500 companies change every single day. New funding rounds need updated amounts and stages. Companies rebrand or move their careers page. Others quietly shut down and need to be flagged or removed. My agent pipeline handles all of this daily, and the API token cost alone is non-trivial every month.

The bigger cost is filtering the noise. The agents pull in everything — two-person side projects, companies that pivoted into something completely unrelated, companies with "AI" in the name that are actually traditional SaaS. All of that has to be screened out.

I've said this before: running this agent system is like managing an intern. It's incredibly productive but has zero judgment. You spend a huge amount of time writing rules, reviewing output, and catching errors. (My constraint file for the pipeline is now 580+ lines — most of which are "DO NOT do X.") I've already spent that time so you don't have to.

Here's the math: if you maintained an equivalent-quality database yourself, between the monthly token costs and the hours you'd spend reviewing data, you'd pay more than a subscription. It's the same logic as making lasagna from scratch — yes, buying the ingredients yourself is technically cheaper than ordering it at a restaurant, but once you factor in the grocery run, layering the noodles, the 3-hour cook, and scrubbing the baking dish afterward, you're better off just letting someone else make it. Take the time you saved and spend it on LeetCode and interview prep. That's a better ROI.

At the end of the day, this project wins on data, not software. Anyone can write the code. Clean, daily-updated, human-reviewed data is the actual moat. I do this work on nights and weekends so you can focus on what actually matters — landing the job.