What your Claude Code setup is missing
When something breaks in production, the fix is usually simple. The hard part is the 30 minutes before it, jumping between Datadog, GitHub, Cloud Run logs, and Slack history, trying to figure out what changed and why.
Claude Code can’t help here. It has no visibility into APM traces, ticket history, or what the team decided last month.
CodeRabbit Agent sits inside Slack and connects to your code, tickets, docs, monitoring stack, and cloud infra. You mention it in a thread, and it pulls traces, cross-references recent PRs, opens a targeted fix, and files the postmortem. The whole incident stays in one thread where the team sees every step.
It also retains what the team decided across conversations, so the same context doesn’t need to be re-derived the next time something breaks.
New workspaces receive $50/user in free agent minutes.
Get started with CodeRabbit Agent →
Thanks to CodeRabbit for partnering today!
[Video] Hermes agent masterclass
We recently released a full issue on the Hermes Agent, and many of you reached out to publish it as a video guide.
We just put together one and you can find the video at the top.
This 48 mins video covers everything you need to understand and customize Hermes Agent.
Self-evolving skills, three-tier memory, GEPA optimization, and going from 1 to 10 agents that work for you 24/7.
Enjoy.
Thanks for reading/watching.
P.S. For those wanting to develop “Industry ML” expertise:
At the end of the day, all businesses care about impact. That’s it!
Can you reduce costs?
Drive revenue?
Can you scale ML models?
Predict trends before they happen?
We have discussed several other topics (with implementations) that align with such topics.
Here are some of them:
Learn everything about MCPs in this crash course with 9 parts →
Learn how to build Agentic systems in a crash course with 14 parts.
Learn how to build real-world RAG apps and evaluate and scale them in this crash course.
Learn sophisticated graph architectures and how to train them on graph data.
So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here.
Learn how to run large models on small devices using Quantization techniques.
Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust using Conformal Predictions.
Learn how to identify causal relationships and answer business questions using causal inference in this crash course.
Learn how to scale and implement ML model training in this practical guide.
Learn techniques to reliably test new models in production.
Learn how to build privacy-first ML systems using Federated Learning.
Learn 6 techniques with implementation to compress ML models.
All these resources will help you cultivate key skills that businesses and companies care about the most.
