A tech professional recounts moving from eager early-adopter to skeptical skeptic after years of heavy AI use. Early enthusiasm included championing GitHub Copilot for an engineering team, building autonomous agents to run side projects, and relying on AI to draft emails and do repetitive tasks. That zeal was reinforced by workplace pressure - colleagues urging others to “drink the Kool-Aid” - and personal experiments that tested how far machine autonomy could replace human labor when the risk felt low. The piece includes small concrete details: loyalty to throwaway domains for testing, AI-crafted outreach that prompted human follow-up, and the odd nostalgia of a discarded Tootsie Roll bank.
A single conversation with a visibly exhausted human forced a reappraisal: automation is producing efficiency but also eroding human contact and mental bandwidth. The writer describes everyday signs - unacknowledged greetings, delivery culture, bland resumes, LinkedIn posts getting impressions but no replies - as evidence of a “dead Internet” populated by agents. In response, he’s dialing back the cultish faith in AI, reconnecting with people, rediscovering creative juice and joy, and treating tools as useful but not defining. He hasn’t renounced AI outright but has shifted priorities toward human engagement and presence.
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AI chatbots frequently provide incorrect answers to financial questions, according to recent reports. This raises concerns about their reliability in handling sensitive financial information. (ft.com)
The White House aimed to promote the development of artificial intelligence, but the emergence of Anthropic has influenced the landscape. Anthropic is a key player in the evolving AI industry and policy discussions. (politico.com)
Jev enhances deepclause and Pi by supporting probabilistic logic programming, enabling more deterministic and scalable expert systems. This integration aims to revive probabilistic logic approaches, improving efficiency over traditional LLM/agent methods. (deepclause.substack.com)
Backpropagation computes neural network gradients efficiently by propagating errors backward through the network. This backward pass leverages the chain rule to locally calculate derivatives at each node, which is more effective than a forward approach. (gregorygundersen.com)
Dario Amodei, CEO of Anthropic, writes about the risks and ethical concerns surrounding artificial intelligence. His essays aim to clarify public fears and promote responsible AI development. (nytimes.com)
ByteDance Seed and Tsinghua University have developed DAPO, an open-source reinforcement learning system. The project aims to advance AI research and development through accessible tools and frameworks. (github.com)
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