3 things engineers need to know before using GPT 5.6
I tested out Luna, Terra, and Sol and analysed telemetry data - here's what I uncovered that you need to know before using GPT 5.6.
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Unified Logs, Traces, and Errors: Why One Tool Beats Three
Last updated: July 2026 Your Rails app throws a 500. You open Sentry and find the exception. The stack trace points to a controller action, but it does not tell you why the database call failed. You switch to Datadog and search for the request trace. The trace shows a 3-second query, but you do not know what the application was logging at that moment. You open your log aggregator, paste in the request ID, and scroll through output until you find the slow query log line that explains the lock contention.
MCP vs CLI: you're probably choosing wrong
Watch the full episode: MCP vs CLI: Does it even make a difference? Live Laugh Logs ep. 3.
When and what should I be logging?
This is a follow-up to Sergiy’s post Errors, traces, logs, metrics: when to reach for what. Modern observability platforms, like Sentry, give developers a lot of choice. For a given problem, should you use traces, profiles, metrics, logs? If you take away one thing from this post, I hope it’s this: when in doubt, start by adding a few targeted log lines.
Time Series Foundation Model: The AI That Stops Disasters
It's time to go from reactive firefighting to predictive prevention. Learn how the Time Series Foundation Models can forecast system failures before they cascade.
Build an SRE Agent Harness for AIOps Without Context Blowout
An agent harness for AIOps is the runtime layer that coding agents like Claude Code were never built to provide: context isolation, decision traceability, and gated execution for tools that touch production. Aura is Mezmo's open-source (Apache 2.0) agent harness, purpose-built for operations work rather than software development.
The future of governing AI agents
How to build governance into autonomous security agents from the architecture up The industry has moved fast on capabilities. Agents now triage alerts, investigate endpoints, create detection rules, and enrich indicators, and they are even capable of performing most actions we as security operators can perform. The architecture patterns are maturing, as are the models, but governance is not keeping pace.
Small Language Models Can Save You a Fortune on AI Costs
Small language models are cheaper, run privately on your own hardware, and often beat the big models on focused tasks like sorting emails or summarizing transcripts — and they're perfect for checking a large model's work. The key is knowing when to use which.
Reducing token usage by 90%?
Watch the full episode: MCP vs CLI: Does it even make a difference? Live Laugh Logs ep. 3.
Called it (mostly): Checking in on 2026 predictions so far
On this episode of Masters of Data, we revisit the predictions Adam White, Zoe Hawkins, and David Girvin made at the end of last year, checking our own scorecard halfway through 2026. The hits: agents running amok and deleting databases, MCP becoming the backbone for tracking what agents actually do, growing security gaps around personal data, and a collective rejection of low-quality AI content. The misses: we underestimated how fast companies would cut staff for AI, then quietly start rehiring once the agents couldn't cover the work, and we're still arguing about whether token burn is a cost problem or a coming attack vector.