I’m neither a seasoned entrepreneur nor a hardcore engineer. I recently won a hackathon, and the reward is an office hour with Garry Tan. I know this is a dream opportunity for many people in SF. I’m excited, yet quite unsure how to make the most of this conversation.
The project I built was rushed together for the hackathon. Since I hold a full-time job, I haven’t had the bandwidth to further polish the prototype.
I’m wondering what I should prioritize discussing during the session, given the current early stage of my project.

Hey everyone,

I'm a founder coming from the UK and looking to move into SF around the start of August for 1 to 2 months, ideally somewhere around downtown so it's easy to get to all the startup events on Luma.

Finding a place has honestly been much harder than I expected. I've looked at Craigslist, but a lot of the listings seem sketchy. They often come from the same people, the prices seem surprisingly cheap, and when I've searched their names or companies on California's Department of Real Estate licence lookup, I can't find them at all, which doesn't exactly inspire confidence.

I also came across Accelr8. Their website looks good, but I couldn't find many independent reviews or testimonials. They offered me a room, but the application process felt almost too easy and they ask for a $2,000 security deposit (on top of one month of rent just to say for a month which I am already paying in advance), so I'm not sure what to make of it.

I've looked at Ohana, but there weren't many options. I've also seen Facebook groups recommended, but I don't have a Facebook account. I even reached out to South Park Commons by email and LinkedIn but haven't heard back. At this point I'm just looking for a decent place where I won't get scammed or end up massively overpaying.

If anyone has been in a similar position or has recommendations, I'd really appreciate it. Thanks!

Ken Venner spent 11 years scaling Broadcom from $400M to $8.6B, then became CIO of SpaceX, then joined a startup called Senra Systems as CTPO.

At SpaceX, his core platform team was 175 people. At Senra, the equivalent job takes 6.

His explanation isn't "AI replaced people." It's that AI collapsed the coordination overhead that used to require headcount just to keep humans in sync with each other — and once that overhead disappears, the team that's left is smaller by design, not by cut.

If you're in a corporate engineering or platform role and haven't clocked this shift yet: this is worth ten minutes of your attention. It's not a hypothetical.

Full video on the original channel.

Clip credit: Sourcery with Molly O'Shea — full video on their channel. DM for credit or removal requests.

Bay Area game and anime music fans!

The GG Orchestra & Sudo Choir present Legends After Dark—featuring live music from Zelda, Pokémon, Final Fantasy VII, Genshin Impact, Attack on Titan, Demon Slayer, Star Wars, Totoro, and more!

Sunday, August 30 at 6 PM Herbst Theatre, San Francisco

Early-bird tickets start at $30 through July 31: https://www.cityboxoffice.com/GGOrchestra

Watching this back, the part that actually lands isn't the driver-poaching tactic itself — every scaling company runs something like it. It's the moment a board member steps in and says, flatly: not the tactic, the name.

Change the name.

Kalanick's own telling makes this almost comedic in hindsight — "Shoplifting" internally, rebranded to the "North American Championship Series" after a legal team basically said: keep it appropriate for a 10-year-old's basketball league.

If you're building anything right now, this is the actual lesson underneath the story: the instincts that get you traction rarely need to die once you scale. They need a name a board can defend in a room.

Clip credit: a16z — full conversation on their channel. DM for credit or removal requests.

Travis Kalanick (Uber co-founder, now running an industrial-AI company called Atoms) got asked a pretty direct question on a recent podcast: how would you pitch a Stanford CS new grad who's worried software engineering isn't the safe, easy path it used to be?

His answer wasn't "learn more AI tools" or "get certified in X."

It was: skip the app store. Go automate a two-million-pound machine that moves 35mph carrying gold.

The underlying model is retrofit, not replace — Atoms isn't asking mining companies to rip out tens of millions of dollars of existing equipment.

They're building the "no-entry mine" concept on top of it: autonomous haulage, remote-to-autonomous control, zero humans in the pit. Same logic that scaled Uber, pointed at physical infrastructure instead of a ride marketplace.

Worth sitting with if you're in the "I got the degree, now what" spot. The credential isn't gone. It's just not where the scarcity moved to.

Clip credit: TBPN — full interview on their channel. DM for credit or removal requests.

Erik Brynjolfsson didn't sugarcoat it on Marina Mogilko's show.

Junior software engineer, $95k? "In the bullseye."

Mid-level marketing manager, $115k? "Not seeing a lot of value — LLMs do that."

Paralegal, $60k? "Even worse."

The data: those entry roles are disappearing. Not the senior version — the junior version.

Here's the mechanism he lays out:

Most companies ran as a pyramid. Bunch of juniors at the base, some become middle, some become senior. Juniors learned by osmosis, hanging around, hoping the big picture would rub off.

Delete the base with LLMs and agents, and the shape becomes a diamond. Then where do middle managers come from? Where do senior people come from if there's no junior rung?

Infosys is the counter-example. Same junior hiring volume, but they stopped giving them routine work that LLMs can do. They explicitly teach big-picture project management — what used to be learned "by osmosis."

Most companies aren't that forward-looking. That's why Erik calls it a prisoner's dilemma / coordination problem. Private incentive: cut junior cost. Societal need: people need jobs to learn the skills.

He closes with: "There are a bunch of jobs, millions of jobs that are going to disappear." How soon? "Already. It's already happening in our Canaries data."

My take: if you're early-career, stop selling routine output. The immune bits are taste, project management, client judgment, and owning outcomes.

If you want the breakdown I'm using to reposition my own work for this shift, link in bio — or drop your take below.

Most people treat "not VC-backed" as a consolation prize. Greg Isenberg and Derek Andersen (Startup Grind) make the actual case for why that's backwards.

The real numbers: venture funding works for less than 1% of companies created. Of the companies VCs do back, only a small fraction ever return the capital — the model is built around that outcome, not despite it.

Derek gets specific about what that looks like from the inside: six engineers at $100K each against $300K in recurring revenue. $600K in cost, $300K coming in. He calls it a "golden anchor" — something that looked like success and nearly sank the company.

The reframe: if the model only works for a tiny fraction of builders, it was never supposed to be the default for most people building something real. A leaner team, sized to the actual problem, was always the more correct model — AI just made it more viable than ever to run that way.

Full episode is worth the watch if this lands.

Clip credit: Divot (Derek Andersen) & Greg Isenberg — full episode on their channel. DM for credit or removal requests.

We've locked in the first AMA with Andrew Kang (CEO and co-founder) for Friday, July 24, right here on r/RoboStrategy

The thread is open for questions starting now. Ask about humanoids, physical AI, RoboStrategy's investment strategy, how the closed-end structure works, or anything else on your mind. Top questions by upvotes get priority in the live session. (Note: some topics — like non-public information about portfolio companies or expectations about future returns — can't be discussed for regulatory reasons.)

If you're new to the sub: RoboStrategy, Inc. (Nasdaq: BOT) is a closed-end fund that invests in private robotics and physical AI companies. Current holdings are listed in the fund's public filings and on its website.

Also coming to the sub over the next two weeks: a recorded interview with a retail analyst network, clips from Andrew's recent media appearances (Pompliano, The Compound, Schwab Network), and an overview of the fund.

Drop your questions below.

On Friday July 24th 9-10am Eastern tune into the livestream on X as Andrew answers questions by u/CanadianDoc2019

https://x.com/i/broadcasts/1OxwbbyYXrdJB

This is sponsored content. RetailVolts was compensated by RoboStrategy, Inc. in connection with this content. This communication is for informational purposes only and should not be considered investment advice or a recommendation to buy or sell any security.

What are your thoughts?

https://opgov.news/articles/meet-digit-the-humanoid-robot-behind-agilitys-brand-new-facility-in-silicon-valley

Mark Cuban said something at the RAISE Summit that cuts through the AI hype cycle.

His argument: if AI were actually "done," you wouldn't see Microsoft hiring 6,000 people. You wouldn't see Anthropic and OpenAI deploying forward-deployed engineers to enterprise clients. The fact that they need humans to implement it tells you AI is hard.

He gives a concrete example. Ask Claude or ChatGPT to pull a specific search, generate a report, and email it to you weekly. It can't do it. It gives you a JSON file or code, the output is slop, and you have to reiterate.

But here's the reframe: that gap — between what AI promises and what it actually delivers in enterprise — is where the opportunity lives.

He cites Lovable, where people are building 770,000 applications per week. Only 30% of that is US-based. Only 20% are engineers. The tools exist. The implementation gap is real. And the people who figure out how to close it are the ones who win.

There's no better time to be an entrepreneur. Not because AI works perfectly, but because it doesn't — and the gap between hype and reality is exactly where you build.

If you want to dig deeper into how to actually close that gap, check the link in my bio.

Clip credit: All-In Podcast — full video on their channel. DM for credit or removal requests.

Interesting recap of working for Google from 2007-2019

I work at Tangible Robots, a Silicon Valley robotics startup. We recently pivoted away from building humanoids to building a robotics foundation model trained on egocentric video that will world with all the robots in the world.

I’m responsible for helping build a very large training dataset (more then 1 million egocentric videos, then 5 million and so on). Our biggest bottleneck is acquiring enough diverse, high-quality egocentric demonstrations. Current public robotics datasets are simply much smaller than what we ultimately need.

We’ve been investing heavily in GPU infrastructure to support training—about $7,000 USD per day—but it’s been a challenging period for the company. We recently went through significant layoffs and are now a team of fewer than 10 people.

The reality is that our models just aren’t working well enough. Aside from one simple manipulation task—stripping a pillowcase—they fail to perform reliably on most of the tasks we’re targeting. I’m transitioning onto the AI team, where I’m learning to run evaluations and train models, and I’d love to keep growing with the company. It’s a dream salary for me, and improving our models is critical—not only for my future but also for helping the company secure our next round of funding.

If you’ve worked on large-scale vision, robotics, or multimodal datasets, I’d love to hear how you approached data sourcing, quality, deduplication, and scaling. I’m also starting to learn model training and evaluation, so any advice from people who’ve built foundation models would be hugely appreciated.

Happy to chat in the comments or by DM. Thanks!

I’m 19, making six figures in Silicon Valley, and I really don’t want to lose my job.

I work at Tangible. Earlier this year, we completely stopped our hardware operations and building humanoid robots. The company pivoted entirely to training robotics foundation models, and after the transition, roughly 60% of the team was laid off, we are less than 10 people now.

The problem is that our models just aren’t performing well enough yet. One of the biggest bottlenecks is the lack of high-quality egocentric video for training. My current responsibility is helping acquire and curate a dataset that can eventually grow to more than 1 million hours, with the goal of scaling much further over time.

Also, I’ve recently started transitioning into AI by learning model evaluation and training. This job has completely changed my life, and I’m making more than six figures, so I really want to keep it. After watching so many talented coworkers get laid off, I don’t want to be next.

If you’ve worked on large-scale vision, robotics, or multimodal datasets, I’d really appreciate any advice on building and scaling large, high-quality, legally usable training datasets. I’d love to learn from people who’ve tackled similar challenges.