Every American flag gets called "Old Glory." That name came from one specific flag, owned by one specific man. In 1824, a young Salem, Massachusetts sea captain named William Driver was given a 24-star flag as a gift, sewn by his mother & some family friends. When it unfurled from his masthead, he called out "Old Glory," and the name stuck. It went around the world with him twice. He retired to Nashville in 1837 and flew it from a locust tree in his yard, adding stars as states joined. Then Tennessee seceded — and two of his own sons enlisted in the Confederate army. Neighbors came looking for the flag, repeatedly, sometimes armed. His daughter sewed it inside a quilt, and the raiders never found it. On February 25, 1862, Union troops took Nashville. Driver walked to the Union commander carrying a calico quilt over his arm, cut it open in front of him, pulled out Old Glory, and asked that it be raised over the state capitol.

We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks: Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better: Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better: Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better: Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work! In summary: - As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding. - Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.

Stages of grief about the effect of AI on open source. Denial: - AI code is slop! Nobody serious will use this. - Open source will keep working exactly like it always has. Anger: - These AI slop PRs are ruining GitHub! - Ban AI-generated code! - If you close PRs, you're not open source anymore! Bargaining: - Okay, AI can write code, but only if a human reviews every line. - Maybe adversarial AI review will make arbitrary external code intake safe again. Depression: - Wait, if code generation is basically free, what is a contribution anymore? - Is GitHub-style open source culture just going away? Acceptance: - Open source is still open source, even if the contribution model evolves. - Good issues, test cases, benchmarks, specifications, and ideas become the scarce inputs. - Upstream may stop accepting arbitrary code while forking becomes much easier. Bonus stage! Reconstruction: - Projects redesign around maintainer-controlled agents, issue-driven development, stronger test/validation infrastructure, and new ways for contributors to earn trust.