AI agents are getting scary good. Tools like clawbot can understand your codebase, write features, fix bugs, and commit changes - all without much human input.
But that doesn’t mean you should let them run wild.
Running AI agents locally is great until you realize they have full access to your machine. One wrong command and you could lose important files.
Docker sandboxes solve this, but the official one lacks flexibility - no persistent workspaces, no multi-instance support, limited customization.
Logging is part of every application, at least they should, but often logs contain sensitive information.
PII (Personally Identifiable Information), PHI (Protected Health Information), and other confidential data often end up in logs - often unintentionally. Storing raw, unmasked logs can lead to compliance violations (GDPR, HIPAA, SOC 2), security risks, and data exposure if logs are misused.
The easiest way to configure your CI/CD pipeline with AWS is to just create long-lived credentials, access and secret key, and store them in GitHub Actions.
While this method works, it has several security risks:
Sharing secrets across microservices creates security risks. Not only when one of the microservices is compromised, as then during rotation of single secret all of your microservices will be affected. Leading to potential downtimes.
Regardless of whether you’re working on a product in a regulated industry like healthcare or finance, or building products that do not require such strict rules, maintaining control over your repositories is essential.
We might argue about what is the important aspect of good communication.
But what are the few basic principles of communication where, at least, two parties will understand each other? Use of the same Glossary, happens in Real Time and uses Same language.
There are multiple strategies to mitigate the number of bugs and problems with the app in production. Some teams focus on unit tests, functional tests, integration tests, manual tests, multiple environments to run those, and build the artifact of the app. Processes for reviewing code, static code analysis, CI/CD pipelines, and so on and so forth.
If you work in the AWS ecosystem, sooner or later, you will deal with AWS Cognito.
AWS Cognito is meant to help you with customer identity and access management.
So you’ve decided to use AWS Cognito for the first time, or you’ve used it already. In both cases, you might want to migrate your existing users from either a legacy system or one Cognito Pool to another.
If you use GitHub to store your repositories, you may also be utilizing GitHub Actions. I’ve been using it for several years across multiple projects. There have been a few instances where I had to debug a failing workflow.
