Tag
An essay arguing that the technological singularity may arise not from a single superintelligent AI, but from constellations of specialized human–AI partnerships, and warns against corporate control of such systems.
GLM 5.2 tops PostTrainBench while being 5x cheaper than Opus 4.8 and 11x cheaper than Fable 5, making personalized AI economically viable for companies and countries.
Engram introduces an AI that learns from user context, scaling compute on personal and enterprise data to create models that understand specific work environments. They offer an API for agents and have partnerships with Notion, Harvey, and Microsoft.
gwern proposed the 'Guardian Angel' approach, advocating for training an LLM digital twin that imitates the user themselves, in order to solve the principal-agent problem and security risks of general AI assistants, and provided a complete roadmap from alignment theory to technical implementation.
A reflective inquiry into the practical gaps and motivations behind personalized AI agents, exploring where current systems fail to 'know' users and the boundary between helpful personalization and a surrogate self.
This paper proposes a unified framework for memory access and selection in long-context dialogue systems, using Bayes factors to quantify the utility of historical turns for modeling changing user preferences. Experiments show it outperforms embedding-based retrieval on preference-intensive tasks.
PersonalAI 2.0 introduces a framework that enhances LLM-based systems by integrating external knowledge graphs with dynamic multistage query processing and adaptive planning mechanisms, achieving reductions in hallucination rates and improved precision across multiple benchmarks.
MaxHermes is an AI agent that retains memory across sessions, auto-generating skills from completed tasks and improving them through repetition, effectively acting as a personalized, always-on assistant for workflows in messaging platforms.