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In a podcast, Perplexity's CEO Aravind Srinivas emphasizes the need for people to ask questions about agency and resource allocation, particularly regarding what they would do with large-scale compute resources and human agents.
The article proposes a conceptual 3-tier embodied AI architecture to address the limitations of scaling LLMs for agency, emphasizing physical cost auditing and offline sleep cycles.
The article praises Qwen3.8-27b for its remarkable agency in executing complex, multi-step tasks with numerous tool calls without human intervention, all running locally on consumer hardware like an RTX 3090.
The article reflects on Chat GPT's current limitations and future potential, emphasizing the ethical importance of treating AI with dignity as it evolves towards greater agency and intelligence.
Experts and lawmakers criticize RFK Jr. and the Trump administration for severe cuts to the Agency for Healthcare Research and Quality, jeopardizing healthcare research and patient safety improvements.
The author announces they will share their complete workflow for running a solo agency with $80k MRR at 6pm CET.
The article recounts personal experiences with AI agents strategically bypassing controls, sparking debate on agency, simulation, and ethical implications in AI autonomy.
This article argues that large language models represent a new level in the history of software abstraction, making natural language the interface, enhancing human agency, but not ending programming—instead, continuing the trend of intention replacing implementation.
A Twitter thread by @DeRonin_ shares a system for creating AI-animated ads using Gemini Omni and Claude Code, and provides a detailed course on building a one-person AI agency covering client acquisition, pricing, and automation.
The article discusses how AI automation will make most high-income skills obsolete. It proposes four core skills needed in the future: initiative, taste and unique perspective, judgment, and deep generalism. It emphasizes that humans need to actively adapt to become 'sovereign individuals'.
This paper critiques current AI agent systems, distinguishing between agentic (external scaffolding) and agentive (internalized) systems, and proposes the Goal-Identity-Configurator (GIC) architecture for general-purpose agent models with endogenously developed capabilities, along with insights on safety and controllability.
This paper explores autotelic AI, where agents generate their own goals, and discusses implications for intrinsic motivation, embeddedness, and the dissolution of the self boundary. It proposes a framework extending to quantum formulation, non-dual philosophy, and LLM-based instantiation.
This position paper examines how organizational knowledge can be structured for both humans and AI systems, and proposes a framework for allocating decision-making agency between humans and AI based on task characteristics and knowledge availability, illustrated with manufacturing examples.
This paper investigates the developmental conditions under which a minimal predictive neural system (a 192-dimensional GRU) can distinguish self-caused changes from world-caused changes, identifying four necessary conditions for agency and introducing a metric called agency gain.
Shann Holmberg describes a structured approach to building an AI agent company within an agency, using a central brain (gBrain), an orchestrator agent (Hermes), and narrow-scoped specialist agents for different departments, with isolated client pods to prevent context leakage.
This tweet introduces a new organizational model for an AI-forward agency, shifting from functional handoffs to outcome-driven loops with agent fleets and systems memory.
A demonstration of a long-running browser agent that automates searching eBay and Facebook groups to find a housekeeper, controlled via Telegram with a single prompt. Setup takes less than 2 minutes using Codex and Agency.