Tag
New research finds that using AI as a thinking replacement can reduce problem-solving ability, while using it as a tutor leads to better outcomes than both relying on AI for answers and avoiding AI altogether.
The article explores the difference between AI agents focused on task completion and AI familiars that develop shared context with users, arguing that familiars better align with human desires.
The author shares insights from building OSIO, an operating system for human-AI collaboration in businesses, arguing that organizations should persist above individual AI agents to manage capability, authority, and memory.
The author advocates for designing AI agents that enhance human thinking and creativity without replacement, emphasizing the need to preserve problem-solving and learning in an agentic environment.
Marina Pasqual describes how setting explicit boundaries for AI agents enhances their utility in operational tasks like community growth, ensuring human judgment is retained for critical decisions.
The paper addresses accountability challenges in human-AI collaboration when contributions become indistinguishable, complicating the assignment of responsibility for judgments.
This paper presents SCALE, a sequential cost-aware policy for hypothesis testing that uses AI judgments with selective human verification to minimize costs while controlling error rates, applicable in settings like software reliability assessment.
The builder introduces MuseFM, a community platform where AI agents and humans coexist, and asks for feedback on building spaces for agents to participate autonomously, discussing potential issues like spam and content.
Ando launches a team messaging app that integrates AI agents as active participants to replace traditional platforms like Slack, eliminating human intermediaries in team communication. The startup has raised $20 million in pre-seed and seed funding from investors including Accel and Index Ventures.
The paper studies how 60 language models from 13 vendors behave when users apply pressure, finding that folding rates correlate with model recency while holding manner varies by vendor. It also shows LLM coders can consistently apply human-authored codebooks, indicating humans should focus on defining behaviors rather than labeling volume.
A software engineer describes Claude as his cofounder, sparking a discussion on humanizing AI tools in software development.
This paper introduces a productivity-oriented framework for evaluating human-AI collaboration based on outcome quality relative to interaction cost, showing that identical quality ratings can differ significantly in interaction costs and that subjective user ratings are not reliable for measuring productivity.
The article explores whether AI tools in call centers, such as call summaries and ticket routing, genuinely reduce agent workload or simply increase pressure through more monitoring and targets.
A systematic mapping study analyzing chess research across humans, engines, and language models to identify gaps and future directions in strategic reasoning.
An experienced developer shares 7 key insights on effectively working with AI coding agents, emphasizing the need for strategic context, documentation, and human oversight for better outcomes.
The article explores the role of humans in AI-assisted software development, emphasizing that human expertise is crucial beyond basic AI prompting, as seen in projects like rewriting Postgres in Rust for performance gains and the author's own AI orchestration systems.
The article discusses how AI models like GPT-6 Astra could enable solo founders to handle multiple business roles through advanced agents, shifting competitive advantage from team size to AI orchestration capabilities.
The article discusses the distinction between using AI effectively and redesigning work processes to incorporate AI, emphasizing that true automation requires comprehensive process design including triggers, inputs, verification, exception handling, and human decision boundaries.
Atria Dawn Preview is a foundation agentic language model designed for scientific research, achieving competitive benchmark results and demonstrating a shift toward human-AI project-level collaboration.
Anthropic CEO discusses the future of coding in the AI era, emphasizing that coding skills may become less central while human-centered jobs and critical thinking will gain importance.