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

||
#claude-code#git-worktrees#supabase#local-development#developer-productivity

// FEATURED

OpenClaw and the Programmable Soul

Four primitives. Agent societies. And a preview of what enterprise AI might become.

Duncan Anderson

||
#AI Agents

// FEATURED

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

||
#AI Agents

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

||
#AI Ethics

// FEATURED

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

||
#AI Agents

// FEATURED

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

||
#Machine Learning

// FEATURED

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

||
#Machine Learning

// FEATURED

Understanding and Processing CT Imaging for Stroke Detection

A practical guide to turning raw brain CT into training-ready data.

William Auroux

||
#Machine Learning

// FEATURED

Building a Reproducible Multimodal Pipeline

Part-2 in a series of posts about proteomics, cancer biology, and AI-driven solutions for oncology.

Kevish Napal

||
#Machine Learning

// FEATURED

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

||
#Machine Learning

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

||
#Context Engineering

// FEATURED

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

||
#AI Agents

// FEATURED

Barnacle Labs Appoints Dr. Oliver Bogler as Scientific Advisor

Distinguished Cancer Researcher and Federal Program Leader to Accelerate AI-Driven Scientific Discovery

Duncan Anderson

||
#Company

// FEATURED

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

||
#Company

// FEATURED

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

||
#Company

// FEATURED

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

||
#AI-Training
Barnacle Labs
Barnacle_Labs

AI for breakthroughs, not buzzwords.

34 Tavistock Street, Covent Garden, London WC2E 7PB

Google Cloud Partner
  • Barnacle Labs Ltd. England & Wales.
  • Company No. 14427097
  • © 2026 Barnacle Labs Ltd.