<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>AI Effectiveness Journal</title><description>Exploring AI effectiveness — measuring AI by how much it helps people, teams, and organizations evolve.</description><link>https://www.mile-hi.ai/</link><item><title>The Forgetting Equation (Part 3/3: The Art of Forgetting)</title><link>https://www.mile-hi.ai/journal/the-forgetting-equation/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/the-forgetting-equation/</guid><description>What if growing requires subtracting? The parallel between human selective recall and AI context pruning hints at something that might be a universal principle of intelligence.</description><pubDate>Sun, 22 Feb 2026 00:00:00 GMT</pubDate></item><item><title>Teaching AI to Forget (Part 2/3: The Art of Forgetting)</title><link>https://www.mile-hi.ai/journal/teaching-ai-to-forget/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/teaching-ai-to-forget/</guid><description>AI systems that remember everything eventually fail. New architectures like Titans and MIRAS show how surprise-driven memory and adaptive forgetting create AI that learns continuously.</description><pubDate>Fri, 20 Feb 2026 00:00:00 GMT</pubDate></item><item><title>Forgetting Makes You Smarter (Part 1/3: The Art of Forgetting)</title><link>https://www.mile-hi.ai/journal/forgetting-makes-you-smarter/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/forgetting-makes-you-smarter/</guid><description>The brain doesn&apos;t forget because it fails — it forgets on purpose. Research shows active forgetting optimizes decision-making, enables flexibility, and is actually a form of learning.</description><pubDate>Wed, 18 Feb 2026 00:00:00 GMT</pubDate></item><item><title>Building AI Learning Curves: A Vibe Coding Journey</title><link>https://www.mile-hi.ai/journal/building-ai-learning-curves/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/building-ai-learning-curves/</guid><description>How an interactive AI learning curves visualization was built in under 2 hours using Claude Code — and what it reveals about measuring AI effectiveness.</description><pubDate>Sun, 15 Feb 2026 00:00:00 GMT</pubDate></item><item><title>Beyond Accuracy: Why Effectiveness Beats Efficiency</title><link>https://www.mile-hi.ai/journal/effectiveness-over-efficiency/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/effectiveness-over-efficiency/</guid><description>What if we&apos;re measuring AI wrong? Accuracy, speed, and cost are efficiency metrics. The more interesting question might be whether the system learns, evolves, and adapts.</description><pubDate>Tue, 10 Feb 2026 00:00:00 GMT</pubDate></item><item><title>The Hidden ERP Personality Types and AI Readiness</title><link>https://www.mile-hi.ai/journal/erp-personality-types/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/erp-personality-types/</guid><description>Before fixing AI, it might help to look at the bones of the enterprise data structure. SAP and Oracle encode fundamentally different philosophies about business relationships — and AI probably can&apos;t reason across either without understanding the difference.</description><pubDate>Thu, 22 Jan 2026 00:00:00 GMT</pubDate></item><item><title>The Tower of Babel in Your Boardroom</title><link>https://www.mile-hi.ai/journal/tower-of-babel-boardroom/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/tower-of-babel-boardroom/</guid><description>From Data Silos to a Knowledge Garden. What if your enterprise doesn&apos;t have a data problem — but a language problem? When Sales says &quot;Revenue&quot; and Finance hears something different, AI may struggle before it starts.</description><pubDate>Tue, 20 Jan 2026 00:00:00 GMT</pubDate></item><item><title>Is Density Dead? The Rise of STEM (Part 3/3: The Sparsity Revolution)</title><link>https://www.mile-hi.ai/journal/is-density-dead/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/is-density-dead/</guid><description>Fine-grained sparsity is not a compromise — it is an upgrade. STEM architecture shows how 70B parameter models can outperform 175B giants across diverse domains.</description><pubDate>Mon, 19 Jan 2026 00:00:00 GMT</pubDate></item><item><title>The Communication Tax (Part 2/3: The Sparsity Revolution)</title><link>https://www.mile-hi.ai/journal/the-communication-tax/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/the-communication-tax/</guid><description>When you split a model across 100 GPUs, the silent killer might not be compute — it could be the cost of GPUs talking to each other. What if we stopped thinking about experts and started thinking about granularity?</description><pubDate>Sat, 17 Jan 2026 00:00:00 GMT</pubDate></item><item><title>We&apos;ve Been Scaling LLMs Wrong (Part 1/3: The Sparsity Revolution)</title><link>https://www.mile-hi.ai/journal/scaling-llms-wrong/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/scaling-llms-wrong/</guid><description>For years, the equation was simple: More Intelligence = More Parameters = More Compute. But we are hitting a wall, and size is becoming a liability.</description><pubDate>Thu, 15 Jan 2026 00:00:00 GMT</pubDate></item><item><title>Is AI Smarter Than We Think, or Just Luckier?</title><link>https://www.mile-hi.ai/journal/ai-smarter-or-luckier/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/ai-smarter-or-luckier/</guid><description>When AI suddenly solves a complex physics problem, is it reasoning or pattern matching? The grokking phenomenon suggests the answer is stranger than either.</description><pubDate>Sat, 10 Jan 2026 00:00:00 GMT</pubDate></item><item><title>PReFLeXOR + ACE: The Self-Correcting AI Engine</title><link>https://www.mile-hi.ai/journal/preflexor-ace-self-correcting-ai/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/preflexor-ace-self-correcting-ai/</guid><description>What if you could stop your AI from making the same mistake twice? A dual-memory architecture — a reasoning map and a procedural manual — explores how AI systems might genuinely learn from their errors.</description><pubDate>Thu, 18 Dec 2025 00:00:00 GMT</pubDate></item><item><title>Your AI Needs a Map: How Sequential Monte Carlo Changes Reasoning</title><link>https://www.mile-hi.ai/journal/sequential-monte-carlo-reasoning/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/sequential-monte-carlo-reasoning/</guid><description>What if your AI is driving without a map — only knowing the next turn? Sequential Monte Carlo might give AI the ability to plan ahead, explore multiple paths, and choose the one most likely to reach the destination.</description><pubDate>Tue, 16 Dec 2025 00:00:00 GMT</pubDate></item><item><title>Building AI That Learns From Its Mistakes (Part 3/3: Context Engineering)</title><link>https://www.mile-hi.ai/journal/ai-learns-from-mistakes/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/ai-learns-from-mistakes/</guid><description>The solution to brevity bias and context collapse probably isn&apos;t bigger windows — it might be smarter context. An evolving playbook approach could turn errors into institutional memory.</description><pubDate>Mon, 15 Dec 2025 00:00:00 GMT</pubDate></item><item><title>Your AI Has Amnesia, Not Hallucinations (Part 2/3: Context Engineering)</title><link>https://www.mile-hi.ai/journal/ai-amnesia-not-hallucinations/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/ai-amnesia-not-hallucinations/</guid><description>What if your AI agent isn&apos;t hallucinating — but has amnesia? Context collapse is what seems to happen when a model is overwhelmed with context it cannot retain, and performance drops at a critical threshold.</description><pubDate>Sat, 13 Dec 2025 00:00:00 GMT</pubDate></item><item><title>Optimizing Prompts for the Wrong Audience (Part 1/3: Context Engineering)</title><link>https://www.mile-hi.ai/journal/optimizing-prompts-wrong-audience/</link><guid isPermaLink="true">https://www.mile-hi.ai/journal/optimizing-prompts-wrong-audience/</guid><description>What if we&apos;re optimizing AI prompts for the wrong audience? The reader is a machine that processes context in fundamentally different ways. This mismatch — brevity bias — might be silently degrading AI output quality.</description><pubDate>Wed, 10 Dec 2025 00:00:00 GMT</pubDate></item></channel></rss>