# Prometheus Prometheus is an open-source monitoring system and time series database for collecting, storing, querying, and alerting on metrics from applications, systems, and services. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## ClickHouse Cloud Announces Private Preview of PromQL Support and Time-Series Table Engine DevFeed: [ClickHouse Cloud Announces Private Preview of PromQL Support and Time-Series Table Engine]() Original publisher: [Read original article]() Author: James Cunningham Published: 2026-09-15T14:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Prometheus](), [Time Series](), [Cloud](), [Grafana](), [telemetry](), [Traces]() Tags: [clickhouse](), [cloud](), [grafana](), [logs](), [metrics](), [prometheus](), [time-series](), [traces]() ### AI overview ClickHouse announces a private preview of PromQL support and a time-series table engine in ClickHouse Cloud, allowing Prometheus metrics to be stored in ClickHouse and queried with existing PromQL. ### Source excerpt ClickHouse PromQL support lets you store Prometheus metrics in ClickHouse Cloud, query them using familiar PromQL, and bring metrics together with your logs and traces without rewriting queries in SQL. ## pgwatch v6: dashboards reimagined, and a reaper that doesn't choke DevFeed: [pgwatch v6: dashboards reimagined, and a reaper that doesn't choke]() Original publisher: [Read original article]() Author: Pavlo Golub Published: 2026-08-28T03:00:58Z Content type: release Language: en Sources: [CYBERTEC PostgreSQL | Services & Support]() Topics: [Grafana](), [PostgreSQL](), [Prometheus](), [incident]() Tags: [blog](), [clusters](), [dashboards](), [features](), [grafana](), [incident](), [linux](), [network](), [news](), [patroni](), [pgwatch](), [postgresql](), [production](), [prometheus](), [release]() ### AI overview This release article describes pgwatch v6.0.0-beta, including a Grafana dashboard overhaul, first-class dashboards for Prometheus sources, Patroni cluster views, four new metrics, and fixes for collection hangs caused by production infrastructure failures. ### Source excerpt This is an extended blog, and talks about the features of pgwatch v6.0.0 beta in more details. Read to know and contribute. The post pgwatch v6: dashboards reimagined, and a reaper that doesn't choke appeared first on CYBERTEC PostgreSQL | Services & Support. ## pgwatch v6: Prometheus becomes a source, not just a sink DevFeed: [pgwatch v6: Prometheus becomes a source, not just a sink]() Original publisher: [Read original article]() Author: Pavlo Golub Published: 2026-08-26T07:43:28Z Content type: release Language: en Sources: [CYBERTEC PostgreSQL | Services & Support]() Topics: [Prometheus](), [PostgreSQL](), [Replication](), [dashboards]() Tags: [metrics](), [news](), [password](), [patroni](), [pgwatch](), [postgresql](), [prometheus](), [release](), [tls]() ### AI overview This blog post introduces pgwatch v6.0.0-beta, whose headline feature allows pgwatch to scrape Prometheus exporters directly as first-class sources alongside PostgreSQL sources. It explains source configuration, metric and label handling, and support for Basic Auth and TLS. ### Source excerpt This blog explains all feature highlights related to PGWatch 6.0.0 beta. You can read to understand and test the features yourself. The post pgwatch v6: Prometheus becomes a source, not just a sink appeared first on CYBERTEC PostgreSQL | Services & Support. ## nixos-telemetry: A NixOS flake for opt-in observability pipelines DevFeed: [nixos-telemetry: A NixOS flake for opt-in observability pipelines]() Original publisher: [Read original article]() Author: palo Published: 2026-08-23T08:27:36Z Content type: article Language: en Sources: [Announcements - NixOS Discourse]() Topics: [telemetry](), [OpenTelemetry](), [Tooling](), [log management](), [Prometheus](), [Grafana](), [Grafana Alloy]() Tags: [announcements](), [collector](), [config](), [grafana](), [grafana-alloy](), [loki](), [opentelemetry](), [prometheus](), [telemetry]() ### AI overview A discussion introduces nixos-telemetry, a NixOS flake that uses an OpenTelemetry collector to connect opt-in scrapers, storage, and visualization components. It supports local stacks, remote forwarding, and fan-out to multiple sinks through a unified option tree. ### Source excerpt Hi everyone, I'd like to share nixos-telemetry, a NixOS flake that makes setting up observability in your infrastructure easy. An OpenTelemetry collector sits at the center of each machine. Scrapers, storage, and visualization are all opt-in. You enable what you need; the collector wires the pipeline together automatically. Why? Wiring up telemetry in NixOS today means gluing together Telegraf, Prometheus, Loki, Grafana, Alloy, each with its own config format, ports, and inter-service dependencies. nixos-telemetry puts all of that behind a single telemetry.* option tree: Turn on the system with telemetry.enable = true. Every app is opt-in. Nothing starts that you didn't ask for. The collector starts automatically once a complete pipeline exists, a matching source and sink for the same signal type. No sink? It waits. Forward to a remote collector, run a full local stack, or both. Fan-out to multiple sinks is supported. What it looks like Full local stack on one machine: { telemetry.enable = true; telemetry.telegraf.enable = true; # host metrics telemetry.alloy.enable = true; # journald logs telemetry.prometheus.enable = true; # metrics storage telemetry.loki.enable = true; # logs storage telemetry.grafana.enable = true; # visualization (datasources auto-provisioned) } Forward to a remote collector: # machine 1 { telemetry.enable = true; telemetry.telegraf.enable = true; telemetry.opentelemetry.exporter.endpoints.remote = "100.64.0.1:4317"; } # machine 2 { telemetry.enable = true; telemetry.opentelemetry.receiver.endpoint = "0.0.0.0:4317"; } Full option reference: OPTIONS.md Thanks! Regarding Discourse LLM Policy : I must disclose that substantial parts of this project are llm generated. And because I coppied parts of the README in this post, substantial parts of this announcmement too. 27 posts - 7 participants Read full topic ## Building open-source observability together: The OpenSearch Observability TAG turns one DevFeed: [Building open-source observability together: The OpenSearch Observability TAG turns one]() Original publisher: [Read original article]() Author: Dotan Horovits Published: 2026-08-11T17:01:57Z Content type: article Language: en Sources: [OpenSearch]() Topics: [observability](), [Open Source](), [OpenTelemetry](), [Prometheus](), [agent observability]() Tags: [agent-observability](), [blog](), [cncf](), [community](), [metrics](), [observability](), [open-source](), [opensearch](), [opentelemetry](), [prometheus]() ### AI overview The OpenSearch Observability Technical Advisory Group marks its first anniversary after a year of community-driven guidance, RFC reviews, architecture decisions, and collaboration across organizations. The article highlights work involving OpenTelemetry, Prometheus, Perses, KEDA, Kubernetes, and the OpenSearch Observability Stack, and explains how contributors can participate in the group's second year. ### Source excerpt The OpenSearch Observability TAG's first year: 10+ orgs shipped OTLP ingestion, Prometheus metrics, an open-source Observability Stack, and AI agent observability. The post Building open-source observability together: The OpenSearch Observability TAG turns one appeared first on OpenSearch. ## Use of VRF in dual WAN setup DevFeed: [Use of VRF in dual WAN setup]() Original publisher: [Read original article]() Published: 2026-08-03T20:58:23Z Content type: tutorial Language: en Sources: [Personal blog of Anurag Bhatia]() Topics: [networking](), [MikroTik](), [Prometheus](), [Containers]() Tags: [containers](), [mikrotik](), [prometheus](), [router](), [routing](), [routing-table](), [vrf]() ### AI overview This article describes migrating a MikroTik home router from multiple routing tables to a VRF-based dual-WAN design. It explains how shared routing domains caused router-bound traffic to return through the wrong ISP during partial outages and made ISP-specific routing for measurement containers difficult. VRFs provide separate layer-3 routing domains for the WAN links, while Prometheus and Semaphore handle packet-loss-based switchover. ### Source excerpt Over the weekend I migrated my home router from multiple routing tables to a VRF-based design, placing each WAN uplink into its own VRF. While multiple routing tables worked for basic policy routing, they have some limitations and that led to several edge cases that became increasingly difficult to work around. Issues with the setup: If the active default route pointed to ISP1, traffic arriving on ISP1 naturally returned via ISP1. However, traffic terminating on the router and arriving via ISP2 would also be replied to through ISP1, since both uplinks still shared the same routing domain. This did not cause issues for traffic on devices below the router but was bad for traffic terminating on the router interface itself. Due to the above reason, I recently lost access to my home router while I was out of the country because ISP1 had a partial outage (their transit went down, peering stayed up) & due to distributed tooling, the auto switch trigger could not happen either to take care of it. Packets from ISP 2 were being returned via the ISP1 route & thus blackholed. I have a special case where I want most of the devices on a redundant setup but some devices (containers) on specific ISP only. These are measurement containers running blackbox exporter behind a specific ISP as well as RIPE Atlas. I don't want these to switch over for accuracy of measurement. Without VRF it was ugly config-wise, as ISP1 failure will lead to ISP2 routing even when the specific routing table did not have that route. Old Setup My old setup was running multiple routing table pairs: ISP1 only & ISP1 as primary (ISP2 as secondary) ISP2 only & ISP2 as primary (ISP1 as secondary) All WAN interfaces, LAN interfaces and routing tables were in the same single "Main" VRF. This by default takes care of full outage events, fibre cuts, etc. For packet loss-based switchover, I have set up Prometheus + Semaphore as covered in this post last year. Understanding VRF VRF is Virtual Routing and Forwarding. It ## How to Connect Prometheus Alerts to an Event-Driven AI Agent for Initial Investigation DevFeed: [How to Connect Prometheus Alerts to an Event-Driven AI Agent for Initial Investigation]() Original publisher: [Read original article]() Author: Pramodh Kumar M Published: 2026-07-23T15:00:31Z Content type: tutorial Language: en Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More]() Topics: [AI Agent](), [Prometheus](), [event driven](), [Kubernetes](), [Slack](), [Deployment]() Tags: [agents](), [ai](), [ai-agents](), [aiops](), [alert-fatigue](), [alert-manager](), [alert-triage](), [architecture](), [auto-remediation](), [automated-incident-response](), [automation](), [devaiops](), [event-driven](), [event-driven-ai-agents-with-prometheus-alerts](), [guide](), [how-to](), [kubernetes](), [kubernetes-ai-agent](), [prometheus](), [prometheus-alert-rules](), [prometheus-alertmanager-webhook](), [root-cause-analysis](), [slack](), [sre](), [sre-automation]() ### AI overview This guide explains how to connect Prometheus and Alertmanager to an event-driven AI agent that investigates alerts before a human responds. It covers the architecture, read-only investigation tools, alert-rule annotations, safety guardrails, and a progression toward guarded remediation. ### Source excerpt Every page interrupts a human, yet most alerts end in the same ten investigation steps. Here is how event driven AI agents catch Prometheus alerts and do that first pass before you even look at your phone. ## Diagnosing a 28,232-Connection SSE Limit in a Go Leaderboard DevFeed: [Diagnosing a 28,232-Connection SSE Limit in a Go Leaderboard]() Original publisher: [Read original article]() Author: Pranshu Raj Published: 2026-07-23T00:00:00Z Content type: tutorial Language: en Sources: [Pranshu Raj - blog on backend systems, performance and sidequests]() Topics: [Server-sent events (SSE)](), [Go Language](), [Docker Compose](), [Redis](), [Grafana](), [Prometheus]() Tags: [docker-compose](), [go](), [grafana](), [prometheus](), [redis](), [sse]() ### AI overview A tutorial on testing and diagnosing the SSE connection limit of a Go leaderboard service. It describes the service architecture, Docker Compose deployment, and monitoring with Prometheus and Grafana. ### Source excerpt Why my Go leaderboard's capped at exactly 28,232 SSE connections, how Docker networking got me to 150k, and the path toward millions. ## Broadcasting to 28K SSE Clients (and learning about Go channels) DevFeed: [Broadcasting to 28K SSE Clients (and learning about Go channels)]() Original publisher: [Read original article]() Author: Pranshu Raj Published: 2026-07-23T00:00:00Z Content type: tutorial Language: en Sources: [Pranshu Raj - blog on backend systems, performance and sidequests]() Topics: [Go Language](), [Server-sent events (SSE)](), [Concurrency](), [backpressure](), [real-time](), [observability](), [Redis](), [Grafana](), [Prometheus]() Tags: [architecture](), [backpressure](), [concurrency](), [go](), [grafana](), [observability](), [prometheus](), [real-time](), [redis](), [sse]() ### AI overview The article describes building a real-time leaderboard in Go that reached 28,232 concurrent SSE connections before exposing a broken broadcast design. It explains the fix using centralized polling, deduplication, fan-out, backpressure handling, and observability with Prometheus and Grafana. ### Source excerpt A post on progressively getting my server to 28k SSE connections, finding a bug in my implementation of fan out using Go channels and the resulting mental model update. ## The open ecosystem around ClickHouse DevFeed: [The open ecosystem around ClickHouse]() Original publisher: [Read original article]() Author: Al Brown Published: 2026-06-17T15:41:30Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Model Context Protocol](), [Open Source](), [OpenTelemetry](), [Apache Iceberg](), [Database](), [MCP Server](), [observability](), [Prometheus](), [schema-evolution](), [Artificial Intelligence]() Tags: [ai](), [clickhouse](), [database](), [mcp-server](), [model-context-protocol](), [observability](), [open](), [open-source](), [opentelemetry](), [prometheus](), [schema-evolution]() ### AI overview ClickHouse's open ecosystem connects databases with observability, AI tooling, and lakehouse technologies through open standards. The article highlights OpenTelemetry and Prometheus support, an open-source MCP server compatible with multiple agent frameworks, and support for Iceberg and Delta Lake formats. ### Source excerpt Around any good database sits the clients you import, the dashboards your team shares, the pipelines that feed it, the projects that build on top of it, and now the agents that query it. ## Harness AI Verification and Rollback for Argo CD: Limitations of Static Thresholds DevFeed: [Harness AI Verification and Rollback for Argo CD: Limitations of Static Thresholds]() Original publisher: [Read original article]() Author: Prasad Satam Akshit Madan Shubhendu Patidar Published: 2026-06-17T00:00:00Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [argo-cd](), [GitOps](), [Kubernetes](), [Artificial Intelligence](), [Prometheus]() Tags: [ai](), [argo-cd](), [gitops](), [harness](), [kubernetes](), [machine-learning](), [maintenance](), [progressive-delivery](), [prometheus](), [rollback](), [software-delivery](), [statistical-significance]() ### AI overview This vendor commentary argues that Argo Rollouts Analysis Templates can be difficult to maintain because they rely on manually defined static thresholds. It presents Harness AI Verification and Rollback as a context-aware alternative that uses unsupervised machine learning and service health trajectories to assess deviations and support rollbacks. ### Source excerpt Stop the maintenance nightmare of static thresholds. Learn how Harness AI Verification and Rollback provides context-aware safety for Argo CD. | Blog ## How the D. E. Shaw group powers high-cardinality observability at scale with ClickHouse DevFeed: [How the D. E. Shaw group powers high-cardinality observability at scale with ClickHouse]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-05-15T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [observability](), [High-Performance Computing](), [Prometheus](), [Time Series](), [benchmarking](), [Compression](), [tracing]() Tags: [clickhouse](), [compression](), [compute](), [high-performance-computing](), [infrastructure](), [metrics](), [observability](), [performance](), [production](), [prometheus](), [scale](), [site-reliability](), [systems](), [time-series](), [tracing]() ### AI overview The D. E. Shaw group uses ClickHouse for high-cardinality observability across millions of compute workloads on its internal grid. The article describes evaluation results showing approximately 7x better performance than alternatives, production ingestion exceeding 500,000 records per second, and improved long-term capacity planning and tracing analysis. ### Source excerpt How the D. E. Shaw group replaced its previous observability platform with ClickHouse to handle high-cardinality metrics at scale, achieving 7x better query performance and enabling multi-year capacity planning across millions of compute workloads. ## Building Blocks for Foundation Model Training and Inference on AWS DevFeed: [Building Blocks for Foundation Model Training and Inference on AWS]() Original publisher: [Read original article]() Author: Keita Watanabe; Pavel Belevich; Aman Shanbhag Published: 2026-05-11T23:18:26Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [foundation-models](), [Amazon Web Services](), [Training AI Models](), [Inference](), [distributed-training](), [cloud-infrastructure](), [Orchestration](), [PyTorch](), [Kubernetes](), [Grafana](), [Prometheus](), [Monitoring]() Tags: [ai](), [aws](), [building](), [distributed-training](), [frameworks](), [grafana](), [hardware](), [inference](), [infrastructure](), [kubernetes](), [latency](), [low-latency](), [model](), [model-development](), [monitoring](), [networking](), [observability](), [open-source](), [prometheus](), [training]() ### AI overview An introductory technical article about the infrastructure and open-source software building blocks required for foundation-model pre-training, post-training, and inference on AWS. It discusses accelerator compute, low-latency networking, distributed storage, orchestration, ML frameworks, and observability tools. ### Source excerpt Figure: Adapted from "AI's Three Scaling Laws, Explained" (NVIDIA Blog). Taken together, these scaling regimes push the foundation-model lifecycle--pre-training, post-training, and inference--toward convergent infrastructure requirements: tightly coupled accelerator compute, a high-bandwidth low-latency network, and a distributed storage backend. ## Why Prometheus couldn't see Cilium metrics at 2 a.m. DevFeed: [Why Prometheus couldn't see Cilium metrics at 2 a.m.]() Original publisher: [Read original article]() Author: Rishi Mondal Published: 2026-05-10T14:00:00Z Content type: article Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [Prometheus](), [Cilium](), [Grafana](), [Kubernetes](), [Cloud Native Ecosystem](), [Monitoring]() Tags: [cilium](), [cloud-native-ecosystem](), [cncf](), [grafana](), [kubernetes](), [monitoring](), [network](), [networking](), [platform-engineering](), [post-contributed](), [prometheus](), [sponsor-cncf](), [sponsored-post-contributed](), [tls]() ### AI overview This article examines the integration tax that platform teams encounter when combining CNCF projects in production. It uses Prometheus and Cilium metrics as an example, and discusses integration failures involving Hubble, Grafana, cert-manager, ingress controllers, and cloud DNS configuration. ### Source excerpt I still remember the first time we lost sleep over something that wasn't a bug. It was a Tuesday. Grafana The post Why Prometheus couldn't see Cilium metrics at 2 a.m. appeared first on The New Stack. ## How Discord Automates ScyllaDB Clusters at Scale DevFeed: [How Discord Automates ScyllaDB Clusters at Scale]() Original publisher: [Read original article]() Author: Peter French Published: 2026-05-08T00:00:00Z Content type: article Language: en Sources: [Discord Blog]() Topics: [Database](), [postgresql clusters](), [Replication](), [Concurrency](), [configuration](), [Orchestration](), [Rust](), [YAML](), [Prometheus](), [API]() Tags: [concurrency](), [database](), [how-to](), [metrics](), [prometheus](), [replication](), [rust](), [scylladb](), [workflow](), [workflows]() ### AI overview Discord describes how it automated ScyllaDB cluster operations with a Scylla Control Plane built around tasks, workflows, and jobs. The system uses explicit condition checks against Scylla's API and Prometheus metrics, idempotent Rust tasks, and YAML-defined workflows with retries, parallelism, configuration, and runtime templating. ### Source excerpt You've been asked to stand up a brand-new database cluster, meaning a whole day of configuring dozens of nodes, validating replication, wiring up dual-write pipelines... what if this whole ordeal took less than two hours? This is how we got ourselves into this exact mess, and how we made our way out of it. ## From Incident Counting to SLIs: How DigitalOcean Rethought Availability DevFeed: [From Incident Counting to SLIs: How DigitalOcean Rethought Availability]() Original publisher: [Read original article]() Author: Miguel Carrera Published: 2026-04-23T09:15:00Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Availability](), [Digital Ocean](), [incident](), [Framework](), [Prometheus]() Tags: [availability](), [digitalocean](), [engineering](), [framework](), [incident](), [metric](), [platform](), [prometheus]() ### AI overview DigitalOcean replaced incident-based availability tracking with an SLI-driven framework covering the control plane, data plane, and error budgets. The article explains that incident counting did not reflect customer experience and describes the newer measurement approach. ### Source excerpt Our journey to truly understand our customer experience began with a hard look at our internal availability numbers at the start of 2025. We saw something uncomfortable: the numbers didn't match our customers' reality. Our monthly availability oscillated between 99.5% and 99.9%. Those peaks and valleys depended more on whether we declared a high-severity incident that month than on how the platform was actually performing. Customers were still experiencing issues and opening escalations, but the metric didn't reflect customer availability. The previous internal measurement served us well in our early days, but its limitations became evident as DigitalOcean expanded. Our incident-based approach treated any declared incident as a total outage and anything below the severity threshold as invisible. This created a structural trap: we couldn't expand coverage to include lower-severity issues without artificially destroying our availability number, because the formula would count every minute of a partial degradation as a full platform outage. The chart above shows monthly platform availability using both methodologies over the same time period. The incident-based (old) swings between roughly 99.5% and 99.9% month to month. The SLI-based metric (new) holds consistently at 99.95% or above. The old metric was measuring noise, while the new metric measures actual availability signals. This isn't a problem unique to DigitalOcean. Any platform that measures availability by counting incident minutes against total calendar time will eventually hit the same wall. The incident-based metric was both too generous and too punitive, depending on where the line was drawn. Thisarticle walks through the operational framework we built to replace that system, the architectural decision to split the measurement into Control Plane and Data Plane, the two different SLI methodologies we use for each plane, the Prometheus recording rules and multi window alerting that make it operational, error ## Shipping logs to Loki DevFeed: [Shipping logs to Loki]() Original publisher: [Read original article]() Author: Marc Plano-Lesay Published: 2026-04-03T10:10:02Z Content type: tutorial Language: en Sources: [Marc Plano-Lesay]() Topics: [Logging](), [Monitoring](), [Prometheus]() Tags: [esphome](), [iot](), [logs](), [loki](), [monitoring](), [observability](), [prometheus](), [server-sent-events](), [terminal]() ### AI overview This tutorial explains how to ship ESPHome device logs to Loki. It contrasts logs with Prometheus metrics, describes the ephemeral nature of device logs, and introduces Server-Sent Events and syslog as ways to extract logs for aggregation. ### Source excerpt When metrics aren't the whole story In the previous post, we set up metrics collection with Prometheus. We can now see what is happening on our devices -- a sensor is failing, the WiFi signal is degrading, or the main loop is running slow. But metrics alone don't tell us why. When a device reboots, the uptime metric resets -- but what caused the reboot? When an entity fails, the _failed metric goes to 1 -- but what error occurred? ## Scraping ESPHome metrics with Prometheus DevFeed: [Scraping ESPHome metrics with Prometheus]() Original publisher: [Read original article]() Author: Marc Plano-Lesay Published: 2026-03-29T03:18:27Z Content type: tutorial Language: en Sources: [Marc Plano-Lesay]() Topics: [esphome](), [Prometheus](), [observability](), [Monitoring](), [Grafana]() Tags: [devices](), [esphome](), [grafana](), [iot](), [monitoring](), [observability](), [prometheus]() ### AI overview A tutorial on exposing ESPHome device metrics in the OpenMetrics format and scraping them with Prometheus. It explains how observability helps monitor device state, connectivity, and diagnostic information, with Grafana mentioned for dashboards and alerting. ### Source excerpt What is observability and why does it matter? ESPHome is an amazing tool. Devices you build with it are very useful. But how do you notice when they don't work? You would eventually notice if a temperature sensor stopped working and your climate control automations aren't working as intended. While that's not ideal, it gets worse: what about a sensor part of an alarm system, detecting that a window is open? ## Introducing Upright: An Open Source Synthetic Monitoring System DevFeed: [Introducing Upright: An Open Source Synthetic Monitoring System]() Original publisher: [Read original article]() Author: Lewis Buckley Published: 2026-02-16T18:00:00Z Content type: release Language: en Sources: [37signals Dev]() Topics: [synthetic monitoring](), [Monitoring](), [Open Source](), [Playwright](), [Rails](), [Prometheus](), [Grafana](), [Deployment](), [SQLite]() Tags: [deployment](), [grafana](), [monitoring](), [open-source](), [playwright](), [prometheus](), [rails](), [sqlite](), [synthetic-monitoring]() ### AI overview 37signals is open-sourcing Upright, a synthetic monitoring system built to monitor Basecamp, HEY, Fizzy, and other services. Upright runs health checks from multiple geographic locations and reports results through Prometheus metrics for alerting and Grafana visualization. ### Source excerpt We're open-sourcing Upright, the synthetic monitoring system we built to watch over Basecamp, HEY, Fizzy and our many other services. ## Why Sysadmins Should Learn Automation with Ansible, Terraform, and Git in 2026 DevFeed: [Why Sysadmins Should Learn Automation with Ansible, Terraform, and Git in 2026]() Original publisher: [Read original article]() Author: Alois Published: 2026-01-20T00:00:00Z Content type: tutorial Language: es Sources: [tengoping.com]() Topics: [Ansible](), [Terraform](), [Git](), [ssh](), [Prometheus](), [Grafana](), [systemd]() Tags: [ansible](), [git](), [grafana](), [prometheus](), [ssh](), [sysadmin](), [systemd](), [terraform](), [yaml]() ### AI overview This Spanish article argues that automation is an essential skill for sysadmins managing growing infrastructure. It explains how Ansible playbooks can coordinate tasks such as kernel patching and staged server reboots, while monitoring and scheduled maintenance can automate remediation and preventive work. ### Source excerpt Por qué la automatización con Ansible, Terraform y Git dejó de ser opcional para un sysadmin, y por dónde empezar sin agobiarte. ## Fork yeah: We're adding ten new open source projects to EmeritOSS DevFeed: [Fork yeah: We're adding ten new open source projects to EmeritOSS]() Original publisher: [Read original article]() Published: 2026-01-15T00:00:00Z Content type: news Language: en Sources: [Chainguard: Unchained]() Topics: [Open Source](), [Prometheus](), [Apache Cassandra](), [Amazon S3](), [observability](), [Grafana](), [data-processing]() Tags: [amazon-s3](), [cassandra](), [chainguard-containers](), [chainguard-emeritoss](), [chainguard-images](), [chainguard-open-source](), [dashboards](), [maintenance](), [minio](), [observability](), [open-source](), [open-source-chainguard-images](), [pgcat](), [prometheus](), [security](), [source]() ### AI overview Chainguard announces ten additional open source projects joining EmeritOSS, a program intended to provide long-term maintenance and stability for mature projects. The supplied text highlights MinIO, Prometheus-related exporters, and integrations involving Apache Cassandra and RabbitMQ, covering object storage, monitoring, metrics, and observability. ### Source excerpt We added 10 open source projects to EmeritOSS--including MinIO, Prometheus exporters, and PgCat--to provide long-term, stability-focused maintenance and security. ## Building Enterprise-Style UniFi Observability with Unpoller, Prometheus, and Grafana DevFeed: [Building Enterprise-Style UniFi Observability with Unpoller, Prometheus, and Grafana]() Original publisher: [Read original article]() Author: Techno Tim Published: 2025-12-21T13:00:00Z Content type: tutorial Language: en Sources: [Techno Tim]() Topics: [observability](), [Grafana](), [Prometheus](), [Homelab](), [Network](), [dashboards](), [Docker Compose](), [API]() Tags: [api](), [compose](), [dashboards](), [docker](), [github](), [grafana](), [homelab](), [metrics](), [network](), [observability](), [prometheus](), [services](), [unifi]() ### AI overview This tutorial shows how to build a UniFi observability stack for a homelab using Unpoller, Prometheus, and Grafana. It explains how metrics flow from UniFi devices through Unpoller into Prometheus for long-term storage and Grafana for visualization, with deployment handled by Docker Compose. ### Source excerpt If you run a UniFi network in a homelab or small environment, there's a point where basic status pages stop being enough. You want to understand how your network behaves over time - what normal looks like, what's changing, and how those changes correlate with real issues. This post walks through building an enterprise-style observability stack for UniFi using Unpoller, Prometheus, and Grafana,... ## Own Your Observability: Supabase Metrics API DevFeed: [Own Your Observability: Supabase Metrics API]() Original publisher: [Read original article]() Author: Steven Eubank Published: 2025-12-16T07:00:00Z Content type: article Language: en Sources: [Supabase Blog]() Topics: [Supabase](), [API](), [telemetry](), [observability](), [Prometheus](), [Grafana](), [Grafana Cloud](), [datadog agent](), [Amazon Web Services](), [Self-hosted]() Tags: [api](), [aws](), [datadog-agent](), [grafana](), [grafana-cloud](), [metrics](), [monitoring](), [observability](), [prometheus](), [replication](), [self-hosted](), [storage](), [telemetry](), [visualization]() ### AI overview Supabase's Metrics API streams roughly 200 Postgres performance and health metrics in Prometheus exposition format, enabling integration with Grafana Cloud, Datadog, AWS Managed Prometheus, self-hosted Grafana, and other Prometheus-compatible backends. The article explains how this unifies database telemetry with existing observability stacks for monitoring, visualization, alerting, and troubleshooting. ### Source excerpt Stream your Supabase database telemetry into any Prometheus-compatible observability stack with the Metrics API. Full control over monitoring, visualization, and alerting. ## Durable Digest: November 2025 DevFeed: [Durable Digest: November 2025]() Original publisher: [Read original article]() Author: Temporal Technologies Published: 2025-11-25T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Monitoring](), [Prometheus](), [Authorization](), [Large Language Model](), [Model Context Protocol](), [SIEM, Security, Observability](), [Amazon S3](), [configuration](), [account](), [export]() Tags: [announcements](), [aws-s3](), [blog-post](), [llm](), [mcp](), [observability](), [permissions](), [preview](), [prometheus]() ### AI overview Temporal's November 2025 Durable Digest reviews recently shipped Temporal Cloud features, including group-based permissions, SCIM user management, environment configuration, workflow history export to object storage, and an OpenMetrics endpoint. It also highlights a durable stateful agents project using Mistral, Temporal, and MCP. ### Source excerpt Recently shipped features, real-world fixes from the field, a Builder Spotlight straight from the Code Exchange, fresh AI how-tos, and where to meet us live. [Next page]()