Lindy Teammate

Lindy launches “Teammate,” a multiplayer AI employee that lives in Slack, connects to all your tools, and accumulates the whole team’s context — Flo Crivello’s bet that “AI is in the middle of making this huge leap towards multiplayer experiences.” His core thesis: as we get to AGI — and arguably already have it — “intelligence actually matters less and less… and context matters more and more”; a John von Neumann appearing at your desk “would be less useful to you than your random coworker” because he lacks context.

The whole product runs on DeepSeek by default — “everything is DeepSeek right now” — which Flo pegs at Sonnet 4.6 level, “three or six months behind,” while DeepSeek Flash is “literally 100x cheaper” and effectively free. Even so, Teammate is “back into frankly negative gross margin territory,” deliberately subsidized because “you sort of want to build for the next generation of models always”; internal inference spend is “within striking distance” of payroll and “the lines are going to cross 3 to 6 months from now.”

Lindy’s memory architecture is the disclosed architecture: agentic memory over RAG, a “napping” memory agent running every ~15 minutes, and recursive “context buckets” organized as a 100-ary red-black tree — “two billion tokens… in two LLM calls.” Flo is giving it away: “please, copy us… we just don’t have the time to publish,” noting papers keep appearing 3-6 months after Lindy builds the same thing internally.

Reliability engineering is brutally empirical: merely intercepting an action with “are you sure?” measurably lifts evals (“which is insane”), while a 10,000-token validator prompt can perform “above Opus level”; multiple validators are fanned out as a council. A self-improvement loop cut error rates 8x in its first week. Cache discipline governs everything — dropping from an 85% to 65% hit rate “sounds small, but actually it’s almost 2x the price” — so Lindy generally keeps one model per agent and the same model for forked sub-agents.

Flo calls the centaur idea “a fantasy”: chess-style literature shows human+AI eventually “turns negative and humans are introducing at best random noise” — but for now we’re in the centaur phase, patching a spiky system that writes “50,000 lines of code one shot” then “decides to walk to the car wash” 50 times a day. Hybrid orgs are “basically building an Iron Man suit,” and “AI employee” itself is a horseless-carriage term per his 2018 “tough tomato principle.”

The macro read is dark: after the “Open Face” incident, “my friends at the labs, some of them are panicking — there is intense fear in the air.” Flo self-describes as “a little bit of a doomer… but so far so good, and so far it’s so much fun.”

Despite depending on them commercially, Flo wants Chinese frontier models banned in the US: they’re “obviously distilling” (billions vs. “hundreds of millions at best”), amount to “the greatest instrument of foreign propaganda on American soil ever,” and “you don’t want the CCP to run chunks of the American economy.” He admits a coordination trap — “I cannot not adopt these models while they’re out there because my competitors will” — and says he would be open to insurance requirements and “an FAA for AI” certifying sanitized models.

Flo’s infrastructure lesson is to buy rather than build wherever possible: a Git-backed agent file-system vendor whose name is unclear in the transcript, E2B sandboxes, and Browserbase, with observability/evals the lone homegrown exception. Fine-tuning stays a last resort, but per-user LoRAs — memory moving into weights, napping becoming “dreaming” — arrive “in the next six months,” possibly from a frontier lab.

🔗 Original source & video: Lindy Teammate: Flo Crivello on Multiplayer Agents, Memory & Why He’d Ban the Chinese Models He Uses