[FEATURE] Maybe this could be better

[naufalkmd]

It’s a plausible project, but it only becomes “good and relevant” if you push past naive duplicate-checking and design it around real dev workflows (CI, local debugging, PRs) rather than “yet another logger.”
Realistic architecture

Aim for a small, local-first core with optional integrations:

Core CLI binary

    Written in something fast and portable (Go/Rust/C++), single static binary.​

    Commands like: noteerr scan, noteerr watch, noteerr status, noteerr explain.

    Operates on stdin or on files (e.g. journalctl | noteerr scan, noteerr scan app.log).​

Log ingestion and parsing layer

    Pluggable parsers for: plain text, JSON logs, common frameworks (Django, Rails, Node, etc.).​

    Normalizes to a small internal event schema: timestamp, level, source (service/file), message, stack trace, tags.

    Minimal config via a YAML/TOML file: patterns, ignore rules, field mappings.

Error fingerprinting and cache

    For each error event, compute a fingerprint hash: message template, top stack frames, error type, maybe normalized file paths.

    Store in a local cache DB (SQLite or Badger/LMDB) with fields like: first_seen, last_seen, count, sample payloads.

    TTL/retention controls so the cache doesn’t grow forever (e.g. drop entries older than N days or beyond M records).​

Deduplication and classification engine

    When a new error arrives, check the cache:

        If fingerprint exists → mark as “known” and increment count.

        If new fingerprint → mark as “new” and store metadata.

    Optional severity classification: patterns, exit codes, or simple rules to elevate “new + high impact” errors.

Reporting and UX

    CLI output that focuses on “what changed since last run”:

        New error types.

        Known errors that suddenly spiked.

        Errors resolved (no occurrences in last X runs).

    Modes:

        Human-readable TUI/summary for local use.

        Machine-friendly output (JSON) for CI scripts to act on.

    Example workflow: noteerr scan ./logs --since last_run --format json for CI to gate merges.

CI / Git / editor integrations

    Pre-commit/CI integration: run after tests, fail build only on “new” errors above a threshold.

    Git metadata: store last seen commit/hash for each fingerprint so you can say “this error first appeared in commit abc123.”

    Optional LSP/editor plugin later: surface “this error has appeared 37 times across 5 branches” inline.

Privacy and optional cloud

    Default is fully local: no data leaves the machine, good for security-focused teams.​

    If you ever add sync, make it opt-in and only sync anonymized fingerprints and aggregate counts.

What would make it genuinely useful (or useless)

Useful if you nail these things:

High signal, low noise

    Most teams already drown in logs; you only win if you clearly answer: “What should I care about now?”

    Surfacing “brand-new” and “resurfaced after 30 days” errors is actually valuable; most SaaS tools treat that as just another event.

Local-first, infra-free

    There’s a niche for tools that don’t need Sentry/Datadog/PostHog setups and still give better-than-grep insights.

    If Noteerr runs on any CI runner and any dev laptop with zero server, that’s a differentiator.

Tight CI feedback loop

    Gate PRs on “no new errors in test logs.”

    That’s a concrete, automatable value prop that teams can adopt without migrating to a new SaaS platform.

Opinionated defaults

    Ships with batteries included: sensible ignore patterns, known noisy frameworks, preconfigured fingerprints for common stacks.

    If users have to spend a day configuring patterns, they’ll drop it and stick with existing tools.

Useless / low relevance if you:

Just re-implement grep + counters

    “Shows errors and counts them” is already done by tons of CLI log viewers (and basic Sentry free tiers).​​

Avoid ecosystems devs already use

    If it doesn’t plug into CI, pre-commit, or editors, it becomes a one-off toy they run twice then forget.

Ignore performance and UX

    Slow scans over big logs, clunky flags, or noisy output will kill adoption quickly, especially in large codebases.

Market and relevance reality check

Existing competition is strong

    Sentry, Rollbar, Bugsnag, Datadog, PostHog, CubeAPM, etc. already do dedup, grouping, regression detection, and UI dashboards.​

    However, they are heavy, infra-y, and not optimized for “I just want to run something on this log file locally.”​

There is a realistic niche

    There’s active interest in lightweight CLI log tooling that adds context and structure without a full observability stack.​

    A tool that turns raw logs into “new/known/resolved errors by fingerprint + commit” and works entirely offline is not obviously commoditized.

As a product vs dev-tool project

    As a commercial product, going head-to-head with existing error trackers is a losing game unless you have a strong wedge (e.g. offline-only, regulated environments, great Git/CI integration).

    As an open-source dev tool, this is absolutely relevant and could get traction if it’s fast, simple, and genuinely reduces log-pain for real projects.

How I’d scope v1

If you want a realistic, non-bullshit v1 that might actually get used:

Single-binary CLI that:

    Reads logs from stdin or a file.

    Detects error lines + stack traces for at least one popular stack (e.g. Node or Python).

    Fingerprints and caches errors in a local SQLite DB.

    Outputs “new vs known” error groups with counts and a short summary.

CI integration:

    Provide a simple GitHub Actions + GitLab CI example: run Noteerr on test logs, fail if N new error fingerprints appear.

Developer ergonomics:

    One config file, sane defaults, clear --help, JSON output mode, good docs and examples.

If you implement that well and dogfood it on your own projects, you’ll know quickly if it’s worth pushing further.