AI agents are taking on the work no one has time to own—flaky tests, noisy logs, broken builds. Swipe through to learn how they're reshaping the way teams ship software ➡️ Full blog post: https://circle.ci/47wULkn
How AI agents automate software delivery tasks
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🌐 TrueConf 8.5 latest update brought not only the ability to use videos as virtual backgrounds, but also to demonstrate information from a flipchart, whiteboard or document right to the conference without using third-party software. 💡 If the board is at an angle, AI will automatically align the image and remove all unnecessary details.
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Why can AI build code so easily, but needs a human detective for tricky data? Software moves faster, backlogs shrink and devs focus more on craft. But when context and business understanding matter most, your data experts are truly irreplaceable. In this new blog by Ben Bausili, he breaks down the “Interrogation Gap” in his new blog. Read how human expertise still drives the best data work. https://lnkd.in/g3KzTxxp
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AI can write your docs. But it can’t run your standup. It won’t pick up on the tension in that “all good.” It definitely won’t help when 3 teams think someone else is presenting. Your dashboards say “All Systems Operational.” But your team? Lost in Slack threads, ghosting on ownership, and waiting for someone else to present. We call it the Silent Outage. ✅ CPU: Green ✅ Network: Green ✅ Deploy: Green ❌ Communication: not found Underneath every AI-augmented delivery cycle is a team that still has to communicate, clarify, and recover together. Join our webinar CTRL + SHIFT: Soft Skills for Hard Systems We’ll go through the language, frameworks, and examples of communication that work in modern teams. Register here: https://kode.wiki/47YQ42Y
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Have you ever wondered how to turn static PDF requirements into living, version-controlled models? In many companies, requirements just sit in PDFs. They’re easy to share, but it’s hard to keep the connections to models and implementations, which can lead to silos and extra manual work. In our video, Gerald Neumann from NANGA SYSTEMS and Philipp Kalenda from LieberLieber Software GmbH show how those static PDFs can come to life as a living Digital Red Thread. AI helps extract requirements automatically and keeps everything fully traceable. To see how you can manage versions and variants in practice, check out the full video here: https://lnkd.in/er4nt4fR
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📣 Today, we’re open-sourcing the Doubleword Control Layer - the world’s fastest AI Gateway, delivering 15× higher throughput and 450× lower latency overhead than LiteLLM. The Control Layer provides a single, secure interface for routing, managing, and governing inference activity across models - whether open-source or proprietary. With the Control Layer, enterprises can: ⚡ Seamlessly switch between models - open or proprietary, hosted or self-hosted ⚡ Turn any model into a production-ready API - with built-in auth, rate limits, and user controls ⚡ Centrally monitor and audit all inference activity - for governance, compliance, and optimization 🔽 Check out the full announcement, repo, docs, demo video and benchmarking write-up in the comments.
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We’ve open-sourced the Doubleword Control Layer - the gateway we built because the two prevailing paths both have failure modes. API models shift underneath you with no guarantees on stability, privacy or residency. Self-hosted inference exposes web APIs with no authentication or authorization - and unless you add something on top, you will ship them exposed. A gateway is the right architectural response: a single stable point of control above both API and self-hosted models, so policy isn’t re-implemented in every application and you don’t depend on whatever guarantees a provider feels like giving you at the time. A gateway should be invisible to the user - it must add control without adding latency. It’s now open source. If you care about owning your inference layer rather than depending on someone else’s, check it out
📣 Today, we’re open-sourcing the Doubleword Control Layer - the world’s fastest AI Gateway, delivering 15× higher throughput and 450× lower latency overhead than LiteLLM. The Control Layer provides a single, secure interface for routing, managing, and governing inference activity across models - whether open-source or proprietary. With the Control Layer, enterprises can: ⚡ Seamlessly switch between models - open or proprietary, hosted or self-hosted ⚡ Turn any model into a production-ready API - with built-in auth, rate limits, and user controls ⚡ Centrally monitor and audit all inference activity - for governance, compliance, and optimization 🔽 Check out the full announcement, repo, docs, demo video and benchmarking write-up in the comments.
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💻 Making it Work. Make it Fast. Make it Pretty. That's not just a catchy line. It's the unspoken rhythm of every software engineer's journey. 🔧Make it work: At the start, it's all about functionality. Getting the logic right. Making sure the product does what it's meant to do, even if it's not perfect yet. Progress Starts with something that works. ⚡Make it fast: Next comes performance. Optimizing the code, improving efficiency, and fine-tuning the system so users don't wait. It's about making technology invisible by making it seamless. 🎨Make it pretty: Finally, the art of refinement. Turning solid functionality into beautiful experience. This is where design, usability and emotion come together. Every developer knows this cycle. It's the dance between creativity and logic, between what's possible and what's polished. At the end of the day, we're not just building apps or systems. We are building trust through thoughtful craftsmanship. #SoftwareEngineering #Developers #DesignThinking #Coding #Innovation
Every technological improvement and iteration boils down to 3 steps. And it does not matter if it's Software, Hardware or AI, it all comes down to: - Make it Work ⚙️ - Make it Fast ⚡ - Make it Pretty 🦋 Focus on these in that order and you'll be fine!
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How should software companies re-define themselves in the AI era? With AI-native startups redefining speed and scale and achieving $100M+ ARR operating in a radically different way, software incumbents hoping to remain competitive have had to fully embrace AI not just to reinvent their value proposition and product, but also in terms of their GTM, business model, internal operations and people strategy. Discover how leading software players are transforming themselves to be AI-centric in in the latest report (incl. insights from a recent software CXO survey) from McKinsey TMT⬇️
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How should software companies re-define themselves in the AI era? With AI-native startups redefining speed and scale and achieving $100M+ ARR operating in a radically different way, software incumbents hoping to remain competitive have had to fully embrace AI not just to reinvent their value proposition and product, but also in terms of their GTM, business model, internal operations and people strategy. Discover how leading software players are transforming themselves to be AI-centric in in the latest report (incl. insights from a recent software CXO survey) from McKinsey TMT⬇️
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