Ollogger is a powerful, flexible logging application that helps users create custom AI-powered logging assistants. Built with React, TypeScript, and modern web technologies.
Cloudflare, an internet infrastructure and security company, has upgraded its logging pipeline by migrating from syslog-ng to OpenTelemetry Collector. This change affects one of Cloudflare's largest data pipelines, processing millions of log events per second.
**Key Points**
* **Motivations:** Language compatibility, easier integration, enhanced metrics, and unified telemetry infrastructure.
* **Custom Components:** Custom exporter, modified file exporter, processors, and rate-limiters.
* **Migration and Future Plans:** Careful rollout, monitoring, and plans for more sophisticated log sampling techniques and open-source contributions.
* **Other Adopters:** Google, Splunk, Shopify, and GitHub are also adopting OpenTelemetry for various use cases.
**Key Points**
* **Motivations:** Language compatibility, easier integration, enhanced metrics, and unified telemetry infrastructure.
* **Custom Components:** Custom exporter, modified file exporter, processors, and rate-limiters.
* **Migration and Future Plans:** Careful rollout, monitoring, and plans for more sophisticated log sampling techniques and open-source contributions.
* **Other Adopters:** Google, Splunk, Shopify, and GitHub are also adopting OpenTelemetry for various use cases.
Use Callbacks to send Output Data to Posthog, Sentry, etc. LiteLLM provides input_callbacks, success_callbacks, and failure_callbacks to easily send data based on response status.
LiteLLM is a library to deploy and manage LLM (Large Language Model) APIs using a standardized format. It supports multiple LLM providers, includes proxy server features for load balancing and cost tracking, and offers various integrations for logging and observability.
Docker offers various logging drivers that dictate the storage location and format of log messages. These include json-file, syslog, journald, fluentd, awslogs, gelf, logentries, and splunk.
A discussion post on Reddit's LocalLLaMA subreddit about logging the output of running models and monitoring performance, specifically for debugging errors, warnings, and performance analysis. The post also mentions the need for flags to output logs as flat files, GPU metrics (GPU utilization, RAM usage, TensorCore usage, etc.) for troubleshooting and analytics.
