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Pierre Sauvignon, Founder of LobsterOne

Pierre Sauvignon

Founder · LobsterOne

Pierre Sauvignon is the founder of LobsterOne — a macOS menu bar app and team dashboard for tracking AI coding tool usage. He built LobsterOne to answer a question he kept hitting in his own engineering work: how much AI is my team actually using, and is it working?

Before LobsterOne, Pierre spent years shipping software across startup and enterprise environments. He writes about AI-assisted development, developer productivity measurement, team adoption patterns, governance, and the cultural shifts that accompany AI coding tools. His writing is practitioner-first: frameworks he has used, trade-offs he has made, and measurement approaches that hold up in production — not hype.

LobsterOne is built by OnSilent Pty Ltd, an Australian software company. Reach Pierre at support@lobsterone.ai or on the social links above.

Published articles (52)

Energy per token chart for Claude Code and Codex
sustainabilityresearch

How Much Energy Does AI Coding Use? A Developer's Guide to LLM Carbon Footprint

The public data on Claude Code and Codex energy consumption — Wh per token, per session, per workday — triangulated from Epoch AI, Google's Gemini disclosure, and peer-reviewed benchmarks. What the numbers actually mean for developers.

Apr 20, 202610 min read
Eco Score leaf rating on the LobsterOne dashboard
sustainabilityproduct-launch

Introducing the Eco Score: The First Sustainability Metric for AI-Assisted Coding

Every Claude Code and Codex turn has an energy cost. The Eco Score makes your AI coding climate impact visible — with a composite 0–100 score, three sub-scores, and a leaf rating you can actually improve.

Apr 20, 20268 min read
Sustainable AI coding playbook
sustainabilitybest-practices

Reduce Your AI Coding Energy Consumption: A Sustainable Developer's Playbook

Seven concrete tactics to cut the energy and carbon footprint of your Claude Code and Codex usage — model selection, prompt caching, session hygiene, and extended thinking discipline.

Apr 20, 20268 min read
Dashboard widgets for debugging AI coding token usage
productmetrics

Five Dashboard Widgets That Show You Where Your Tokens Go

New widgets for tools per session, cache efficiency, cost by project, token burn, and tools insights help you debug runaway token usage and get more from your AI coding quota.

Apr 8, 20269 min read
Best vibe coding tools compared in 2026
vibe-codingtools

Best Vibe Coding Tools Compared (2026)

A fair comparison of the top AI coding tools — IDE-integrated, CLI-native, and browser-based — and why the tool matters less than how you use it.

Apr 3, 202610 min read
Vibe coding for engineering teams
vibe-codingteams

Vibe Coding for Engineering Teams: Adoption Without Chaos

How engineering leaders can roll out AI-assisted development across their team — with visibility, shared practices, and measurable outcomes.

Apr 2, 202612 min read
10 vibe coding best practices
vibe-codingbest-practices

Vibe Coding Best Practices: 10 Rules for AI-First Development

Practical rules for getting real results from AI-assisted coding — from PRD-first workflows to token budgets and measurement.

Apr 1, 202620 min read
How gamification and streaks improve AI developer productivity
ai-adoptionleaderboards

How Gamification and Streaks Improve AI Developer Productivity

How streaks, badges, and leaderboards leverage behavioral psychology to make AI coding tool usage stick — the science behind the habit.

Mar 31, 202612 min read
From 10x developer to 10x AI-assisted developer
developer-transitionproductivity

From 10x Developer to 10x AI-Assisted Developer

Why the best developers get disproportionately more value from AI tools — and how to close the gap if you are not there yet.

Mar 30, 202610 min read
AI-assisted coding workflow patterns that ship faster
developer-transitionproductivity

AI-Assisted Coding Workflow Patterns That Ship Faster

Five proven workflow patterns for AI-assisted development — scaffold-then-refine, test-first-then-implement, review-loop, spike-and-stabilize, pair-with-AI.

Mar 30, 202614 min read
Enterprise AI coding strategy — 30/60/90 roadmap
enterpriseguides

The Enterprise AI Coding Playbook: A 30/60/90 Roadmap

A CTO's 30/60/90-day rollout plan for AI coding tools at enterprise scale. Week-by-week milestones, owner assignments, and links to the specific artifacts each phase produces.

Mar 30, 20268 min read
Business case template for AI coding tool budget approval
ai-adoptionroi

A One-Page Business Case for AI Coding Tools

A fill-in-the-blanks business case template for the meeting where you ask your CFO for AI coding tool budget. Cost model, savings math, risk framing, and the single slide that closes the decision.

Mar 29, 20268 min read
AI coding tool procurement contract checklist
enterprisetools

AI Coding Tool Contracts: The Clauses Procurement Needs to Negotiate

A contract term checklist for AI coding tool procurement. Data ownership, training rights, exit, DPA and BAA coverage, indemnification, and the vendor answers that disqualify a tool before pricing matters.

Mar 28, 202611 min read
Weighted scorecard for AI coding tool bakeoff
ai-adoptiontools

AI Coding Tool Bakeoff: A Weighted Scorecard for Tech Leads

A scorecard for running a head-to-head AI coding tool evaluation. Weighted criteria, hands-on test tasks, tie-breaker rules, and the comparative structure that produces a defensible choice.

Mar 27, 20267 min read
Privacy-first AI coding analytics — measuring without monitoring
metricsrisk-governance

Privacy-First AI Coding Analytics: Measuring Without Monitoring

How to get full visibility into AI tool adoption and efficiency without collecting code, prompts, or sensitive data. The privacy-first approach.

Mar 27, 202613 min read
How to prevent AI coding doom loops in production codebases
risk-governanceproductivity

How to Prevent AI Coding Doom Loops in Production Codebases

What doom loops are, how to detect them in your codebase, and the metrics-driven approach to breaking the cycle before it compounds.

Mar 26, 202612 min read
AI coding risk assessment template — pre-filled
risk-governanceguides

AI Coding Risk Assessment: A Filled-In Template for Your Next Steering Committee

A pre-filled AI coding risk register with likelihood, impact, existing controls, and residual scores. Copy the structure, adjust for your context, walk into the meeting with a deliverable.

Mar 25, 20269 min read
AI coding compliance — regulation-by-regulation mapping
enterpriserisk-governance

AI Coding Compliance: A Regulation-by-Regulation Mapping

What SOC 2, HIPAA, PCI-DSS, GDPR, and the EU AI Act actually require when your code is AI-generated — mapped to specific controls, evidence artifacts, and audit-time answers.

Mar 22, 202610 min read
AI coding governance policy template
enterpriserisk-governance

AI Coding Governance: A Policy Document Template

Fill-in-the-blank policy language for an internal AI coding tools standard. Scope, acceptable use, approval, review, enforcement — copy into your policy library and adjust the bracketed placeholders.

Mar 21, 20269 min read
AI-generated code testing strategy — what to test differently
risk-governancedeveloper-transition

AI-Generated Code Testing Strategy: What to Test Differently

Why AI-generated code needs different test strategies — edge case coverage, integration testing emphasis, and property-based testing approaches.

Mar 20, 202614 min read
CI/CD gating decision tree for AI-generated code
risk-governanceguides

AI Code CI/CD Gating: A Decision Tree for Blocking, Flagging, and Passing

When to block an AI-generated commit at merge, when to flag it for extra review, and when to let it through. A concrete gating tree for staff engineers responsible for production safety.

Mar 19, 20268 min read
Code review best practices for AI-generated code
developer-transitionsecurity

Code Review Best Practices for AI-Generated Code

How code review changes when the author is an AI — what to look for, common failure patterns, and a review checklist for AI-assisted development.

Mar 18, 202614 min read
AI code audit trail — forensic provenance for compliance officers
risk-governanceenterprise

AI Code Provenance: The Five Questions an Auditor Will Ask

A practical git-trailer spec and retention table for proving AI code provenance during an audit or post-incident review. What to capture, what to keep, and how long.

Mar 17, 20268 min read
How to build an AI code quality gate for your CI/CD pipeline
risk-governanceproductivity

How to Build an AI Code Quality Gate for Your CI/CD Pipeline

Linting rules, test coverage thresholds, and automated checks specifically tuned for AI-generated code patterns in your build pipeline.

Mar 17, 202612 min read
SAST ruleset patterns for AI-generated code
risk-governancesecurity

An AppSec SAST Ruleset for AI-Generated Code

Concrete Semgrep rules, regex patterns, and tuning guidance for the AI-specific security baseline. What to flag, what to skip, and how to calibrate FP rates when half your commits are AI-assisted.

Mar 16, 20269 min read
Building an AI-first engineering culture at scale
enterpriseai-adoption

Building an AI-First Engineering Culture at Scale

What 'AI-first' actually means in practice — not replacing developers but changing how every developer works. Cultural and process shifts that stick.

Mar 12, 202610 min read
Scaling AI coding tools from 10 to 1,000 developers
enterpriseteams

Scaling AI Coding Tools from 10 to 1,000 Developers

What breaks when you scale AI coding beyond a single team — tooling fragmentation, cost explosion, inconsistent practices, and how to fix each one.

Mar 10, 202611 min read
Best AI toolsets to roll out in a dev team
ai-adoptiontools

Best AI Toolsets to Roll Out in a Dev Team (2026)

How to evaluate and select AI coding tools for your team — criteria that matter, categories to consider, and what most evaluations miss.

Mar 9, 202615 min read
How to calculate ROI on AI coding tool investment
metricsroi

How to Calculate ROI on AI Coding Tool Investment

A step-by-step ROI model for AI coding tools — license cost plus token cost versus hours saved, quality delta, and velocity gains.

Mar 5, 202624 min read
The engineering manager's first 90 days with AI coding tools
ai-adoptionteams

The Engineering Manager's First 90 Days with AI Coding Tools

A week-by-week guide — tool selection, pilot group setup, measurement framework, and full rollout in 90 days.

Mar 3, 202612 min read
How to transition from traditional development to AI-assisted coding
developer-transitionguides

How to Transition from Traditional Development to AI-Assisted Coding

A practical guide for experienced developers making the shift to AI-assisted workflows — mindset changes, new skills, and daily workflow patterns.

Mar 2, 202613 min read
How senior developers can lead the AI transition
developer-transitionteams

How Senior Developers Can Lead the AI Transition

Why senior devs are the best positioned to lead AI adoption — their architectural judgment makes AI output dramatically better.

Feb 27, 202613 min read
How to help traditional developers embrace AI coding tools
developer-transitionteams

How to Help Traditional Developers Embrace AI Coding Tools

Practical guidance for team leads — pairing sessions, gradual adoption paths, celebrating early wins, and removing friction for experienced developers.

Feb 26, 202612 min read
How to build an AI coding champions program
ai-adoptionteams

How to Build an AI Coding Champions Program

Select early adopters, give them a mandate, measure their impact, and let results sell the tooling — a playbook for internal AI advocacy.

Feb 25, 202611 min read
Why developers resist AI coding tools and what to do about it
ai-adoptionteams

Why Developers Resist AI Coding Tools (And What to Do About It)

The five real reasons developers push back on AI tools — fear of deskilling, trust, workflow disruption — and evidence-based strategies to address each one.

Feb 24, 202613 min read
How to motivate developers to adopt AI coding tools
ai-adoptionteams

How to Motivate Developers to Adopt AI Coding Tools

Behavioral tactics — not mandates — that drive organic AI tool adoption. Internal champions, pairing sessions, and visible leaderboards.

Feb 23, 202610 min read
How to run an AI coding pilot program that actually proves value
enterpriseai-adoption

How to Run an AI Coding Pilot Program That Actually Proves Value

Pilot design that produces actionable data — team selection, duration, control metrics, success criteria, and how to present results.

Feb 20, 202612 min read
How to roll out AI coding tools across your engineering team
ai-adoptionteams

How to Roll Out AI Coding Tools Across Your Engineering Team

A phased playbook for engineering leaders deploying AI coding tools — from pilot group to full adoption, with change management and measurement built in.

Feb 20, 202614 min read
How to measure AI adoption in engineering teams
metricsteams

How to Measure AI Adoption in Engineering Teams

What to track when your team uses AI coding tools — tokens, cost, acceptance rate, sessions — and how to build a measurement practice that drives decisions.

Feb 19, 202615 min read
AI coding adoption benchmarks for 2026
ai-adoptionmetrics

AI Coding Adoption Benchmarks: What Good Looks Like in 2026

A framework for setting realistic AI adoption targets by team size, domain, and maturity — not vanity metrics, but actionable benchmarks.

Feb 18, 202612 min read
The AI-assisted developer skill stack — what changes and what stays
developer-transitionproductivity

The AI-Assisted Developer Skill Stack: What Changes, What Stays

Which traditional developer skills become more important with AI tools and which become less central — a realistic assessment, not hype.

Feb 17, 202611 min read
AI coding team dashboard design and analytics
metricsteams

AI Coding Team Dashboard: What Your Team Analytics Should Show

What a well-designed AI coding analytics dashboard looks like — key views, drill-downs, alert thresholds, and the metrics that actually drive decisions.

Feb 16, 202628 min read
12 AI development KPIs every engineering leader should track
metricsguides

12 AI Development KPIs Every Engineering Leader Should Track

The essential KPIs for AI-assisted development — from token consumption and acceptance rate to cost per session and adoption velocity.

Feb 13, 202615 min read
How to track AI coding token usage across your team
metricsteams

How to Track AI Coding Token Usage Across Your Team

A practical guide to tracking token consumption at individual and team level — what tokens reveal about usage patterns and where value hides.

Feb 12, 202612 min read
AI coding session analytics - what to look for
metricsproductivity

AI Coding Session Analytics: What to Look For

How session duration, prompt count, and token cost per session reveal developer efficiency and tool-fit signals you can act on.

Feb 11, 202612 min read
What AI code acceptance rate tells you about developer productivity
metricsproductivity

What AI Code Acceptance Rate Tells You About Developer Productivity

A deep dive on acceptance and rejection rates — what they mean, what good looks like, and why low acceptance is a coaching signal, not a failure.

Feb 10, 202614 min read
AI prompting skills every developer needs in 2026
developer-transitionproductivity

AI Prompting Skills Every Developer Needs in 2026

Practical prompting techniques for developers — context setting, constraint specification, iterative refinement, and PRD-first prompting patterns.

Feb 9, 202611 min read
How to measure your personal AI coding productivity
metricsdeveloper-transition

How to Measure Your Personal AI Coding Productivity

An individual developer's guide to tracking tokens, sessions, acceptance rates, and streaks — improve your AI coding practice with your own data.

Feb 9, 202611 min read
AI pair programming — how to treat your AI like a junior dev
developer-transitionproductivity

AI Pair Programming: How to Treat Your AI Like a Junior Dev

The mental model for AI collaboration — set context, give clear instructions, review everything, iterate on feedback. A practical guide.

Feb 6, 202614 min read
Why having the right AI metrics changes everything
metricsproductivity

Why Having the Right AI Metrics Changes Everything

Teams that measure the wrong things — lines of code, commit count — actively harm themselves. Here's what to measure instead and why it matters.

Feb 5, 202611 min read
Strava for AI developers — why visibility drives better coding
ai-adoptionleaderboards

Strava for AI Developers: Why Visibility Drives Better Coding

Making AI coding activity visible — not surveillant — is the key to sustained adoption. The fitness tracker analogy explains why.

Feb 4, 202611 min read
What is vibe coding — a developer's guide
vibe-codingguides

What Is Vibe Coding? A Developer's Guide (2026)

Vibe coding means building software through natural-language prompts to an AI. Here's what it is, when it works, and why measuring it matters.

Feb 3, 202623 min read