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

Web Developer Travis McCracken on Running Backend Services in a Monorepo

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

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via DEV Community: rust (author: Dissertation Online Uk)

Why We Built a Blockchain That Can't Be Hacked by Quantum Computers — And Why We Did It Before Anyone Else

via DEV Community: rust (author: MUHAMMAD ATTAHIR)

The Significance of Eric Park'…

via DEV Community: rust (author: Norvik Tech)

Why your media parsers are a 0-click attack surface (and how to harden them)

via DEV Community: rust (author: Alan West)

Shipping 31 Rust crates in five days: a small-LLM dev stack, in the open

via DEV Community: rust (author: Mukunda Rao Katta)

Arroyo: Discover Real-Time Data Processing in Rust

via DEV Community: rust (author: Ns5)

Building in public, week 9: cross-links, positions going up, and 246 new dev.to followers in 7 days

via DEV Community: rust (author: Serhii Kalyna)

Build a Self-Improving AI Agent in Rust with Garudust — Daily Briefing Bot in 10 Minutes

via DEV Community: rust (author: Garudust)

Bun – Rust Project

via DEV Community: rust (author: tharshan)

Indexing Solana Programs in Rust: Notes From a Python Backend Engineer

via DEV Community: rust (author: Brian Ting)

Securing the Stack: How to Build Zero-Trust Applications with WebAssembly

via DEV Community: rust (author: Alain Airom (Ayrom))

Clipboard Monitor + Gemini in a Tauri App — Building a Smarter Dev Tool

All tests run on an 8-year-old MacBook Air.
All results from shipping 7 Mac apps as a solo developer. No sponsored opinion.
HiyokoHelper monitors the clipboard and optionally sends content to Gemini for analysis. It sounds simple. The implementation has specific gotchas.
Here's what I learned.

Clipboard monitoring in Tauri
There's no built-in file-watcher equivalent for the clipboard. You poll.
rustuse tauri_plugin_clipboard_manager::ClipboardExt;
use std::time::Duration;
use tokio::time::interval;

async fn watch_clipboard(handle: AppHandle) {
let mut last = String::new();
let mut ticker = interval(Duration::from_millis(500));

loop {
    ticker.tick().await;
    if let Ok(current) = handle.clipboard().read_text() {
        if current != last && !current.is_empty() {
            last = current.clone();
            handle.emit("clipboard-changed", current).ok();
        }
    }
}

}
500ms polling is fast enough to feel responsive. Lower than 200ms and you're burning CPU for no user benefit.

What to do with clipboard content
For HiyokoHelper, clipboard content goes to Gemini for command explanation and danger detection. The flow:

Clipboard changes
Frontend receives clipboard-changed event
User clicks "Analyze" (or auto-analyze is on)
Content sent to Gemini with a structured prompt
Response shown in the UI

Auto-analyze on every clipboard change is too aggressive. Users copy passwords, personal data, sensitive content. Analyze on demand, not automatically.

The dangerous content problem
Terminal error messages and shell commands are the primary use case. But users also copy:

Passwords
API keys
Personal information
Code with secrets in it

Before sending to Gemini: strip obvious secrets. API keys (long alphanumeric strings), password manager output, anything that looks like a credential.
rustfn looks_like_secret(text: &str) -> bool {
// Long random strings
let has_long_random = text.split_whitespace()
.any(|w| w.len() > 30 && w.chars().all(|c| c.is_alphanumeric()));

// Common secret patterns
let has_secret_pattern = text.contains("sk-")
    || text.contains("AIza")
    || text.contains("ghp_");

has_long_random || has_secret_pattern

}
Not perfect. Good enough to avoid the most obvious cases.

The history cache
Store clipboard history in SQLite with timestamps. Let users browse, search, and pin items.
Don't store everything forever. Cap at 100 items. Auto-delete items older than 7 days. Clipboard history that grows unbounded is a privacy and storage problem.

The verdict
Clipboard monitoring is polling. Gemini integration needs explicit user action, not auto-send. History needs a retention policy.
The useful part — AI analysis of terminal errors and commands — is genuinely useful once you get the UX right.

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)

Web Developer Travis McCracken on My Favorite VSCode Extensions for Backend Dev

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

Five problems every agent loop has. No framework needed.

Most agent failure modes are not interesting. They are boring. They are the same five problems in different costumes. After eighteen months running agent loops in production, I keep meeting these five and only these five.

I do not build agent frameworks. I build small libraries that fix one failure mode each. You install the one you need. The composition emerges from your code, not a framework's architecture diagram.

Here they are in roughly the order you will hit them.

1. The JSON is not JSON

Your model returns Sure, here you go:\njson\n{...,}\n``. You parse it. You crash.

Fix: repair before validate. Strip the fence. Extract the largest balanced JSON object from surrounding prose. Remove trailing commas. Then validate against your schema. If validation fails, send the model back a precise hint, not a generic "invalid JSON, please try again."

The hint is the trick. Smaller models self-correct beautifully on a structural complaint. They do not self-correct on a vague reprimand.

2. The tool args are wrong

The model picks the right tool. It calls it with units: "kelvin" against an enum of ["c", "f"]. You run the tool. Bad things happen.

Fix: validate every tool call against its schema before running it. Validation issues become the tool's response. Feed them back. The model fixes the call on the next turn.

Always return all validation issues at once, not the first. The model fixes them in one retry, not five.

3. The agent wanders to the wrong network

The minute your agent can pick URLs, you handed it URL-picking power. A confused-deputy bug or a prompt injection sends a fetch to a domain you did not authorize. By the time you notice, an API key is in an attacker's log.

Fix: declarative domain allowlist. List the four hosts your agent legitimately needs. Block everything else with an error message at the HTTP layer.

4. The context budget runs out

You stack five turns of chat history. You drop the system message accidentally during truncation. The agent forgets what it is doing. Or you drop the trailing user turn and the model answers a question you never asked.

Fix: anchored truncation. Preserve the leading system message and the trailing user turn. Drop from the middle.

Drop-oldest is the right default for chat. Drop-middle is better when you want both early grounding and recent context. Both keep the load-bearing pieces of the prompt.

5. Regressions sneak in

You tweak a system prompt. The agent now picks tools in a slightly different order. Sometimes that is fine. Sometimes it is a regression that breaks the deployed app and you only notice next Friday.

Fix: snapshot tests for agent traces. Record one run end-to-end. First test run writes the snapshot. Later runs diff and fail with a unified diff if anything changed. Refresh with an env var when the change is intentional.

The framework I did not write

A naive agent loop hitting all five of these:

`rust
let fitted = Fitter::new(8_000).fit(messages, Strategy::DropOldest);
let raw = call_model(&fitted).await?;
let action = caster.parse(&raw)?;
if action.kind == "tool" {
validator(&action.tool)?.validate(&action.args)
.map_err(|e| anyhow!(e.for_llm()))?;
run_tool(&action).await
} else {
Ok(action.text)
}
`

Ten lines. Five concerns. Each concern is a separate 200-line library that does one thing and ships independently.

The framework version of this is 2000 lines, locks you to one HTTP client, opinionated about which provider, and bundles all five concerns into a single API you cannot pry apart when one of them is wrong.

I have shipped both kinds. The small libraries win every time.

If you are building an agent and have not hit problems 1 or 2 yet, you will. Skip the framework. Pick the small library when the problem actually shows up. Compose.

That is the whole stack.

via DEV Community: rust (author: Mukunda Rao Katta)

Deploying a Rust MCP Server to Azure Appservice

via DEV Community: rust (author: xbill)

Turn Your File System Into a SQL Database

via DEV Community: rust (author: Mukhtar)

Building a Low-Cost Image Converter on AWS With Rust Lambda

Project demo link: https://image-ignite.vercel.app/

This started as a hobby project. I was thinking about a simple image conversion service for resizing ,convert, compress uploaded images.

Since it was a personal project, cost mattered a lot.
Keeping EC2 instances running 24/7 felt unnecessary for a workload that only existed for a few seconds per request.

So I started experimenting with AWS Lambda.

First Attempt: Node.js + Sharp on Lambda

My initial stack was:

The architecture was very simple:

Upload → Lambda → Process Image → Store Result

At first, Lambda felt like the perfect solution:

● no idle server costs
● automatic scaling
● no server management
● pay only when used

Exactly what I wanted for a hobby project.

The Problem I Started Noticing

After some usage, cold starts became noticeable.
Especially for image-heavy requests.

The actual image processing was fast enough, but startup time sometimes became a large part of the total request.

Approximate numbers from my experience:

For a workload that only runs a few seconds, that startup overhead feels significant.

Why I Switched to Rust

I rebuilt the processor using Rust Lambda.
Not because of hype.

The workload simply matched Rust better.
The difference became noticeable almost immediately.

For this type of workload, lower startup time mattered a lot more than I initially expected.

Final Thoughts

This project started as a small hobby image converter where I wanted to avoid the cost of always-on infrastructure. I initially used Node.js with Sharp on AWS Lambda, but cold starts became noticeable for short-lived image processing tasks. Moving to Rust improved startup time and reduced memory usage significantly. More importantly, it changed how I think about infrastructure — some workloads work better as temporary, event-driven compute instead of persistent servers.

via DEV Community: rust (author: fayismahmood)

Building an open-source Windows file transfer app with Rust, Tauri and QUIC

via DEV Community: rust (author: Kerim Sabic)