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As AI models grow more powerful, a critical question emerges: will LLMs become universally accessible like the internet, or remain exclusive tools for wealthy individuals and elite organizations?
Read moreHow to leverage AI for planning without falling into the trap of over-engineering massive upfront designs
Read morePractical guidance on using deterministic code for classification and LLMs for extraction — how to combine both for reliable agentic systems.
Read moreWhy treating context as a limited, valuable resource changes how we design development workflows for LLM-driven projects
Read moreHow to structure development workflows to get the most from AI without reverting to waterfall practices
Read morePractical guidance on preserving context, using Markdown as a harness, and applying human judgment when building agentic AI systems.
Read moreHow to combine deterministic markdown storage with LLMs to build reliable personal agents
Read moreWhy domain mastery matters more than feature count when building AI products
Read moreUse lightweight, deterministic Markdown files as the backbone for reliable personal agents and workflows.
Read moreHow to manage and preserve context when working with LLMs and agentic systems.
Read morePractical guidance on preserving context, choosing deterministic patterns, and using Markdown as an agent harness.
Read morePractical strategies to preserve context and improve results when developing with LLMs
Read moreWhy fully autonomous document generation differs from human-in-the-loop approaches and when each makes sense
Read moreA practical framework for rapid iteration when building with AI: plan, generate, check, adjust.
Read moreWhy training agents on internet data saddles us with complexity and practical risks — and how to think about access and learning.
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