Abraham Nevado

New article: The Feynman Guide to Effective AI: Building a $10M Business with Zero Employees Demystifying the strategic use of AI using the Feynman Technique. Learn how to stop treating AI like a toy and start building it as an exoskeleton. Read it here: https://lnkd.in/gsYF22Y5

1

Guillermo Rauch, CEO of Vercel, explained why modern apps don’t need you to reinvent backends, just build the frontend and plug into services. Full episode: https://lnkd.in/e6-Bf7PJ

52

2 comentarios

The bottleneck for building AI projects isn't the AI. It's everything else. Next.js frontend. Python packages and dependencies. Postgres? A vector store? Maybe Redis. Getting that stack running locally is brittle. Deploying it is a headache. Getting it working consistently for the whole team? Even harder. I've spent the last three years working with teams building AI products. Same pattern every time, days wasted on infrastructure before anyone writes a single line of code. So I built Spinbox. It's a CLI that spins up containerized prototyping environments using Docker and dev containers. Pick a predefined profile or mix and match components. Full stack in seconds. Every developer I know builds tools like this for themselves. We never open-source them. I decided it was time to change that. I sat down with Claude and gave Spinbox a proper plugin architecture. It's now one of my most-used terminal tools. If you're learning AI this Christmas, skip the infrastructure headache. https://lnkd.in/epMt-_VE Still early days, feedback and PRs welcome.

42

9 comentarios

For years, Sonar has helped millions of developers master code quality. Today, we are taking that mission to the next level, and we are announcing the beta version for architecture capabilities in SonarQube Cloud. Why now? First, we were pretty busy working on other topics :), and more importantly AI agents are increasing significantly the volume of code changes on applications. If we don’t provide developers—and their AI agents—with clear architectural guardrails, we aren't accelerating innovation; we are simply accelerating entropy and the drift from intended architecture. We built this to provide a pragmatic solution to a complex problem: helping you visualize your actual structure, formalize your intent, and prevent the drift that kills productivity.I invite you to join the Beta and read my full blog post on the announcement to see how we are helping teams build software that lasts: https://lnkd.in/e4uz53Qu

276

9 comentarios

At SonderMind, we’ve been inspired by the concept of Constitutional AI. While we haven’t adopted the model in its purest form, we’ve created our own AI Constitution — a framework that codifies our values and defines what’s appropriate, safe, and effective for AI in mental health. It’s our first step toward building AI we can trust and reason about, together. #constitutionalAI #safeAI

31

5 comentarios

They had the frameworks. The pilots. The executive buy-in. And they still couldn't scale AI past proof of concept. Luis de Abreu Branco just crystallized what I've been seeing in the field: four governance mechanisms that separate organizations scaling AI from those stuck in pilot purgatory: 𝟭. 𝗩𝗮𝗹𝘂𝗲 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗯𝗲𝗮𝘁𝘀 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 Stop asking "Are you ready for AI?" (episodic checkpoint thinking). Start asking "What are the non-negotiable boundaries?" (continuous observability). Most PMOs are still running approval gates. The ones exiting pilots have switched to observation + intervention. 𝟮. 𝗟𝗶𝘃𝗶𝗻𝗴 𝗮𝗿𝘁𝗶𝗳𝗮𝗰𝘁𝘀 𝗯𝗲𝗮𝘁 𝘀𝘁𝗮𝘁𝗶𝗰 𝗰𝗵𝗲𝗰𝗸𝗽𝗼𝗶𝗻𝘁𝘀 Quarterly governance reviews react too late. Decision dashboards surface drift in real-time. The phrase I'm stealing: "Legible, not permissioned." Your AI systems should explain themselves continuously, not wait for audit season. But frameworks alone don't fix this. It comes down to who owns the tension. 𝟯. 𝗦𝘁𝗲𝘄𝗮𝗿𝗱𝘀𝗵𝗶𝗽 𝗯𝗲𝗮𝘁𝘀 𝗮𝗽𝗽𝗿𝗼𝘃𝗮𝗹 𝗰𝗼𝗺𝗺𝗶𝘁𝘁𝗲𝗲𝘀 Committees diffuse accountability. A clear stewardship function—with mandate to observe, intervene, and redirect without killing learning—is rare. The organizations with AI in production have this. The rest are still debating who gets to say no. 𝟰. 𝗥𝗲𝘃𝗲𝗿𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗯𝗲𝗮𝘁𝘀 𝗽𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻 Most risk frameworks optimize for "avoid failure." Adaptive systems optimize for "fail safely." Shift from "Can we prevent this from going wrong?" to "Can we shut it down cleanly if it does?" That changes the entire architecture. Luis closed with this: "This frontier is not technical. It is organizational and ethical." That's where I see the gap. Not in the frameworks. In the leadership capacity to hold the tension between exploration and control when the board asks hard questions. The real assessment isn't "Can your tools do this?" It's "Can your leadership sustain dual-mode operation when pressure hits?" Are the governance structures or the leadership readiness the bigger blocker for you? 💾 Save this if you're designing governance for AI initiatives. Follow Markus Kopko ✨ for strategic AI in PM. #AIGovernance #ProjectManagement #CPMAI

19

22 comentarios