Latest
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AI-generated abandonware is hollowing out open source
When everyone can build, the scarce resource becomes maintainers.
Editor’s picks
What is a staff engineer? Technical leaders who aren’t managers
Staff engineers are technical leaders who have deep domain knowledge, walking the line between tech and the business.
New York • September 8 & 9, 2027
Loved LDX3 New York? Pre-sale tickets for 2027 are now available.
Essential reading
On our StaffPlus playlist
How to master the four Staff archetypes and elevate your impact
While the specifics of the job can vary widely, Will Larson has famously categorized the Staff+ experience into four archetypes: Tech Lead, Architect, Solver, and the Right Hand.
Understanding the role as a Staff engineer
How to define, develop and deliver in your role on the technical track.
How to balance technical direction and leadership work
But as is often the case in business, priorities constantly shift and where you focus your efforts will too. So how do you strike the right balance between working on the technical direction of the business and those tasks that require you to put your leadership hat on?
Start with an exit in mind: How to be effective by being selfish as a staff engineer
Staff engineers often get overwhelmed by long-term ownership of critical projects. This talk explores how to avoid burnout by starting every project with an exit strategy—whether transferring ownership, pausing or bootstrapping a team.
The festival for modern engineering leadership
New York • September 15 & 16, 2026
More for Staff engineers
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The secret behind Java’s success at 30-years-old
The three ingredients behind the programming language’s extraordinary staying power.
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AI-first hiring is everywhere and it’s not slowing down
Engineers might start being penalized for not using AI in technical interviews.
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Why untested AI-generated code is a crisis waiting to happen
Here’s why the sector should embrace the principle of more haste, less speed.
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MCP and the future of AI tools
What is the Model Context Protocol (MCP) and how does it simplify data access, enhance AI reliability, and accelerate development?
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How AI will change software engineering
No one can agree how AI will change software engineering. Here’s how to prepare anyway.
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7 principles for balancing agility and durability
Engineering is a game of trade-offs – move fast and break things, or build slow and last forever? Know when to do which.
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Be the best ally to your business development team
As a staff+ engineer, being the bridge between engineering and the business development team isn’t always straightforward.
Videos for Staff engineers
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Ms. Frizzle would be a great SRE
Five frameworks from a century of education research that map directly to eval design for AI agents, and why building the eval system before the product changes everything.
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What handcrafted servers taught us about handcrafted code
Whether AI-generated code should be treated as disposable, what the shift to immutable infrastructure taught us, and how those lessons apply to the way teams write and maintain code today.
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114 miles to the final cut
How Walter Murch edited a major motion picture on a $999 tool nobody believed in, and what that approach offers anyone trying to lead through a wave of technological disruption.
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Re-engineering the software development playbook
How AI is shifting bottlenecks upstream as implementation costs near zero, and what that means for planning, communication, and where engineering expertise now sits.
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Refactoring LIVE – In conversation with Rands
Luca Rossi sits down with Michael Lopp, aka Rands, to explore engineering leadership in the age of AI.
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Game time: A playbook to (unsuccessfully) 10x in a week and (successfully) 10x in a year
A candid scaling story about why rushed 10x efforts fail, and how disciplined metrics, culture, and architecture win over time.
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Rethinking how distributed teams deliver complex tech
Practical patterns for delivering complex work across distributed teams while improving inclusion, decision quality, and team autonomy.
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The shadow culture: Why engineering principles fail under load
Why engineering principles collapse under pressure, and how redesigning systems makes the right behavior the easy default.
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In partnership with incident.ioAccelerating sustainably with AI
Scaling AI coding across teams requires culture shifts, standards, documentation, and thoughtful sustainable engineering practices.
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Planning next moves: Improving performance when half your stack is someone else’s problem
Learn how to measure latency, set realistic goals, and improve performance even when critical parts of your system are out of your control.
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Rebuilding the kitchen: Human connection in distributed engineering teams
This is a practical, leadership-focused talk for managers who want their remote teams to feel connected, aligned, and human – not just productive.
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Four dimensions of burnout: An anti-burnout framework for the AI era
A practical framework to help engineering leaders recognize burnout early, rebalance demands, and recover while staying effective and healthy.