Founder and CEO of OutcomeOps.ai

Joined October 2017
5 Photos and videos
Check out my new post on #AWSBuilder about Building Auditable RAG on Amazon Bedrock: A Five-Component Walkthrough: builder.aws.com/content/3E2b…

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Drag Race All Stars 11 starts in just 4 days! 18 legendary queens. Tournament of All Stars format. $200K prize Hall of Fame on the line (from the show). Build your fantasy team on myfantasy.ai/?v1 — real-time AI scoring, weekly AI trash talk, and leaderboards. Spots filling fast. Sign up now and lock in your picks before the May 8 premiere! 👉 myfantasy.ai/shows/drag-race… Who’s your #1 draft pick? Comment below 👇 #DragRace #AllStars11 #RuPaulsDragRace #FantasyLeague #MyFantasyAI
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DevOps is dead. Not because the ideas were wrong — but because the implementation lost the plot. I built the same internal platform 5 times across Pearson, Aetna, Liberty Mutual, Comcast, and Gilead. What worked in 2012 still works in 2026. We’re now in Phase 3 of AI — The Reckoning. OutcomeOps is Phase 4: Organizational intelligence that actually ships. 16 hours → 20 minutes. $2.24 per feature. 90% first-time PR approval. Your code. Your standards. Your outcomes. Watch the full story #OutcomeOps #DevOps #PlatformEngineering #EnterpriseAI
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Full transparency. No black boxes. No hidden fees. OutcomeOps deploys straight into YOUR AWS account via Terraform. Your code. Your prompts. Your Bedrock usage. All stay in-region. Fully auditable. Real-time refusal alerts. We don’t train on your data. You pay AWS directly. Zero markup. No other platform does this. #OutcomeOps #EnterpriseAI #AWSBedrock #chatgpt #cursor
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I just published What AI-Assisted Development Actually Looks Like in Two Years medium.com/p/what-ai-assiste…

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Just published: "What AI-Assisted Development Actually Looks Like in Two Years" Most predictions miss the pattern. I've lived through cloud, containers, platform engineering, and now AI in large enterprises. The cycle is the same: individual wins → local chaos → reckoning → organizational intelligence wins. We're entering the second half of that cycle faster than people think. outcomeops.ai/blogs/what-ai-…
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Optimists say infinite productivity with no downside. Pessimists say mass developer displacement. Both miss the middle: teams that encode knowledge into platforms guardrails will ship faster with smaller, higher-leverage teams. Everyone else stays stuck in the reckoning. History (cloud, DevOps, platform eng) says the split is coming.
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If you're leading engineering or platform at an enterprise, this is the practical view from someone who's done the transformations—not hype, not doom. Context Engineering is the missing discipline. Read the full post → outcomeops.ai/blogs/what-ai-… Would love your takes—especially if you've seen the reckoning phase up close.
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You’re probably using the wrong Bedrock model. We ran the *exact same* enterprise RAG pipeline (13 ADRs compliance docs context engineering) on 8 models for a real SOC 2 sales query. Winner? The cheapest one: Claude Haiku 4.5. It shipped a full sales playbook (email, objection handlers, guardrails, CRM notes). Sonnet? Good but incomplete. The rest? Surface-level at best. The most expensive model isn’t the best. The *right* model is. Full results task-matching framework → outcomeops.ai/blogs/youre-pr… #ContextEngineering #AWS #Bedrock #GenAI
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Just dropped a new one: "LLMs Are Ruining My Craft" Remember that 2012 classic about DevOps destroying the soul of development? Yeah, we're doing it again—but this time the villain is pristine, perfectly scoped commits from Claude Opus 4.6. No more glorious git log chaos. No more "ops..." at 11pm carrying the weight of regret. Just bullet points, Conventional Commits, and co-author credits. The suffering was the art. Read it here: briancarpio.com/blog/llms-ar… #LLM #SoftwareEngineering #DevLife #GitLogJazz
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I built the same product twice — 14 years apart. 2012: Nibiru — self-service AWS platform at Pearson that let 70 dev teams deploy infra in minutes instead of months. 2026: OutcomeOps — same core idea, now for AI code generation. The pattern? Organizational intelligence encoded into the platform. outcomeops.ai/blogs/same-pro…
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The winning pattern never changed: Make the right path the easy path. Encode ADRs, code-maps, compliance, and decisions into the generation layer → Context Engineering. Puppet ENC in 2012 → Vector store Bedrock in 2026. Same philosophy, one level higher.
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Real results from production: → 16h tasks → 20 minutes → $2.24 per feature → 90% first-time approval This is what Nibiru looked like in 2014 If this pattern hits home, what AI code gen friction are you seeing? Reply or DM. outcomeops.ai #PlatformEngineering #ContextEngineering #outcomeops
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