Human computer interaction & Ethics
Atlanta, GA
Joined December 2009
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Today, I’m excited to share that we’ve raised $55M in Series A funding at a $1.25B valuation, led by @IVP, with support from @a16z, @ForerunnerVC, and an incredible group of founders and investors including @Benioff, @tobi, @dickc, and @marissamayer.
Everyone has an operating system for their phone. We’re building one for your life.
Right now, life is scattered across apps, group chats, calendars, and tabs. You’re the one connecting the dots and keeping everyone on the same page.
@wabi is a personal AI that helps you get your whole life organized. It brings your people, context, and apps together so you can coordinate plans, share the mental load, discover new things, and show up for each other.
Because AI shouldn’t just help us get things done. It should help us do more of what we love, with the people we love.
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The real story behind Fable vs Kimi isn’t just benchmarks.
Labs know the quiet truth: Most subscribers aren’t power users.
The majority of paying customers use AI for basic, everyday tasks - quick code snippets, simple prototypes, summaries, light debugging, casual brainstorming. Not the deep, multi-hour agentic workflows or ultra-complex reasoning where Fable’s superior expertise actually matters most.
That’s why Kimi K3 can win (or tie) so many practical coding arenas - Frontend Code, SWE Marathon, Terminal Bench, etc. - while being dramatically cheaper.
For the 80-90% of usage that’s “good enough,” price + accessibility + solid real-world performance beats peak intelligence every time.
Fable wins the hardest, most impressive tasks.
Kimi wins the wallets of normal subscribers doing normal work.
The loudest voices online are the ones pushing models to the limit.
But they’re not the majority of the subscriber base.
This is why “slightly behind on the absolute hardest stuff but way better value for how people actually use AI” is such a dangerous position for frontier labs to ignore.
What percentage of your AI usage actually needs Fable-level depth?
As an experiment, I implemented ByteDance’s “Molecular Structure of Thought” - strong LLMs don’t reason freely, they follow structured patterns: EXPLORE, DEEP, REFLECT, and NORMAL. This repo makes that explicit via a Markov transition graph. Give it a try!
github.com/supremeb/molecula…
