Rich text editor (BlockNote) + drawing canvas (Excalidraw) in a browser popup Everything stored in localStorage — no server, no account, no telemetry Works offline Opens in ~200ms

Why I built it: I kept opening new tabs and typing in the URL bar as a scratchpad. That's embarrassing enough to fix. Tech stack:

Chrome Extension Manifest V3 BlockNote for rich text Excalidraw for sketches Zero backend

What I'm looking for: Honest feedback on the use case. Is "private local notepad" a real pain point or am I solving a non-problem? 193 installs with zero marketing so far — but I don't know if that's signal or noise.

Comments URL: https://news.ycombinator.com/item?id=47254873

Points: 3

# Comments: 0

I built @duck_ui/embed — a set of React components that turn SQL queries into interactive dashboards, powered by DuckDB-WASM running entirely in the browser.

     `npm install @duck_ui/embed @duckdb/duckdb-wasm`

 The idea: pass your data (arrays, CSV, Parquet, JSON, or a fetch callback), write SQL, and get charts, tables, KPIs, and filters — no backend required.

     
       
         
           
         
         
           
         
         
           
         
       
     

- DuckDB-WASM boots in the browser, data loads into in-memory tables - SQL queries run against real DuckDB (full SQL support, window functions, CTEs) - Parquet files use HTTP range requests — only fetches needed row groups - FilterBar auto-detects column types and injects WHERE clauses via subquery wrapping - Connection pool (max 4), LRU query cache (100 entries, 5min TTL) - ~95KB bundled (ESM), tree-shakeable

Components: Chart (line/bar/area/scatter/pie), DataTable (paginated, sortable, resizable columns), KPICard (with sparkline + comparison), FilterBar (auto or manual config), Dashboard (responsive grid layout), ExportButton (CSV/JSON).

For connecting to external databases (Postgres, MySQL, etc.), there's a "gateway pattern" — your backend runs the SQL and returns JSON, the embed renders it via a fetch callback.

Built with: React 18+, DuckDB-WASM, uPlot, @tanstack/react-table, Zustand. Apache-2.0 licensed.

Live playground: https://duckui.com/play Docs: https://duckui.com/embed/docs npm: https://www.npmjs.com/package/@duck_ui/embed

This is part of the Duck-UI project (https://github.com/ibero-data/duck-ui), a browser-based SQL IDE for DuckDB with 525+ stars. The embed SDK lets you take the same DuckDB-powered analytics and drop them into any React app.

Would love feedback on the API design and what components/features you'd want next.

Comments URL: https://news.ycombinator.com/item?id=47172109

Points: 1

# Comments: 0

I've been building CH-UI, an open-source ClickHouse workspace. Today I'm releasing v2, which is a complete rewrite from scratch.

v1 was a React SPA served via Docker. v2 is a single Go binary with an embedded Svelte frontend — download, run, open browser. No Docker, no Node, no runtime dependencies.

What it does:

- Multi-tab SQL editor with autocomplete - Database/table explorer with data preview - Saved queries - Tunnel architecture: run `ch-ui server` on one machine and `ch-ui connect` next to your ClickHouse — secure WebSocket tunnel, no VPN needed - Self-update (`ch-ui update`), OS service install (`ch-ui service install`)

Pro edition adds dashboards, scheduled jobs, AI assistant (BYO provider), governance (lineage, access matrix, policies), and alerting.

Core is Apache 2.0. Pro uses a signed license model (Ed25519).

Stack: Go 1.24, Svelte 5, SQLite (embedded state), Chi router, WebSocket tunnels.

GitHub: https://github.com/caioricciuti/ch-ui Install: `curl -fsSL https://ch-ui.com/install.sh | sh`

Happy to answer questions about the architecture, the tunnel model, or the open-core licensing approach.

Comments URL: https://news.ycombinator.com/item?id=47095026

Points: 9

# Comments: 0

I built Glintlog because I wanted a simple way to aggregate logs without spinning up Elasticsearch, Loki, or a SaaS that costs my kidney…

What it is: A self-hosted log aggregation tool that runs as a single binary. No Docker, no dependencies, no config files required.

How it works: ``` curl -fsSL https://raw.githubusercontent.com/ibero-data/glintlog/main/s... | bash ```

That's it. Open localhost:8080 and you have a log viewer with search, filtering, and live tail.

Tech: - Go backend with embedded DuckDB for storage - Native OTLP support (gRPC on 4317, HTTP on 4318) - Works with any OpenTelemetry SDK out of the box

Why I built it:

I run small side projects and wanted observability without the complexity. Existing solutions either require too much infrastructure (ELK), are SaaS-only, or don't support OpenTelemetr natively. Glintlog is meant to be the SQLite of log aggregation – simple, embedded, good enough for most use cases. I use a lot for ETL’s now… (I’m a data engineer too)…

It's not meant to replace SaaS that has a 500-person company. It's for small teams, and enthusiasts who want logs without the overhead, also covering all we need for it…

Docs: https://glintlog.com/docs GitHub: https://github.com/ibero-data/glintlog

Would love feedback. What features would make this useful for you? Would you pay per year?

Comments URL: https://news.ycombinator.com/item?id=46883970

Points: 10

# Comments: 0

What makes it different:

Direct SQL access to your data. Not just pre-built reports – write actual queries against your raw events. Want to know which UTM campaigns convert best for mobile users in Germany? Write the query, get the answer, save it as a dashboard panel.

Full analytics stack:

Web analytics: pageviews, sessions, traffic sources, UTM tracking, device/browser/geo data Error tracking: JavaScript exceptions, unhandled promises, stack traces, filtering by browser/version Performance monitoring: Core Web Vitals (LCP, FID, CLS, INP), page load times, TTFB

Custom dashboards: Drag-drop panels anywhere. Time series, bar charts, pie charts, maps, tables. Monaco editor with SQL autocomplete. Export data as Parquet files – full ownership.

Privacy-first: EU-hosted infrastructure (no data transfers), GDPR-compliant, no cookies needed, lightweight script (<2KB).

Domain verification via DNS ensures only your sites can send data.

Current state: Beta with 7 verified domains tracking production traffic. Looking for 10 EU companies to test for 3 months free.

Want feedback on SQL interface and what analytics patterns matter most for your business.

Try it: https://yaat.io/beta

Built in Valencia, Spain. All data stays in EU.

Comments URL: https://news.ycombinator.com/item?id=45995996

Points: 6

# Comments: 3

What it does:

Real-time charts for CPU, memory, disk... Quick actions menu - one key to flush DNS, fix WiFi, kill processes... Clean up all the dev cache garbage in one place (npm, Homebrew, Xcode, Go, Yarn) System insights with performance scoring

Basically everything I was doing manually or with different tools, now in one TUI.

Written in Go, GPL-3.0.

https://github.com/caioricciuti/dev-cockpit

Install:

curl -fsSL https://raw.githubusercontent.com/caioricciuti/dev-cockpit/m... | bash

Would appreciate feedback on the UI and what other quick actions or monitoring would be useful.

Comments URL: https://news.ycombinator.com/item?id=45824307

Points: 3

# Comments: 1

The Problem: Every time I needed to query csv, parquet, or even to play with SQL, I had to either: (a) spin up a Jupyter notebook (b) use the CLI (c) upload to a hosted service.

Friction at every step (TOO MUCH to load a csv or even to test some sql (study)...

The Solution: DuckDB's WASM runtime lets us run SQL analysis client-side. Load CSV/JSON/Parquet files from disk or URL, write SQL, get results instantly. Data stays on your machine. What It Does:

SQL editor with autocomplete & syntax highlighting Import CSV, JSON, Parquet, Arrow (local or remote URLs) Query history, keyboard shortcuts, theme toggle Persistent storage via OPFS (data survives browser refresh) Optional: Connect to external DuckDB servers One-liner Docker deployment or Node 20+ dev server

Technical Details:

DuckDB compiled to WASM; query execution in-browser OPFS-backed persistence Apache 2.0 licensed Runs on Chrome 88+, Firefox 79+, Safari 14+

Use Cases:

Learning SQL without setting up databases Ad-hoc data exploration (CSV → SQL in seconds) Quick prototyping before shipping to production Privacy-conscious workflows (no data leaves your browser)

GitHub: https://github.com/ibero-data/duck-ui Live Demo: https://demo.duckui.com Quick Start: docker run -p 5522:5522 ghcr.io/ibero-data/duck-ui:latest

Would love feedback on: (1) Use cases I'm missing (2) Performance bottlenecks you hit (3) Features that would make this your default SQL scratchpad.

Comments URL: https://news.ycombinator.com/item?id=45633453

Points: 213

# Comments: 60

I've been working on a personal project I wanted to share with the community: CR - Mac Helper. It's a collection of shell scripts designed to help you manage, clean, and optimise our Macs :)

https://github.com/caioricciuti/cr-mac-helper

As a data engineer/developer, I've accumulated a ton of apps and tools on my Mac over the years, and I was tired of manual cleanup and maintenance. So, I built this to automate the process.

Here's what CR - Mac Helper can do:

App Cleanup: Thoroughly remove applications and their associated files. Path Management: Fix broken PATH entries and optimize your environment. Cache & Temp File Cleanup: Free up disk space by clearing out caches and temporary files. Login Items Management: Control startup apps to improve boot times. System Maintenance: Run maintenance scripts, verify disk permissions, and more. And more! (Battery optimization, security cleanup, system auditing...) Why I built it:

I tried existing tools, but they often lacked the specific customisation I needed. Plus, I wanted something lightweight that didn't require installing more software.

Important Notes:

Back up your system before use! While I've made efforts to ensure safety, I'm not responsible for any data loss. Some operations require sudo. The project is open-source under the MIT license. I'm looking for feedback and contributions! And if you can a little start is really appreciated :D

Check it out on GitHub: https://github.com/caioricciuti/cr-mac-helper

Let me know what you think, and feel free to contribute! Any feedback, suggestions, or bug reports are greatly appreciated!

Thanks!

Comments URL: https://news.ycombinator.com/item?id=43458359

Points: 8

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=42917835

Points: 6

# Comments: 0