10 Things You Can Do With Logs Using Garudust Agent 🦅

via DEV Community: rust (author: Garudust)

Core Rust Concepts Every Developer Should Know

via DEV Community: rust (author: mihir mohapatra)

DataZen: a 10 MB open-source database client built with Tauri and Rust

DataZen is a free, MIT-licensed desktop app for PostgreSQL, MySQL, SQLite, and Redis.

Why another client?

● TablePlus is great but paid for many teams
● DBeaver is powerful but heavy on RAM and startup time

DataZen targets daily dev work: connect, browse, run SQL, export — in a <10 MB installer.

Stack

● Tauri v2 + Rust backend (sqlx, redis, russh for SSH)
● React + CodeMirror 6 frontend
● Credentials encrypted locally (AES-256-GCM)

Features

● Multi-window workflow
● Built-in SSH tunnels (no local ssh binary)
● SQL editor with table/column autocomplete
● Virtual scrolling for large tables
● Backup to SQL, CSV/JSON import/export
● PG ↔ MySQL schema + data sync
● Redis key browser
● Dark theme, English + Chinese UI

Status

Early v0.0.3, but I use it as a daily driver for SQL + Redis.

● Download: https://github.com/flyxl/datazen/releases
● Site: https://flyxl.github.io/datazen/
● Repo: https://github.com/flyxl/datazen

macOS: if Gatekeeper blocks the app, run xattr -cr /Applications/DataZen.app after install.

Feedback: wuxiaolongklws@gmail.com (mailto:wuxiaolongklws@gmail.com) — stars and issues welcome!

via DEV Community: rust (author: flyxl)

I Built "harumi" — A Pure Rust PDF Editing Library with CJK Support

Overview

harumi is a Pure Rust library that lets you dynamically add CJK text (Japanese, Chinese, Korean) to existing PDFs. Unlike bindings-based solutions, it has zero C dependencies and handles font subsetting automatically.

● crates.io: https://crates.io/crates/harumi
● GitHub: https://github.com/kent-tokyo/harumi

Why Another Rust PDF Crate?

The existing Rust PDF ecosystem leaves a gap:

harumi fills that gap: append-only editing of existing PDFs, Pure Rust, with automatic CJK font subsetting and ToUnicode CMap generation built in.

The Three Hard Problems of CJK in PDF

Getting Japanese (and CJK in general) right inside a PDF isn't just about "embedding a font." There are three distinct challenges:

1. Font Subsetting

A full Japanese font file can easily exceed 10 MB. For practical file sizes you must extract only the glyphs actually used and rebuild the font binary — this is subsetting. harumi does this automatically at save time.

2. ToUnicode CMap Generation

PDFs separate rendering (Glyph IDs) from semantics (Unicode code points). Without a ToUnicode CMap, copy-paste and text search produce garbled output. harumi generates this mapping for every font it embeds.

3. Glyph Advance Width Recalculation

After subsetting, Glyph IDs are reassigned. The advance widths stored in the PDF must be recalculated to match — otherwise text spacing breaks. harumi handles this as part of the save pipeline.

Lazy Subsetting Pipeline

harumi uses a lazy subsetting design to handle all three problems in one pass:

1. embed_font() — store raw font bytes; no processing yet
2. Collect all text draw calls across all pages
3. Walk every page at save() time, gathering the complete set of used characters
4. Subset the font to only those glyphs
5. Reassign Glyph IDs
6. Build the ToUnicode CMap
7. Recalculate advance widths and write the final CIDFont object

This single-pass approach avoids redundant font processing and keeps the implementation straightforward.

Feature Overview

use harumi::Document;

let mut doc = Document::open("input.pdf")?;

// Append text (including invisible text for search layers)
doc.page(0).add_text("Hello, 世界！", font, 12.0, x, y)?;

// Draw shapes and embed images
doc.page(0).draw_rect(x, y, width, height, color)?;
doc.page(0).embed_image(image_bytes, x, y, width, height)?;

// Page operations
doc.rotate_page(1, 90)?;
doc.delete_page(2)?;
doc.reorder_pages(&[2, 0, 1])?;

// Merge and split
let other = Document::open("other.pdf")?;
doc.merge(other)?;
let parts = doc.split_at(&[3])?;

// Extract text
let text = doc.extract_text(0)?;

// Metadata
doc.set_title("My Document")?;

doc.save("output.pdf")?;

Current Status & Roadmap

harumi is published on crates.io and the source is available on GitHub.

Planned improvements:

● Broader CJK font format support
● Form field editing
● Performance optimizations for large documents

Feedback, issues, and contributions are very welcome!

via DEV Community: rust (author: kent-tokyo)

Auth multi-tenant que prova, não promete: como o GarraIA fecha 110 cenários de RBAC + 81 de RLS antes do beta da Fase 3

via DEV Community: rust (author: Michel)

Deploying a Rust A2A Agent to Google Cloud Run

via DEV Community: rust (author: xbill)

[Solved] 12 Best Sites to Buy Mix Gmail Accounts in Bulk &

via DEV Community: rust (author: marcellapa)

Building a Browser-Based RPG Map Editor with Rust, WebAssembly, WebGL2, and React

via DEV Community: rust (author: TheXper)

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via DEV Community: rust (author: Writer Support Uk)

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via DEV Community: rust (author: Assignment Service Uk)

Web Developer Travis McCracken on The Power of Make and Bash

via DEV Community: rust (author: Travis McCracken Web Developer)

Ubuntu 26.04: Goodbye sudo, Hello Rust and Post-Quantum SSH

via DEV Community: rust (author: Arthur)

Localizing a Tauri App for Japanese and English — What Actually Works

All tests run on an 8-year-old MacBook Air.
All results from shipping 7 Mac apps as a solo developer. No sponsored opinion.
All my apps ship in Japanese and English. Not separate codebases — one app, two languages, runtime switching.
Here's the implementation that works in production.

Why bother with dual language
Japanese users convert better on Japanese UI with JPY pricing. English users are a global market. Both are worth serving. One codebase is the only viable approach for a solo developer.

The i18n library
I use react-i18next in the React frontend:
bashnpm install i18next react-i18next
typescript// i18n.ts
import i18n from 'i18next'
import { initReactI18next } from 'react-i18next'

i18n.use(initReactI18next).init({
resources: {
en: { translation: enTranslations },
ja: { translation: jaTranslations },
},
lng: 'en',
fallbackLng: 'en',
})

export default i18n

Translation file structure
typescript// en.json
{
"sync": {
"start": "Start Sync",
"stop": "Stop Sync",
"status": {
"idle": "Ready",
"running": "Syncing...",
"complete": "Sync complete",
"error": "Sync failed"
}
},
"device": {
"connect": "Connect Device",
"disconnect": "Disconnect",
"notFound": "No device found"
}
}

// ja.json
{
"sync": {
"start": "同期開始",
"stop": "同期停止",
"status": {
"idle": "待機中",
"running": "同期中...",
"complete": "同期完了",
"error": "同期エラー"
}
},
"device": {
"connect": "デバイスを接続",
"disconnect": "切断",
"notFound": "デバイスが見つかりません"
}
}

Detecting system language
Get the system locale from Rust and pass to the frontend on launch:
rust#[tauri::command]
fn get_system_locale() -> String {
std::env::var("LANG")
.unwrap_or_default()
.split('.')
.next()
.unwrap_or("en")
.to_string()
}
typescriptconst locale = await invoke('get_system_locale')
const lang = locale.startsWith('ja') ? 'ja' : 'en'
i18n.changeLanguage(lang)

Localizing Gemini prompts
AI prompts need localization too. Japanese users get Japanese responses:
rustpub fn build_prompt(input: &str, lang: &str) -> String {
match lang {
"ja" => format!(
"以下のエラーを日本語で説明し、修正方法を提案してください。\nエラー: {}",
input
),
_ => format!(
"Explain this error and suggest a fix.\nError: {}",
input
),
}
}
A Japanese user who gets an English AI response will be confused. Localize the prompts.

The separate Gumroad listings
One app binary, two Gumroad listings — JP and EN with different prices and descriptions. The JP listing uses JPY pricing. The EN listing uses USD.
Users self-select. The binary is identical. The storefront is localized.

The verdict
react-i18next + system locale detection + localized AI prompts covers the full localization stack. The overhead is low. The market expansion is real.

If this was useful, a ❤️ helps more than you'd think — thanks!
Hiyoko PDF Vault → https://hiyokoko.gumroad.com/l/HiyokoPDFVault
X → @hiyoyok

via DEV Community: rust (author: hiyoyo)

We Are Leaving Bun… For Now

via DEV Community: rust (author: Jhon Caldeira)

Modeling a nut, in Rust

via DEV Community: rust (author: Satoshi Misumi)

PDF to Text Conversion with Rust on AWS Lambda

via DEV Community: rust (author: fayismahmood)

I don't want to be a programmer, I want to remind developers that they are failing us and they must take action

use Claude for project planning (it's expensive), use DeepSeek or a cheap Chinese model for coding, use Gemini for review. Errors here, errors there, GitBrain add-ons or VS Code add-ons depending on what you use. A problem in the code? Go to the browser to fix it. Then I need to pay for some subscription from my phone.
This is the curse of wasted time and scattered focus.
I'm not a Rust programmer, but it's the fastest language.
So I built an interface that integrates chat with 30+ AI providers simultaneously, payments, seven memory layers, and a simple but effective embedded IDE — Monica IDE — plus Microsoft Phi Nano.
A browser with advanced privacy tech. Orchestration between models and payments. A unique user experience for each person.
That's my idea. I don't know how to complete it, polish it, or test it on Windows or Mac — I only work on Linux. These are my financial limits. I need your help and advice.

github

via DEV Community: rust (author: aly ghaly)

I built a local-first hybrid vector database in Rust from scratch

A few months ago I started building vecdb — a vector database that
runs entirely on your own machine. No cloud, no API keys, no subscription.

The problem

Most vector databases make you choose — semantic search OR keyword search.
Semantic search finds meaning but misses exact keywords. Keyword search
finds exact matches but misses meaning.

vecdb combines both in a two-stage pipeline:

1. HNSW dense index retrieves candidates by meaning
2. BM25 sparse index re-scores by keyword relevance
3. A fusion function combines both scores

What it can do

● Hybrid HNSW + BM25 retrieval
● SQL-like query language with VECTOR_SIM predicate
● Python and TypeScript SDKs
● Single binary, Docker support
● 187 tests
● MIT license

Example query

SELECT * FROM documents
WHERE VECTOR_SIM(vec, [0.1, 0.2, 0.3]) > 0.75
AND payload->>'region' = 'US'
LIMIT 10;

Try it

GitHub: https://github.com/zaydmulani09/vecdb

Would love feedback from the community — especially on the
architecture and what to tackle in v0.2.0.

via DEV Community: rust (author: Zayd Mulani)

ให้ AI พูดภาษาไทยได้ด้วย Garudust Agent + iApp TTS

ถ้าคุณใช้ Garudust Agent อยู่แล้ว การเพิ่มความสามารถให้ AI พูดภาษาไทยออกมาเป็นเสียง ทำได้ในไม่กี่ขั้นตอน — ไม่ต้องแก้โค้ดใด ๆ เพราะ Garudust มีระบบ script tool ที่ pluggable อยู่แล้ว

TTS คืออะไร และทำไมต้อง iApp

TTS (Text-to-Speech) คือการแปลงข้อความเป็นเสียงพูด เหมาะกับ use case เช่น

● ตอบกลับผู้ใช้ด้วยเสียงแทนข้อความบน LINE / Telegram
● สร้างไฟล์เสียงสำหรับ podcast / narration อัตโนมัติ
● Accessibility — ผู้ใช้ที่อ่านหน้าจอยาก

iApp Technology เป็นบริษัทไทยที่มี TTS API รองรับภาษาไทยโดยเฉพาะ เสียงฟังเป็นธรรมชาติ รองรับสูงสุด 10,000 ตัวอักษรต่อ request คิดค่าใช้จ่าย 1 IC ต่อ 400 ตัวอักษร สมัครได้ที่ iapp.co.th

ขั้นตอนที่ 1 — ติดตั้ง tts tool จาก Hub

Garudust มี Hub รวม script tool ให้ install ได้ทันที

garudust tool install tts

ระบบจะดึง tool.yaml และ run.py มาไว้ที่ ~/.garudust/tools/tts/ และ register ใน registry อัตโนมัติ

ขั้นตอนที่ 2 — เพิ่ม Provider Profile ใน config.yaml

Garudust ใช้ระบบ provider profile จัดการ API endpoint และ key — ไม่ต้อง hardcode ใน script

เปิด ~/.garudust/config.yaml แล้วเพิ่ม:

providers:
  # ... providers ที่มีอยู่แล้ว ...
  tts-iapp:
    url: https://api.iapp.co.th/v3/store/audio/tts
    key: ${IAPP_API_KEY}

tools:
  tts:
    model: tts-iapp   # ชี้ไปที่ profile ข้างบน

Garudust จะ inject GARUDUST_BASE_URL และ GARUDUST_API_KEY เข้า script อัตโนมัติตอน runtime

ขั้นตอนที่ 3 — เพิ่ม API Key ใน .env

# ~/.garudust/.env
IAPP_API_KEY=iapp_live_xxxxxxxxxxxxxxxx

รับ key ได้จาก iapp.co.th/dashboard

ทดสอบผ่าน CLI

garudust "ใช้ tts tool แปลงข้อความว่า สวัสดีครับ ผมคือ Garudust ผู้ช่วย AI ของคุณ แล้วบอก path ไฟล์ที่ได้"

ผลลัพธ์:

สร้างไฟล์เสียงสำเร็จ
เสียงอยู่ที่: /tmp/tts_88b5bba372dd41a7acc7e37ea75df89b.wav

เปิดฟัง:

open /tmp/tts_88b5bba372dd41a7acc7e37ea75df89b.wav

เบื้องหลัง — run.py ทำงานยังไง

iApp TTS API คืน raw PCM มาโดยไม่มี WAV header ดังนั้น run.py จัดการ wrap header ให้เอง:

def pcm_to_wav(pcm: bytes, sample_rate: int = 24000, channels: int = 1, bits: int = 16) -> bytes:
    byte_rate   = sample_rate * channels * bits // 8
    block_align = channels * bits // 8
    data_size   = len(pcm)
    header = struct.pack(
        "<4sI4s4sIHHIIHH4sI",
        b"RIFF", 36 + data_size, b"WAVE",
        b"fmt ", 16,
        1, channels, sample_rate, byte_rate, block_align, bits,
        b"data", data_size,
    )
    return header + pcm

ผลที่ได้คือ RIFF WAV 16-bit mono 24kHz — เปิดได้กับทุก audio player

เปลี่ยน Provider ได้ทันทีโดยไม่แตะโค้ด

นี่คือจุดแข็งของ Garudust provider profile — ถ้าอยากลอง provider อื่นเช่น AIS หรือ local model แค่เพิ่ม profile และเปลี่ยน tools.tts.model บรรทัดเดียว:

providers:
  tts-ais:
    url: https://your-ais-tts-endpoint/api/tts
    key: ${AIS_TTS_API_KEY}

tools:
  tts:
    model: tts-ais   # เปลี่ยนแค่นี้

run.py ไม่ต้องแตะเลย เพราะอ่าน GARUDUST_BASE_URL และ GARUDUST_API_KEY จาก profile เสมอ

ขั้นต่อไปที่ทำได้

● LINE voice message — เพิ่ม OutboundMessage::Audio ใน Garudust core แล้วส่งไฟล์ WAV เป็น voice message บน LINE ได้เลย
● Auto TTS reply — สั่งให้ agent เรียก tts ทุกครั้งที่ตอบ โดย inject ใน system prompt
● Speaker selection — iApp มีหลายเสียง ตั้ง TTS_SPEAKER_ID ใน .env เพื่อเลือกเสียงที่ชอบ

สรุป

Garudust Agent เป็น open source ที่ github.com/garudust-org/garudust-agent — ติดตามหรือ contribute ได้เลย 🦅

via DEV Community: rust (author: Garudust)

Rust for Web3 Hackers: Welcome to the Cyber-Mecha Repair Dock 🤖🔧

via DEV Community: rust (author: Sudip)

Advanced Rust Concepts: Iterators, Closures, Generics & More (Part 2)

via DEV Community: rust (author: mihir mohapatra)