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Author: drweb
I have been experimenting with MCP servers in a few ways, including the Redgate Monitor MCP Server. It took me a few tries, and some help from engineers to get connected. I’ll cover how I did this in Claude and Visual Studio Code in this post, and try to warn you about a few places I made mistakes.This is part of a series of posts on Redgate Monitor. Click to see the other posts.Creating A TokenI’m not going to bore you with the setup and configuration of the Redgate Monitor instance. That procedure might change by release, so I’ll assume…
Most sysadmins only use cat to dump a file and tail -f to watch logs. That leaves a lot of useful behavior untouched and a lot of time wasted scrolling through files that could be sliced in seconds. Every Linux system whether it’s a cloud VM, a bare-metal server, or a container running a single servic; generates files constantly. Config files, log files, CSV exports, deployment outputs, and cron job records are everywhere. Being able to read and inspect those files quickly, without opening an editor or writing a Python script, is one of the most underrated command-line skills. The…
TL;DR — Key Takeaways Knowledge retrieval is a major incident-response bottleneck. Engineers often spend substantial time searching old tickets, runbooks, postmortems and internal documentation before they can fix the actual problem. Traditional AIOps tools do not solve the whole problem because much of the most useful incident knowledge lives in unstructured text rather than metrics and traces. The RAG system combines incident data, document-aware chunking, vector retrieval, re-ranking, LLM reasoning and a feedback loop. It achieved 87.3% root-cause identification accuracy across 2,400 incident scenarios, while mean diagnosis time fell 59%. If you’ve ever been paged at 2 a.m. for a…
What AI thinks a DBA does all day is a calm, orderly, deeply reassuring Tuesday. It has backups, documentation, proactive health checks, and apparently a door that closes. I asked. Then I wrote down my actual Tuesday. Its day has six items. Mine has thirteen.I asked it to describe a typical day for a database administrator. It answered instantly, with the confidence of something that has never been cc’d.One Tuesday, according to the job description and according to Tuesday.What AI Thinks HappensWhat Actually Happens9:00. Review overnight backup jobs and monitoring alerts.9:04. Somebody asks whether the database is slow. The database is not…
TL;DR — Key Takeaways isolated-vm, a popular Node.js sandbox considered more secure than vm2, has a critical flaw that lets malicious code escape its isolation and potentially hijack the host process for remote code execution. The bug is in the C++ glue code that transfers data between V8 isolates, where a time-of-check-to-time-of-use flaw in ExternalCopy can turn a seemingly safe ivm.Reference into a full guest-to-host escape. With isolated-vm used by more than a million projects/downloads a week—including AI and automation platforms—developers should urgently upgrade to versions 7.0.1 or 6.2.0 and scrutinize how their sandboxes could cross security boundaries. Developers for…
TL;DR — Key Takeaways Tessl Code Review targets a growing bottleneck in agentic development: AI-generated code is arriving faster than human reviewers can keep up. The tool stores review standards as versioned files in the repository, giving teams control over the criteria used to assess pull requests. Tessl reviews the full pull request context, including surrounding code, team standards and previous discussion, rather than only analyzing the latest diff. Every team that has turned coding agents loose on real work has run into the same wall. The agents write fast. They write a lot. And somewhere between the tenth and…
TL;DR — Key Takeaways PQC migration starts with discovering where vulnerable cryptography exists—not simply swapping algorithms. Cryptographic dependencies can hide across source code, libraries, containers, CI/CD, Kubernetes, cloud services and SaaS platforms. SBOMs help map software components, but CBOMs and cryptographic inventories provide deeper visibility into keys, certificates, protocols and algorithms. CI/CD pipelines can continuously collect cryptographic metadata and connect it to builds, images and deployed workloads. A living inventory is essential for crypto agility, migration prioritization and reducing “harvest now, decrypt later” risk. Post-quantum cryptography is often framed as an algorithm replacement exercise. The discussion usually moves quickly toward…
TL;DR — Key Takeaways Waterfall is becoming useful again in the LLM era, not as a slow bureaucratic process but as a lightweight control structure for fast AI-generated work. Waterfall 2.0 follows five stages: vision, ADRs, prototypes, architectural skeleton, and code plus tests. Backtracking becomes a strength. LLMs can regenerate earlier artifacts quickly, making it practical to revisit assumptions without weeks of rework. A shared knowledge corpus is central, bringing requirements, ADRs, source code, schemas, diagrams and telemetry into one context. The engineer’s role shifts toward orchestration and validation, with humans managing context, checking outputs and making trade-off decisions. Waterfall…
TL;DR — Key Takeaways Traditional SRE metrics can stay green while an AI system is failing customers. Latency, uptime and error rates do not measure whether an LLM is giving correct, grounded answers. Production-grade AI needs evaluation at three stages: before release, during CI and on sampled live production traffic. Use a layered evaluator stack. Cheap deterministic checks can run broadly, while more expensive LLM judges and human domain reviews should run selectively. The best eval sets come from real production failures. Customer complaints, anomalies and low-confidence traces should be promoted into permanent regression tests. For RAG systems, retrieval quality…