Software Engineering
Software Engineering on Beyond Market Intelligence: a running collection of 6 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.
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."