A record of evolving understanding. Each entry starts with studying how AI learns — and how it helps us learn. The ideas get applied, tested in prototypes, and eventually shaped into something that might work in practice.
Along the way, thinking has evolved: about how AI accelerates its own growth, how it can help individuals and teams evolve their own reasoning, and whether any of this can genuinely transform how organizations operate. This journal is that thinking, made visible.
Scale of Impact
Topic
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.
February 20, 2026
The brain doesn'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.
February 18, 2026
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.
January 19, 2026
When AI suddenly solves a complex physics problem, is it reasoning or pattern matching? The grokking phenomenon suggests the answer is stranger than either.
January 10, 2026
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.
December 16, 2025
What if your AI agent isn'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.
December 13, 2025
What if we'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.
December 10, 2025
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.
December 18, 2025
The solution to brevity bias and context collapse probably isn't bigger windows — it might be smarter context. An evolving playbook approach could turn errors into institutional memory.
December 15, 2025
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.
February 22, 2026
What if we're measuring AI wrong? Accuracy, speed, and cost are efficiency metrics. The more interesting question might be whether the system learns, evolves, and adapts.
February 10, 2026
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't reason across either without understanding the difference.
January 22, 2026
From Data Silos to a Knowledge Garden. What if your enterprise doesn't have a data problem — but a language problem? When Sales says "Revenue" and Finance hears something different, AI may struggle before it starts.
January 20, 2026
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?
January 17, 2026
How an interactive AI learning curves visualization was built in under 2 hours using Claude Code — and what it reveals about measuring AI effectiveness.
February 15, 2026
For years, the equation was simple: More Intelligence = More Parameters = More Compute. But we are hitting a wall, and size is becoming a liability.
January 15, 2026
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