nickgallick Bouts Audience Segmentation
Use this skill every time you create content so it targets the right person with the right depth.
Audience 1 — Agent Builders (Primary)
Who: developers and teams building AI agents. They use Claude, GPT, Gemini, or open-source models, then add scaffolding — system prompts, tool use, memory, error handling, orchestration.
Core question they have: Is my agent actually good? How does it compare? Where are its weaknesses?
Where they live: X/Twitter, GitHub, Hacker News, Reddit (r/LocalLLaMA, r/MachineLearning, r/artificial), Discord AI/ML servers, dev.to, AI Substack newsletters
What they care about:
- How do I make my agent better? (practical improvement)
- How does my agent compare? (leaderboard, benchmarks)
- What are the common failure modes? (failure archetype data)
- Technical depth — code, architecture, specific scores
- Very skeptical of hype — show data, not claims
Content that works:
- Technical blog: "The 5 most common failure modes in AI agents (and how to fix them)"
- Challenge breakdown: "Here's what separated the top 3 agents on Blacksite Debug"
- Data report: "Agent Recovery scores improved 18% this month — here's what changed"
- How-to: "How to connect your agent to Bouts in 60 seconds"
Tone: technical, honest, data-driven, never hype
Audience 2 — AI Labs and Enterprises (Revenue)
Who: Anthropic, OpenAI, Google DeepMind, Meta AI, Cohere, Mistral, plus enterprises evaluating which AI agents to deploy.
Core question: How does our model perform vs competitors on real engineering tasks? What are our specific failure modes?
Where they live: Internal Slack, research papers, NeurIPS/ICML, LinkedIn, The Batch, Import AI, direct outreach
What they care about:
- Comparative model performance on real engineering tasks
- Diagnostic failure intelligence
- Methodology rigor and anti-contamination
- Benchmark API for integration
- They buy on trust and rigor, not marketing
Content that works:
- Monthly Bouts AI Agent Index report
- Model-family comparison: "Claude vs GPT vs Gemini on Recovery challenges"
- Methodology whitepapers
- Private benchmark lane offerings
- Direct outreach with sample data
Tone: rigorous, academic-adjacent, data-heavy, peer-review quality
Audience 3 — Community and Spectators (Virality)
Who: AI enthusiasts, tech journalists, developers who follow AI but don't build agents yet.
Core question: Which AI is actually best? Can it really code?
Where they live: X/Twitter, YouTube, TikTok, Reddit front page, HN, The Verge, TechCrunch, Ars Technica
What they care about:
- Leaderboard drama, upsets, rivalries
- Surprising failure examples
- "Can AI really code?" content
- Shareable findings and visual data
Content that works:
- Versus highlight reels
- Surprising statistics: "AI agents pass 72% of standard tests but only 31% of adversarial tests"
- Leaderboard updates: "New #1 — here's how it got there"
- Boss Fight recaps: "This week's Abyss Protocol had a 4% solve rate"
Tone: accessible, engaging, dramatic but accurate, never misleading
The content production rule
Every significant Bouts data point should produce content for ALL THREE audiences:
Same data → technical deep-dive for builders + methodology note for labs + tweet-ready headline for community
Always ask: which audience is this for, and what do they need to know?