
Latest
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Most companies still aren’t measuring AI coding tools
AI adoption is soaring, but 82% of organizations still aren’t measuring the impact of AI coding tools.
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The one way to crack technical estimations under pressure
Technical estimations require overview of many different moving parts. Why not solicit the help of your team to move things along?
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In partnership with Stytch
Are you ready for AI agents?
AI is changing how apps get accessed – are your systems built to keep up?
Editor’s picks

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Essential reading

How to build an effective technical strategy
Building a tech strategy requires a lot of moving parts. Learn about what routes to take and whether decisions should be top-down.
On our Technical Direction playlist

Modernizing legacy systems: A technical strategy for evolving monoliths into modern architectures at HelloFresh
Gain insights into transforming legacy systems into scalable architectures, with practical strategies for balancing stability, managing technical debt, and enabling growth opportunities at HelloFresh.

Technical Vision vs. Technical Strategy: The difference and why it matters
Jonathan Maltz digs into the nuts and bolts of setting a successful technical strategy. Startin by talking about the difference between technical vision and technical strategy.

How to implement platform engineering at scale
In this webinar, we’ll hear from enterprise engineering leaders who’ve overcome cultural barriers and team silos, and successfully adopted platform engineering practices in their orgs.

Good technical debt
Jon Thornton discusses how this framework was used to rapidly build and ship Squarespace’s Email Campaigns product in less than 15 months. Along the way, you’ll get several practical guidelines for how tech debt can supercharge your technical investments.

Creating, defining, and refining an effective tech strategy
Having a defined tech strategy creates alignment and keeps everyone on the same page. So how can you ensure yours is most effective? Panelists Anna Shipman, Randy Shoup, Papanii Nene Okai, Nimisha Asthagiri and Anand Mariappan share their tips.
More about Technical Direction
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Tech debt traps to avoid
Tech debt is a term that often catches people out. Here are some ways to make sure you avoid falling into that trap!
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The 6 biggest generative AI risks for developers
The future of work will almost certainly involve the use of generative AI tools, but is big tech being too hasty in integrating these capabilities into the software we use every day?
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Can platform engineering help you do more with less?
As tech faces a downturn, platform engineering offers a way to decrease cognitive load, cut cloud costs, and boost delivery speed. What’s the catch?
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When to migrate from a monolithic to a distributed frontend architecture
Deciding when to move from a monolithic to a distributed frontend architecture is an important technical decision as any company scales. Here’s what you need to consider before making the move.
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Kubernetes for engineering managers
If your organization is modernizing how it develops software, you are probably going to need a container orchestration platform like Kubernetes. Here’s why.
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Practicing engineering transparency
Being more transparent as a Staff+ engineer can help build trust and encourage best practice across entire engineering teams.
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The business case for headless CMS – a quick guide for developers
Headless CMS is a major upgrade for organizations looking to streamline their content delivery, but building a business case that suits all stakeholders is no mean feat.
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Riding the ever-changing waves of front-end development
The frontend has undergone an impressive evolution in the past few decades, but just how much has this role changed over time?
Top Technical Direction videos
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Managing the marathon: Leading teams through lengthy migration projects
In this short talk, Lawrence will reflect on his experience leading teams through multi-year migration projects.
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Iterate to Greatness: Building High Performance, AI-native Engineering Teams
In this talk, we’ll discuss how Vercel transitioned to an AI-native company and how you too can operate highly effective, AI-native product engineering teams, from the tools used to the way to stay organised amid the rapidly changing pace of AI.
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Making smart investments: A framework for maximizing your ROI in technical decisions
In this talk, we’re going to explore a framework for evaluating the return on investment (ROI) of complex tech decisions, illustrated with real-world examples that highlight both the traps to avoid and the paths to success. I’ll share methods for pinpointing key metrics that matter, and how to design experiments or proof-of-concepts to measure ROI. Finally we will discuss the importance of staying objective and adaptable throughout the process.
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Data Science Demystified
This talk gives an overview of Data Science and delves deep into the pipeline data scientists use – right from fetching the data, the Python tools and frameworks used to creating models, gaining insights and telling a story.
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How to use technology radars to make transparent tech decisions
The talk centres around the benefits of building such a tool, such as transparency, alignment and faster onboarding.
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Tech debt as innovation, reframing this forever problem as an opportunity
Tech Debt is a natural by-product of software engineering, yet we, in the software industry, don’t attack it with the same excitement or fervor as we do with product innovation or feature development.
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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.
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Explosive overflow: Lessons from rocket science
Thirty-nine seconds after launch, the Ariane 5 rocket exploded—caused by software design errors. In this talk, Mark analyzes these historical flaws to explore key lessons in resilience and product security. We’ll discuss testing, validation, legacy code, design assumptions, and the challenge of proving when things don’t go wrong.