Hugh KaznowskiandClaude Sonnet 5
e8baf2b004
Fold ProfileStore's separate profiles.json into Settings itself, so settings.json is the single source of truth instead of two files kept in sync by hand. Settings now carries `profiles: Vec<Profile>` and `active_profile: Option<String>` directly; every former ProfileStore mutation (create, delete, set_active, record_param_declaration, record_metric_declaration, dashboard CRUD, ensure_default) is now a method on Settings that persists via Settings::save. A custom Deserialize (via SettingsOnDisk) keeps an old settings.json (with the removed Profiles pointer struct) loading cleanly, and Settings::load runs a one-time migration: if profiles is empty and a legacy <settings_dir>/profiles/profiles.json exists, its contents are merged in, settings.json is resaved, and the old file is renamed so it isn't re-imported. Updated all call sites in api.rs and main.rs to the single-path API, and ported profile.rs's ProfileStore tests to exercise Settings directly. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01XFUNZwCvz3kcod3352ZR2o
ottyl
Observability for any command's terminal output — no logging backend, no agents, no config to ship.
your_app --verbose | ottyl
Pipe a program's stdout into ottyl and turn it into a searchable log timeline, ad-hoc metrics extracted from the log lines themselves, and Grafana-style dashboards — served from a single local binary, for the lifetime of that process:
- Search: A syntax-aware filter bar over your logs — regex matches and metric comparisons like
latency_ms[value]<200, with relative (5h,-3m) and absolute datetimes. - Log timeline: A bucketed bar chart of message volume over time, Kibana-style. Drag across it to set the active time range.
- Metrics from logs: Declare a metric by matching a regex against your log lines and extracting a value. It applies retroactively to everything already ingested, and to everything that follows.
- Dashboards: Bar, line, and pie chart panels driven by PromQL-style queries over your declared metrics.
- Mixed sources: Poll an existing Prometheus/OpenMetrics endpoint alongside log ingestion, in the same dashboard.
- Profiles: Parsing rules and dashboards are saved per app, so they're there again next time that app runs through ottyl.
Use cases
- Debugging a noisy CLI or long-running script, instead of scrolling a terminal buffer.
- Getting metrics and charts out of a program that only ever prints text.
- Local dashboards without standing up a metrics stack.
- Combining scraped Prometheus metrics with log-derived ones during a debugging session.
Get started
Requires Rust and Node.js. The webapp is built and embedded into the binary, so the result is a single self-contained executable.
git clone <this-repo>
cd ottyl
make build
your_app --verbose | ottyl
Or point it at a log file instead of stdin, and mix in a Prometheus/OpenMetrics endpoint:
ottyl --file /var/log/your_app.log --metrics-url http://localhost:9090/metrics
ottyl prints the URL of the local web server it starts (an OS-assigned free port by default; pin one with --port). See ottyl --help for the rest, including where settings and profiles live (--settings-path).
Full documentation — filtering syntax, parameters, metrics, dashboards — is built into the app itself, under the nav menu's docs section.
Development
make dev # frontend dev server with hot reload, proxying to a separately running `cargo run` backend
make test # backend + frontend tests
make lint # clippy + oxlint