How GitKraken Insights measures AI's impact on developer productivity

The rise of AI-assisted coding and integration for enterprise environments AI may be writing code faster — but enterprises are learning that speed without structure can create new bottlenecks. 🧩 GitLab’s latest research calls it the AI Paradox: dev teams gain efficiency through AI coding tools, yet lose productivity to scattered toolchains, compliance hurdles, and data silos. The enterprise solution? Platform engineering and model-based integration. By uniting your mobile and backend ecosystems through hubs like Planview, companies can create a single source of truth—cutting down on duplicate effort, reducing compliance risks, and enabling AI agents to work safely within enterprise-grade boundaries. It’s no longer about whether AI can code — it’s about whether your organization is architected to scale that intelligence responsibly. #EnterpriseApps #AICompliance #MobileInnovation #IntegrationStrategy #PlatformEngineering #AIMaturity

How do you prove that AI is really helping your developers — beyond just the hype? That’s the question GitKraken Insights was built to answer. 🧠 With teams adopting tools like Copilot, Claude, and Cursor, leaders are asking: what’s the ROI? Are we truly shipping better, faster, and smarter — or just differently? GitKraken’s new Insights platform blends traditional DORA metrics with AI-specific analytics, developer sentiment, and code quality data. The result? A real-time, contextual picture of how AI affects your workflow — minus the surveillance vibe that developers hate. It’s not just about measuring lines of code or PR velocity anymore. It’s about understanding how people and AI collaborate to deliver better software. In a way, this feels like the start of open transparency in AI-driven dev — data that empowers, not polices. #OpenSource #GitKraken #AIinDevelopment #DevTransparency #DeveloperExperience #OpenInnovation

One of the biggest challenges of AI adoption in dev teams today? Proving that it actually works. Enter GitKraken Insights, a brand-new platform that helps companies measure the ROI of AI in software development. Built with GitClear, it combines metrics like DORA, code quality, technical debt tracking, and AI impact analysis into one unified view. But what makes it stand out? GitKraken built it for developers — not to monitor individuals, but to help teams surface friction points, identify what AI is really helping with, and make smarter workflow decisions. It’s a fresh take on developer analytics for the AI era — practical, transparent, and built to make teams stronger together. 💪 #GitHubNews #GitKraken #AIProductivity #DevAnalytics #OpenSourceCommunity

"The complexity of building enterprise-grade AI should not require thousands of lines of code." For too long, scaling intelligent applications—especially those utilizing complex patterns like Retrieval-Augmented Generation (RAG)—has been bottlenecked by fragmented tools and heavy engineering dependencies. This is the old way. mlangles LLMOps changes the fundamental equation of AI development. This video demonstrates how we transform ideas into powerful, intelligent systems without writing a single line of code: - Design, Not Code: Drag, drop, and connect your tools, apps, and LLMs seamlessly in a visual workflow. - Built-in RAG & Memory: Instantly deploy complex applications like secure, memory-enabled chatbots powered by your internal documents (PDF Ingestion) and vector databases. - Logic to Action: Every block brings your application to life, ensuring rapid deployment and effortless governance. Stop coding complexity. Start orchestrating intelligence. Watch to see how mlangles makes powerful LLMOps accessible to your entire team. #LLMOps #AIGeneration #NoCodeAI #RAG #MachineLearning #Mlangles #EnterpriseAI

🚀 Safely Incorporating AI Agents in Software Development AI coding tools are powerful - but without guardrails, they can introduce risks. Here’s how to get real productivity gains (15–20%) safely: ✅ Use AI for code suggestions, test generation & documentation ✅ Keep human-in-loop review and CI/CD protections ✅ Apply agents to real data engineering workflows (Airflow, dbt, Great Expectations) ✅ Track measurable gains: faster delivery, fewer bugs, better collaboration #AIAgents #SoftwareDevelopment #DataEngineering #MLOps #Productivity #BestPractices #GenAI

If you haven’t done this in your CI pipeline, you’re leaving time and money on the table. It’s one of the easiest ways to harness the power of AI — with immediate, measurable impact. At Zight, I just added an AI-powered checkpoint to our CI pipeline. It’s a smart gate that helps us catch regressions early, reduce cost, and give developers faster feedback without compromising quality. Here’s how it works: 📂 We send the git diff + test directory structure + list of tests to an LLM 🧠 It identifies a targeted slice of tests (unit, integration and functional) that verify the impacted areas ⚡ Those tests run in few minutes ✅ If they pass, we move on to the full test suite ❌ If they fail, the pipeline exits early — saving time and compute The results speak for themselves: 🚀 41% reduction in CI run time 💰 18% drop in compute spend 🧘♂️ Faster feedback keeps devs in flow, reduce context switching This is one of the best practical use cases for AI in engineering today - simple to implement, high leverage, and developer-friendly. Happy to share prompt and more details over DM - just reply with "CICD" on this post #AI #DeveloperExperience #Zight #EngineeringEfficiency

Yesterday, I gave an AI agent a complex debugging task and walked away. Three hours later, it had completed 11 deployment iterations without asking me a single question. It tested the code. Identified encryption failures. Researched solutions. Modified the implementation. Redeployed. Found new issues. Adapted its approach. Persisted until everything worked. This wasn't a chatbot answering questions. This was an agent doing work. After 18 months of hands-on experimentation with AI agents—building coding agents, research agents, and production SaaS applications, I've learned something profound: 𝐓𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐚 𝐜𝐡𝐚𝐭𝐛𝐨𝐭 𝐚𝐧𝐝 𝐚𝐧 𝐚𝐠𝐞𝐧𝐭 𝐢𝐬𝐧'𝐭 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞. 𝐈𝐭'𝐬 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐲. A chatbot waits for your questions and provides answers. An agent takes your goal and autonomously works toward achieving it—iterating through failures, learning from mistakes, and persisting until the job is done. 𝐈 𝐜𝐚𝐥𝐥 𝐭𝐡𝐢𝐬 𝐭𝐡𝐞 "𝐀𝐮𝐭𝐨𝐥𝐨𝐨𝐩" 𝐚𝐧𝐝 𝐢𝐭'𝐬 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐡𝐨𝐰 𝐰𝐞 𝐛𝐮𝐢𝐥𝐝 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞. I𝐧 𝐦𝐲 𝐥𝐚𝐭𝐞𝐬𝐭 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 (conducted under the COTRUGLI Business School initiative), 𝐈 𝐛𝐫𝐞𝐚𝐤 𝐝𝐨𝐰𝐧: ✅ How agents evolved from research labs to production systems ✅ What industry leaders (OpenAI, Anthropic, Google, Microsoft) actually mean by "AI agent" ✅ The 5 levels of agent maturity (and why Level 3-4 is sufficient to revolutionize work) ✅ 18 months of practical lessons from building agents across domains ✅ Why November 2024's Model Context Protocol changed everything The infrastructure is here. The tools exist. What remains is learning to orchestrate them. Will you learn to orchestrate agents, or be orchestrated by those who do? Read the complete research: https://lnkd.in/dx2e_RpT #AIAgents #ArtificialIntelligence #Automation #FutureOfWork #Research #COTRUGLI

🌟 7 Steps to Make Your OSS Project AI-Ready 🤖👨💻 AI is changing how open-source projects are discovered, used, and maintained. Here’s how to make your project ready for the era of AI-assisted development — per persona 👇 For Users 🔹 Add llms.txt – help LLMs find your docs 🔹 Add chat to docs – instant Q&A v/ kapa.ai Inkeep 🔹 Expose APIs via MCP – let AI agents use your project For Contributors 🔹 Add AGENTS.md – teach AI tools how to build/test 🔹 Define AI use rules - update CONTRIBUTING.md (great example: OpenInfra Foundation 👏) For Maintainers 🔹 AI code reviews - first line of defence: CodeRabbit 🔹 Automate triage & issue mgmt - try Dosu 💡 If unsure where to start: add AGENTS.md + clear AI policy first. Then a chat with docs! Full guide ↓ 🙏♻️ #OSS #OpenSource #AI https://lnkd.in/epmETRrt https://lnkd.in/epmETRrt

🧐 Not all AI in software teams is built the same. While AI-assisted tools, like automated code review and suggestion engines, boost developer productivity by up to 30%, these gains often plateau as project complexity grows. 💡 In contrast, fully AI-native systems, where automation isn’t an add-on, but the operating principle, are driving up to 2x faster release cycles and 45% fewer manual interventions in CI/CD pipelines. When should teams stick with AI on the side, and when is it time to fully embed intelligence at the core? Here’s a practical comparison to guide your next decision. #AINative #AIAssisted #DevTeams #Productivity #SoftwareDevelopment #Analytics #Velx

Most founders think they need to build everything from scratch. My client wanted an AI knowledge base chatbot for internal use. Their original plan: 6 months of full custom development. Backend architecture from zero. Custom APIs. Complex database design. I showed them what actually works. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝘀𝗰𝗿𝗮𝘁𝗰𝗵: → Months of development → Complex codebase to maintain   → Delayed validation and feedback 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗻𝟴𝗻: → Zero custom code → Same functionality → Faster learning from real users Result? We shipped their internal tool using n8n automation. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: → Speed wins in early stages → Don't reinvent the wheel → Same outcome, fraction of the effort What they got: Full RAG chatbot with OpenAI + Pinecone + custom knowledge base. The n8n workflow connects everything visually. No coding. Same result. 𝗪𝗵𝗲𝗻 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝘀𝗰𝗿𝗮𝘁𝗰𝗵 𝗺𝗮𝗸𝗲𝘀 𝘀𝗲𝗻𝘀𝗲: → Proprietary algorithms → Extreme customization needs   → Unique IP protection Most founders don't need perfect. They need working. I build solutions with AI automation from day one - whether it's your core product or internal tools that make your team more efficient. Over to you: What's one thing you're planning to build custom that might already exist? #AI #Automation #NoCode #Founders