Influences
I keep a small collection of arguments, research and writing that changed, sharpened or challenged how I think about a problem.
This is not a general reading list. These are sources I expect to return to.
Readings connected to my Thinking
External articles that made me think and helped clarify, challenge or extend my notes.
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Common software project conflicts and how to navigate them (opens in new tab)
A breakdown of common sources of conflict in software projects, from priorities to ownership and expectations. What stands out is that many of these aren't real disagreements, but people making decisions with different context. Several conflict types described here feel like symptoms of the same underlying issue: unevenly distributed information.
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This Is How Successful People Make Such Smart Decisions (opens in new tab)
Introduces the idea of having strong opinions, weakly held. While the principle encourages flexibility, in practice many disagreements are not caused by strong opinions but by incomplete context. What appears as healthy debate is often people optimizing for different realities.
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AI Coding Is Not the Same as Software Engineering - And It Matters (opens in new tab)
Generating code quickly is not the same as building systems that can evolve, scale, and be maintained over time. As AI accelerates implementation, the long-term complexity and quality of software increasingly depend on architecture, trade-offs, and engineering discipline rather than raw output speed.
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Blurring boundaries: Toward the collective empathic understanding of product requirements (opens in new tab)
Based on a study of 18 cross-functional teams, the paper shows how product understanding improves when knowledge and participation cross role boundaries. Product & Engineering do not need to own the same work, but they do need enough shared understanding to interpret requirements, question assumptions, and plan together.
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Boundaries Between Product & Engineering (opens in new tab)
Draws a useful boundary between shared product responsibility and absorbing another discipline's work. Better collaboration does not make Product accountable for bugs, technical debt, or architecture; it requires clearer ownership and more direct paths for information and escalation.
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CPU work and GPU work (opens in new tab)
Introduces a useful distinction between work that can tolerate probabilistic exploration and work that still needs guarantees, validation, and deterministic controls. The useful idea is not the metaphor itself, but the operational reminder that the shape of the work should determine the shape of the process.
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Coding Is No Longer the Constraint: Scaling Developer Experience to Teams and Agents at Spotify (opens in new tab)
Shows how AI leverage compounds earlier investments in developer platforms, standardization, ownership data, and automated feedback loops. As implementation gets faster, the limiting work moves toward review, prioritization, and the human judgment needed to decide what should change.
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Context Engineering: A Practical Guide for AI Agents (2026) (opens in new tab)
Shows why useful context for AI agents is not just retrieved information, but structure across code, history, systems, and decisions. It gives a concrete software-systems example of why better access to information still does not remove ambiguity, hidden dependencies, or the need for interpretation.
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Even with AI, cognitive digital twins retrieve information more than they actually understand work (opens in new tab)
Draws a useful boundary between retrieving explicit traces of work and actually understanding how work happens under real constraints. It reinforces the idea that better access to context does not automatically create shared understanding, especially when tacit knowledge, judgment, and interpretation are still required.
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How I Escaped AI Autopilot (opens in new tab)
A strong articulation of how fluent AI output can make passive review feel like real understanding. It extends the distinction between contribution and mastery by showing how attention, ownership, and verification become more important when output already looks polished and plausible.
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How we really build production-grade AI agents: beyond models, toward data and API quality (opens in new tab)
Argues that production agent reliability depends on the data, APIs, and controls around the model, not only on model capability. Its most useful lens is to treat APIs as policy surfaces: contracts that bound actions, expose evidence, and make validation, observability, and human approval explicit.
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Programming in 2026: excitement, dread, and the coming transformation (opens in new tab)
The article explores the growing tension between the speed of AI-assisted software creation and the long-term complexity of maintaining real systems. It questions whether rapid AI-generated output is actually improving software engineering, or simply accelerating the accumulation of fragile and difficult-to-evolve systems.
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Revisiting "No Silver Bullets" in the age of AI (opens in new tab)
A modern revisit of Fred Brooks' No Silver Bullet in the context of AI-assisted software development. The article explores whether AI meaningfully changes software engineering productivity, or whether the essential complexity of software systems still dominates. What stands out is the distinction between accelerating implementation work and reducing the deeper organizational, contextual and decision-making complexity involved in building software systems.
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The AI Great Leap Forward (opens in new tab)
Many organizations are pushing for rapid AI adoption, often prioritizing visible progress over actual value. This leads to systems that look functional but lack validation, reliability, and long-term maintainability.
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The Product-Minded Engineer (opens in new tab)
Argues that engineers should engage with product decisions, not just implementation. Decisions improve when context is shared across roles instead of being handed off. Highlights how Product & Engineering collaboration leads to better trade-offs and outcomes.
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The broken ladder: AI, remote work, and early-career hiring (opens in new tab)
The paper challenges an easy explanation for declining junior hiring. Across 243 million new hires and 407 million job postings, the association with working from home remains robust when estimated alongside GenAI exposure, while the apparent GenAI effect weakens. It complicates my interest in remote work as career access: widening where experienced people can work may coexist with weaker entry routes. This is a discussion paper covering four anglophone labour markets, not a final verdict on remote work.
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Thoughts on slowing the fuck down (opens in new tab)
Coding agents make it possible to build much faster, but they also remove the natural constraints that used to limit mistakes. Small issues that would normally be manageable can quickly compound into systems that are difficult to understand, maintain, or trust.
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Using Platform Engineering to simplify the developer experience — part one (opens in new tab)
John Lewis treats its paved road as a compelling default rather than a mandatory route. The part I find most useful is what happens outside it: repeated exceptions become product-discovery evidence, while one-off needs can remain explicit and locally owned. This is a vendor-hosted operator account, so I read the mechanism as a case to think with rather than a universal rule.