Exciting milestone for Opsera and an even bigger signal for where this market is going. Speed alone is no longer a competitive advantage. Leaders don’t just want visibility into developer activity they want clarity on impact, accountability, and risk in an AI-first world. That’s why we believe Developer Productivity Insights must evolve from measurement to action. Excited about what we are building, and even more energized by the conversations ahead.

AI has compressed the time to write code, but expanded the surface area of risk. Speed without governance isn't productivity, it's deferred liability. What excites me here is the shift from observability to enforceability — moving the compliance conversation upstream, into the inner loop, where it actually changes behavior. That's the maturity the industry needs right now.

Opsera has been named a Leader in the 2026 Gartner® Magic Quadrant™ for Developer Productivity Insight Platforms, positioned furthest for Completeness of vision among all other vendors. AI coding tools made developers faster. They also introduced more vulnerabilities, longer review cycles, and growing compliance gaps. The inner loop accelerates. The outer loop absorbs the debt. Most platforms in this category measure what happened. Opsera governs what's allowed to happen. → Real-time compliance enforcement in the inner loop → Policy enforced across pipelines and agentic workflows → Every commit and deployment connected to business outcomes  → Plain language answers through Hummingbird AI Read the full perspective from our CEO Kumar C. → https://lnkd.in/e7hqYEPp

Two years. One week of use. A week to rebuild. In the late 1980s, on one of my first software development teams at the CIA, we built a system for our counter-terrorism mission. Five to ten developers. Tens of thousands of lines of custom code. Relational databases. Ingest pipelines for thousands of incoming text messages a day. And at the heart of it, a purpose-built piece of TRW silicon called the Fast Data Finder, 3,600 custom processors doing Boolean logic, proximity searches, and fuzzy matching directly in hardware. Genuinely cutting-edge for its day. Two years to deliver. Millions of dollars spent. Do you know how long the system has been used in production? One week. In the two years we spent building, the technology moved, the mission shifted, and the sponsors who asked for it rotated to new assignments. We built something impressive. We just couldn't deliver it at the speed of the mission. I told this story at Google Cloud Next a few weeks ago, and the point I was trying to make was: Every layer of that system, the code, the databases, the ingest, even the custom silicon, exists today as something I can compose, not build. With a modern agentic coding environment, Antigravity, Claude Code, Gemini CLI, take your pick, pointed at frontier models running in commercial cloud, I could spend a week assembling a more performant, more secure version of that entire system. By myself. A week. Not two years. That's the shift I want every mission leader to internalize: in 1988, custom work was necessary because there were no alternatives. In 2026, we have the power to compose solutions, using frontier models, managed services, vector stores, agentic tooling, and assemble them in days with small teams. The main takeaway is that the key question has changed from "can we build it?" to "what is still worth building from scratch, and what should we now compose?" If we're still defaulting to two-year builds when composition will do, we're choosing to lose ground. The main takeaway: only mission speed matters; everything else supports that goal. #AI #NationalSecurity #AgenticAI

Brilliant breakdown by Sam Newman of the Pocket OS database snafu, on the Modern Software Engineering channel. Considered, thoughtful, insightful, actionable, empathic. We need more of this kind of discourse around #genAI and #agentic development—and outages and failures in general—and less shrieky hype, whether pro or anti. Nice job, Sam. https://lnkd.in/eCZVJkeX

Interesting to see how quickly momentum is building around open standards for agentic systems.    Over the last several months, conversations around AI agents have increasingly shifted away from just model capability and toward operational questions around interoperability, identity, governance, trust, and coordination across systems.    The growing ecosystem participation around the Linux Foundation’s Agentic AI Foundation reflects how important these infrastructure layers are becoming as agents move closer to real operational workflows and production environments.    GoDaddy continues contributing to those conversations through ANS and broader open infrastructure collaboration efforts.    https://lnkd.in/ezHg_hK2    #AI #AIAgents #OpenStandards #LinuxFoundation #Interoperability #AIInfrastructure #ANS #AgenticAI 

𝗧𝗵𝗲 𝗔𝗜 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗜𝗹𝗹𝘂𝘀𝗶𝗼𝗻 Most teams bolt LLMs onto legacy stacks like it's magic glue. It's a disaster... Latency isn't just the model's fault. It's the overhead of unoptimized middleware and non-existent prompt caching. Stop treating AI as a plug-and-play fix. If your data pipeline is a bottleneck, a smarter model won't save the user experience. Fix the core architecture first. Follow my journey here: https://lnkd.in/gmc7u3Qt #SoftwareEngineering #TechDebt #AI #WebPerformance #CodeCoreGlobal ✅ My Daily Tech Post #46285

IBM and Red Hat today unveiled Project Lightwell, a $5 billion effort to help secure the open source software underpinning modern enterprise systems and AI infrastructure. The initiative combines AI-driven vulnerability detection with a global team of more than 20,000 engineers focused on identifying and patching weaknesses in widely used open-source code. “Open source is the backbone of today’s digital economy and the foundation of modern AI,” IBM CEO Arvind Krishna said.

AI hype is colliding with harsh infrastructure reality. While AI promises to replace talent and automate workflows, developers are now building custom monitors because agents fail too often. Container complexity persists, JavaScript strains under load, and Rust is quietly emerging as the backbone for next-gen financial systems. Reliability is becoming the ultimate competitive moat. Targeted insights and strategic opportunities available in Premium: https://lnkd.in/emW6CUXp

Saw a tweet this morning that stopped me mid-scroll 👇 "Sandboxing the agent process is useful. It still doesn't answer the uncomfortable part: what did the agent send out, what policy allowed it, and what proof do you have after the run? That's where agent security gets real." Honestly? Spot on. For the last few months, I've been talking about Docker Sandboxes as the way to keep agents from nuking your laptop. And it works ~ hard VM boundary, only the target workspace mounted, blast radius contained. But sandboxing is the floor, not the ceiling. The uncomfortable questions still remain: • What did the agent send out? → you need network controls • What policy allowed it? → you need MCP + sandbox policy • What proof do you have after? → you need centralized enforcement and audit This is exactly the gap Docker AI Governance is built for. Sandbox + network + MCP controls. Defined once. Enforced across every developer's machine. No migration required. Sandboxing = blast radius. Governance = the receipts. You need both. I'll be diving into this afternoon at the Agentic AI Unplugged meetup in Bengaluru ~ walking through AI Coding Agent Horror Stories, what problem does Docker sandboxes solve, what they don't, and where governance picks up. If you're in Bengaluru, come hang out. 🔗 https://lnkd.in/gWm6tnK8 #Docker #AIAgents #AgentSecurity #MCP #DevRel