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AI agents that run order-to-cash operations Discussion | Link

Unify data analysis and plotting in one powerful workspace Discussion | Link

A native macOS terminal you can skin and theme Discussion | Link

"On the first attempt, reaching orbit, I never thought it was possible."

GitHub热门项目 | 《深入理解 AI Agent：设计原理与工程实践》（李博杰 著）开源主仓库：全书正文、编译版 PDF 与按章配套代码 | Stars: 5,299 | 1,734 stars today | 语言: Python

Last week I was reviewing the staking engine of a protocol before its mainnet launch. Deep in a 1,400-line contract, I found what looked like a serious bug. The reward math multiplied three values before dividing: uint256 delta = (lot.amount * rBase * midpointRate) / (RAY * RAY); Multiply-before-divide. If that intermediate product overflows uint256 , the whole epoch settlement reverts — and since every stake , withdraw , and setStake runs it, the post's funds get permanently frozen . That's a High-severity, fund-locking DoS. I had the finding half-written. lot.amount can reach 10M tokens ( 1e25 ). rBase grows with elapsed time. midpointRate can hit RAY . Multiply those and you blow past 2^256 ... I was ready to send it. Then I did the one thing that separates a real audit from false-positive spam: I checked the actual constants before writing the claim. uint256 private constant RAY = 1e18; // I'd assumed 1e27 RAY was 1e18 , not the 1e27 I'd been carrying in my head. And the interest rate had a hard cap — MAX_RATE_MAX_RAY = 5e18 , enforced even against a fully-captured timelock. I ran the numbers with the real values: For that multiplication to overflow, the protocol would need to go 2.3 × 10¹⁵ years without a single state update. Not reachable. The bug didn't exist. I deleted the finding. Why this matters more than the bug would have If I'd sent that report, here's what happens: the team's engineer clones the repo, plugs in the real constants, and realizes in ten minutes that I flagged an overflow that can't happen. Every other finding in my report now gets read with a raised eyebrow. My credibility — the entire product — is gone. This is the dirty secret of automated smart-contract auditing: the bottleneck isn't finding issues. It's not drowning the real ones in false positives. Anyone can run a scanner and paste 40 "criticals." A team that has to triage 40 flags to find the 2 that matter will — correctly — stop trusting you. The bar I hold: zero false positives o

.The V8 engine speeds up JavaScript by dynamically compiling frequently run bytecode into optimized native machine code. However, if you pass inconsistent argument types to these optimized functions, V8 panics and deoptimizes back to bytecode. Keeping your functions monomorphic (single-typed) prevents this costly deoptimization loop, ensuring maximum runtime execution speed. If you’ve spent as much time digging into V8 execution flags as I have, you quickly realize that JavaScript is constantly rewriting itself under the hood. We like to think of JavaScript as a dynamically typed scripting language. But at runtime, engines like V8 are working tirelessly to turn your code into a highly optimized, statically typed powerhouse. When we violate that type stability, we pay a massive performance tax. How does the V8 engine optimize JavaScript at runtime? V8 uses a multi-tiered compilation pipeline that starts with an interpreter for fast startup times, then upgrades hot functions to optimized machine code using a JIT compiler. By tracking runtime type patterns, the engine can safely make assumptions to skip expensive dynamic lookups. When I look at V8’s execution pipeline, I see two primary systems working in tandem: Ignition (the interpreter) and TurboFan (the JIT compiler). Initially, Ignition compiles your raw JavaScript into bytecode so your app can boot instantly. As this bytecode executes, V8 allocates a data structure called a Feedback Vector for each function. Inside this vector are Feedback Slots (managed by Inline Caches, or ICs). These slots act as recorders, capturing the exact types (or "shapes") of the variables passing through your code. Once a function runs frequently enough to cross an execution threshold, V8 marks it as "hot" and hands it to TurboFan. TurboFan reads those feedback slots, assumes the types will remain identical in the future, and compiles a highly streamlined, native machine code version of that function. What happens when you pass differe

Modern web applications demand responsive, non-blocking user flows. This tutorial demonstrates a production-grade implementation for handling form submissions without full-page reloads using the native Fetch API and clean async/await syntax. The Complete Source Code Create a file named app.js and drop in the following event-driven architecture: JavaScript document.getElementById('registrationForm').addEventListener('submit', async (event) => { event.preventDefault(); // 1. Stop full page reload const form = event.target; const formData = new FormData(form); const submitBtn = form.querySelector('button[type="submit"]'); const responseMessage = document.getElementById('responseMessage'); // 2. UI Feedback: Disable button during network request submitBtn.disabled = true; submitBtn.textContent = 'Processing...'; responseMessage.textContent = ''; try { // 3. Asynchronous Fetch Request const response = await fetch(form.action, { method: 'POST', body: formData, headers: { 'X-Requested-With': 'XMLHttpRequest' } }); // 4. Status Code Validation if (!response.ok) { throw new Error(`HTTP error! Status: ${response.status}`); } const result = await response.json(); // 5. Dynamic UI State Handling if (result.success) { responseMessage.style.color = '#155724'; responseMessage.textContent = result.message; form.reset(); // Clear form on success } else { responseMessage.style.color = '#721c24'; responseMessage.textContent = result.error || 'Submission failed.'; } } catch (error) { // 6. Global Error Catching responseMessage.style.color = '#721c24'; responseMessage.textContent = 'A network error occurred. Please try again.'; console.error('Submission tracking error:', error); } finally { // 7. Reset UI State Guaranteed submitBtn.disabled = false; submitBtn.textContent = 'Submit Data Securely'; } }); The Problem with Synchronous Form Lifecycles Traditional submissions trigger a synchronous navigation cycle: the browser constructs a payload, issues a full HTTP request, and replaces the

Understand anything online without leaving the page. Discussion | Link

This is a submission for DEV's Summer Bug Smash: Clear the Lineup powered by Sentry . Project Overview Formbricks is an open-source survey and experience management platform built with Next.js, TypeScript, React, and Tailwind CSS. It lets teams create and deploy surveys across websites, apps, and email. Developers can self-host it or use the cloud version. With over 12,000 GitHub stars and hundreds of contributors, it is one of the most actively maintained open source alternatives to Qualtrics. Bug Fix or Performance Improvement Formbricks supports custom regex validation rules on survey questions. A survey creator can set a pattern that user responses must match before they are accepted. The problem was in how that pattern was stored. The validation schema for regex pattern rules only checked that the input was a non-empty string: // Before the fix export const ZValidationRuleParamsPattern = z . object ({ pattern : z . string (). min ( 1 ), flags : z . string (). optional (), }); z.string().min(1) means "give me any string with at least one character." It does not verify that the string is actually a valid regular expression. So a survey creator could type [invalid as their pattern, the schema would accept it, it would be saved to the database, and then when a real user submitted a response, the system would try to run new RegExp("[invalid") , JavaScript would throw a SyntaxError , and the survey would crash silently at runtime. The bug never surfaced during setup. It only appeared when a real user was trying to submit a real response. Code Pull Request: github.com/Tobore005/formbricks/pull/1 Here is the fix: const isValidRegexPattern = ( pattern : string ): boolean => { try { new RegExp ( pattern ); return true ; } catch { return false ; } }; const isValidRegexFlags = ( flags : string | undefined ): boolean => { if ( flags === undefined ) return true ; try { new RegExp ( "" , flags ); return true ; } catch { return false ; } }; export const ZValidationRuleParamsPa

So I've been working on this app for a very long time now. Started off by wanting an app that separates long form notes from short form notes visually and keeps everything organized on a canvas. The app features: -Sticky notes for quick, small notes and A4 notes for long form documents -A quick mode that uses local storage to quickly open the app and jot down a thought on the canvas -A PDF export option that exports your notes into 3 custom designed PDF styles -Visual hierarchy to replicate file