In part 3, we implemented comprehensive observability for our restaurant agent using LangFuse. Now we're taking it further by adding automated evaluation that not only measures performance but also sends evaluation scores back to LangFuse for centralized monitoring. - Source: dev.to / about 1 month ago

Selecting the right observability platform is critical for ensuring your AI agents perform reliably, efficiently, and safely in production. The following features are essential for modern AI agent observability platforms, as demonstrated by industry leaders like Maxim AI, Langfuse, Arize AI, and others. - Source: dev.to / 2 months ago

For monitoring, there are separate full-fledged monitoring solutions like Opik, PostHog, Langfuse or OpenLLMetry, maybe will try some next time. - Source: dev.to / 4 months ago

Langfuse has emerged as a favorite in the open-source community, and for good reason. It is incredibly powerful, offering deep, detailed tracing and extensive features for monitoring, debugging, and analytics. It requires a few more environment variables for its public key, secret key, and host, but the setup is still minimal. - Source: dev.to / 4 months ago

And then there’s evaluation and observability—two things you must consider when your AI app is live. You need to know if the model is doing its job, and why it failed when it didn’t. Tools like LangSmith and LangFuse can help with this, but you’ll need to spend time experimenting with what works best for your stack. - Source: dev.to / 4 months ago

A special kind of validation is enforcing that record fields are not null. (Un)fortunately, records do not have any special behavior regarding nullability. You can use tools like NullAway or Error Prone to prevent null in your code in general, or you can add checks to your records:. - Source: dev.to / almost 3 years ago

I am all in favour to more third side libraries adding functionalities, like Lombok, Manifold and error prone. As well as smaller projects like this shameless plug and what appears in this list. Source: almost 3 years ago

While at it you could also point them to static code analyzers such as error_prone, spotbugs and pmd (use all 3 at once - they complement each other in detecting different issues). Source: almost 4 years ago

Linters can check for this sort of thing, for example Error Prone[0] has a lint[1] for this. Totally agree this is better to be in the language proper so we don't need this extra tooling. [0]: https://errorprone.info [1]: https://errorprone.info/bugpattern/ParameterName. - Source: Hacker News / about 4 years ago