Best Programming Master

Most supply chain practitioners already understand the value of a Software Bill of Materials. SBOMs give you visibility into the libraries, frameworks, and dependencies that shape modern software, allowing you to respond quickly when vulnerabilities emerge. But as AI native systems become foundational to products and operations, the traditional SBOM model no longer captures the full scope of supply chain risk. Models, datasets, embeddings, orchestration layers, and third-party AI services now influence application behavior as much as source code. Treating these elements as out of scope creates blind spots that organizations can no longer afford. This shift is why the concept of an AI Bill of Materials is starting to matter. An AI BOM extends the logic of an SBOM to reflect how AI systems are actually built and operated. Instead of cataloging only software components, it records models and their versions, training and fine-tuning datasets, data sources and licenses, evaluation artifact...

Anthropic makes Skills an open standard Skills—a capability that allows users to teach Claude repeatable workflows—was first introduced in October, and now the company is making it an open standard. “Like MCP, we believe skills should be portable across tools and platforms—the same skill should work whether you’re using Claude or other AI platforms,” the company wrote in a blog post. Additionally, the company announced a directory of pre-built skills from companies like Notion, Canva, Figma, and Atlassian. Other new features, which vary by plan, include the ability to provision skills from admin settings and easier methods for creating and editing skills. OpenAI GPT-5.2-Codex released This is a version of GPT-5.2 that is optimized for the company’s coding agent Codex. It includes “improvements on long-horizon work through context compaction, stronger performance on large code changes like refactors and migrations, improved performance in Windows environments, and significantly st...

Enterprises are making faster progress with agentic AI than many expected, not because the tooling is mature, but because companies have realized they can’t afford to wait. The leading 10 to 20% of organizations are racing ahead, standing up internal “agent platforms” that handle planning, tool selection, long running memory, workflow coordination, and human in the loop approvals. Capabilities they once assumed off the shelf copilots would provide. They aren’t trying to become orchestration framework vendors; they’re filling gaps because enterprise needs for reliability, auditability, and policy enforcement are higher than what the current ecosystem offers. Yet despite these limitations, enterprises are making real, operational progress, not theoretical claims. They are learning, shaping patterns, and validating what will become the backbone of agentic systems for years to come. From Glue Code to Repeatable Patterns The first major stride is the shift from improvisation to repeatabl...

AI workslop is any AI-generated work that masquerades as professional output but lacks substance to advance any task meaningfully. If you’ve received a report that took you three reads to realize it said nothing, an email that used three paragraphs where one sentence would do, or a presentation with visually stunning slides containing zero actionable insight—congratulations, you’ve been workslopped. The $440,000 hallucination In July 2025, consulting giant Deloitte delivered a report to the Australian Department of Employment and Workplace Relations. The price tag: $440,000. The content: Chock-a-block with AI hallucinations: fabricated academic citations, false references, and a quote wrongly attributed to a Federal Court judgment. The message was clear: a major consulting firm had charged nearly half a million dollars for a report that couldn’t pass basic fact-checking. No surprise there, as LLMs are probabilistic machines trained to give *any* answer, even if incorrect, rather t...

As 2025 comes to a close, SD Times is looking back at the top software development news stories of the year across the industry. Here are 10 of what we believe to be the biggest stories we covered throughout the year: Linux Foundation forms Agentic AI Foundation to be new home for MCP, goose, and AGENTS.md The Linux Foundation earlier this month announced that it is forming the Agentic AI Foundation (AAIF) to promote transparent and collaborative evolution of agentic AI. Three major projects have been donated to the foundation at launch: Anthropic’s Model Context Protocol (MCP), Block’s goose, and OpenAI’s AGENTS.md. Additionally, AAIF member Obot.ai will donate its MCP Dev Summit events and podcast to the foundation. The AAIF is launching with several members, including larger platinum members Amazon, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft and OpenAI; gold members Adyen, Arcade.dev, Cisco, Datadog, Docker, Ericsson, IBM, JetBrains, Okta, Oracle, Runlayer, SAP,...

I’ve been teaching at the Rhode Island School of Design for 18 years. This semester, for the first time, students lined up after my opening lecture, asking the same question: “What’s the role of AI in the design field? How do I prepare for this?” Their concerns mirror what I’m hearing from senior UX leaders across industries. And quite frankly, the narrative has been predictable: automation will eliminate jobs, compress budgets, and reduce the need for human designers. However, new data my team collected from 500 senior managers across the U.S., UK, and Germany tells a different story than the anxiety suggests. AI isn’t replacing UX professionals. Instead, it shows signs of elevating them to strategic status and making their roles more critical than ever. UX has crossed into recession-proof territory For years, UX lived in a precarious position during economic downturns. When budgets tightened, design was often among the first areas to face cuts. That’s changing. Our research shows...

Articles about AI dominated media coverage in 2025, as the ability to perform more than just rudimentary tasks grew into making software development life cycle processes autonomous. The widespread piloting and implementation of generative AI, agents and MCP servers were among the topics that produced the top trending articles on sdtimes.com this year. But AI wasn’t the only thing on the minds of developers and their managers. Here’s a look at the top trending stories on sdtimes.com for 2025. The most-viewed article this year asked the question: “ Is Agile Dead in the Age of AI? ” Author Adam Sandman, CEO of Inflectra, posited that Agile processes are in fact not dead, but are evolving. “The future of software development isn’t Agile vs. AI, it’s Agile with AI. Strategic alignment, mentorship, and smart governance make sure that AI’s power enhances safety, maintainability, and long-term product value.,” he wrote. No. 2: 88% of companies are contemplating leaving Oracle Java SD Time...

Flaky tests have long been a source of wasted engineering time for mobile development teams, but recent data shows they are becoming something more serious: a growing drag on delivery speed. As AI-driven code generation accelerates and pipelines absorb far greater volumes of output, test instability is no longer an occasional nuisance. This constant rise has been recorded by all manner of developers, from small teams to Google and Microsoft . The recently launched Bitrise Mobile Insights report backs up this shift with hard numbers: the likelihood of encountering a flaky test rose from 10% in 2022 to 26% in 2025. Practically, this means that the average mobile development team now encounters unreliable test results during a typical workflow run. That level of unpredictability has real consequences for organizations that depend on fast, confident release cycles. Flaky tests undermine trust in CI/CD infrastructure, force developers to repeat work and introduce friction at the point wh...

As this year comes to a close, many experts have begun to look ahead to next year. Here are several predictions for trends in AI in 2026. Ariel Katz, CEO of Sisense From agent hype to outcome accountability 2025 was the year agents exploded; 2026 is the year enterprises demand proof they actually work. After millions spent on tokens, tools, and experiments that never reached production, companies shift from buying AI components to buying measurable business outcomes. The winners will offer outcome-as-a-service – owning the workflow, the integration, the semantics, and the last mile – because customers won’t pay for agents. They’ll pay for certainty.   Andrew Sellers, VP of technology strategy and enablement at Confluent 2026 will see new protocols for multi-agent coordination and metadata exchange Two critical standards are likely to emerge in 2026 as AI operations become autonomous. First, as single-agent systems evolve into complex multi-agent teams, the industry needs a...