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A blog post explaining that cache read costs dominate LLM inference spending for agentic workloads, with cumulative costs growing quadratically as context is re-read each turn, and advice to reduce tool call count to cut costs.
A tweet highlights a CS survey paper arguing that bigger context windows are a dead end, and that memory engineering — treating agent memory as an operating system — is what separates real AI agents from autocomplete, enabling stateless models to self-evolve.
A new survey from Renmin University reviews nearly 1,000 studies on long-horizon AI agents, arguing that reliable long-horizon intelligence depends on the whole model-harness system, not just larger context windows or stronger models.
A claim that GPT-5.6 Sol achieves state-of-the-art on ARC-AGI-3 by enabling reasoning across multiple context windows using canonical compaction.
The post argues that AI agent architecture should shift from monolithic agents that hold all context to a routing model where agents delegate tasks to specialized services, similar to how software evolved from monoliths to microservices.
A hot take arguing that context windows are a distraction from the real problem of AI memory, which remains unsolved and leads to forgetting context and duplicating bad info.
Context forking is a technique used by coding agents to rewind the conversation history, allowing developers to reuse high-quality context, save time and tokens, and explore different design paths. This guide explains how to use context forking effectively.
A comprehensive guide on building reliable AI agents, explaining core components of perception, decision logic, and action interface, with insights from a former Meta engineer.