Information from Documents
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@docs2info
Read documents. Not too many. Mostly PDFs. Our parents read and wrote documents. Having the world's knowledge an internet connection away doesn't change this.
If your kid’s lemonade stand processes 0.5–1% of US GDP, then yes, that’s a fair analogy for @tryramp. Ramp’s data is useful for the same reason it gets cited at all: it is quite consistent with the revenue figures OpenAI and Anthropic release. If it weren’t, no one would care.
I’ve been at a small conference this week, one where the AI people have been presenting early in the week and the domain science people will be presenting later in the week. At the end of the talks last night, the conversation turned very doomer with all the AI people talking about how well Claude Code or Codex can do hill-climbing AI research and how we (the AI people) are maybe all about to lose our jobs! The domain science people expressed their shock at this attitude because, though Claude Code can be let loose to complete lots of banal hill-climbing AI research projects, basically no experimental science is hill-climbing or even metric driven. Most scientific fields are about much more taste-driven exploration that is incredibly difficult to make metrics for or to parameterize, and this misunderstanding from the AI community is one of the most damaging things to the realization of great science with AI. Seems like we’re actually pretty far from having AI models do that… Over the summer, @evijit and I wrote about this (and some other things hindering AI for science) at a bit more length, and today that work is out in Patterns! So, if you care about these problems and the real challenges in bringing AI to science in the real work, I recommend giving it a read!
I get why AI labs are so focused on software development (it helps them get recursive improvement, and also they are coders so they think coding is the most vital thing), but there are 9.5x more managers than there are coders & efforts to build tools for them are very nascent.
In the last few months, I've spoken to many CS professors who asked me if we even need CS PhD students anymore. Now that we have coding agents, can't professors work directly with agents? My view is that equipping PhD students with coding agents will allow them to do work that is orders of magnitude more impressive than they otherwise could. And they can be *accountable* for their outcomes in a way agents can't (yet). For example, who checks the agent's outputs are correct? Who is responsible for mistakes or errors?
Our cracked team just used Software Factory to rebuild and replace Jira in a little more than a month. We first spent 3.5 weeks planning. This is Software Factory’s superpower. It allowed our lead PM, Designer and Architect to thoughtfully describe and detail exactly what they wanted. Software Factory then did the heavy lifting in filling in the blanks and allowing our senior tech folks to sharpen the direction of what they wanted. Then in 2.5 weeks 2.5 junior devs built a replacement. This will launch as an updated Planner module inside of Software Factory on Tuesday. It’s beautiful, clean and super useful. Try it here: 8090.ai
We just nuked all phd students and mfers are still talking about how AI is just a bubble WAKE UP
