Darren LI

Tasks with soft edges, where there isn't a clear definition of the optimal solution, are more suitable for AI agents. In games, for example, there may be multiple acceptable ways to behave, and users are forgiving of variations. However, tasks with hard edges, where specific actions or results are expected, can be more challenging for agents.... See more

2. They also have ~55% of volume of assets going towards exchanges.

The main differences between the AutoGPT project and traditional LangChain agents can be attributed to different objectives. In AutoGPT, the goals are often more open ended and long running. This means that AutoGPT has a different AgentExecutor and different way of doing memory (both of which are more optimized for long running tasks).

If popular Creators are using Beacons, their followers will start using Beacons too, and the loop continues. The loop is working: Beacons generates 77% of new users from this viral growth loop, and 18% from an incentivized referral program, meaning that 95% of new users come from other users.

These conversations identified 4 key innovations on the horizon: steering, memory, “arms and legs,” and multimodality.

原来线性强调的是TPF,technology problem fit,但在AI应用这件事上,技术门槛并不高,因为核心技术是由大模型提供的。但应用层对创始人想法的要求是更高的,对快速动作、快速组队的要求也更高。经过了几次决策流程我们发现了原来做法的问题,所以决定,只要找到right problem、right people

In the aftermath of the 2016 Presidential election, these attitudes radically shifted. For different reasons, both sides of the political spectrum began to immensely distrust the platform monopolies. The algorithmically curated streams that had once seemed so futuristic suddenly became Orwellian. Today, it’s not only acceptable to move more of your... See more

Language is a richer form of supervision than classical closed-set labels.