Brenda

building @abstractpbc. prev. maths @uwaterloo, @contrary, @longdotxyz

[Brenda @librendaa072]

Great builds are to come…

Great people are inside…

Great ideas come from within.

@akatoshouse x @BDC_Capital x @Canada x @HackTheNorth x @cansbridgeproj ❤️

[Brenda @librendaa072]

@mikisafr Come to our robotics pancake flipping thing over the weekend, there will be lots of errors, we need you!!!

[Brenda @librendaa072]

@mikisafr Stellar!!

[Miki S-Y @mikisafr]

Robots are getting better at completing tasks. But what happens when they make a mistake?

We fine-tuned π₀.₅ on two real-world manipulation tasks to showcase whether recovery demonstrations can improve policy reliability more effectively than additional successful demonstrations. 🧵

[Brenda @librendaa072]

@claireymao @lucyjcai @i2rt_robotics haha the best bartender in sf, might rival ;) @DX_aniel

[Brenda @librendaa072]

@SleppyEric @amiklas @borisjabes @fanjiewang @ycombinator @UWaterloo hey love this :)

[Brenda @librendaa072]

@donaldjewkes @physical_int i love this

[Eric Zou @SleppyEric]

started WaterlooAGI

we do experiments, evals, and research about AGI from different directions

my first experiment was on noisy vs structured corruption on LLM dreaming

looking for researchers to exchange ideas and sponsors with spare compute. dm me 🔎

links below :)

[Brenda @librendaa072]

@yeokch flashback to china days

[Brenda @librendaa072]

@yeokch 🐐

[Yeok @yeokch]

I've spent years in China building robots. I can tell you this playbook won't work:

Pre-seed $: Invent the ultimate actuator.
Seed $$: Figure out mass production.
The hard part isn't inventing a beautiful topology. It's process engineering.

The real delta between the US and China is the ability to execute the enormous amount of physical x mental work required to iterate thousands of times on product designs and manufacturing processes. Partly because much of the tacit knowledge / management systems has left the US, but also because of the sheer human labor required.

This isn't something you can solve by buying traditional automation lines either. It requires:

1. Ideation driven by human intuition. Walking up and down the line, talking to assembly workers, and developing hypotheses about what and where improvements can be made. Raw human hours and attention spent understanding a specific manufacturing process.

2. Large batches of experiments across multiple variables. Coordinating experiments that can easily involve 50+ people, issuing instructions, and tracking the execution and results of each experiment.

3. Purpose-built test design to identify key metrics. For example, a change in gear hobbing profile might require accelerated aging tests on a dyno, while a change in bearing tolerance might require NVH testing.

4. Data + human intuition-driven analysis. Interpreting test results and deciding whether to implement a process change or run the next set of experiments.

5. Strict process control. Reissuing work instructions to the line and ensuring every process is followed to the letter.

The best manufacturers in Asia, from Foxconn to TSMC, have built their competitive advantage on systematically turning thousands of physical experiments into better manufacturing processes.

Their moat isn't just production scale. It's the organizational machinery that makes manufacturing learn and improve faster than anyone else.

I don't think most people in SF clock this.

Say you're manufacturing actuator gears in-house in the US. After you've made your first article, will you hire a team of 10 process engineers to coordinate the 50 technicians running concurrent experiments with designs, processes, and materials?

To understand wear patterns across every pin in a cycloidal drive after 10,000 hours? To reduce backlash from 15 arcmin to 5, then to 1? Just to keep pace with China's progress over the next few years?

Do you have the skill set and management systems in place to do this?

That's why our approach isn't just to automate manufacturing. It's to automate the process of learning how to manufacture better.

Automate simulation and design optimization.

Automate assembly and physical testing.
Automate the design and execution of process experiments.
Feed real-world experimental data back into simulation to continuously improve sim-to-real accuracy.
The goal is to turn what currently takes 50 engineers coordinating thousands of experiments into a closed-loop system that continuously learns and improves.

Then the same robotic line mass-manufactures the product.

PS: Coming back to actuator design, many actuator designs are already commoditized in China. The project in the video is open source, and CNC machining cost in China is just $27 (watch the video).

I hope people understand this reality. I'll link more examples in the replies.

[Brenda @librendaa072]

@kunvar_ holy 👀

[Kunvar Thaman @kunvar_]

The billion dollar raise isn't even the most exciting part about this announcement

[Brenda @librendaa072]

@jojokompella nice nice

[Brenda @librendaa072]

@jojokompella yooo this is so cool

[Brenda @librendaa072]

@jojokompella Hahaha

[Brenda @librendaa072]

@bertgodel Yooo this is so sick

[Brenda @librendaa072]

@arankomatsuzaki the last one slapped

[Brenda @librendaa072]

Come by and meet very interesting people.

Whether you’re working on LLMs in a lab, ex-lab, or building your stealth company.

Good connections are made every single time.

Private event. Select attendance only.

Thanks for posting @arankomatsuzaki! I look forward to seeing you there.

[Aran Komatsuzaki @arankomatsuzaki]

Robotics research meetup happening at SF tomorrow night!

​Enjoy pizzas and papers :)

https://t.co/Ra5JH6wqht

[Brenda @librendaa072]

@sighjith this is what we need

[Brenda @librendaa072]

@sighjith holy 👀🔥