Eduardo

I think, bit by bit.

[Ryan Peterman @ryanlpeterman]

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.

In this episode:

• Useful and useless programming languages
• Rust vs C
• Haskell vs OCaml
• Why functional programming matters
• Static languages and their value for LLMs
• Why Excel is his 2nd favorite programming language

Where to watch:

• YouTube - https://t.co/72aR1f1a9D
• Spotify - https://t.co/ltqlAmVjYQ
• Apple Podcasts - https://t.co/jOYDGtGVnt
• Transcript - https://t.co/bRFoE5uyhD

Thank you to the sponsor of this episode 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://t.co/y8noBzFEem

Chapters:

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 is 2nd favorite programming language
01:25:04 - Advice for his younger self

[Eduardo @curupa]

I've been driving nixos in a vm inside macos for about a year now and let me tell you, this is the way.

[Eduardo @curupa]

@ryanlpeterman, you’re about 2 interviews away from completing the Distributed Systems Turing Award bingo card. Jokes aside, yet another banger episode! Thank you, you’re such a skillful interviewer.

[Ryan Peterman @ryanlpeterman]

Barbara Liskov is a Turing award winner famous for her contributions to programming languages and distributed systems. I interviewed her recently about:

• Being rejected from college based on gender
• The software crisis of the 1970s
• Paxos vs Viewstamped replication (her invention) and why one is more well known
• Stories of Dijkstra and how his work influenced hers
• Why her Turing award was questioned

Where to watch:

• Youtube - https://t.co/1D4fgSym22
• Spotify - https://t.co/91fDmuPrtr
• Apple Podcasts - https://t.co/DM7faEiAxX
• Transcript - https://t.co/NjEMywF8W8

[Eduardo @curupa]

Another banger episode! Stonebraker is one of my CS heroes.

[Ryan Peterman @ryanlpeterman]

Mike Stonebraker is a Turing award winner famous for his fundamental contributions to databases (e.g. Postgres, C-Store and much more). I interviewed him recently about:

• The story behind Postgres & the hardest technical challenge in building it
• Where he disagreed with Google's technical decisions
• Future problems in databases
• Literature recommendations to learn databases
• Why LLMs score 0% on his text-SQL benchmark
• What if you replaced all state in an OS with a DB

Timestamps:

0:00 - Intro
1:03 - How he got into databases
6:43 - Competing with Oracle
9:07 - What made Postgres special
15:55 - One size fits none
21:37 - Why he disagreed with Google
29:14 - Why he chose academia over big tech
30:58 - Replacing state in an OS with a DB
42:02 - Future problems in databases
51:36 - Technical book recommendations to learn databases
52:20 - Advice for younger self
55:52 - Outro

Where to watch:

• YouTube: https://t.co/YCunRSEIUK
• Spotify: https://t.co/7cCzATzN8z
• Apple Podcasts: https://t.co/jOYDGtGVnt
• Transcript: https://t.co/36BL7eGNmq

[Eduardo @curupa]

Holy shit! Leslie Lamport is on @ryanlpeterman ‘s podcast: https://t.co/w7cVZhk8oY

[Eduardo @curupa]

There is no royal road to geometry. Or any serious subject really.

[Eduardo @curupa]

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[Aakash Gupta @aakashgupta]

Karpathy’s 2025 retrospective is the clearest articulation I’ve seen of what foundational AI labs are actually building.

We’re not “evolving animals,” we’re “summoning ghosts.”

LLMs have completely different optimization pressures than biological intelligence. Humans evolved for tribal survival. LLMs optimize for imitating text, solving puzzles, and winning upvotes on LM Arena. Different pressures, different shapes in the intelligence space.

This framing finally explains what confuses everyone about AI capability.

GPT-5 aces the bar exam but gets tricked by simple jailbreaks. Claude writes PhD-level philosophy but hallucinates citations. Gemini solves competition math that stumps IMO medalists but fumbles spatial reasoning.

Capability spikes near verifiable domains where RLVR concentrates optimization pressure. Everywhere else, you get a different entity entirely.

I’ve been thinking about what this means for AI product builders.

The teams struggling with AI deployment are treating capability as uniform. They ask “can AI do this task?” and expect a yes/no answer. But ghost intelligence doesn’t work that way.

The teams winning are asking a different question: “Does this task live near a verifiable domain?”

If yes, the ghost might be superhuman. Build for autonomy. If no, the ghost needs guardrails. Build for human-in-the-loop.

This is why Cursor works. This is why Claude Code runs on localhost instead of the cloud. The best AI products in 2025 mapped the jagged edges and designed around them.

The companies that internalize Karpathy’s ghost framing will build better products than the ones still thinking in terms of “smarter or dumber than humans.”

There’s no single axis. Just different shapes.

[Mike Maples, Jr @m2jr]

This is one of the better takes on the AI "bubble" I've seen recently. https://t.co/sMJsm1vnT2

[Eduardo @curupa]

LLMs give us a faster horse on the royal road — but the road still doesn’t reach geometry.

[Felipe O. Carvalho @_Felipe]

Proper *Data* modeling wins over *Domain* modeling every time. Computers deal with data not real world objects. Start with the Relational Model as your guide for everything, deviate where it makes sense, but not by default.

[Eduardo @curupa]

Turns out someone did: https://t.co/mPDTk5FICe

[Eduardo @curupa]

Who's thinking about running service meshes on FPGAs?

[Eduardo @curupa]

@zetalyrae @leoshatrushin1 I’d be interested

[Eduardo @curupa]

"we talk about programming like it is about writing code, but the code ends up being less important than the architecture, and the architecture ends up being less important than social issues."
From https://t.co/rqqL5u3w9d

[Eduardo @curupa]

@AsgerSR SWIFT

[Eduardo @curupa]

@_Felipe Reminded me of https://t.co/jYTVnUfBd4

[François Chollet @fchollet]

Search engines used to be regarded as "AI" not long ago. It wouldn't be surprising if interpolative databases (foundation models) stop being regarded as AI in the future, too. They're more a static data structure -- an embedded dataset -- than a cognitively capable agent.

[Eduardo @curupa]

@algebraic_sofia Opa, manda aí, por favor

[Eduardo @curupa]

getting LLMs integrated into your dev environment has been transformational indeed. All emacs users out there: https://t.co/7quAOYS6gs. gpt3.5|4, gemini, and many more at your fingertips.

[Eduardo @curupa]

@programad @floydophone Richard Stallman.

[Eduardo @curupa]

tailscale is pure magic.