Small server, small monitoring. Statlite watches your applications and their host with a single binary that idles at about 15 MiB, so the observer never becomes the heaviest thing on the box. Point it at a Spring Boot Actuator endpoint and it polls health plus a fixed, curated set of Micrometer metrics: request traffic, latency, CPU, and runtime memory, all drawn as clean charts. Quarkus, Micronaut, and anything else that can answer a fixed JSON metrics profile can join the same dashboard. History lives in one local SQLite file, raw samples only, kept for 90 days by default, so yesterday's latency spike is still there when you go looking. The dashboard also watches the host itself: processor load, memory pressure, and filesystem capacity, including a warning before the metrics database fills its own disk. Restart detection tells you when an application bounced, and per-target Basic Auth support lets you poll protected Actuator endpoints safely. Configuration is one YAML file listing your targets and polling interval, friendly to systemd and Docker alike. Everything is deliberately scoped: no alert manager, no external service, no observability platform to operate. Just open the page and see whether your apps are healthy. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Logging every drive, charge, and idle stretch of a Tesla to a database you own, TeslaMate shows where the battery went, including degradation, vampire drain, and charging cost. Battery health comes first: the dashboard estimates usable capacity, range lost, and degradation against mileage. Idle losses come next, with Vampire Drain listing how much charge disappears during every idle period and a States view recording every online, asleep, and offline stretch. The car is left alone to fall asleep, so logging adds no extra vampire drain. Charging sessions show curves, energy added and used, efficiency, and cost, using per-location prices attached to geo-fences drawn on a map. Each drive records second-by-second speed, power, elevation, and temperature, plotted on a route map beside tire pressure and consumption charts. Trip, Drives, Efficiency, Drive Stats, Mileage, Updates, Statistics, and a lifetime driving map fill in the rest. Live vehicle data goes out over MQTT to Home Assistant, Node-RED, and Telegram, and old history imports from TeslaFi or tesla-apiscraper. Several vehicles on one Tesla account work together, addresses are looked up automatically, and the web interface offers light, dark, or system themes in 19 languages. Sign-in uses Tesla access and refresh tokens that TeslaMate encrypts with a key you set. It works best on an always-on server. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Every ride, run, hike, and swim you have ever recorded finds a home in Dreeve, a private fitness dashboard that lives on your own server rather than in a social feed. Import FIT, TCX, or GPX files from a watch folder, sync Strava, or add connectors for Garmin Connect, Wahoo, Polar Flow, Hammerhead, and Zepp/Amazfit. Weekly charts follow distance, time, and elevation beside training goals, heart rate zones, and training load analysis, and monthly stats pair per-sport totals with an interactive calendar. A heatmap paints every kilometer you have covered. Best efforts rank your fastest 5k, 10k, 50k, and 100k across every sport, while Eddington numbers, a milestones timeline, and a yearly Rewind mark the long game. Strava sync also brings segments, segment efforts, and challenges. Gear pages total the distance on each bike and shoe, and maintenance tasks send alerts through ntfy, Gotify, Discord, Telegram, Slack, or email when an interval comes due. Automation rules assign gear, flag commutes, and set sport types by device, distance, weekday, time of day, or location, so new imports arrive organized. Ask the AI workout assistant about your training, using cloud models, OpenRouter, or a local Ollama model. An installable mobile app and embeddable badges carry your stats to phones and blogs, and a password-protected admin panel keeps the settings. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

The de facto dashboard of observability: Grafana is the open-source frontend that turns the data stores you already run into interactive graphs. It does not store metrics itself; it connects to the data stores you already run and turns their contents into interactive dashboards. Supported sources number over 150 via plugins: Prometheus, Loki, Tempo, InfluxDB, Elasticsearch, MySQL, PostgreSQL, Microsoft SQL Server, AWS CloudWatch, Azure Monitor, Google Cloud Monitoring, and many more. Dashboards are built from a large library of panel types (time series, heatmaps, tables, gauges, logs) with template variables for reusable, parameterized views. Unified alerting evaluates rules against any connected data source, not just Prometheus, and routes notifications to Slack, PagerDuty, email, and other channels with grouping and silencing - unlike Prometheus Alertmanager, a single rule can combine a Loki log pattern, a PostgreSQL query result, and a CloudWatch metric. Dashboards serialize to JSON and data sources configure via provisioning files, so the entire observability setup can live in Git and deploy repeatably across environments. Explore mode adds ad-hoc querying outside dashboards, with split view for correlating a metric spike against the matching log lines, and access control spans organizations, teams, folder permissions, and OAuth, LDAP, and SAML integration. Written in Go and TypeScript, AGPL-licensed. Self-hosting gives you unlimited users, dashboards, and queries at flat hosting cost, without Grafana Cloud's usage-based pricing.

Backed by Y Combinator with 4,300 GitHub stars and growing rapidly since its September 2024 launch, Briefer delivers the first truly unified notebook-and-dashboard platform that eliminates the fragmented workflow of juggling Jupyter for analysis, Tableau for visualization, and Notion for documentation — combining all three in a single Notion-like workspace where SQL query results automatically become Python DataFrames accessible in subsequent code blocks. The built-in AI analyst understands your database schema and notebook context to generate SQL queries, write Python transformations, create visualizations, and fix errors on demand using configurable OpenAI or private LLM backends. Connect directly to PostgreSQL, MySQL, BigQuery, Redshift, Snowflake, and Amazon Athena as data sources, or upload CSV files for immediate analysis. Native point-and-click visualizations produce charts, tables, and dashboards without writing code, while interactive data apps use inputs, dropdowns, and date pickers to create parameterized reports for non-technical stakeholders. Scheduled execution runs notebooks and dashboards periodically with results delivered via Slack integration or public shareable links. Write-back queries modify production data directly from notebooks for ad-hoc pipeline testing. The architecture runs as three Docker containers — web frontend, API server, and optional AI service — backed by PostgreSQL and a Jupyter server for Python execution, deployable via single Docker command, Docker Compose, or Helm charts for Kubernetes. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPLv3 licensed.