The Peterman Pod

Thariq Shihipar is an engineer on Anthropic’s Claude Code team I asked him how Anthropic makes the most out of the models for engineering and how the industry will change soon.

• My ergonomic keyboard project I mentioned, you can follow along here: https://read.compose.llc/
• The Kickstarter page for it: https://www.kickstarter.com/projects/ryanlpeterman/compose-simple-ergonomics-beautifully-done

Podcast links:

• YouTube: https://youtu.be/2Kch3tWMnw8
• Apple: https://podcasts.apple.com/us/podcast/the-peterman-pod/id1777363835
• Transcript: https://www.developing.dev/p/how-anthropic-builds-and-how-engineering

Thank you to this episode's sponsor for supporting my work:

• WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at https://workos.com/

Timestamps:

(00:00) Intro
(00:29) Onboarding at Anthropic
(02:53) Internal capabilities vs external perception
(06:16) Model vs Harness
(08:55) What percent of Anthropics changes are fully autonomous
(14:51) Computer use
(17:42) How to make the most out of your compute
(20:45) Loop engineering
(22:47) Where the industry will go soon
(26:02) Which model do Anthropic engineers use
(27:38) Is learning a particular model worth it
(30:56) Prompting tips for todays models
(35:04) How to get the models to do tasteful work
(39:00) How much of writing is done by AI at Anthropic
(45:36) Code ownership and maintenance at Anthropic
(52:04) How Anthropic prevents breakages
(55:24) Visibility and sharing your work
(58:42) Luck surface area example
(01:00:57) Should people still learn to code
(01:07:42) Advice for his younger self
(01:09:58) Outro

Where to find Thariq:

• X/Twitter: https://x.com/trq212
• LinkedIn: https://www.linkedin.com/in/thariqshihipar/
• Personal Website: https://www.thariq.io/

Where to find Ryan:

• Newsletter: https://www.developing.dev/
• X/Twitter: https://x.com/ryanlpeterman
• LinkedIn: https://www.linkedin.com/in/ryanlpeterman/
• Threads: https://www.threads.com/@ryanlpeterman
• Instagram: https://www.instagram.com/ryanlpeterman
• TikTok: https://www.tiktok.com/@ryanlpeterman

Referenced in this episode:

• Anthropic's post on removing 80% of Claude Code's system prompt: https://claude.com/blog/the-new-rules-of-context-engineering-for-claude-5-generation-models

Martin Odersky is the creator of Scala and I interviewed him to compare different languages designs (Rust vs Zig vs Python vs Scala) and how AI will impact programming languages.

• My ergonomic keyboard project I mentioned, you can follow along here: https://read.compose.llc/
• The Kickstarter page for it: https://www.kickstarter.com/projects/ryanlpeterman/compose-simple-ergonomics-beautifully-done

Podcast links:

• YouTube: https://youtu.be/LdN4sPWM-WY
• Apple: https://podcasts.apple.com/us/podcast/the-peterman-pod/id1777363835
• Transcript: https://www.developing.dev/p/creator-of-scala-comparing-languages?r=n49ky

Thank you to this episode's sponsors for supporting my work:

• WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at https://workos.com/
• Jira by Atlassian: Get more work done with your favorite agents and models all in one place, check them out at https://jira.dev/

Timestamps:

(00:00) Intro
(00:44) Why care about functional programming
(06:35) Why should people learn Scala
(09:26) Rust vs Scala
(12:42) Rust vs Zig
(15:45) Scala vs Python
(18:31) The programming languages that influenced him
(22:16) How running on the JVM works
(26:33) Why writing a compiler is hard
(29:19) Why Twitter adopted Scala early on
(31:00) How he believes AI will impact programming languages
(43:40) Will there be less engineers in ten years
(44:34) Top programming languages to learn to grow
(46:18) Top technical book recommendation
(46:51) Why he chose academia instead of industry
(48:28) Reflecting on Scala
(55:42) Advice for his younger self
(56:33) Outro

Where to find Martin:

• Wikipedia: https://en.wikipedia.org/wiki/Martin_Odersky
• Website: https://people.epfl.ch/martin.odersky
• X/Twitter: https://x.com/odersky
• GitHub: https://github.com/odersky

Where to find Ryan:

• Newsletter: https://www.developing.dev/
• X/Twitter: https://x.com/ryanlpeterman
• LinkedIn: https://www.linkedin.com/in/ryanlpeterman/
• Threads: https://www.threads.com/@ryanlpeterman
• Instagram: https://www.instagram.com/ryanlpeterman
• TikTok: https://www.tiktok.com/@ryanlpeterman

Referenced in this episode:

• Structure and Interpretation of Computer Programs: https://web.mit.edu/6.001/6.037/sicp.pdf
• A Brief, Incomplete, and Mostly Wrong History of Programming Languages (book): http://james-iry.blogspot.com/2009/05/brief-incomplete-and-mostly-wrong.html

Sergey Levine is one of the world's top robotics researchers and co-founder of Physical Intelligence. We talked about where humanoid robotics is today, thoughts on the Chinese robotics ecosystem, and his predictions for future timelines.

• My ergonomic keyboard project I mentioned, you can follow along here: https://read.compose.llc/
• The Kickstarter page for it: https://www.kickstarter.com/projects/ryanlpeterman/compose-simple-ergonomics-beautifully-done

Podcast links:

• YouTube: https://youtu.be/9OSbaPjv0Rc
• Apple: https://podcasts.apple.com/us/podcast/the-peterman-pod/id1777363835
• Transcript: https://www.developing.dev/p/sergey-levine-current-state-of-humanoid?r=n49ky

Thank you to this episode's sponsor for supporting my work:

• WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at https://workos.com/

Timestamps:

(00:00) Intro
(00:37) Where are we today
(04:20) Most surprising capabilities so far
(07:03) The most inspiring real world robotics
(08:36) If OpenAI or Anthropic got into robotics
(10:22) Chinese robotics
(13:15) Will one lab breakout from the rest
(16:59) Thoughts on a concrete roadmap
(21:03) Generalization and demonstrating it
(26:04) Types of data and which is best for robotics
(34:34) Why humanoid robotics differs from Waymo
(37:10) If humanoid robotics failed here is why
(39:55) Are there hot take modeling architectures in robotics
(42:05) Thoughts on AI safety in robotics
(46:44) Top robotics research paper recommendation
(49:35) Why is Boston Dynamics less top of mind
(53:47) Advice for his younger self
(56:42) Outro

Where to find Sergey:

• Google Scholar: https://scholar.google.com/citations?user=8R35rCwAAAAJ&hl=en
• Website: https://people.eecs.berkeley.edu/~svlevine/
• Wikipedia: https://en.wikipedia.org/wiki/Sergey_Levine
• X/Twitter: https://x.com/svlevine?lang=en
• LinkedIn: https://www.linkedin.com/in/sergey-levine-5a31a24/

Where to find Ryan:

• Newsletter: https://www.developing.dev/
• X/Twitter: https://x.com/ryanlpeterman
• LinkedIn: https://www.linkedin.com/in/ryanlpeterman/
• Threads: https://www.threads.com/@ryanlpeterman
• Instagram: https://www.instagram.com/ryanlpeterman
• TikTok: https://www.tiktok.com/@ryanlpeterman

Referenced in this episode:

• Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware (ALOHA / ACT paper): https://arxiv.org/abs/2304.13705
• Emergence of Human to Robot Transfer in Vision-Language-Action Models: https://arxiv.org/abs/2512.22414
• Summary of human to robot paper: https://www.pi.website/research/human_to_robot

Anders Hejlsberg is the creator of TypeScript and C#, and I asked him about how the TypeScript compiler got 10x faster through a rewrite in Go and his thoughts on how AI has impacted software engineering.

• My ergonomic keyboard project I mentioned, you can follow along here: https://read.compose.llc/
• The Kickstarter page for it: https://www.kickstarter.com/projects/ryanlpeterman/compose-simple-ergonomics-beautifully-done

Podcast links:

• YouTube: https://www.youtube.com/watch?v=cywK3XYYJ2o
• Apple: https://podcasts.apple.com/us/podcast/the-peterman-pod/id1777363835
• Transcript: https://www.developing.dev/p/creator-of-typescript-10x-faster

Thank you to this episode's sponsors for supporting my work:

• WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at https://workos.com/
• Jira by Atlassian: Get more work done with your favorite agents and models all in one place, check them out at https://jira.dev/

Timestamps:

(00:00) Intro
(00:48) Why write a compiler in JavaScript
(07:29) Why rewrite the compiler in Go
(14:49) LLMs for large migrations
(20:12) Why Javascript is so popular
(26:32) Why ever use Javascript on the backend
(32:59) What it takes to build a programming language
(37:06) Will there be fewer languages in 10 years
(42:57) Hands on engineering vs delegation
(49:14) Why fast tooling matters more now
(51:16) AI software engineering predictions
(58:52) The most technically challenging work
(01:02:04) Top book recommendation
(01:03:50) Advice for his younger self
(01:05:00) Outro

Where to find Anders:

• GitHub: https://github.com/ahejlsberg
• X/Twitter: https://x.com/ahejlsberg
• Wikipedia: https://en.wikipedia.org/wiki/Anders_Hejlsberg
• LinkedIn: https://www.linkedin.com/in/ahejlsberg/

Where to find Ryan:

• Newsletter: https://www.developing.dev/
• X/Twitter: https://x.com/ryanlpeterman
• LinkedIn: https://www.linkedin.com/in/ryanlpeterman/
• Threads: https://www.threads.com/@ryanlpeterman
• Instagram: https://www.instagram.com/ryanlpeterman
• TikTok: https://www.tiktok.com/@ryanlpeterman

Referenced in this episode:

• Flow type checker repository: https://github.com/facebook/flow
• TypeScript compiler repository: https://github.com/microsoft/TypeScript
• Algorithms + Data Structures = Programs (book): https://en.wikipedia.org/wiki/Algorithms_%2B_Data_Structures_%3D_Programs
• TypeScript native rewrite: https://devblogs.microsoft.com/typescript/announcing-typescript-7-0/

Leonardo de Moura is the creator of Lean and the Z3 theorem prover. I talked with him about how Lean works and why LLMs plus Lean will fundamentally change how we write software and do math.

• My ergonomic keyboard project I mentioned, you can follow along here: https://read.compose.llc/
• The Kickstarter page for it: https://www.kickstarter.com/projects/ryanlpeterman/compose-simple-ergonomics-beautifully-done

Podcast links:

• YouTube: https://youtu.be/KzdYKeAqWhY
• Apple: https://podcasts.apple.com/us/podcast/the-peterman-pod/id1777363835
• Transcript: https://www.developing.dev/p/creator-of-lean-the-end-of-handwritten

Thank you to this episode's sponsor for supporting my work:

• WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at https://workos.com/

Timestamps:

(00:00) Intro
(00:28) How formal verification works
(05:21) A new way of writing software
(13:15) Proof assistants vs programming languages
(21:06) How Lean has assisted in mathematical breakthroughs
(32:03) When is it worth formalizing software
(33:29) How Lean will impact handwritten math
(38:55) The Z3 theorem prover project he started
(45:44) The most technically challenging work of his career
(51:10) Lean vs its competitors
(01:00:37) The future of Lean
(01:04:10) Technical book recommendations
(01:06:15) Advice for his younger self
(01:07:10) Outro

Where to find Leonardo:

• Wikipedia: https://en.wikipedia.org/wiki/Leonardo_de_Moura
• Website: https://leodemoura.github.io/
• GitHub: https://github.com/leodemoura
• LinkedIn: https://www.linkedin.com/in/leonardo-de-moura-26a27b5/
• X/Twitter: https://x.com/Leonard41111588

Where to find Ryan:

• Newsletter: https://www.developing.dev/
• X/Twitter: https://x.com/ryanlpeterman
• LinkedIn: https://www.linkedin.com/in/ryanlpeterman/
• Threads: https://www.threads.com/@ryanlpeterman
• Instagram: https://www.instagram.com/ryanlpeterman
• TikTok: https://www.tiktok.com/@ryanlpeterman

Referenced in this episode:

• Lean 4: https://github.com/leanprover/lean4
• Mathlib: Lean Mathematical Library: https://github.com/leanprover-community/mathlib4
• Lean4Lean: https://github.com/digama0/lean4lean
• Liquid Tensor Experiment: https://xenaproject.wordpress.com/2020/12/05/liquid-tensor-experiment/
• Veil protocol verification language: https://veil.dev/
• Z3 theorem prover: https://github.com/Z3Prover/z3
• seL4 formally verified microkernel: https://github.com/seL4/seL4