Culture
Build vs Buy in the Age of AI 5 minutes read.
This is capturing so well what I believe many of us see today around SaaS and Generative AI (buy vs. build will become buy and build): "Companies will continue to buy complex and valuable component services for important parts of their business, but these components will be designed to be accessed and controlled by both humans and software. Some of that software will be AI agents acting on our behalf, and some will be customer (or system integrator) defined workflows generated from gen AI tools. I expect that these AI agents will be created by the vendors themselves, by systems integrators, and by end customers. [...] That said, as more non-technical people aspire to create solutions beyond simple personal time-savers, they will need to learn much of what the product world has learned. And the most important lesson of all is that the hard part is rarely building and delivering the solution; the hard part is discovering the right solution to build."
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How to Delete Everything: The Clean-Slate Approach to Technical Strategy (Video) 24 minutes read.
Engineers often fail to come up with creative ideas because they either treat constraints as a lack of permissions to do anything ("it's too much effort, nobody will approve it") or mistake it as good enough metrics ("it takes 5 minutes in the p99, which is pretty good as is"). In my experience, using time capping ("let's give it a week and see where we stand") and having at least one engineer with both skills and optimism can drive asymmetric results that will surprise everyone and create the momentum to push beyond your current limitations. Plum Ertz shares her practices on how to reset your mental state to develop new ideas as a team.
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Practical Notes "AI for Everyone" by Andrew Ng – A Strategical Guide for Tech Leaders 8 minutes read.
"Everyone says AI will change the world, but very few talk about how to actually make it useful inside a business. [...] AI adoption isn’t about replacing people [yet]. It’s about redesigning roles so your team can focus on the parts of work that really need creativity and judgment. [...] One reason many AI projects fail is because leaders treat them like traditional software projects. But AI is different. It’s probabilistic, it needs lots of data, and its results can be unpredictable. That means you need to do your homework on two levels: technical and business." -- Nanda Reynaldi's notes from Andrew Ng’s course, I think, best represent 2025. We're still in the age of considering the outcomes we can leverage AI Agents to solve, but it's mainly on a task or tasks level, versus a role. Build the muscle. Experiment.
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