The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 12 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Superhuman views OpenAI and ChatGPT as direct competitors in productivity
“Oh, the answer is like ChatGPT, or like OpenAI and superhuman are competitors. Like this is what we fight against to some extent.”
Superhuman engineers grew pull request throughput from four to six per week
“In Q one, we were roughly set at four PR per engineer per week. In Q one, in Q two, we were closer to five PR per engineer per week, and in Q three, we're closer to six. So the global throughput, and again, PR per engineer per week, we can debate, but that's a…”
Pricing AI inference per million tokens is amateur thinking at enterprise scale
“A lot of people think about cost in terms of dollars per million tokens, right? And I think that that is actually amateur thinking. This only, only the kind of pricing you care about if you're a solo developer, but once you're in a large scale, like you guys a…”
Web browsers will become thinner and eventually embed directly into the OS
“I think the browser would be like more and more thin. I believe they would be like thinner and thinner, but they will disappear or they would be like just embedded in the OS eventually.”
Superhuman operates with 50 engineers and roughly 100,000 paying users
“Superhuman may, like, we have 50. 50 engineers. And your user base is roughly a million? Yeah, like, less than that. Like paying users probably 100,000.”
Houssier: Superhuman starts with top models before optimizing inference cost
“We would always start with the highest and more expensive model. To get the right quality. And when the quality is nailed, then we can spend time trying to optimize.”
Houssier: AI agents that hand trivial choices back to users waste time
“And I don't need, typically, the agent to say, hey, I found this lot, and this lot, which one do you prefer? I just asked for 15 minutes. Find it. Do it. I have an admin, when I was asking her, like, on Slack, find me 15 minutes. She's not asking me if I need,…”
Superhuman builds dynamic on-the-fly aggregation lambdas with Anthropic
“We're working right now with Anthropic to basically do kind of like a building on the fly, small, kind of a key component of lambdas that will build the code to do the aggregation.”
Superhuman CTO: Ask AI uses agentic paginated search across email batches
“So we had to implement the pagination search. So like semantic search for like the first, I would say, 40 and deep search. Not that one. Okay. Next 40, next 40. So I'm kind of like using this agentic loop, and while you don't have find the answer, continue and…”
Superhuman uses Turbopuffer on the backend to store search vector embeddings
“We use a Turbo Puffer on the back end to store like .”
Houssier: Superhuman testing beta auto-drafting meeting availability in emails
“Right now it's in beta. But right now my emails, like internally, when someone is asking, hello, can we meet next week for lunch? Automatically I will have like three slots proposed in the draft and I can just like send the draft that is prepared for me.”