Matej Senožetnik’s Post

97% of developers now use AI coding tools. That number sounds huge — but what’s actually changing inside delivery pipelines? A GitHub survey found that nearly every developer has adopted AI assistance in some form, mostly for code suggestions, documentation, and testing. And in a year-long enterprise study (arXiv, Sept 2025), teams using AI review/generation shipped 28% more code and cut PR review cycles by 31.8%. That’s not just productivity, that’s shorter iteration loops and faster business response. The catch? Gains are uneven. Projects without code-quality guardrails saw higher revert rates and more flaky tests. 👉 Real productivity = velocity + reliability. What’s your experience — have AI tools genuinely improved your team’s delivery metrics, or just moved the bottleneck? #SoftwareEngineering #AICoding #DeveloperProductivity #GammaEdge

Our team has been diligently working on the enhanced next-generation capabilities, and we are thrilled to announce its launch today! Our new benchmark study reveals that:  20x more productive than Claude Code and GitHub Copilot  Tests that always compile and pass  Fixed-price licensing vs unpredictable token costs  Hours of unattended generation vs constant oversight Read all the details about this launch on our blog: https://hubs.ly/Q03RxMBM0 Sounds too good to be true? Check it out yourself! #SoftwareTesting #TestAutomation #DevOps #EnterpriseJava #QualityEngineering #SoftwareDevelopment

Today, we're announcing the next generation of Diffblue Cover. Our reinforcement learning-powerd agentic capabilities deliver a 20x productivity advantage versus AI coding assistants – and a new benchmark study proves it. Three breakthrough capabilities transform enterprise test coverage: 🔍 Test Asset Insights - Diffblue Cover now analyzes your pre-existing unit test coverage and learns from it. - The agent learns from your patterns, reuses established helper methods and fixtures, and builds upon domain-specific test infrastructure to generate high quality, idiomatic test coverage. 🧠 LLM-Augmented Intelligence - Fuses customer-approved LLMs with our reinforcement learning-based test generation. - Better coverage and quality than any single generative AI technique can deliver in isolation – while maintaining our signature promise: deterministically generated tests that always compile and capture application behavior. 📊 Guided Coverage Improvement - Dramatically speeds up coverage improvement beyond the agent's first pass. - Automatically identifies and prioritizes underlying problems, then generates an actionable turnkey coverage improvement plan. Internal testing shows 50% coverage increase beyond out-of-box in just one hour. Our new benchmark study reveals that: ✅ 20x more productive than Claude Code and GitHub Copilot ✅ Tests that always compile and pass ✅ Fixed-price licensing vs unpredictable token costs ✅ Hours of unattended generation vs constant oversight Read all the details about this launch on our blog: https://lnkd.in/ditVPuwp #SoftwareTesting #TestAutomation #DevOps #EnterpriseJava #QualityEngineering #SoftwareDevelopment

Autocomplete is now a thing of the past. AI assistant isn't just guessing what the next line will be; it's reading whole codebases. Full-system awareness is the next big thing. The big tech companies are racing to own it, and two clear plans are starting to take shape:  𝐓𝐡𝐞 "𝐌𝐚𝐬𝐬𝐢𝐯𝐞 𝐌𝐞𝐦𝐨𝐫𝐲" 𝐰𝐚𝐲: Claude 3.5 Sonnet from Anthropic uses a huge 200,000-token window to look at and think about your whole project at once.  𝐓𝐡𝐞 "𝐒𝐦𝐚𝐫𝐭 𝐈𝐧𝐝𝐞𝐱" 𝐰𝐚𝐲: GitHub Copilot Enterprise indexes all of your repository and uses semantic search to find the exact context it needs for any job.  What makes it so important? We went from writing codes faster to solving bigger problems. AI partner can now help find bugs that affect more than one system, suggest ways to improve your architecture, and even understand technical debt.  Developers adapting to this shift aren't just coding faster. They're solving complex problems they couldn't tackle alone How are you planning to leverage AI that understands your full system context? Share your thoughts below. 👇 #AICoding #DeveloperProductivity #SoftwareDevelopment #TechTools #AI

The Developer Renaissance in AI. Remember when "knowing AI" meant a PhD? Today, early access to language models turned curious builders into product teams of one—and it’s messy, creative, and wildly effective. The barrier to build has crashed. We’re seeing developers compose workflows, not just code, and leaders rethinking how software gets shipped. Adoption data backs it up: AI is moving from pilot to profit across industries [McKinsey](https://lnkd.in/g8ucfiRt), and the open-source surge shows where builders are heading next [GitHub Trends](https://lnkd.in/gQygdR_G). My turning point? I once duct-taped prompts to spreadsheets and prayed the Zapier step didn’t break. Now I use my Signals-to-Systems method: 1) Workstream X-Ray to run productivity analysis inside a specific business, 2) an Automation Spine in n8n with MMR (Multi-Model Routing) across OpenAI, Anthropic, Perplexity, and Grok, and 3) Agent Guardrails for evals and fallbacks. For a 60-person SMB (small and mid-size business), we cut onboarding time by ~40% and turned tribal know-how into repeatable, data-driven workflows. If you’re a senior leader, the opportunity isn’t “buy an AI tool.” It’s empower your developers to ship small, compoundable wins that move decisions closer to data and customers. What’s one workflow you’d automate this quarter if a two-week build was guaranteed? If you want my Signals-to-Systems checklist or a quick diagnostic on your teams’ workflows, comment “RENAISSANCE,” follow for playbooks, or DM me to map a no-fluff roadmap for your org. #AI, #Automation, #DigitalTransformation, #Productivity, #SoftwareDevelopment, #NoCode, #WorkflowAutomation, #AIAutomation, #OpenSourceAI, #AIForBusiness, #GenAI, #FutureOfWork, #AIBuilders

Tech With Tim: Finally, an AI Database That Actually Makes Sense Finally, an AI Database That Actually Makes Sense This video dives into Tiger Data, a game-changing AI database built for seamless agent development. You’ll get a live demo of its standout features, step-by-step setup instructions, and even sample data creation—ultimately showing how easy it is to accelerate your projects with intelligent DB tooling. Plus, there’s a quick plug for DevLaunch, a hands-on mentorship program that helps devs move from tutorials to real-world projects and job offers. With clear timestamps for each segment—overview, demo, setup, data creation, and an agentic workflow example—it’s the perfect crash course for anyone ready to level up. Watch on YouTube https://lnkd.in/gs3HRYRS

I’ve been experimenting with parallelizing strategies with AI, and shipping a bug gave me clarity into its importance. 🦾 💪 Shipping bugs happens — it’s part of building software. Last week, I merged one into main that blocked other developers. ‼️ Instead of rushing to debug under pressure, we forked the process: - We opened an issue and assigned the urgent fix to GitHub Copilot. - In parallel, our team focused on the root cause analysis — understanding how the bug slipped through and how to prevent it. By offloading the immediate fix to AI, we reduced the mental pressure and gave ourselves space to think clearly. 😌 Copilot delivered a working patch, which we reviewed and shipped in 20 minutes, while we continued our analysis and, over the next several hours, completed a full refactor that eliminated the pattern causing the bug. This experience reinforced a key principle for us: AI isn’t just for speed — it’s for clarity, focus, and better flow. When you can, use it to handle the urgent so humans can stay focused on the important. #AIinEngineering #DeveloperExperience #SoftwareEngineering #AIWorkflow

How to start your AI automation journey — even if you don’t have a technical background In the last 3 weeks, five people asked me how to begin learning AI and no-code tools. All of them had one thing in common: curiosity, and a little bit of overwhelm. Here’s what I shared 👇 1️⃣ Pick 1–2 no-code platforms and explore Start free accounts on Make, Zapier, or n8n. Take a quick tour of their Academy lessons (they’re free!) and try one simple automation. ➡️ Make is more visual, Zapier is simpler, n8n a bit more technical. 2️⃣ Don’t fear missing out. You’ll hear about Apify, Hunter, Airtable, Notion, Snov, Lemlist, PhantomBuster… Ignore the noise for now. Build muscle memory with the basics first. 3️⃣ Experiment with LLMs. Try OpenAI, Gemini, Claude, DeepSeek, even Grok — see which fits your work style. It’s not about which is “best”; it’s about which helps you think better. 4️⃣ Learn like you’d train in a gym. If you lift too heavy on day 1, you’ll be sore. So give yourself 1–1.5 hours a day, five days a week. In 2–3 months you’ll feel stronger, faster, more confident. 5️⃣ Use free credits and free content wisely. List YouTube creators whose tutorials feel easy to follow. Keep notes. Build small automations. Celebrate small wins. AI + no-code aren’t just for engineers. They’re for the curious, the disciplined, and the ones willing to learn every day. 💡 #NoCode #AI #Automation #LearningJourney #Anautomate

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

Working with ai in the terminal is crazy...🥵🥵💪🏿💯 Ever imagined your terminal becoming your smartest teammate? That’s exactly what’s happening right now with the new generation of AI command-line tools and MCP-based systems (Model Context Protocol). I’ve been experimenting with AI directly in the terminal — and honestly, it’s next-level. 🚀 Here’s what’s blowing my mind so far: ⚙️ Run different agents side by side — each with unique skills and memory. 📂 Read and modify files instantly — the AI can suggest, rewrite, or refactor code right from your workspace. 🔐 Permission-aware control — you decide what the AI can access or edit. 🧠 Context persistence — it remembers your recent commands, goals, and even mistakes. 🌐 Cross-tool synergy — imagine AI talking to Docker, Git, or even Notion right from your shell. It’s like the command line just evolved from a text interface to an intelligent operating environment. We’re moving toward a world where the terminal isn’t just where you type commands — it’s where you collaborate with AI. 💬 I’m curious — would you trust an AI agent to modify files or execute shell commands for you? Or would you rather keep full manual control? #AI #AgenticAI #Developers #Automation #PromptEngineering #AIAgents #Innovation #MachineLearning #FutureOfWork #ArtificialIntelligence #TechTrends