The Peterman Pod

Ryan Williams is a professor at MIT and the winner of the Gödel Prize in theoretical computer science. I interviewed him all about his work starting by asking him a popular Leetcode question (3 SUM).

• 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/AaK1SL2i_4Y
• Apple: https://podcasts.apple.com/us/podcast/the-peterman-pod/id1777363835
• Transcript: https://www.developing.dev/p/mit-complexity-theorist-on-leetcode

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
(00:41) Asking him a popular Leetcode question
(03:54) Doing better than the popular optimal solution
(08:26) Fine grained complexity
(17:00) A severe strengthening of P vs NP
(24:38) SAT problems and solvers
(34:51) Hot takes on famous open questions
(46:57) Simulating space with time
(01:01:02) Why he solves hard problems
(01:02:35) How to pick good research direction
(01:07:14) Technical book recommendations
(01:08:31) Advice for his younger self
(01:11:56) Outro

Where to find Ryan:

• Wikipedia: https://en.wikipedia.org/wiki/Ryan_Williams_(computer_scientist)
• Website: https://people.csail.mit.edu/rrw/
• LinkedIn: https://www.linkedin.com/in/r-ryan-williams-a1b534a/
• X/Twitter: https://twitter.com/rrwilliams

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:

• Some Estimated Likelihoods for Computational Complexity: https://people.csail.mit.edu/rrw/likelihoods.pdf
• Simulating Time with Square-Root Space: https://arxiv.org/abs/2502.17779
• Cook and Mertz's tree evaluation paper: https://dl.acm.org/doi/10.1145/3618260.3649664

Charlie Marsh is the founder of Astral, the Python devtool startup that was acquired by OpenAI. I inteviewed him about how software engineering is changing and learnings from starting his own company as an engineer.

• 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/Iw65FD4MGgs
• Apple: https://podcasts.apple.com/us/podcast/the-peterman-pod/id1777363835
• Transcript: https://www.developing.dev/p/openai-eng-and-dev-tools-founder

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
(00:40) Origin story
(06:04) The front page of Hacker News
(14:35) Why he chose Rust
(20:10) Full codebase migration from Zig to Rust
(28:40) LLM generated code and open source
(35:34) Performance optimizations
(44:54) Optimization with AI and combating slop
(01:02:08) Learnings as an eng starting a company
(01:17:55) Top technical talk recommendation
(01:18:56) Advice for his younger self
(01:22:00) Outro

Where to find Charlie:

• LinkedIn: https://www.linkedin.com/in/marshcharles/
• GitHub: https://github.com/charliermarsh
• X/Twitter: https://x.com/charliermarsh

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:

• Python tooling could be much, much faster: https://notes.crmarsh.com/python-tooling-could-be-much-much-faster
• The coolest PR he's ever seen: https://github.com/astral-sh/uv/pull/789
• Andrew Kelley’s data-oriented design talk: https://www.youtube.com/watch?v=IroPQ150F6c
• Ruff: https://github.com/astral-sh/ruff
• uv: https://github.com/astral-sh/uv
• ty: https://github.com/astral-sh/ty
• Salsa: https://github.com/salsa-rs/salsa

Vlad Feinberg is Google DeepMind’s pre-training area lead and I asked him all about how to land a job at a frontier lab like Google DeepMind, Anthropic or OpenAI.

• 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/cDyi91onoJ8
• Apple: https://podcasts.apple.com/us/podcast/the-peterman-pod/id1777363835
• Transcript: https://www.developing.dev/p/google-deepmind-pre-training-lead

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
(00:33) Skills frontier labs need
(08:45) The difference between AI research and engineering
(21:41) Domains that matter for the frontier
(30:50) Marketing yourself to frontier labs
(35:13) Concrete steps engineers can take
(38:29) Overview of pre-training areas
(47:23) Jeff Dean spot bonus story
(50:14) Favorite Gemini war story
(58:59) Advice for his younger self
(01:03:07) Outro

Where to find Vlad:

• Personal Website: https://vladfeinberg.com/
• Twitter/X: https://x.com/FeinbergVlad
• LinkedIn: https://www.linkedin.com/in/vladimirfeinberg/

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:

• How to Land a Job at a Frontier Lab: https://vladfeinberg.com/2026/05/10/how-to-land-a-job-at-a-frontier-lab.html
• ThunderKittens: https://github.com/HazyResearch/ThunderKittens
• Deedy's doomer Tweet: https://x.com/FeinbergVlad/status/2056383124829872466?s=20
• Jacob Steinhardt's "Research as a Stochastic Decision Process": https://cs.stanford.edu/~jsteinhardt/ResearchasaStochasticDecisionProcess.html
• The Scaling Book: https://jax-ml.github.io/scaling-book/
• Dwarkesh and Reiner's video: https://www.youtube.com/watch?v=xmkSf5IS-zw

Simon Peyton Jones is the co-creator of Haskell (pure functional programming language) and I interviewed him about functional programming, why it matters, and his thoughts on other programming languages.

• My ergonomic keyboard project I mentioned, you can follow along here: https://read.compose.llc/

Podcast links:

• YouTube: https://youtu.be/xcB_LF3cdqw
• Apple: https://podcasts.apple.com/us/podcast/the-peterman-pod/id1777363835
• Transcript: https://www.developing.dev/p/co-creator-of-haskell-functional

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
(00:39) What functional programming is
(09:18) Downsides of functional programming
(10:53) Specialized hardware for functional programming
(21:47) Haskell is useless
(25:59) Rust vs C
(28:26) Haskell vs OCaml
(35:26) Side effects in Haskell
(44:26) Type systems
(57:30) How the Haskell compiler works
(01:04:35) Why Haskell is talked about more than used
(01:09:07) Avoiding success at all costs
(01:11:12) LLMs and programming languages
(01:13:57) New programming language design
(01:15:59) Should students continue to learn programming
(01:22:33) Why Excel is his 2nd favorite programming language
(01:25:04) Advice for his younger self

Where to find Simon:

• LinkedIn: https://www.linkedin.com/in/simonpj/
• Wikipedia: https://en.wikipedia.org/wiki/Simon_Peyton_Jones
• Personal Website: https://simon.peytonjones.org/

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:

• Haskell is useless: https://www.youtube.com/watch?v=iSmkqocn0oQ
• John Backus Turing Award lecture: https://worrydream.com/refs/Backus_1978_-_Can_Programming_Be_Liberated_from_the_von_Neumann_Style.pdf
• Why functional programming matters: https://www.cs.kent.ac.uk/people/staff/dat/miranda/whyfp90.pdf
• Excel is his 2nd favorite programming language: https://www.youtube.com/watch?v=_M4P5M85KO8

Avi Wigderson is the only person in history to have won both a Turing Award (computer science) and Abel Prize (math). I interviewed him all about his field.

• My ergonomic keyboard project I mentioned, you can follow along here: https://read.compose.llc/

Podcast links:

• YouTube: https://youtu.be/5GUcvSAJcJw
• Apple: https://podcasts.apple.com/us/podcast/the-peterman-pod/id1777363835
• Transcript: https://www.developing.dev/p/turing-award-winner-p-vs-np-zero

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
(01:08) P vs NP
(14:51) What if you relaxed correctness
(25:38) Why NP complete problems are equivalent
(30:33) Space vs time complexity
(43:06) Why people use SAT solvers
(45:53) Randomness is a resource
(55:48) Randomness depends on computational power
(01:21:20) Zero knowledge proofs and their significance
(01:38:30) Quantum computation and why it matters
(01:56:24) Math vs computer science
(02:08:16) Major breakthroughs and his experience
(02:12:31) Advice for his younger self
(02:14:48) Outro

Where to find Avi:

• Wikipedia: https://en.wikipedia.org/wiki/Avi_Wigderson
• Personal Website: https://www.math.ias.edu/avi/home

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:

• PCP Theorem paper: https://www.cs.umd.edu/~gasarch/TOPICS/pcp/AS.pdf
• Paper on SAT approximation hardness: https://www.cs.umd.edu/~gasarch/BLOGPAPERS/max3satl.pdf
• Turing's paper: https://www.cs.virginia.edu/~robins/Turing_Paper_1936.pdf
• Original paper on NP completeness: https://www.cs.toronto.edu/~sacook/homepage/1971.pdf
• Ryan William's breakthrough result on space vs time: https://people.csail.mit.edu/rrw/time-vs-space.pdf
• Old result on space vs time: https://www-wjp.cs.uni-saarland.de/publikationen/HPV75.pdf
• Paper describing constant space majority solution: https://people.cs.umass.edu/~barring/publications/bwbp.pdf
• Fast primality test paper: https://www.sciencedirect.com/science/article/pii/0022314X80900840/pdf?md5=6f748cd82fa8efa1a637efab5f632baa&pid=1-s2.0-0022314X80900840-main.pdf
• Deterministic primality test paper: https://www.cse.iitk.ac.in/users/manindra/algebra/primality_v6.pdf
• Randomness vs observer paper: https://people.csail.mit.edu/silvio/Selected%20Scientific%20Papers/Pseudo%20Randomness/How_To_Generate_Cryptographically_Strong_Sequences_Of_Pseudo-Random_Bits.pdf
• Hardness vs randomness paper: https://www.math.ias.edu/~avi/PUBLICATIONS/MYPAPERS/NOAM/HARDNESS/final.pdf
• Erdos original sum vs product paper: https://users.renyi.hu/~p_erdos/1983-18.pdf
• Terrence Tao sum vs product paper: https://arxiv.org/pdf/math/0301343
• Seminal interactive proof paper: https://www.cs.miami.edu/home/burt/learning/csc609.221/goldwasser-micali-rackoff-knoweldge-complexity.pdf
• Zero knowledge proof paper: https://www.math.ias.edu/~avi/PUBLICATIONS/MYPAPERS/GMW86/GMW86.pdf
• Shor's algorithm original paper: https://arxiv.org/pdf/quant-ph/9508027
• Lattice paper (new hard problems): https://dl.acm.org/doi/epdf/10.1145/258533.258604
• MIP* vs RE paper: https://arxiv.org/pdf/2001.04383
• Zero knowledge non-interactive proofs: https://eprint.iacr.org/2025/1296.pdf