Wolf's goal for Pixi: git clone a repo, then something like pixi run start sets up dependencies and actually runs the project. Cross-platform. Not "hope Ubuntu's apt has the right CMake."

I talk with Wolf Vollprecht (founder & CEO) and Ruben Arts (software engineer) at Prefix.dev about Pixi as a Conda-ecosystem package manager rewritten in Rust, RoboStack turning ROS packages into Conda binaries (Linux, Mac, Windows subsets), and why robotics teams get stuck juggling apt, pip, and the ROS index.

You'll like this if you ship ROS workspaces, teach workshops, or care about Mac/Windows robotics tooling beyond "build from source and pray." The stack explainer (about 4:32–19:30) is the densest conceptual stretch; demos start around 25:50.

EPISODE LINKS

- Prefix.dev: https://prefix.dev/

- RoboStack: https://robostack.github.io/index.html

- Wolf Vollprecht LinkedIn: https://www.linkedin.com/in/wolfvollprecht/

- Ruben Arts LinkedIn: https://www.linkedin.com/in/ruben-arts/

- Wolf on X: https://x.com/wolfvollprecht

- Ruben on X: https://x.com/rarts_ruby

PODCAST LINKS

- Podcast Website: https://audrownashpodcast.com/

- YouTube: https://www.youtube.com/@audrow

- Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786

- Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162

TIMECODES

0:00 Cold open

0:50 Guest intros

1:44 Motivation behind Pixi

4:32 Package management basics

9:24 Robotics setup pain

14:43 RoboStack cross-platform

19:30 Prefix business model

25:50 Demo: setting up Pixi

46:49 Quick setup and caching

49:00 Building ROS packages

51:01 Features and activation

52:55 Tasks in Pixi

59:37 Building Conda packages

1:16:01 Future and community

A bag of gene-edited cells can be worth about half a million dollars (Fred's range for a dose is roughly $400K to $2M), and today that work is still done by hand in clean rooms, over thousands of steps.

I talk with Fred Parietti, Co-founder and CEO of Multiply Labs, about robots that manufacture cell and gene therapies using the same instruments, reagents, and consumables the scientists use, why they train from video of scientists instead of teleoperation, and where a ~$30K humanoid fits in their plant (loading and unloading carts, not the science).

You'll like this interview if you're a roboticist curious about pharma, a founder picking high-value problems over pizza robots, or anyone who wants Fred's take on "infinite market" pitches and a robotics winter. The biology explainer (16:08–34:00) is the most technical stretch.

EPISODE LINKS

- Multiply Labs: https://www.multiplylabs.com

PODCAST LINKS

- Podcast Website: https://audrownashpodcast.com/

- YouTube: https://www.youtube.com/@audrow

- Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786

- Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162

TIMECODES

0:00 Cold open

0:53 Multiply Labs overview

7:17 Pharma robotics gap

16:08 Next-gen pharma

34:00 Multiply Labs automation

43:54 Deployment size

1:00:04 Thoughts on humanoids

1:10:01 Training process

1:21:39 High-value problems

1:24:37 Key takeaways

For a wheeled robot, "shut it off" can be a safety move. For a biped that has to stay upright, killing power is the hazard. Pras walks through why Digit needs different rules.

I talk with Pras Velagapudi, CTO of Agility Robotics, about Digit (two arms, two legs) doing paid material handling on full shifts, why a dynamically stable biped beats a heavy wheeled base for narrow aisles, the tradeoffs of wheels on feet, and how they're working with others (including Boston Dynamics) on safety standards for robots that can't just lock their wheels and call it safe.

You'll like this interview if you're into humanoids, warehouse automation, or the control and safety problems that show up once a robot has to balance. The legs-and-wheels stretch (about 5:26–23:15) is the densest design argument; safety and standards (36:42 on) is where the shut-it-off problem shows up.

EPISODE LINKS

- Agility Robotics: https://www.agilityrobotics.com

PODCAST LINKS

- Podcast Website: https://audrownashpodcast.com/

- YouTube: https://www.youtube.com/@audrow

- Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786

- Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162

TIMECODES

0:00 Cold open

0:42 Agility overview

5:26 Why humanoid design

13:26 Why legs, not wheels

23:15 Digit's application

36:42 Safety in humanoids

49:11 Standardizing safety

1:06:50 Digit in other industries

1:14:41 How AI fits into robotics

1:29:23 Is data still gold?

1:35:54 The future of Agility

1:38:13 Key takeaways

Stefan's cold open: the best robot in the world today is less valuable to its owner than an average computer was in 1960. They argue robotics still makes teams rebuild the same navigation stack instead of buying commodity software.

I talk with Stefan Seltz-Axmacher (CEO) and Ilia Baranov (CTO), co-founders of Polymath Robotics, about packaging point-to-point navigation for off-highway machines (tractors, bulldozers, mines), why SaaS feels easy because you don't rebuild payments and backends, labor shortages and vehicle SKU sprawl, and why they think humanoid-as-general-purpose is often the wrong bet next to Roomba-scale appliances.

You'll like this if you build autonomy for industrial vehicles, sell into mining/ag, or want a blunt take on demos vs products. The ROI/history stretch (about 12:14–24:15) and the humanoid/minimally-viable-humanoid bit (52:02 on) are the densest arguments.

EPISODE LINKS

- Polymath Robotics: https://www.polymathrobotics.com

PODCAST LINKS

- Podcast Website: https://audrownashpodcast.com/

- YouTube: https://www.youtube.com/@audrow

- Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786

- Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162

TIMECODES

0:00 Cold open

0:12 Polymath overview

6:04 Robotics standards gap

12:14 Market reality

16:43 Labor shortages

24:15 Cutting mining costs

33:55 AgTech needs action

43:16 Practical autonomy

52:02 Humanoid future

1:12:31 LLMs and coding

1:21:10 Work-kids balance

1:36:04 Key takeaways

Twenty years ago, SaaS had no rule of 40. Brannon Jones's take is that deep tech is in that same stretch now: the rules are still unwritten.

I talk with Brannon Jones, investor on AlleyCorp's deep tech team, about why they started the vertical in robotics, how they underwrite hardware with a value-creation multiple instead of SaaS metrics, and what that looks like in the portfolio: EyeBot's 90-second eye exam kiosk, Portal Space's satellite thrusters, and Aescape's robotic massage table (which he says raised $83M). We also get into why AlleyCorp hasn't backed a humanoid yet, and why he thinks a lot of embodied-AI demos are still pick and place.

You'll like this if you raise for a robotics company, invest in hardware, or want an operator-turned-investor view of New York deep tech. The portfolio walk (about 15:43–56:00) is concrete; the investing framework (56:08 on) is the densest stretch.

EPISODE LINKS

- Brannon Jones X: https://x.com/robotjonez

- Brannon Jones LinkedIn: https://www.linkedin.com/in/jones-brannon

- AlleyCorp website: https://alleycorp.com

- AlleyCorp LinkedIn: https://www.linkedin.com/company/alleycorp

PODCAST LINKS

- Podcast Website: https://audrownashpodcast.com/

- YouTube: https://www.youtube.com/@audrow

- Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786

- Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162

TIMECODES

0:00 Cold open

0:23 AlleyCorp and Brannon Jones

3:26 What deep tech means at AlleyCorp

10:14 New York vs West Coast portfolio

15:43 Deep tech portfolio overview

17:11 EyeBot eye exam kiosk

22:25 Why deep tech vs SaaS

28:46 Portal Space satellite thrusters

32:35 Aescape massage robot

44:39 Valar Atomics

49:24 Koop Technologies

50:58 Glacier recycling robots

54:46 Civ Robotics solar surveying

56:08 Funding robotics without SaaS playbooks

1:09:50 Market-focused startups

1:14:17 Early robotics investments

1:23:40 Humanoids and value creation

1:28:32 Deglobalization and China

1:32:48 Hype vs pick and place

1:38:19 Key takeaways

Somebody draws a house on a napkin. BotBuilt turns that into a 3D nail schedule, cuts twisted lumber in a warehouse, and ships panels so on-site framing drops from weeks to about four hours.

I talk with Barrett Ames, co-founder of BotBuilt, about why they automate framing (the bones of the house) instead of the whole build, how they plan and grip imperfect lumber, and the business of warehouse construction that still has to win on jobsites.

You'll like this if you care about construction robotics, founder-market fit in hardtech, or how to pick an automation wedge that isn't a race to the bottom. The lumber-handling stretch (about 6:59–17:41) is the densest engineering bit; time and cost (56:52 on) is the densest business bit.

EPISODE LINKS

- BotBuilt: https://www.botbuilt.com

- Barrett Ames: https://www.cbames.com

PODCAST LINKS

- Podcast Website: https://audrownashpodcast.com/

- YouTube: https://www.youtube.com/@audrow

- Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786

- Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162

TIMECODES

0:00 Cold open

2:22 Overview

6:59 Challenges

17:41 Sketch to structure

27:20 Generative solutions

30:36 Structuring build

35:49 When things go wrong

42:31 Traditional vs automated

56:52 Time and cost

1:07:36 Long-term vision

1:14:35 Construction opportunities

1:23:51 Lessons learned

1:33:32 Final thoughts

Tesla gets far on vision alone, but shipping that stack means hardware, bandwidth, AI, and sync. Luxonis's bet is cameras that are eyes, ears, and a chunk of brain at the edge.

I talk with Bradley Dillon, CEO of Luxonis, about AI-enabled cameras for robots (he cites more than 150,000 sold), fusing sensors on-device, multi-device linking, models and labeling, and where they aim the platform next (including harder industrial and security screening angles).

You'll like this if you build robots and don't want to become a camera company, or if you care about edge vs cloud for vision. The edge-compute stretch (about 25:01–31:41) and applications (20:26 on) are the densest product bits.

EPISODE LINKS

- Luxonis: https://www.luxonis.com

- Bradley Dillon LinkedIn: https://www.linkedin.com/in/bradley-dillon/

PODCAST LINKS

- Podcast Website: https://audrownashpodcast.com/

- YouTube: https://www.youtube.com/@audrow

- Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786

- Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162

TIMECODES

0:00 Cold open

1:54 Bradley Dillon and Luxonis overview

8:22 Luxonis equipment

13:01 Integrating multiple sensors

20:26 Applications of Luxonis vision

25:01 Luxonis and computing power

28:21 Linking multiple devices

31:41 Luxonis AI models

34:53 Data generation and labeling

44:53 Future predictions

49:01 AI for scene understanding

59:31 Targeting industries

1:04:54 Smart security screening

1:09:43 Growth story

1:18:58 Chip supply challenges

Three short live interviews from a stealth robotics meetup between Austin and San Antonio: legged-robot safety in human spaces, automating mining exploration tasks, and robots that frame houses.

I talk with Kyle Morgenstein (UT Austin PhD, legged safety / RL), David Venegas (Head of Operations at Durin, mining), and Barrett Ames (co-founder of BotBuilt, construction framing).

You'll like this if you want a sampler across research and startups rather than one deep company dive. Kyle's safety stretch opens (about 1:24–20:36); David covers mining (20:36–38:03); Barrett closes on BotBuilt (38:03 on).

EPISODE LINKS

- Kyle Morgenstein: https://www.kylemorgenstein.com/

- Durin: https://www.durin.com/

- BotBuilt: https://www.botbuilt.com/

PODCAST LINKS

- Podcast Website: https://audrownashpodcast.com/

- YouTube: https://www.youtube.com/@audrow

- Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786

- Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162

TIMECODES

0:00 Cold open

1:24 Kyle: legged robot safety

3:55 Improving robot safety for humans

7:25 Humanoid progress and future

10:39 Boston Dynamics vs Unitree

12:14 Reward models for safer robots

15:05 Foundation models

16:34 Teleoperation efficiency

18:45 Training from YouTube data

20:36 David: mining basics

28:58 Automating mining tasks

34:09 Access challenges in excavation

36:57 Use of simulation

38:03 Barrett: construction robots

41:27 Custom home focus

43:06 BotBuilt's approach

44:16 BotBuilt's data sourcing

49:47 Working with robots

49:57 Perception stack

52:20 Handling lumber challenges

53:39 Margins vs prefab homes

53:57 Transport costs

56:02 BotBuilt's lifecycle

58:17 Outro

Vision and audio have digital sensors. Smell mostly doesn't. Kordel's bet is to make olfaction as usable for robots as cameras are today.

I talk with Kordel France, founder of Scentience, about how smell sensing works, what goes wrong when you try to identify complex odors, near-term use cases (from quality control to security-style detection), datasets and standardization, and what an API for smell would look like for robot builders.

You'll like this if you're curious about a new sensing modality, or you're hunting for application wedges that aren't another gripper demo. The mechanisms stretch (about 8:14–15:08) is the densest science bit; API / integration (38:38 on) is the densest product bit.

EPISODE LINKS

- Scentience: https://scentience.ai/

- Kordel France: https://kordelfrance.ai/

PODCAST LINKS

- Podcast Website: https://audrownashpodcast.com/

- YouTube: https://www.youtube.com/@audrow

- Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786

- Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162

TIMECODES

0:00 Cold open

1:22 Kordel France and Scentience overview

2:39 Smell's role in robotics

5:19 Smell sensor problem solving

8:14 Smell identification mechanisms

15:08 Use cases

16:42 Near-future innovations

25:09 Application development

27:53 Scent technology standardization

33:02 Scent recognition dataset

33:33 Industry vs academia

36:56 Startup landscape in smell robotics

38:38 Smell sensing API

42:40 Sensor integration

49:58 Weather data's impact on smell sensors

ARIA is the UK's DARPA-like funder without the defense mission. Jenny Read's program bets on robot hardware, especially dexterity, with a budget she puts around £50 million.

I talk with Jenny Read, Programme Director at ARIA, about how the agency works, why she thinks hardware lagged while software ate the attention, gear ratios and touch/state awareness, integrating body design with neural control, and how they pick and measure high-risk projects.

You'll like this if you raise or apply for ambitious robotics hardware research, or you want a funder's view of the dexterity bottleneck. The hardware/dexterity stretch (about 8:12–33:19) is the densest technical bit; funding logistics (21:52 on) is the densest process bit.

EPISODE LINKS

- ARIA: https://www.aria.org.uk

- Jenny Read: https://www.jennyreadresearch.com/

PODCAST LINKS

- Podcast Website: https://audrownashpodcast.com/

- YouTube: https://www.youtube.com/@audrow

- Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786

- Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162

TIMECODES

0:00 Introduction to Jenny Read and ARIA

3:49 Jenny's role in ARIA

6:26 Jenny's robotics focus

8:12 Areas of focus within hardware

9:40 High versus low gear ratios

11:56 Touch sensing and fragility

13:40 Integrating body design and neural control

18:47 Jenny's background and funding approach

21:52 Selecting focus areas, funding, logistics

27:56 Dexterity challenge and investment focus

33:19 ARIA's allocation strategy

42:59 Bottlenecks: manufacturing and motors

45:38 Measuring success

48:28 Key takeaways

51:49 Outro