Articles, tutorials, and updates about the AI tools ecosystem.
A practical look at MCP servers that connect AI agents to Git and GitHub, covering what they can do, how they work, and what security risks come with write access.
A practical look at what MCP servers for AWS, GCP, and Kubernetes actually do, how permissions work, and where they save meaningful engineering time.
AI agents can now participate directly in team communication channels. Here's what that looks like in practice, and what you need to watch out for.
Latency, throughput, and resource consumption benchmarks across popular MCP servers, plus a methodology for testing that reflects real production conditions.
When a tool appears in multiple curated directories, it tells you something that no single listing can. Cross-referencing is one of the most underused quality signals in the AI tool ecosystem.
With dozens of AI tool directories each offering partial views of the ecosystem, the real challenge is not finding a directory. It is getting a complete picture without checking all of them.
With thousands of AI tools competing for attention, getting discovered requires more than just being good. The discovery channels that work in this market are specific and worth understanding.
A practical walkthrough of MCP server architecture, tool definitions, resource handling, and testing using the official TypeScript SDK.
Giving AI models access to external tools introduces real security considerations. A practical look at the risks and how to mitigate them without giving up the benefits.
An AI agent that monitors your systems and sends you smart, contextual notifications can replace the firehose of alerts that everyone ignores.
Individual MCP setups are straightforward. Getting a whole team on the same MCP configuration, with consistent behavior and shared standards, introduces challenges nobody warned me about.
AI agents frequently break down when tasks require more than a few sequential steps. The failure modes are predictable and, once you understand them, largely avoidable.
Testing AI skills is fundamentally different from testing deterministic software. The outputs are variable, quality is subjective, and edge cases are infinite. Here are approaches that produce practical results.
When an AI agent produces wrong results or gets stuck in loops, systematic debugging techniques reveal the root cause faster than guessing. Here is a practical approach.
When an MCP server returns an error, the quality of that error message determines whether the AI agent can recover or just gives up. Most servers write terrible error messages.
AI-powered code review catches bugs, style issues, and security problems before human reviewers see the PR. Here's how to wire it up with MCP servers and your existing Git workflow.
AI agents can use tools to read data, execute code, call APIs, and modify systems. But tool use is not as seamless as it appears, and understanding the gaps matters for building effective agents.
Reusing proven AI skills instead of building from scratch compresses development timelines. Skill libraries provide tested, ready-to-use capabilities that accelerate AI-powered feature development.
As AI models gain the ability to process images, audio, and video alongside text, the tools ecosystem is evolving to support these new modalities. The implications are broader than you might expect.
Everyone uses these terms interchangeably, but agents and assistants are genuinely different things. The distinction matters when you're deciding what to build.