ADVENTURES
IN AI
// TOPICS
All our insights across AI, technology, and innovation.
16 posts
// FEATURED
The Missing Piece of the Claude Code Workflow: Isolated Worktree Databases
Git worktrees + Claude Code need isolated databases. Here's how to spin up separate Supabase stacks per branch with automatic port allocation.
JD Wuarin
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OpenClaw and the Programmable Soul
Four primitives. Agent societies. And a preview of what enterprise AI might become.
Duncan Anderson
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Building AI products means managing API costs. Here's what actually worked
A practical guide to making LLM costs debuggable in production: how to log tokens at the right granularity, understand prompt caching behavior, and identify the real drivers behind cost spikes.
Thibault Boutet
||Seatbelts for AI: Lessons from the Grok Image Controversy
Grok's image abuse problem is engineering, not ideology—and we already know how to fix it
Duncan Anderson
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Building a Production Multi-Agent System for Document Writing
A deep dive into the architecture of a briefing generation system that uses 15 specialized AI agents to transform PDF source materials into structured documents.
Thibault Boutet
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Mean Pooling Beats Attention: Predicting Telomerase Activity from Whole-Slide Images
From global signals to local clues with ABMIL—and what it taught us about telomerase as a tissue-wide phenotype.
Kevish Napal
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The Dimension Dilemma: Why 2.5D Models Outperform 3D CNNs for Stroke Classification
Lessons & Experiments on training deep learning models on 3D medical data.
William Auroux
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Understanding and Processing CT Imaging for Stroke Detection
A practical guide to turning raw brain CT into training-ready data.
William Auroux
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Building a Reproducible Multimodal Pipeline
Part-2 in a series of posts about proteomics, cancer biology, and AI-driven solutions for oncology.
Kevish Napal
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Understanding Cancer and Telomerase: From Biology to New Treatments
Part-1 in a series of posts about proteomics, cancer biology, and AI-driven solutions for oncology.
Kevish Napal
||Stop Treating AI Like Magic: Why Context Engineering Beats Bigger Models
Smaller models with a disciplined context rival flagships at a fraction of the cost
Duncan Anderson
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The 4 Levels of AI Agents: When to Use Workflows vs Autonomous Systems
Stop over-engineering simple problems and under-engineering complex ones
Duncan Anderson
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Barnacle Labs Appoints Dr. Oliver Bogler as Scientific Advisor
Distinguished Cancer Researcher and Federal Program Leader to Accelerate AI-Driven Scientific Discovery
Duncan Anderson
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Why We're Going All-In on Forward Deployed Engineers for AI Projects
Forward Deployed Engineers help to scope and define new projects
Duncan Anderson
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Me and an AI: Building a New Website Together
Our new website was built by Claude and o1. A human (me) oversaw its construction, but AI wrote every line of code.
Duncan Anderson
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Beyond Data Hoovering: The Nuanced Reality of Training Large Language Models (LLMs)
Training Large Language Models (LLMs) is an evolving science — or, perhaps, an art form. In this post I set out to shed some light on exactly what is meant by training a model.
Duncan Anderson
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