GlobalChubby

Reminds me of Children of the Magenta Line: https://youtu.be/5ESJH1NLMLs?si=wozPZOKAFKNcjPSm

Fly the plane, not the automation.

This was just the default where I worked. We called the exercise Wheel of Misfortune: facilitator sets up an outage / bug in a non-prod environment (inspired by real events or not), spins a wheel, picks a victim, and the problem is solved collectively, manually. That's the standard format, but it could be much more freeform than that: the point is to educate each other regarding "incident management."

I will say, I'd always been skeptical that AI could be better at incident management than an experienced engineer with deep domain / institutional knowledge, but I was recently proven wrong: Claude root-caused and helped resolve a Postgres lock queue pileup that I believe would have taken me much longer to fix.

Re: aviation being a perfect industry to learn from, I think https://m.youtube.com/watch?v=5ESJH1NLMLs might be a good watch. I was introduced to this at a Wheel of Misfortune around 5 years ago, so it's interesting watching it today when AI is encroaching upon everything.

Corollary: there will now be a new generation of programmers who will never know the joy of building, designing, or debugging a system by hand, using their own thinking in collaboration with other humans.

Just today I suggested to a very junior engineer (more of an FDE, really) to write a short design / implementation proposal on an infrastructure change that spans about 8-10 code reviews. He replied with a link to a short, Claude artifact, clearly written by AI.

It dawned on me: to him, this approach of offloading thinking and understanding to AI, from design to implementation, is totally natural. He sees nothing wrong with his job being reduced to a middle-man between more senior engineers and AI.

My understanding when I was practicing is that the trend in modern mathematics is to focus on spaces with a certain kind of structure, and maps between those spaces that preserve it, and then what are invariants are preserved by those maps. Structure-preserving maps between categories of such spaces - "functors" in the language of category theory - are especially neat.

That's certainly different from Olympiad-style problems.

The Broken Earth Trilogy by N.K. Jemisin

Just to play devil's advocate: why? There's nothing a priori that suggests AI-written content is less pedagogically sound than human written content?

Piramidal | Senior Backend, Infra, Frontend Engineers | NYC onsite | Full-time

Piramidal is looking for senior software engineers - frontend, backend, and infra - who are excited about solving difficult problems at the boundary of foundational AI and neuroscience. We work on real-time monitoring of neurological data to help clinicians better treat their patients.

Our core backend and infra stack is Python, Go, and Terraform deployed to various cloud and on-prem, while our frontend stack is React and TypeScript. Bonus points for either: standing up and maintaining reliable streaming systems with a production/ops mindset; or building smooth, data-intensive React UIs with modern plotting/visualization libraries.

You would take an active role in architecture and design, implementation, and hiring from your early days, and will have the opportunity to have ownership commensurate to your capacity and interests.

Apply for jobs here: https://job-boards.greenhouse.io/piramidalinc

  Location: PST, USA
  Remote: Yes
  Willing to relocate: No
  Technologies: Go, Python, C++, AWS, GCP, Postgres, etc
  CV: Available on request
  Email: sapien.reversing.entropy@gmail.com

Backend / infra SWE with 10 YOE. Most recently I was a SWE-SRE at FAANG (5 years) and a Fortune 500. Interested in working with teams working towards something for the general benefit of society and not just looking to maximize shareholder profit. Examples areas include: education, healthcare, GovTech (public interest tech, grassroots organizing), climate / sustainability.

Software Engineer (10 YOE) | Backend/Infrastructure

Spent the last 5 years at Google building and operating critical services with huge scale. I designed and shipped distributed monitoring systems, migrated a huge global, sharded database to safe rollout automation, and shipped code in Go, Python and C++, both internal tooling, as well as instrumenting and adding features to user-facing binaries serving many QPS. Half the time I was production oncall for these services too. Before this, moved fast at startups and mid-sized US companies. Now seeking: Backend or infrastructure engineering roles where I can build resilient systems that matter. Strong preference for companies with meaningful mission, but open to any team with high ownership culture. In my work, I care a lot about resilient design, long-term operability, and reliability and would like to work with others who are similarly oriented.

Location: Currently Japan, relocating to US (PNW preferred) or remote Remote: Open to remote or hybrid Willing to relocate: Yes Technologies: Golang (current focus), Python, C++, Kubernetes, protocol buffers, PostgresQL, AWS, CI/CD, GitHub Actions, batch / streaming pipelines...More than the specific technologies, I think it's important to know what questions to ask of it, its limitations, and how to pick up anything. Résumé/CV: https://drive.google.com/file/d/1EP4ME2XGhXWFRhf77pgPE6bSlqr... (PII removed) Email: sapien.reversing.entropy@gmail.com