Topic
Essays, profiles, and reading notes on AI products, AI systems, adoption, agents, and company strategy.
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Start with the control layer for agents, context, tools, permissions, and human review.
Series Index AI Adoption That Actually Changes the CompanyUse the index instead of dropping into a middle chapter of the series.
Series Index Seven Powers in the AI EraRead the strategic power series from the table of contents.
AI The AI Builder’s Edge: 23 Patterns Across 264 ProfilesAcross 264 ai & machine learning profiles and 18,726 lessons, 23 patterns recur often enough to matter. They are not universal rules; recurrence indicates breadth within this collection, not agreement across an entire field.
Research Explainers When Do AI Agents Actually Need Blockchains?Jeremy Allaire argues that AI agents need economic infrastructure for payments, identity, and coordination. This explainer separates the functions that genuinely benefit from blockchains from those better handled by conventional systems, clarifying where onchain architecture earns its complexity.
Research Explainers AI Traffic Is Becoming Workflow TrafficOpenRouter's 100 trillion token usage study suggests AI demand is shifting from simple text generation toward reasoning, tools, code, and context-heavy workflows. This explainer maps what that change means for model providers, infrastructure, and the economics of serving AI.
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A survey argues that agent reliability depends as much on the execution harness as on the model and gives builders a vocabulary for the infrastructure surrounding agents. It organizes execution, context, state, tools, recovery, and evaluation into one reliability model.
AgingBench argues that deployed agents can degrade as their memory state changes, so reliability needs lifespan testing rather than only day-one benchmarks. Its evaluation tests how accumulated state and environmental change affect performance over time.
Redpoint's InfraRed report argues that AI value is shifting from thin application wrappers into infrastructure: post-training, inference, agents, security, sandboxes, observability, and compute.
Language Models Need Sleep argues that long-context systems may require offline consolidation time, not merely larger memory, to reason over context they can no longer attend to. The analogy motivates architectures that compress experience between active reasoning cycles.
A legal history argues that AI authorship is not a new copyright problem but the latest form of mediated authorship. Earlier disputes over photography and mechanical creation reveal recurring legal tests for control and originality.
Polar shows how to train coding agents through the harnesses they already use instead of rebuilding those environments for reinforcement learning. The approach preserves realistic tools and feedback while making training operationally tractable.
Epicure shows how ingredient embeddings can become navigable tools for cooking, menu design, and food AI. The system turns flavor relationships into controllable dimensions for substitution, exploration, menu planning, and creative composition rather than returning another opaque ranking.
SkillOpt treats reusable agent skills as operating assets that can be trained, validated, and reused. It shows how procedural instructions can be optimized against tasks, checked for regressions, and distributed with evidence.