Yishai Beeri
CTO at LinearB
Tel Aviv, Israel
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Yishai Beeri likes to solve problems, and that’s why he was so fascinated with programming when first encountered Logo back in the 80s, where the possibilities seemed endless.
He has made it a focus of his career to solve complex programming problems, both as a consultant and entrepreneur. In 2014 he joined the CTO office of a fast-moving cloud security startup, which later was acquired by a networking giant. At this startup he also met Ori Keren and Dan Lines, now co-founders of LinearB. He joined them shortly after the company was established, in order to get back to what he loves most about engineering, solving big challenges, and this time he is focusing on the world of Dev Productivity and Experience. He is also the host of the very successful Hebrew Podcast - Dev Interrupted.
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Software Engineering Intelligence for Product Leaders
SEI, short for Software Engineering Intelligence, is an upcoming market category for tools and services that help leaders understand, track, and optimize software delivery in their organizations.
Connecting to the key SDLC tools, SEI platforms provide visibility into the bottlenecks plaguing software engineering teams, helping to unblock delivery. The DORA metrics are a well known example for such visibility.
In this talk I'll review the origins of this category, and where it is going. I'll cover the key solution areas in SEI:
- Developer Experience,
- Developer Productivity,
- Predictable Delivery, and
- Profitable Engineering
and how each of these matter to Product leaders and agile practitioners.
You'll leave this session with a good understanding of what pains tools in this space are solving, and how they apply to your daily work.
Finally, I'll dig into automation as a key capability for optimizing delivery, and how it is crucial for enabling the GenAI revolution for code delivery.
How Can Engineering Leaders Measure the Impact of GenAI
Generative AI has taken the world by storm since its general availability to the public in early 2023. Going from novel technology to commodity in months, this disruptive technology quickly found applications in the software development world - and many engineering organizations are trying to capitalize on the disruptive technology.
Nearly every engineering organization has adopted, or is thinking about adopting GenAI as a tool for developers. It has become wildly popular for everything from generating code, to writing and deploying tests, pull request reviews, experimentation and PoCs and much more. But who checks the machines?
In this talk I'll dive into the actual measurable outcomes - positive and negative - of adopting GenAI in engineering organizations. While engineering leaders rapidly jumped on the GenAI bandwagon, many are still struggling to quantify its value, not to mention manage its risks. This talk will discuss how and what to measure to understand adoption, quantify benefits and control risks in your initiative to leverage AI in your engineering organization. Join us to learn about the specific metrics you should be monitoring to ensure maximizing ROI on your GenAI engineering org initiatives.
A CTO Perspective: Refactoring Your Engineering for Scale
Companies at the very early phases are often focused on achieving product-market fit. Once this phase is completed successfully and the organization discovers the core of its repeatable business, a new phase of growth starts to unfold––scaling up and selling up-market to larger organizations.
What worked for your first 300 customers won’t necessarily scale or meet the requirements of the larger, often highly-regulated corporations you’re pitching to now. In this talk, I will share the story of a modern scale-up that now has to enter its next phase of maturity and level-up many aspects on the engineering side to unlock new business opportunities. This talk will focus on technical and product aspects from UX design through security and privacy, and into scaling considerations that require attention to make this possible. We will provide real tangible examples through areas we are investing in including: UX scalability, SSO, GRC considerations, role-based access control, designing for hybrid and on-prem deployments, stability, auditing, monitoring and much more. All of these have a direct impact on the fundamentals of DevOps, SRE and platform engineering - and the art of choosing when to plan and invest for them (and when is too early) is important to master as you scale.
A DORA Metrics Adventure in Navigating the Seas of Engineering Change
DORA metrics have now been widely adopted by progressive engineering organizations to provide hard data on the health of engineering delivery through the lens of speed and quality.
In this talk, I'd like to share some practical use cases for navigating the choppy seas of change in engineering organizations with the help of DORA metrics.
Embark on this climactic voyage to learn about how engineering leaders utilize engineering metrics to chart the course for common complex engineering challenges, tracking the crucial lighthouses of adoption, impact, and risk . From acquisitions and M&As, RIFs, firefighting and engineering quality hellholes, to restructuring and reorgs - the sea is restless and the change monsters lurk. Come to this talk for an odyssey through a mythical landscape of engineering challenges armed with DORA metrics as your torch.
Are Your Engineering Teams Really Prepared for the GenAI Revolution?!
GenAI has gone from generally available, novel technology to widely adopted in a matter of months. Most engineering organizations are using GenAI to generate code, write tests, and assist in code reviews. New code is becoming dirt cheap to write - but our delivery pipelines remain miserably unprepared for the tsunami of new code flowing at a much more rapid pace.
Our current pipelines need a hard reset to prepare us for the GenAI revolution - and engineering managers need to get started TODAY.
This talk will dive into where GenAI is starting to break down our delivery pipelines. While scaling CI/CD is easy, and we can always add a few more workers, scaling the humans in the process is the hard part. This talk will demonstrate how massive amounts of new machine-generated code will impact our pipelines, in ways that will require either greater headcount, or smarter, automated pipelines. You'll come away with ideas for how to modernize your delivery pipeline so you can fully embrace the GenAI revolution.
DevOps for the GenAI Age
When DevOps first started to manifest over a decade ago, a lot of the focus was on streamlining engineering, much in the same way as assembly lines. Starting with the most repetitive and automatable processes, we created pipelines for continuous operations––testing, integration and deployment (AKA CI/CD). Concepts were borrowed from Kaizen (Japanese assembly lines) to ensure these processes flowed and worked, and it’s hard to imagine ever going back on the process and quality improvements brought on by CI/CD. But it only got us so far.
After solving for automation of programs - tests, integrations, builds and deploys are all basically scripts - our attention can turn to much heavier sources of friction, which are also harder to automate. These are the touch points between humans: pickup time for code reviews, who do I even assign this review to?!, what do I need to line up to get this PR merged? And between humans and programs: flaky builds and tests, builds + tests that take too long, post-deployment ownership during outages, synchronizing access to dev environments, and much more.
With GenAI getting embedded in the SDLC, the human / machine interface points are even more important. We will soon have 10x the amount of [AI generated] code, with agents writing code, tests & reviews. How will our processes and pipelines deal with this new scale? Who will review and approve all this code? How do we deliver it in a safe manner?
Enter the DevOps for the GenAI age. We must rethink our processes and eliminate the amounting friction. In this talk we’ll bring data and research from millions of PRs and developers, and share how simple tweaks and automations have unlocked velocity by orders of magnitude.
Methods such as automating trivial code fixes, better understanding of ownership and stakeholders in the process, improved communication (the backbone of DevOps!) all map to greater velocity and developer happiness, and are crucial to reap the benefits of the GenAI revolution in the SDLC.
Join us to learn how.
From AI to Burnout: Shaping Developer Productivity in 2025
The software engineering landscape is undergoing a seismic shift, driven by the rapid evolution of generative AI, the complexities of developer productivity, and the need to balance innovation with human creativity. This talk reviews 2025's key trends - from AI-enhanced tools to the experimentation with agentic AI in software development life cycles - and ventures a peek into 2026.
Attendees will gain insights into how engineering teams can integrate AI into toolchains effectively, preserve creativity amidst automation, and navigate emerging challenges in developer experience and cybersecurity. With bold predictions and actionable frameworks, this session empowers leaders to balance efficiency with empathy, ensuring both organizational success and developer well-being in the face of accelerating technological change.
Key Takeaways:
- Understand the dual trajectory of AI in 2025: mature assistance tools versus experimental agentic AI.
- Integrate AI into existing workflows while preserving human creativity and innovation.
- Measure and enhance developer experience using a combination of qualitative and quantitative metrics.
- Mitigate burnout by fostering a balance between efficiency, empathy, and team well-being.
- Adopt a data-driven approach to tackle productivity challenges and maximize AI’s potential.
Dev Productivity will Dip in 2025. The pains and gains of GenAI in Software Development
GenAI is revolutionizing software development, offering new ways to automate repetitive tasks and improve collaboration. But with these opportunities come challenges: how do you integrate AI into your workflow without disrupting creativity or team dynamics? How do you avoid over-reliance on automation while maintaining quality and efficiency? In this session, explore practical approaches to using AI for tasks like code reviews, testing, and daily development, while overcoming the productivity dips and adoption hurdles that GenAI might bring in 2025. Whether you’re optimizing your own workflow or contributing to team success, you’ll leave with actionable strategies to thrive in the evolving developer landscape.
Key Takeaways:
- Explore how AI tools can improve dev workflows and where they still fall short.
- Navigate the productivity challenges of GenAI adoption, from integration hurdles to maintaining quality.
- Avoid burnout by working smarter, not harder, with the right mix of tools and techniques.
- Apply data-driven strategies to measure and improve developer experience in your team
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