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Are your engineers drowning in debugging work instead of building innovative solutions? A groundbreaking study reveals that engineers spend 35-50% of their time debugging software, with AI-generated code making this problem even worse. But here's the game-changer: Deductive AI just demonstrated how machine learning can automate production failure diagnosis. Key breakthrough: DoorDash's advertising platform, which processes real-time auctions in under 100 milliseconds, integrated Deductive AI and achieved remarkable results. The system diagnosed approximately 100 production incidents over recent months, projecting 1000+ annual engineering hours saved with millions in revenue impact. The technology uses reinforcement learning to build knowledge graphs that map relationships across codebases, telemetry data, and documentation. When incidents occur, AI agents work together to form hypotheses and converge on root causes in minutes rather than hours. This represents a fundamental shift from reactive firefighting to proactive problem-solving, allowing engineers to focus on innovation rather than manual debugging. Read more: https://lnkd.in/dNvNWJr2 Need support with Website Design, Development, Automations or AI Solutions? Visit our website at https://orbodigital.com/ or book a meeting with us here: https://lnkd.in/gGXujAYh #AI #Automation #SoftwareDevelopment #MachineLearning #TechInnovation #DigitalTransformation #WebDevelopment #Zapier

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📣 Today we’re announcing new features for Jellyfish AI Impact that bring together system data, developer sentiment, and guided insights - giving leaders a clearer, faster, and more complete picture of how AI is transforming their organizations and the overall SDLC. As AI becomes embedded in every corner of software development, engineering leaders face a new challenge: proving that these tools actually deliver value. From autonomous agents to code assistants, the landscape is evolving faster than most teams can measure. Keep up with the AI evolution with Jellyfish AI Impact: https://lnkd.in/g7t3wx5h

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LiteLLM is an open-source proxy that connects your app to LLMs through one unified interface. Paired with Redis, it gives AI and ML teams a simple yet powerful way to unify access to LLMs, accelerate response times, and make AI apps real-time. Redis handles performance, memory, and data coordination that modern AI apps demand, while LiteLLM handles abstraction and routing. Our AI Product Marketing Manager Rini Vasan dives into: ▶️ What LiteLLM is ▶️ Why it works well with Redis ▶️ How LiteLLM and Redis work together Learn how to scale your LLM gateway with LiteLLM & Redis: https://lnkd.in/gqsTebF7

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Your CI/CD bill is not exploding because of “expensive runners,” it is bleeding out through flaky tests, over-parallelized jobs, and slow feedback loops that quietly tax every deploy This piece shows you how to redesign pipelines so you cut compute waste, shrink queues, and speed up developer flow without adding yet another approval gate or YAML religion Full breakdown in the comments. #DevOps #CICD #PlatformEngineering

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The Redis AI Challenge ends in less than a week (Aug 10th deadline)! Think it's too late to join? Think again! Here are 3 project ideas that you can build in a week. Project 1: Real-Time Poll App Prompt: Beyond the Cache Build a web app where users can create polls and vote, with results updating live for everyone. A perfect project to showcase Redis for real-time applications. Redis Use: Hashes for poll data, Sorted Sets for vote counts, and Pub/Sub for live updates. Project 2: Simple Job Queue Prompt: Beyond the Cache Create a background job system. A web app submits a task (like sending an email) to a queue, and a separate worker process handles it. This is a core backend development pattern. Redis Use: Redis Lists as a simple and reliable message queue using LPUSH and RPOP. Project 3: AI-Powered Article Suggester Prompt: Real-Time AI Innovators Build a blog that recommends similar articles to the one being read. It's a fantastic, beginner-friendly entry into the world of AI. Redis Use: Use Redis as a vector database to store AI-generated embeddings of your articles and perform lightning-fast similarity searches. --- Head to the challenge page to see entry details and peep what other people are building. Can't wait to see what you build! https://lnkd.in/eH_Dk2wu #Redis #AI #VectorDatabase #MachineLearning #Backend #Programming

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🚀 Are your pull requests slowing your team down? Most teams treat every pull request the same. But not every change needs the same level of attention. That’s where Ship / Show / Ask comes in. With DevSensei, you can structure your PR workflow into three simple categories: ✅ Ship For small, low-risk changes. No need to wait, just merge and move on. 👀 Show For changes you want visibility on, but without blocking progress. 💬 Ask For complex work that needs discussion and proper review. This approach helps teams: • Reduce waiting time on trivial PRs • Focus reviews where they actually matter • Keep delivery speed high without sacrificing quality Instead of one rigid process, you get a workflow that adapts to the change itself. Because not every PR should slow you down. 👉 Curious how it works in practice?

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In the architectural landscape of 2026, the "one-size-fits-all" API is a relic of the past. As enterprises scale across web, mobile, and even IoT devices, the diverse data requirements of these platforms have made a single, monolithic backend a...

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Julia Blase, Product Manager at Chronosphere, discusses how observability needs to evolve alongside Kubernetes. She explains that Kubernetes brought dynamic, scalable infrastructure, and monitoring systems must follow suit. The future requires on-demand instrumentation and dynamic data collection that can scale up and down based on needs, helping teams optimize costs while getting the right insights at the right time. Watch the full interview: https://ku.bz/X6tgWrG0P

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