Darrell Booker

AWS CodeCommit is back and open for fresh sign-ups now. We're bringing the band back together and will be adding new features and expanding to even more #AWS Regions in 2026. Read all about it at https://lnkd.in/guauiRBB

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It was an absolute pleasure sharing the stage with brilliant minds today - Kate Kallot and Sandra Hermoso Rodríguez As we celebrate Black History Month, I’m proud to be part of the evolving intersection of AI and the Black community. The conversation spanned multiple AI domains. Key reflections from the conversation: 🔹 Explainable AI One simple promise - not just what the model said but why it said it, in a way a human can inspect, verify, and challenge. Transparency should be a feature, not a footnote. Reviewing data to identify patterns of bias or unfairness that may come from past inequalities or underrepresented groups. 🔹 AI Hallucination When AI confidently produces false or made-up information, especially in sensitive areas like law, health, or education, it erodes trust. We must keep human oversight central. AI systems can “hallucinate” or make wrong, biased decisions when their training data reflects unfair patterns or lacks representation, especially in areas like facial recognition or hiring. 🔹 Be involved - Better Questions, Better Outcomes At every decision point, design reviews, product meetings, roadmap planning... we should ask: Whom does this AI affect? Whose data trains it? Where can bias enter? Who’s missing from the room? You don’t need to be technical to shape outcomes. Bias often begins with assumptions, not algorithms. Normalising these questions drives inclusion from the ground up. In summary: Hope → AI can expand equity and amplify underrepresented voices if we build it that way. Concern → Without intentionality, AI can quietly scale bias faster than any system before. Action → Whether you're technical or not, ask better questions, demand transparency, and shape not just what AI does, but whom it serves. Kudos to the team - Black @Arm Kashima Brown Femi Dada Tsitsi M. Mabel Esho #AI

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A little over a month ago, I wrote a post about preparing for #AWS #reInvent2025 and looking to connect with people in the community. One response stood out. Tony C. and I had never met or even connected before, but he commented that he’d be at re:Invent and that we should meet — since we were both working in #FinOps and maybe we could help each other. Fast forward to re:Invent: we met in person, shook hands, and spent time talking about business, FinOps, and life in general. Tony was incredibly generous and offered to help however he could — a reminder that many of us are here today because others helped us along the way. After the conference, we met again over Google Meet so I could learn more about CloudForecast and what Tony and his co-founder François LAGIER are building. There were a few takeaways that really stood out about the company: 1️⃣ You don’t need to raise massive amounts of capital to build a great company. CloudForecast is fully bootstrapped and deeply focused on delivering value to customers. 2️⃣ They’ve solved a very specific problem — a lightweight, proactive cost visibility tool for engineering teams that don’t need (or want) a complex FinOps platform. 3️⃣ Generosity still matters. Tony and François embody that mindset. If you want to learn more about CloudForecast — including a deeper, independent analysis based on our conversation — you can read it in the CloudXray AI FinOps Directory. 👉 Direct link in the comments #FinOps #CloudCostManagement #AWS #ReInvent #StartupJourney #Bootstrapped #CloudEconomics #SaaS #BuildInPublic

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🧠 Cloud Computing + AI isn’t just for developers. Last night, I gave away extra pizza at our Orlando AWS meetup and ended up talking about cloud computing with a film student. That convo inspired my latest blog post: “Why Everyone Should Learn About Cloud Computing and AI” Whether you're in healthcare, farming, education, marketing—or yes, even film—cloud and AI are reshaping every industry. You don’t have to be a coder. But you do need to understand the tools shaping your world. 👉 Read the post: https://lnkd.in/erQwS_Rf 🎯 TLDR: Curiosity > Obsolescence. #AWSCommunityBuilders #AI #TechForEveryone #LifelongLearning #AWSUserGroup

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🚀 New episode of the AWS Developers Podcast! In this interview, Seb speaks with Trevor Rowe from the Amazon Web Services (AWS) SDK team about creating SDKs that cover over 380 services across 13 programming languages. They discuss Smithy, a tool for code generation, and the team's commitment to innovation in enhancing the developer experience. If you build with AWS, this one’s packed with insights. 🎧 Listen now in your favorite podcast app or here 👉 https://lnkd.in/euwEDied #aws #sdk #cloud

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Black History Month — Tech Edition | Day 20 Today we highlight Kelsey Hightower. Kelsey Hightower became one of the most respected voices in cloud-native infrastructure during the rise of Kubernetes and container orchestration. But what made him stand out wasn’t just technical depth. It was clarity. In the early Kubernetes era, the ecosystem was: • Complex • Rapidly evolving • Vendor-influenced • Often confusing to enterprise teams Hightower built trust by doing something rare: He explained distributed systems in a way operators could reason about. He didn’t sell abstractions. He deconstructed them. That mattered. Because Kubernetes wasn’t just a developer tool. It became enterprise control plane infrastructure. And enterprise teams needed: • Conceptual models • Failure-mode awareness • Practical operational guidance • Straight answers about trade-offs Hightower represented a new archetype in infrastructure: The engineer who can operate at scale — and teach at scale. That combination changes ecosystems. Enterprise IT leaders know this instinctively: Tools evolve quickly. Understanding compounds slowly. The lesson for today’s generation: Complex systems demand interpreters — not just implementers. Kelsey Hightower helped enterprises move from experimentation to operational maturity in the cloud-native era. More tomorrow.

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