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FUNDA
@FundaAI
The full-stack research platform for public equity investors. sales@funda.ai
Joined November 2012
1.1K Following    31.4K Followers
The success of a personal agent product requires organizational commitment, user research, use-case iteration, application integrations, and access to CPUs and compute. $META has historically committed heavily to major consumer-product opportunities, and the required capabilities align with Meta’s strengths in consumer behavior and rapid product iteration.
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Muse is winning at model-market fit. I haven’t seen anything it does today that ChatGPT Work couldn’t do months ago, or that Instinct or another agent couldn’t do. But using it feels different. It’s fast, smooth, and you actually want to keep using it. A faster, cheaper model paired with a great harness makes a huge difference. For an agent taking dozens of steps to get something done, every bit of latency adds up. You feel it every time you ask for something and if it's too slow it will feel clunky and annoying. And cost matters to the product experience too. It determines how much work you can afford to run in the background, how often the agent can check things, and how many attempts it can make. It's so obvious that they have a cheap model because they run the Feed updates hourly, they also create memories hourly too and not nightly like other agents. You can have the same capabilities on paper and end up with very different products. IMO that’s what I think Muse is getting right: the underlying model is very good.
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