software engineering
software engineering on Beyond Market Intelligence: a running collection of 40 stories we have gathered and hand-picked because they are worth your time. Every post here touches on software engineering in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around software engineering, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Google says it fixed more Chrome bugs in June than over the past two years, thanks to AI
Google significantly accelerated its bug-fixing capabilities in June, resolving more issues than in the preceding two years—a trend experts predicted with the rise of AI. Leveraging large language models (LLMs) and AI tools, Google is now identifying and patching bugs at an exponential rate, mirroring similar advancements at companies like Microsoft. This shift highlights a growing reliance on AI to maintain software quality and underscores the transformative impact of these technologies on product development.
AI-Assisted Software Development: Team Profiles and Capabilities for Putting Research into Action
Harness the power of AI to accelerate software development. DORA’s 2025 research, detailed by Ben Linders, identifies key team profiles and success capabilities, demonstrating that strategic focus on organizational systems yields the greatest returns—AI acts as a powerful amplifier. Explore actionable insights from this research to transform your development workflows. For deeper context on the evolving AI landscape, see "Microsoft is openly competing with OpenAI, Anthropic more than ever," and discover how these shifts impact the industry.
Presentation: Getting Rid of LeetCode Interviews in the World of AI
Traditional LeetCode interviews are failing to identify senior engineering talent. Daniel Doubrovkine, sharing his own experience, reveals why these algorithm-focused tests often miss the mark, even for seasoned leaders. This presentation introduces actionable frameworks for a redefined interview loop, prioritizing human judgment, system design, and practical AI collaboration – yielding far stronger hiring signals. Discover how to move beyond rote memorization and evaluate real-world problem-solving capabilities. Explore this shift further with our article, "Graph Engineering for AI Agents."
GM redesigned its engineering workflows around AI agents — and tripled its merged pull requests
General Motors has fundamentally redesigned its autonomous vehicle engineering workflows around AI agents, yielding remarkable results. By shifting focus from simply adding AI coding assistants to automating broader processes—analyzing data, triaging issues, and running experiments—GM engineers now spend just 15% of their time writing code. This strategic shift has tripled merged pull requests, accelerating feature releases and significantly reducing defects.
Uber’s Zero Growth Stack: Scaling Services, While Optimising Infrastructure and AI Cost
Uber’s "Zero Growth Stack" represents a progressive approach to scaling services, decoupling capacity growth from business demand to optimize infrastructure and AI costs. This innovative framework prioritizes scalable architecture, with garbage collection optimization as a core component. Furthermore, generative AI is strategically integrated into the development process, boosting developer productivity while implementing crucial cost management strategies. As Ben Greene explores in "The Future of Engineering," adapting to AI-driven automation is increasingly vital—discover how Uber is leading the way.
Presentation: The Future of Engineering: Mindsets That Matter When Code Isn’t Enough
As AI code automation accelerates, how can software engineers not just survive, but thrive? Ben Greene, drawing on his startup experience, tackles this critical question in "The Future of Engineering: Mindsets That Matter When Code Isn’t Enough." Greene identifies key principles—starting simple, maintaining comprehension, prioritizing difficult challenges, and focusing on customer impact—highlighting why human empathy and practical problem-solving remain irreplaceable.
Why Adding More AI Agents Made Our System Slower
Scaling AI agent systems isn’t always linear. We recently encountered a surprising bottleneck: asynchronous task management. As we expanded to hundreds of LLM agents, seemingly minor CPU tasks quietly became our largest performance constraint, slowing overall system speed. This post details how we identified and addressed this hidden cost, offering practical insights for anyone building complex AI workflows. Learn from our experience – a challenge we’ve explored further, alongside broader lessons from 8.5 years of machine learning.
Presentation: From Copy-Paste to Composition: Building Agents Like Real Software
Jake Mannix’s presentation, "From Copy-Paste to Composition," addresses a critical evolution in AI agent design. Moving beyond rudimentary architectures, Mannix outlines a framework for building agents akin to robust software – versioned, encapsulated "virtual tools." This approach leverages an intermediate protocol layer to enable key capabilities like interface mapping and dynamic schema projection. Crucially, it incorporates runtime taint tracking to proactively mitigate data exfiltration risks, all while maintaining development velocity.
Agentic AI vs AI Automation: What’s the Real Difference?
Across engineering teams, the distinction between AI automation and Agentic AI is becoming increasingly critical. While looping LangChain calls might initially appear to create an "AI agent," production environments often reveal vulnerabilities. Agentic AI represents a more robust architecture, designed for adaptability and resilience. Explore the real differences – and why understanding them is vital for reliable AI deployments. For deeper insights into the broader AI landscape, consider "AI and the rise of the universal entertainment app."
GitLab Brings Carbon Awareness to CI/CD to Measure the Environmental Cost of Software Delivery
GitLab is pioneering a new era of Green DevOps with the introduction of carbon awareness within its CI/CD pipelines. Now, software engineering teams can directly measure the environmental cost associated with their delivery processes, fostering more sustainable development practices. This innovative approach allows for data-driven optimization, minimizing emissions without sacrificing speed or efficiency. Explore how GitLab empowers you to build responsibly – a critical step toward a future-focused approach to software development, as further detailed in our recent article, "GitLab 19.
Presentation: Engineering AI for Creativity and Curiosity on Mobile
Join us for a compelling presentation by Bhavuk Jain, exploring the engineering behind bringing powerful AI to mobile devices. Jain details the challenges and solutions in translating foundational AI into scalable products like AI Wallpapers and Circle to Search, focusing on runtime guardrails, fine-tuning, and OS integration. This session offers critical insights for engineering leaders navigating the balance between user experience, model latency, and infrastructure costs—essential for delivering safe and reliable AI experiences.
Am I focusing on the wrong skills as a CS student in the AI era? (Need brutally honest advice) [D]
The AI landscape is rapidly evolving, prompting a critical question for aspiring Computer Scientists: are current skill priorities still relevant? Your concerns about balancing traditional software engineering fundamentals—architecture, system design, and debugging—with the rise of AI are valid. While AI-powered code generation tools are advancing, a deep understanding of underlying principles remains paramount.
Are there some textbooks that take a primarily engineering approach to machine learning (as opposed to a "scientific" approach)? [D]
Many find the transition from theoretical machine learning to practical software implementation challenging, especially when navigating complex organizational structures. While many textbooks prioritize a scientific, statistical foundation, fewer focus on the engineering principles needed to build robust, production-ready ML components. If you're seeking a more pragmatic approach—one that emphasizes efficient software development and integration—consider exploring resources that prioritize engineering workflows. As discussed in "Platform Engineering for Everyone," successful ML implementation requires more than just technology; it demands a well-defined platform.
Thinking Machines open sources first multimodal language model, Inkling, focused on low cost and 'resistance to censorship'
Today, Thinking Machines released Inkling, its first major language model under a permissive Apache 2.0 open-source license, offering enterprises a powerful new option for agentic AI workloads. This 975-billion-parameter, natively multimodal model distinguishes itself with a novel "controllable thinking effort" mechanism, balancing cost and performance. While not state-of-the-art across all benchmarks—GLM 5.2 leads in reasoning—Inkling excels in software engineering and demonstrates remarkable resistance to censorship.
Stripe Benchmark Shows AI Agents Build Integrations but Struggle with Validation
Stripe’s new benchmark reveals a significant hurdle in the rise of AI agents: while capable of constructing Stripe integrations across key workflows, they consistently struggle with validation. This suite assesses end-to-end software engineering capabilities, highlighting critical gaps in execution, testing, and validation—particularly under production-like conditions. The findings underscore that achieving reliable agentic systems requires focused improvements beyond initial build phases. For deeper insights into a related challenge, explore "Most RAG Hallucinations Are Retrieval Failures" to understand how data retrieval impacts AI accuracy.
Meta’s Adam Mosseri says AI token budgets could soon be capped per engineer
Adam Mosseri, head of Instagram, anticipates a significant shift in how companies manage AI development. He predicts AI "token budgets" – essentially, the computational cost of using AI tools – will soon be capped per engineer, mirroring traditional expense controls like payroll. This move reflects a growing awareness of the escalating costs associated with AI innovation. For deeper insights into the broader conversation around AI governance, explore our article, "DeepMind CEO calls for an independent standards body to regulate frontier AI."