I think a lot about technical debt, technical financing, and how to move fast without putting future state at risk. It’s one of the most difficult balancing acts in the tech sector. It’s also a challenge that is at the core of nearly every business problem when a tech org is involved.
It might be a familiar situation or one you’re in right now. Senior leadership says you’re too slow. Engineers say they can’t go faster unless they spend time rewriting. Deadlines are made, band-aids are used, and the cycle continues.
Both groups have the same hopes and desires but end up talking past each other. There’s nothing inherently wrong with either side's position. Still, one group tends not to have enough involvement, depth, or understanding to figure out how to reach across the aisle and reach a common experience.
I saw a post earlier today that noted the “move fast, break things” motto had sunk their startup, with one engineer noting they were leaving because they were tired of “putting bandages on broken bones.”
What an image. We’re all hiking up the same trail with compound fractures, covered in Band-Aids that are doing their best to keep our bones in place.
So, how do you keep forging ahead? As an engineering leader, how do you explain a compound leg fracture to a group that might not understand what a leg should look like?
I’ve not found a fool-proof answer, but I’m going to find one…or fall off the mountain trying.
How have you solved this problem in the past?
Been a bit, so the backlog of links is large and full of terrors:
📌 Strategy, Priorities, and the Big Picture
How FAANG Really Prioritizes Work
A teardown of how top tech companies prioritize without clinging to Agile dogma. If your roadmap feels more like a wish list, this one might recalibrate your approach.The Software Architecture Decision Canvas
Architecture discussions dragging on for hours? Use this canvas to focus debate, clarify trade-offs, and stop decision fatigue before it starts.Illich’s Law: The Real Cost of “More”
A reminder that more hours ≠ more output. Especially relevant if your team is sprinting but barely moving, Illich’s Law is also called “the Law of Diminishing Returns.” This law explains why, and how, to course-correct.The CTO’s Ouroboros
A thoughtful piece on the cyclical nature of tech leadership, and how we often find ourselves solving the same problems with fancier tools (and higher stakes).The CTO’s Calculated Absence
Yes, we’re linking this one again. Because stepping back strategically is still underrated—and this nails the why and how.
🔧 Tools, Techniques, and Ways to Build Smarter
GreptimeDB and the Future of Observability
A purpose-built, time-series database designed to meet modern observability needs. If you’ve ever cursed your logging setup, this one’s worth a peek.Properly Logging Tech Debt
Tech debt isn’t just a nuisance, it’s a window into your culture. Documenting it well builds trust, shows accountability, and helps teams think long-term.Engineering Metrics That Drive Focus
LeadDev’s metrics guide remains a banger, ideal for separating signal from noise when performance reviews and planning season hit at the same time.Jack Danger’s Pyramid Career Model
Worth a revisit: grow outward before upward. Cross-disciplinary breadth builds the kind of depth that lasts longer than any title bump.
🤖 AI and the New Developer Toolkit
Zero Human Code
AI wrote the code, fixed the bugs, and stumbled all the way through. A brutally honest case study in just how far we are, and how close we’re getting (maybe).DX Guide to AI-Assisted Engineering
A soup-to-nuts framework for embedding AI into your workflows; from planning to production. Less hype, more how.Prompt Engineering Whitepaper (Kaggle)
A surprisingly deep dive from Kaggle on what makes prompts actually work, whether you're coding with GPT-4 or running research-grade inference.OpenAI Prompting Guide
If you’re still guessing at prompts, stop. This guide gives structure to the madness and is an essential read for anyone building with LLMs.OpenAI Academy
Self-serve, well-structured, and designed for builders who want to get good, fast. Still one of the best places to start your AI journey.Netflix’s Foundation Models for Recs
How Netflix is using massive foundation models to supercharge personalization. It’s enterprise-grade, high-stakes LLM work, and it’s live.The End of Programming as We Know It
O’Reilly lays out the future: less “write code,” more “design intent.” The skill shift is coming, time to start practicing. Tim O’Reilly then recently wrote this “AI First Puts Humans First” piece. Interesting combo of articles.
🧠 People, Motivation, and Team Dynamics
How to Motivate Your Team
Skip the fluffy platitudes. This is a refreshing, real guide to figuring out what your team actually needs to stay engaged.Async Communication for Leadership
Async isn’t just for ICs. If you want deeper, more focused collaboration, especially with other leads, this is the case for slowing down to go fast.Lenny + Cutler on Team Rituals
Bookmark this one. From calendar audits to reflection prompts, these are the high-signal habits that separate chaotic teams from consistent ones.Learning is a Learned Skill
Still one of the best pieces on intentional growth. Especially useful if you're onboarding a team, coaching a direct, or just hitting a wall.The Productivity Phantom
You feel productive, but are you moving the needle? This piece calls out the illusion, and offers strategies for measuring what really matters.