Hot Take: AI agents are not in their teenage years
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MCP Design Principles
You can give AI agents everywhere fingers & eyes into your tool or service, by implementing an MCP (Model Context Protocol) server. It’s a great idea! It’s also a new kind of design and engineering. Jessica describes how it’s different from implementing an API or a GUI, and why it’s more exciting than either.
GitKraken MCP: Give Copilot & Cursor the Git Context They're Missing
AI assistants like Cursor and GitHub Copilot are fun to play with. They autocomplete code, refactor functions, and occasionally argue with you about whether you really needed that semicolon. But the moment you ask them to do something grounded in your repo, say, “start work on JIRA-123”… you hit a wall. They don’t know your branching conventions. They don’t know how your team links issues.
How to Responsibly and Effectively Contribute to Open Source Using AI
With the influx of AI tooling, it’s never been easier to contribute to open source communities. These tools are capable of gathering context quickly, “understanding” repositories faster than ever before. They provide instant summaries about repositories that, previously, would have meant reading lines and lines of code. They can fix bugs in programming languages you don’t know, and ultimately allow more contributors to get involved, which (almost) every open source project wants.
Building and Monitoring AI Agents and MCP servers
Whether you’re building agents in your applications, or standing up an MCP server because its the new cool thing, working with AI is just different. Trying to figure out why it does the weird things it does is hard.
#049 - The AI Translator: Using LLMs & MCP for K8s Operations & Self-Healing Infra with Alexei Le...
In this episode, Itiel Shwartz kicks off a series on MLOps, LLM, and GenAI in Kubernetes. Starting with Alexei Ledenev, who has over two decades in software development and deep experience in cloud architecture and distributed systems. He shares his journey from CoreOS Fleet to his current role on the Platform Team at Doit.
AI Browsers and AI Agents: A New Way of Working Online
People still head online for information of every kind. Traditional browsers got us there, but they stayed deliberately passive. You opened a tab, loaded a site, then did it again. The actual work moving content around, logging into accounts, drafting and sending messages-always fell to us. That old setup no longer fits how professionals operate today. Workflows are far more complex, with countless SaaS platforms, globally distributed teams, and nonstop communication streams. The gap isn't about needing yet another tool. It's about having a smarter partner right inside the browser.
AI's Role in Elevating Telecom Customer Experience
Telcos have added apps, chatbots, and new call scripts, yet customers still face long waits and generic offers. The problem isn't effort-it's outdated systems and reactive service models. AI offers a reset. By predicting needs, personalizing journeys, and automating support, it moves telcos from reactive service to proactive partnership. And given that large operators handle millions of daily customer interactions, the opportunity is enormous. AI doesn't just improve customer experience-it redefines it.
Agentic AI Becomes Essential: Why Adoption Is Accelerating and What Comes Next
The cautious optimism business leaders held towards AI agents has evolved into more widespread enthusiasm. In our last survey from April 2025, just over half (51%) of companies had deployed AI agents in their organization. Six months later, 75% of companies are deploying more than one agent, according to PagerDuty’s latest research.
Smarter AI Cost Optimization With Guardrails That Scale
AI adoption is reshaping how organizations innovate. It’s also driving cloud costs higher. CloudZero’s State Of AI Costs In 2025 report finds that for mature FinOps and engineering leaders, visibility into AI costs is a critical first step, but it’s not enough. To enable fast, responsible AI and machine learning innovation at scale, teams need pragmatic, flexible guardrails. They don’t need rigid budgets or knee-jerk shutdowns that slow progress or push teams into shadow ML.