software development
software development on Beyond Market Intelligence: a running collection of 69 stories we have gathered and hand-picked because they are worth your time. Every post here touches on software development 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 development, 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.
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.
Prompt Engineering Is Solved—Prompt Management Isn’t
Prompt engineering offers a powerful path to improved AI interactions, yet a critical gap remains: prompt *management*. A surprisingly common production failure—a simple variable rename—can silently break live calls, highlighting the need for robust safeguards. This article introduces a lightweight static analysis tool that treats prompts as contracts, proactively catching breaking changes before deployment. Discover how this approach ensures stability and reliability, building upon the foundational work of prompt engineering, as explored in articles like "Nimble claims its new, domain-specialized Web Search Agents…"
How to Give an LLM Agent a Browser
Empower your LLM agents to navigate the web with confidence. This guide explores building a browser-enabled agent using OpenAI's Agents SDK and Playwright’s MCP, unlocking a new dimension of data access and automation. Discover how to equip your AI with the ability to interact with websites, extract information, and perform tasks previously beyond its reach. This approach moves beyond static datasets, enabling dynamic, real-time data processing. For further insights into AI agent capabilities, see "You Can Hand One AI Agent Your Worst Recurring Task.
Prentis, new AI lab co-founded by Reid Hoffman, Mark Pincus in talks to raise $100M
Prentis, a new AI lab backed by Reid Hoffman and Mark Pincus, is poised to reshape how we interact with computers. Currently in discussions to secure $100 million in funding, Prentis is betting on a future where automating routine tasks surpasses coding as AI’s primary application. This represents a significant shift, empowering users to streamline workflows and unlock greater productivity. Explore the potential of AI-powered automation – a future where tedious tasks simply disappear.
Why Cognition bought Poke: AI personality is becoming a competitive advantage
Cognition’s acquisition of Poke signals a pivotal shift: AI personality is emerging as a core competitive advantage. Integrating Poke’s conversational style and interaction model into our coding agent, Devin, underscores our belief that user experience is paramount. It’s not just *what* AI can do, but *how* it communicates that drives adoption and productivity. This move reflects a future where seamless, intuitive interaction unlocks AI’s full potential. Explore this concept further in our article, "Loop Engineering for RAG Generation," which details innovative approaches to AI interaction.
Article: The Self-Building Agent: A LangChain4j Experiment
Explore the future of AI-assisted coding with our recent experiment: "The Self-Building Agent: A LangChain4j Experiment." Kevin Dubois and Mario Fusco detail how a code assistant autonomously designed and built an agentic system using LangChain4j, demonstrating a framework capable of independent coding, testing, and debugging. Their findings reveal that supervisor and workflow architectures offer distinct trade-offs in debugging speed and flexibility. For further exploration into AI agents and their capabilities, see our article, "Agentic coding goes hands-free…"
Agentic coding goes hands-free as OpenAI brings GPT-Live's full duplex voice control to Codex and ChatGPT on the desktop
OpenAI is redefining developer workflows with the integration of GPT-Live's full-duplex voice control into the ChatGPT desktop application, now powering both Codex and ChatGPT Work. This innovative move allows engineers to orchestrate coding tasks—from debugging to reviewing pull requests—hands-free, ushering in a new era of productivity. The system intelligently manages complex operations, even supporting multi-folder projects and remote execution. As AI Insider journalist @ChrisGPT noted, this represents a significant step towards personal AGI, mirroring advancements like Anthropic’s recent Claude voice mode updates.
Build an LLM Agent That Can Write and Run Code
Unlock the potential of AI-powered code generation and execution. This hands-on walkthrough guides you through building an LLM agent using the OpenAI Agents SDK and Docker. Learn to empower your workflows by seamlessly integrating code writing and running capabilities. We’ll demonstrate a practical approach to leveraging these tools, offering a future-focused solution for data professionals. For those interested in a deeper dive into LLM runtimes, explore "How To Build Your Own LLM Runtime From Scratch" for a comprehensive understanding of the underlying infrastructure.
Android Studio Quail 2 Redesigns Agent Mode, Streamlines AI-Assisted Coding
Android Studio Quail 2 delivers a significant advancement in AI-assisted coding, now stable and ready for adoption. The expanded Gemini/AI Agent Mode allows for parallel conversations within the IDE, streamlining workflows and reflecting Google’s commitment to AI integration. This release prioritizes developer productivity with enhanced debugging and profiling tools, alongside simplified access to experimental features. Discover how these improvements empower you to build more efficiently – a shift mirrored by the increasing prevalence of AI-generated content, as seen with Deezer’s recent surge in daily uploads.
RSPack 2.0: Performance Gains, Leaner Dependencies and ESM Core
Rspack 2.0, developed by ByteDance, marks a significant advancement in web tooling. This update delivers substantial performance gains alongside a leaner dependency footprint, centered around a pure ECMAScript modules (ESM) core. Improved static analysis and support for React Server Components (RSC) further enhance its capabilities. Early benchmarks demonstrate considerable reductions in build times, reflecting the project's impressive growth—now exceeding 5 million weekly npm downloads. For those interested in agentic automation, consider exploring our recent article on "GitLab 19.2."
GitLab 19.2 Puts AI Agents to Work on the Security Backlog
GitLab 19.2 introduces agentic automation to tackle the growing security and review backlog resulting from AI-assisted coding. This release directly addresses the challenge of maintaining code quality as AI tools accelerate development. Key features include Dependency Scanning Auto-Remediation, a streamlined Security Review Flow, and the GitLab Duo CLI, all designed to empower teams. Notably, Custom Flows enter public beta, offering unprecedented flexibility. For those exploring broader AI model management strategies, consider "Yelp Unifies ML Model Training with Training Orchestrator" for additional insights.
X relaunches a rebuilt Android app after year-long effort
After a year-long development effort, X is pleased to announce the global relaunch of its rebuilt Android app. This reimagined version delivers a significantly enhanced user experience, prioritizing accessibility and intuitive data management. We’ve focused on streamlining workflows and empowering users to achieve more with their data, wherever they are. For those interested in leveraging AI to further optimize their productivity, consider exploring our recent article, "How to Run Claude Code Agents for 24+ Hours," for deeper insights.
Java News Roundup: Value Objects, WildFly 41, TornadoVM, LangChain4j, Oracle AI Agent Studio
This week's Java News Roundup, published July 13th, 2026, showcases significant advancements across the ecosystem. Leading the highlights: a preview release of Value Objects—a welcome return for streamlined data modeling—and the General Availability of WildFly 41. Further updates include point releases for TornadoVM and LangChain4j, alongside maintenance releases for key frameworks like Micronaut. Notably, Oracle’s AI Agent Studio for Fusion Applications now offers new capabilities.
How to Run Claude Code Agents for 24+ Hours
Unlock sustained coding productivity with Claude Code Agents running continuously – even for 24+ hours. This guide explores how to leverage these powerful AI assistants to streamline your engineering workflows and tackle complex projects with unprecedented efficiency. Discover practical techniques for maintaining and optimizing long-running agents, transforming your coding process. For a foundational understanding of setup and configuration, see "A Beginner’s Guide to Setting Up Claude Code for High Performance Agentic Programming" and elevate your agentic programming skills.
Presentation: Platform Engineering for Everyone - Success Can’t Be Coded
Successful internal development platforms demand more than just technical prowess. In "Platform Engineering for Everyone – Success Can’t Be Coded," Max Körbächer argues that a purely infrastructure-first approach often falls short. This presentation explores the critical need for a product mindset, actionable DevEx and SPACE metrics, and a thriving community to drive lasting adoption. Learn how to align teams, manage technical debt, and unlock real value—a perspective mirrored in our recent article, "Stripe Benchmark Shows AI Agents Build Integrations but Struggle with Validation."
Podcast: Strands Agents with Clare Liguori
Welcome to the podcast! Today, Thomas Betts speaks with Clare Liguori, technical lead for the Strands Agents SDK, a rapidly evolving open-source project. The discussion charts Strands Agents’ progression from a Python SDK to a robust, production-ready agent harness. Clare shares valuable lessons gleaned from scaling agents, including the strategic shift to a model-driven architecture. As the underlying LLMs continue to advance, explore what's next for this transformative technology—a topic further illuminated in "Many Companies Use AI.
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.
KDnuggets Weekly Roundup: Week of July 13, 2026
This week’s KDnuggets Weekly Roundup delivers practical insights for data professionals. We're prioritizing efficiency, starting with a clear alternative to cumbersome if-else chains in Python – embrace the Registry Pattern. Level up your portfolio with five real-world SQL projects, stay current with ten top AI YouTube channels, and explore structured language model generation. For deeper exploration of related topics, consider "Pinecone Introduces Nexus Engine," now generally available, for compiling business context into structured data for AI agents.
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.
Don’t Let Claude Grade Its Own Homework
Self-reviewing AI models—like asking Claude to grade its own homework—introduces inherent bias. Our latest post explores a more reliable approach: cross-provider PR review using Codex within GitHub Actions. A second opinion from a different lab consistently delivers more objective and insightful evaluations than internal assessments. This method ensures rigorous quality control and identifies potential blind spots. As Anthropic and Blackstone recently highlighted, successful AI implementation demands more than just powerful models; it requires robust validation—and that starts with impartial review.
Reflection inks $1B compute deal with Nebius
Reflection AI, founded in 2024, is accelerating its development of open-source AI technology with a significant $1 billion compute agreement with Nebius. This substantial investment underscores Reflection’s commitment to scalable AI solutions and reflects a growing demand for dedicated compute resources. The move highlights a broader trend within the industry, as evidenced by New York State’s recent temporary halt on new data center construction, signaling a need for more efficient resource management. This deal positions Reflection to deliver transformative AI capabilities.