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If you use Claude, you’re missing this. These MCP servers add live browsing, automation, scraping, and media generation—turning your workflow into a fully connected AI system. #ClaudeAI #MCPServers #AIWorkflow #AIAutomation #AIForDevelopers #AutomationTools #DeveloperTools

Something big is happening and I don't think everyone has noticed yet. 100+ model labs have come online in the last 6 months. Not companies building AI apps but companies building the powerful models that power them. Every modality, every use case, every research direction is getting its own lab. Because the tools and talent to do frontier research are no longer locked inside a handful of organizations and it is a great thing! I've had the chance to talk to many of them over the past few months. And what I keep hearing is the same thing: While training the model is hard in and out of itself, there is also a significant challenge to go from trained weights to a paying developer. Auth. API keys. Rate limiting. Billing. White-label branding. Months of engineering work that has nothing to do with their research. Today we're launching Baseten Frontier Gateway and it built exactly for this moment. A production-grade, white-labeled inference API with no separate gateway to build or buy. Native to Baseten infrastructure, so no extra latency and no integration headaches are felt. Poolside went from kickoff to production in 7 weeks. P50 TTFT: 146ms on Laguna XS.2, 605ms on Laguna M.1. I am really proud of this one and the incredible team I have had the chance to work with. Thanks for the vision and collaboration Bola Malek. Go check out the blog to learn more: https://lnkd.in/gZ_jfajG

🚀 One month into the $20 Claude plan. Here's what actually got built. I'll be honest — I wasn't sure it was worth it. But the last few weeks changed that. 🏗️ A full hub ecosystem — from scratch I built four fully functional hubs: • FinanceHub — tracks expenses, budgets, and financial summaries in one place • WeatherHub — real-time weather data with clean, at-a-glance conditions • RouterHub — monitors network status and connectivity metrics • EnergyHub — tracks energy usage and helps spot patterns over time Each one is something I'd been meaning to build for ages. All four shipped in a matter of weeks. 🕐 A UTC Time Converter Simple, clean, and actually useful. Pick a UTC time and instantly see it across every timezone you care about. The kind of utility that sounds trivial until you're scheduling calls across 5 countries. 🐺 Discovered OpenWolf This one changed how I use Claude Code entirely. OpenWolf is open-source middleware that sits invisibly between you and Claude Code — giving it a persistent memory of your project, a file index so it knows what to read before reading it, and a token ledger that tracks everything. It reduced my token usage by over 65%. If you're using Claude Code and not using OpenWolf, you're leaving a lot on the table. All of this in a single month. But here's the honest part — it's not magic. There's a real learning curve. You need to be creative with how you prompt, break problems down, and know when to push back on the output. The people getting the most out of AI aren't just typing requests — they're thinking alongside it. Is the $20 worth it? For me, absolutely. But only because I took the time to learn how to use it well. #AI #BuildingWithAI #ClaudeAI #ClaudeCode #OpenWolf #Developer #SideProjects #Productivity

Runway recurring homepage loading failures are becoming a serious reliability concern. A production platform cannot afford repeated access instability multiple times per day. When users cannot consistently reach the dashboard, workflows stall, timelines slip, and trust erodes. This is no longer a minor bug. It is a core platform reliability issue. Creative AI tools positioned as professional infrastructure must deliver: • Stable platform access • Consistent uptime • Transparent incident communication • Reliable production continuity Users are not asking for new features right now. They are asking for the homepage to load consistently. #RunwayML #UnstableApplication #ServerIssues

What if your monitoring tool could actually learn? We do not mean "machine learning" just to check a box on a marketing list, but a tool that truly understands your specific environment and adapts to it. Here is how anomaly detection works at Logic Insight: 1. We gather data from your systems, including every metric and every cluster. 2. Our models build flexible baselines for each metric, taking into account the time of day, the day of the week, and seasonal trends. 3. When something behaves unusually compared to that baseline, we flag it and provide context about what changed and what else might be affected. 4. The models continuously update themselves as your system grows and changes. To see the difference this makes, think about standard alerts versus data-driven ones. A standard alert tells you to look when processing power goes over a fixed number, like 80%. A data-driven alert tells you that processing power is only at 65%, but notes that this is highly unusual for a Monday morning, and points out that it matches a slowdown in your storage. One approach just gives you a raw number. The other gives you an actual answer. This approach dramatically reduces false alarms because the system learns what is normal for your specific infrastructure, rather than relying on a generic industry average. Machine learning is not magic, but when it is applied to the right data with the right context, it changes how teams manage their daily operations. You can see how it works at logicinsight.io #MachineLearning #AnomalyDetection #Observability #AIOps #Nutanix #DevOps #LogicInsight

It's a big day at LogicMonitor. We're taking the next step in making Edwin AI the front door to IT operations. Enterprise IT has become harder to manage than ever. Teams are working across cloud, SaaS, infrastructure, digital experience, and AI-driven workloads, while still being asked to move faster, reduce risk, and keep critical services running. The old model of more dashboards, more alerts, and more manual triage is not enough. That is what we are focused on changing. Edwin AI is becoming the starting point for how teams understand what is happening, why it matters, and what action to take next, backed by the telemetry, context, automation, and governance of the LogicMonitor platform. This is not about replacing people or claiming everything becomes autonomous overnight. It is about helping teams cut through noise, reduce manual effort, and take safer action with better context. I’m grateful to the customers, partners, and LM teams helping shape this work through the Autonomous IT Innovation Program. This is how we make Autonomous IT practical, trusted, and real. Let's get to work. ➡️ https://ow.ly/uLky50Z4Vng

May the flow be with you. Today, we unleashed the force with the #walkerOS v4 release. Changes to the core event model might be a small step for us, but a giant leap for mankind. Every source now speaks the same shape, every destination receives it the same way. From browser to warehouse, no glue code, no per-tool quirks. One standardized pipeline, source to destination. The biggest new thing: flows can now talk to flows. Your web flow can reference your server flow directly. URLs, settings, and deployment targets resolve at bundle time. No more copy-pasting endpoints between configs. Client and server finally feel like one setup. Both are fully integrated and linked together. And v4 is fully schema-based. Your editor autocompletes every field. Your AI agent can read the configs, validate references, and help you build flows. You can literally talk to your flow.json. Big enough to break v3. Small enough to fit on one migration page. And as you can see, there's another major release coming soon 🖖

Into The Box 2026 was more than a conference recap. It was a clear signal of where the CFML and BoxLang ecosystem is heading next. We’ve published a complete overview of everything announced at ITB 2026, including: -BoxLang’s momentum since 1.0 -Real customer modernization stories from government, travel, finance, and enterprise systems -BoxLang AI and agentic development updates -ColdBox 8 and native BoxLang support -Modern application templates -CommandBox, BX-CLI, TestBox, CBWire, ContentBox, and ecosystem updates -Our Florida International University partnership to help train the next generation of BoxLang developers The core message is simple: Modernization does not have to start with throwing everything away. For organizations with years of ColdFusion, Lucee, and CFML investment, BoxLang offers a practical path forward: preserve what works, modernize what matters, and prepare your applications for cloud, AI, and the next decade of development. Read the full recap here: https://lnkd.in/eeDBgpXT #BoxLang #CFML #ColdFusion #Lucee #ColdBox #OrtusSolutions #IntoTheBox2026 #SoftwareModernization #Java #AI #DevOps

What if Lovable already knew everything about your project before you typed a single prompt? Well it turns out it can. With its MCP server, Lovable understands your entire workflow much better. But what does that mean? MCP stands for Model Context Protocol. Think of it as a universal plug that lets AI agents connect to external apps, reading their data and taking actions based on it. Lovable can now connect to your existing tools. Notion, Granola, Linear, n8n, and many more. That way, the Lovable agent can read your docs, meeting notes, and workflows while it builds. Imagine your team decides on a new feature in a meeting. Granola captures the meeting notes, decisions and action points. Because Granola is connected to Lovable, you can then go to Lovable and say “build a prototype based on what we decided in this morning’s meeting”. The more you connect, the better Lovable understands you and the faster you ship. What are you waiting for? Connect all your agents to Lovable now with a 20% discount on the Pro version using this link 👉 https://lnkd.in/e24zWRGS And for more information on the tools you can integrate to Lovable 👉 https://lnkd.in/eG_jD29A

Open Source Landscape for Industrial Agent Platforms — 2026 Edition 🚀 When an enterprise has 1,000+ AI agents running across teams, the question is no longer: “How do we build an agent ❓ ” It becomes: How do we build an Agent Platform that can run, govern, observe, secure, and evolve them at scale? Over the past year, the open source ecosystem has started moving toward this problem. My current mental model is a 5-layer architecture 👇 Runtime - Gateway - Observability - Security - Memory 🚗 Runtime (Agent runtime + tool execution runtime) Agents need isolated, scalable execution. Each agent session should ideally run in its own sandbox: hard isolation, no shared state leakage, and scale-to-zero when idle. OSS: Kagent/OpenSandbox 🏛️ Gateway Controlled entry point to model access, MCP tools, agent-to-agent traffic, routing, spend, audit, and policy. OSS: Agentgateway/Agentregistry 👁️🗨️ Observability and Monitoring Traces, evals, cost, latency, and failure modes OSS: OpenTelemetry/MLflow / Langfuse / Prometheus / Grafana 💂♀️ Security What the LLM can say, what the agent can do and how tool is allowed to execute OSS: NeMo Guardrails/LLM Guard 🧠 Memory manage of short-term, long-term memory OSS: Mem0/Dapr ⛩️ Gaps I still see in the open source landscape 🧩 1️⃣ Browser-use content security and domain specific filter 2️⃣ Central observability tool supporting all the metrics, logs and spans 3️⃣ Integration and operations burden to all 5 layers 4️⃣ Framework on fast incubating/mutating/inheriting horizontal agents for non-IT #AgentPlatform #PlatformEngineering #MCP #AIAgents #AIObservability #AISecurity #AIGovernance #OpenTelemetry #OSS #Kubenetes