Bob and I have spent many years as Python devs, and 6 years coaching with Pybites and we can safely say that being a Senior Developer is only about 1/3 Python knowledge. The other 60% is the ecosystem. It's the tooling. It's all of the tech around Python that makes you stand out from the rest. This is the biggest blind spot keeping developers stuck in Tutorial Hell. You spend hours memorising obscure library features, but you crumble when asked to configure a CI/CD pipeline.
When you learn on your own, you're responsible for: Choosing what to learn next Deciding what "good enough" looks like Knowing when you're ready to move on Evaluating whether your work reflects real-world expectations Most beginners don't struggle because they lack discipline. They struggle because they don't yet have the context to make good learning decisions.
This extends to the software development community, which is seeing a near-ubiquitous presence of AI-coding assistants as teams face pressures to generate more output in less time. While the huge spike in efficiencies greatly helps them, these teams too often fail to incorporate adequate safety controls and practices into AI deployments. The resulting risks leave their organizations exposed, and developers will struggle to backtrack in tracing and identifying where - and how - a security gap occurred.
The software industry is collectively hallucinating a familiar fantasy. We visited versions of it in the 2000s with offshoring and again in the 2010s with microservices. Each time, the dream was identical: a silver bullet for developer productivity, a lever managers can pull to make delivery faster, cheaper, and better. Today, that lever is generative AI, and the pitch is seductively simple: If shipping is bottlenecked by writing code, and large language models can write code instantly, then using an LLM means velocity should explode.
