Join us

ContentUpdates and recent posts about Grafana Tempo..
Link
@kala shared a link, 2 weeks, 5 days ago
FAUN.dev()

Nathan Lambert: Open Models Will Never Catch Up

Open models will be the engine for the next ten years of AI research, according to Nathan Lambert, a research scientist at AI2. He explains that while open models may not catch up with closed ones due to fewer resources, they are still crucial for innovation. Lambert emphasizes the importance of int.. read more  

Nathan Lambert: Open Models Will Never Catch Up
Link
@kala shared a link, 2 weeks, 5 days ago
FAUN.dev()

My AI Adoption Journey

A dev walks through the shift from chatbot coding toagent-based AI workflows, think agents that read files, run code, and double-check their work. Things only clicked once they built outcustom tools and configsto help agents spot and fix their own screwups. That’s the real unlock... read more  

Link
@kala shared a link, 2 weeks, 5 days ago
FAUN.dev()

Generative Pen-trained Transformer

MeetGPenT, an open-source, wall-mounted polargraph pen plotter with a flair for generative art. It blends custom hardware, Marlin firmware, a Flask web UI running on Raspberry Pi, and Gemini-generated drawing prompts. The stack? Machina + LLM. Prompts go in, JSON drawing commands come out. That driv.. read more  

Link
@kala shared a link, 2 weeks, 5 days ago
FAUN.dev()

Towards self-driving codebases

OpenAI spun up a swarm of GPT-5.x agents - thousands of them. Over a week-long sprint, they cranked out runnable browser code and shipped it nonstop. The system hit 1,000 commits an hour across 10 million tool calls. The architecture? A planner-worker stack. Hierarchical. Recursive. Lean on agent ch.. read more  

Towards self-driving codebases
Link
@kala shared a link, 2 weeks, 5 days ago
FAUN.dev()

Self-Optimizing Football Chatbot Guided by Domain Experts on

Generic LLM judges and static prompts fail to capture domain-specific nuance in football defensive analysis. The architecture for self-optimizing agents built on Databricks Agent Framework allows developers to continuously improve AI quality using MLflow and expert feedback. The agent, such as a DC .. read more  

Link
@devopslinks shared a link, 2 weeks, 5 days ago
FAUN.dev()

Demystifying : Why You Shouldn’t Fear Observability in Traditional Environments

OpenTelemetry is friendly with the past. It now pipesreal-time observability into legacy systems- no code rewrite, no drama. Pull structured metrics straight from raw logs, Windows PDH counters, or SQL Server stats. It doesn’t stop there. Got MQTT-based IoT gear? OTLP export or lightweight adapters .. read more  

Demystifying : Why You Shouldn’t Fear Observability in Traditional Environments
Link
@devopslinks shared a link, 2 weeks, 5 days ago
FAUN.dev()

How GEICO lowered its $300M cloud spend and decoupled security from the network

GEICO's IT infrastructure transformation journey highlights the shift from legacy network-centric security model to a more modern, identity-first approach. By centralizing identity and secrets management using HashiCorp Vault, GEICO improved security, reliability, and compliance across their hybrid .. read more  

Link
@devopslinks shared a link, 2 weeks, 5 days ago
FAUN.dev()

Scaling PostgreSQL to power 800 million ChatGPT users

OpenAI pushedPostgreSQLto handle millions of QPS across 800M users. How? Nearly 50 read replicas, heavy read offloading, and serious trimming on write pressure. Writes? Sent elsewhere. Sharded systems likeCosmosDB, lazy writes, and app-level tweaks helped sidestep PostgreSQL’sMVCCwrite amplification.. read more  

Scaling PostgreSQL to power 800 million ChatGPT users
Link
@devopslinks shared a link, 2 weeks, 5 days ago
FAUN.dev()

CloudBees CEO: Why Migration Is a Mirage Costing You Millions

A new CloudBees survey shows 57% of enterprises dropped over $1M on cloud migrations last year. Each effort blew past budget by an average of $315K. The kicker? Many teams still treatmodernization as migration- a shortcut that usually leads to drained budgets, burned-out devs, and delays in shipping.. read more  

Link
@devopslinks shared a link, 2 weeks, 5 days ago
FAUN.dev()

The only Terraform pipeline you will ever need: GitHub Actions for Multi-Environment Deployments

A sharp new GitHub Actions pipeline can now sniff out which Terraform environments changed - anywhere in the repo, no matter how nested - and run them in parallel. Fast, clean, and automatic. It leans onmatrix jobs,Checkovfor static analysis,Workload Identity Federationfor secure cloud access (no ha.. read more  

The only Terraform pipeline you will ever need: GitHub Actions for Multi-Environment Deployments
Grafana Tempo is a distributed tracing backend built for massive scale and low operational overhead. Unlike traditional tracing systems that depend on complex databases, Tempo uses object storage—such as S3, GCS, or Azure Blob Storage—to store trace data, making it highly cost-effective and resilient. Tempo is part of the Grafana observability stack and integrates natively with Grafana, Prometheus, and Loki, enabling unified visualization and correlation across metrics, logs, and traces.

Technically, Tempo supports ingestion from major tracing protocols including Jaeger, Zipkin, OpenCensus, and OpenTelemetry, ensuring easy interoperability. It features TraceQL, a domain-specific query language for traces inspired by PromQL and LogQL, allowing developers to perform targeted searches and complex trace-based analytics. The newer TraceQL Metrics capability even lets users derive metrics directly from trace data, bridging the gap between tracing and performance analysis.

Tempo’s Traces Drilldown UI further enhances usability by providing intuitive, queryless analysis of latency, errors, and performance bottlenecks. Combined with the tempo-cli and tempo-vulture tools, it delivers a full suite for trace collection, verification, and debugging.

Built in Go and following OpenTelemetry standards, Grafana Tempo is ideal for organizations seeking scalable, vendor-neutral distributed tracing to power observability at cloud scale.