Insights
Thinking Out Loud
Essays on biotech R&D, AI architecture, and the seams where deep technical work meets executive strategy.
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AI Engineering
Five Models, One Spec: Notes from Routing the Same Work to Different LLMs
When you dispatch the same tightly-specified task to GPT-5, GLM-5.1, GLM-4.6, and DeepSeek-V3 in parallel, the failure modes are model-specific in ways that matter. A working set of rules for choosing the right model per task.
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AI Architecture
The Hidden Tax on AI Agent Swarms
Decomposing work across multiple AI agents often costs more than a single well-designed agent — here is why that happens and what the numbers actually look like.
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Biotech
When Your Fermentation Data Talks Back: AI-Augmented Bioprocess Development
AI and ML are shifting bioprocess development from trial-and-error to data-driven optimization by giving scientists pattern recognition across thousands of fermentation runs that no human can replicate manually.
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Biotech
The Synthetic Biology Stack: Why Biotech Needs Software Engineering Principles
Biotech companies that adopt software engineering practices (version control for strains, CI/CD for genetic constructs, automated testing for phenotypes) will outcompete those that don't.
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Data Science
Validating Synthetic Genomic Data: The Missing Quality Layer
As synthetic genomic data becomes critical for ML training and privacy-preserving research, we need validation frameworks that measure fidelity, utility, and privacy, not just statistical similarity.
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AI Architecture
The Git Repo as the AI Workflow System Boundary
A git repository is the natural and complete system boundary for AI-driven document workflows. Everything the agent needs already lives in or can be referenced from a repo.
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AI Architecture
What Is the Smallest Unit of Work in Agent Decomposition?
When decomposing tasks for AI agents, there's a granularity floor: a point below which further decomposition creates more overhead than value.
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AI Architecture
Knowing That You Know: External Memory Architecture for AI
Most AI memory architectures have two states: knowing and not knowing. A third state, knowing that you know, lets systems scale beyond their context window without loading everything at once.
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R&D Strategy
Innovation Driven by Ability, Not Self-Awareness
Capability expansion precedes need articulation. The most consequential technologies in history weren't solutions to articulated problems.
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Innovation
Starting With What's Possible: Why R&D Should Lead Innovation
The R&D-First Bullseye Model reverses traditional market-led approaches by starting with technical possibility and working outward toward market validation.
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Business Development
The Gold Mine in Your Lost Proposals
Your organization's lost proposals and unpursued opportunities aren't failures. They're an asset library waiting to be indexed.
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AI Architecture
The Context Window is a Viewport, Not a Bucket
Knowledge map traversal (navigating structured graphs on-demand instead of loading entire databases) is the superior pattern regardless of context window size.
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AI + Tools
The Syntax Tax
Enterprise and scientific software has a long tail of capability most teams never reach because it is locked behind syntax. AI coding assistants change where in the workflow that translation happens.