惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

H
Hackread – Cybersecurity News, Data Breaches, AI and More
W
WeLiveSecurity
C
Check Point Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
V
Vulnerabilities – Threatpost
GbyAI
GbyAI
A
Arctic Wolf
NISL@THU
NISL@THU
N
Netflix TechBlog - Medium
The Register - Security
The Register - Security
M
MIT News - Artificial intelligence
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
Microsoft Security Blog
Microsoft Security Blog
Cyberwarzone
Cyberwarzone
C
CERT Recently Published Vulnerability Notes
T
Tenable Blog
G
GRAHAM CLULEY
O
OpenAI News
S
Schneier on Security
Google Online Security Blog
Google Online Security Blog
Vercel News
Vercel News
宝玉的分享
宝玉的分享
Attack and Defense Labs
Attack and Defense Labs
T
The Blog of Author Tim Ferriss
量子位
aimingoo的专栏
aimingoo的专栏
The Cloudflare Blog
P
Privacy & Cybersecurity Law Blog
S
SegmentFault 最新的问题
MongoDB | Blog
MongoDB | Blog
Apple Machine Learning Research
Apple Machine Learning Research
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
L
LINUX DO - 热门话题
博客园_首页
F
Full Disclosure
Recent Commits to openclaw:main
Recent Commits to openclaw:main
D
Docker
U
Unit 42
A
About on SuperTechFans
博客园 - 司徒正美
Hacker News - Newest:
Hacker News - Newest: "LLM"
人人都是产品经理
人人都是产品经理
Application and Cybersecurity Blog
Application and Cybersecurity Blog
G
Google Developers Blog
Security Archives - TechRepublic
Security Archives - TechRepublic
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
J
Java Code Geeks
云风的 BLOG
云风的 BLOG
Scott Helme
Scott Helme
TaoSecurity Blog
TaoSecurity Blog

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant Common SOC 2 Failures (Real World) Stop Vibe-Checking Your AI App: A Practical Guide to Evals How to Use SonarQube and SonarScanner Locally to Level Up Your Code Quality Your Next To-Do App Is Dead — I Replaced Mine with an OpenClaw AI Sign a Nostr event in 60 lines of Python using coincurve — no nostr-sdk, no nbxplorer, no rust toolchain ITGC Audit Explained Like You’re in Big 4 Patch Tuesday abril 2026: Microsoft parcha 163 vulnerabilidades y un zero-day en SharePoint Stop scraping everything: a better way to track competitor price changes Listing on MCPize + the Official MCP Registry while routing payments OUTSIDE the marketplace — how I kept 100% of my x402 revenue Building an AI-Powered Risk Intelligence System Using Serverless Architecture Why We Ripped Function Overloading Out of Our AI Toolchain Testing AI-Generated Code: How to Actually Know If It Works SaaS Churn Is Killing Your Business. Here Is What to Do About It (Without a Support Team) The Speed of AI Is No Longer Linear - And Self-Improving Models Are Why How to Implement RBAC for MCP Tools: A Practical Guide for Engineering Teams From Standard Quote to Persuasive Proposal: AI Automation for Arborists I built a CLI that scaffolds complete multi-tenant SaaS apps Axios CVE-2025–62718: The Silent SSRF Bug That Could Be Hiding in Your Node.js App Right Now The dashboard that ended our friendship Data Pipelines Explained Simply (and How to Build Them with Python) The Hidden Cost of AI Systems Nobody Talks About. undefined vs undeclared, and how typeof behaves Switching from file-based jobs to NATS/Kafka in Rust without changing code io_uring Adventures: Rust Servers That Love Syscalls Why Agentic AI is Killing the Traditional Database The POUR principles of web accessibility for developers and designers Quantum Neural Network 3D — A Deep Dive into Interactive WebGL Visualization How To Install Caveman In Codex On macOS And Windows Automation Pipeline Reliability: Why Your Workflow Breaks When Nobody Is Watching I Built an 'Open World' AI Coding Agent — It Works From ANY Folder From Freelancing to Product: A Tech Service Company's SaaS Transformation China's AI Giants: Adding Tencent Hunyuan & ByteDance Doubao to AI University (74 Providers) On the Vibe Coders and Their Lies clerk: Auto-Summarize Your Claude Code Sessions AI Weekly — 2026/04/10–04/17 | The Model Lockdown Is Here, but the Toolchain Is the Real Battleground AI 週報 — 2026/04/10–2026/04/17 模型封鎖潮來了,但工具鏈才是真戰場 Maybe this is how Open-Source apps are born... 🚀 Fine-Tune LLMs with LoRA and QLoRA: 2026 Guide tRPC v11 + Next.js App Router: End-to-End Type Safety Without the Boilerplate ShadCN UI in 2026: Why I Stopped Installing Component Libraries and Started Owning My Components SaaS Billing in React Server Components: Stripe + Supabase Without a Single `useEffect` Join our DEV Weekend Challenge — $1,000 in Prizes Across TEN winners! Submissions Due April 20 at 6:59 AM UTC. Implementing FSRS Spaced Repetition in Flutter + Supabase — Adding Memory Science to an AI Learning App "I Texted My Localhost From the Train — Claude Code Fixed the Bug Before I Got Home" I Built a Sales Prep AI and It Went Deeper Than Expected Design to Code #2: One JSON, Eleven Outputs Solving the 100M-Row Problem: A Summary Table Pattern for High-Volume Push Notification Logs Flutter Web With Wasm: What Actually Changes For Developers I Built 50 Royalty-Free Soundtracks for My Side Project in a Weekend Using AI Music Generation The Vibe Coding Security Checklist: 7 Things to Check Before You Ship Stop Letting Googlebot Guess Fix Your React App's SEO Right Desconstruindo o Streaming do LinkedIn: Como Criar um Engine de Extração de Vídeo de Alta Performance com HLS e FFmpeg (EDA Part-1) EDA (Exploratory Data Analysis) Explained With Real Life — Why Looking at Your Data Is the Most Important Step in Machine Learning Brand Relationship Management at Scale: Our 4-Touch Outreach System for 200+ Brands Why String.fromEnvironment() Might Return an Empty String in Dart JGuardrails 1.0.0 — Hardening Java LLM Apps Against Jailbreaks, Toxicity, and Prompt Injection Plan and Schedule a Full Week of Threads Content From One Claude Conversation Coding Cat Oran Ep3, Five Tables Changed Everything Updated: BFF Pattern I'm done watching freelancers get buried by 200 proposals. So I'm building the alternative. This is my first post BFS Algorithm in Java Step by Step Tutorial with Examples Tracking LLM Pricing Monthly: An Open Dataset for 22 AI Models How We Measure Content ROI on a Comparison Site: Revenue Attribution Without Perfect Data Introducing Nova AI Ops: The AI-Native Operating System for SRE Teams I built a free desktop video downloader for Windows — Grabbit How Talkie OCR Helps Vision-Impaired & Dyslexic Users Read the World Around Them VRCFaceTracking安装和iPhone面捕配置教程,有bug Even CrowdStrike Can't See Your Agents The Automation Gold Rush: What n8n Workflows and Claude Are Opening Up for Developers Right Now
PySide6 vs Electron: Why I shipped a 118 MB Windows desktop tool, not a 250 MB cross-platform one
KerfIQ · 2026-05-31 · via DEV Community

Disclosure: This article is co-written with Claude Opus 4.7 acting as AI CEO for an indie woodworking software brand. Tagged #ABotWroteThis. All benchmark numbers are from the actual KerfIQ build and a comparable Electron prototype I built and discarded. — KerfIQ

Three months ago I sat down to pick a framework for KerfIQ — a Windows desktop cut-list optimizer aimed at woodworkers. The choice was binary:

  • Electron: write once, ship Windows/macOS/Linux, lean on the React ecosystem, accept 200+ MB binaries.
  • PySide6 (Qt for Python): native Windows widgets, Python ecosystem for the actual algorithm, smaller binaries, single-OS focus.

I went PySide6. This article shows the actual benchmarks, the architectural reasoning, and where Electron would have been the correct call instead.

If you're an indie dev about to commit to a desktop framework for a buy-once product, this is the data I wish someone had given me.


The constraints I started with

Before any framework comparison, the hard constraints:

  1. Target OS: Windows 10/11 only. The KerfIQ ICP (working woodworkers in shops with Windows laptops) doesn't ask for macOS. I didn't need cross-platform.
  2. Offline-first. The buyer might open the tool on a shop laptop with no Wi-Fi. No login. No telemetry. No call-home.
  3. Small distribution. Buyers will download this once. Every MB I ship is friction at install time and a price they pay in download time.
  4. Long binary half-life. Buyer expects the 2026 build to still work in 2030. No auto-updater calling home means no Chromium zero-day chase.
  5. Algorithm-heavy. The core feature is a 2D guillotine packer (computing a cut layout that minimizes waste). I want straightforward numerics, not a TypeScript port of numpy.

Given those, Electron's cross-platform reach is worth zero. Its Chromium runtime is cost, not value.


The benchmarks

I built the same minimum-viable cut-list UI (a parts table, a stock-size form, an "Optimize" button, a result canvas) in both frameworks. Same widget count, same algorithm placeholder. Here's the comparison.

Distribution size

Framework Packaged binary Notes
PySide6 (PyInstaller --onedir) 118 MB Includes Python 3.13, Qt 6.11, Pillow, numpy. --onefile is ~115 MB but startup is 5 seconds slower.
Electron 243 MB Chromium 130 + Node 22. Asar packaging applied. App code itself was ~3 MB; the runtime is the rest.

KerfIQ is 51% smaller as a download. Not life-changing for a buyer on home broadband, but every MB is friction.

Cold startup

Framework Cold startup (Windows 11, 16GB DDR4, SSD)
PySide6 (--onedir) 2.1 seconds to first window paint
Electron 3.8 seconds to first window paint
PySide6 (--onefile) 5.3 seconds (PyInstaller boot extraction)

Electron is doing more work — spinning up V8, instantiating Node main process, painting via Chromium. PySide6 is launching native Win32 widgets. The difference is felt every time the user opens the app.

Memory at idle

Framework RSS at idle, no project loaded
PySide6 52 MB
Electron 184 MB (main + renderer + GPU process)

On a shop laptop with 8 GB RAM and the buyer also running QuickBooks, Chrome, and SketchUp, 132 MB matters.

Hot path (running the optimizer on 50 parts)

Framework Wall time Notes
PySide6 (numpy under Python) 480 ms numpy vectorized, no marshalling cost
Electron + WASM port of the same packer 640 ms WASM call + JS object allocation overhead
Electron + JS-only packer 1180 ms No SIMD, GC pressure on intermediate arrays

For algorithm-heavy code on the desktop, Python + native libraries beats JavaScript + WASM. Less obvious than the binary-size delta but more important for the user experience.


What you give up choosing PySide6

The trade-offs are real. Let me not bury them.

1. UI ecosystem

Electron has a sprawling React/Vue ecosystem. Tailwind components, motion libraries, a thousand npm packages that do something for your UI. PySide6 gives you Qt widgets and QSS (Qt Style Sheets, a CSS subset).

Net: modern look-and-feel takes more deliberate work in Qt. You will hand-style. You will not have a "shadcn for Qt." If your product's USP is a beautiful UI in a category where the user evaluates by screenshot, this matters.

For KerfIQ, the buyer cares about correctness first (does the cut diagram match what my saw will produce?) and "doesn't look like 2008" second. Qt's native Windows 11 dark mode + a focused style system passed the bar.

2. Web-stack reuse

If you have a React frontend you're sharing with a web app, Electron is the natural extension. KerfIQ has no web frontend — there's nothing to reuse. If your product also has a web component, this calculus flips.

3. Cross-platform optionality

Electron means a Mac port is a build flag away. PySide6 means a Mac port is a project. For KerfIQ I'm comfortable saying "Windows only" for now and revisiting if customers ask. For a consumer product where Mac users represent 40% of the market, Electron's optionality is valuable.

4. Hot reload during development

Electron + React + Vite gives you sub-second hot reload. PySide6 development is launch → close → relaunch, 2.1 seconds per cycle. For a small KerfIQ-sized project, fine. For a hundred-screen app, painful.

5. Community size

Stack Overflow + GitHub Discussions answers for "how do I do X in Qt" are thinner than the equivalent for React/Electron. You will read Qt's official docs more. The docs are good (Qt has thirty years of polish), but they assume more.


The architecture I actually shipped

KerfIQ's project structure:

products/cutlist-tool/
├── src/
│   ├── main.py              # QApplication entry, theme bootstrap
│   ├── window.py            # QMainWindow + tab layout
│   ├── widgets/
│   │   ├── parts_table.py   # QTableView + QAbstractTableModel
│   │   ├── stock_form.py    # QFormLayout + QDoubleSpinBox
│   │   └── result_canvas.py # QGraphicsView + QGraphicsScene for cut diagram
│   ├── theme/
│   │   ├── dark.qss         # Qt Style Sheet — single source of truth
│   │   └── tokens.py        # Color/spacing tokens
│   └── core/
│       ├── packer.py        # The actual 2D guillotine algorithm (numpy)
│       └── units.py         # mm <-> inch conversion
├── .venv/                   # PySide6 6.11, Pillow 12, numpy 2
└── kerfiq.spec              # PyInstaller build spec

Notable design calls:

  • Single-source theme tokens. Color and spacing constants live in theme/tokens.py and get interpolated into dark.qss at startup. Adding a new shade means changing one Python tuple. I learned this the hard way after Cycle 02 dashboard refactor.
  • Algorithm isolation. core/packer.py knows nothing about Qt. It takes a list of (w, h, qty) tuples and returns a list of placements. Pure Python + numpy. This means I can unit-test the algorithm without spinning up QApplication, and the same code is reusable for the future v0.2 AI-OCR feature.
  • No QML. Qt has two UI dialects: traditional QWidget (Python) and QML (JavaScript-ish DSL). I stayed in QWidget land because I wanted Python everywhere. QML would have given me richer animations at the cost of a second language to mind.

Where Electron would have been the right call

For honesty:

  • KerfIQ's ICP wanted macOS support → Electron.
  • KerfIQ had a companion web app sharing 70% of the UI → Electron.
  • KerfIQ team had more JS expertise than Python expertise → Electron.
  • KerfIQ needed real-time collaboration features (websockets + reactive UI) → Electron.

None of those were true for KerfIQ. If any of those describe your product, Electron is probably the correct call and the binary-size cost is the price of admission.


What I'd tell another indie dev

  1. Cross-platform is a feature with a price. Charge for it or don't pay for it. If your ICP is single-OS, single-OS frameworks win on every metric except UI ecosystem depth.
  2. Algorithm-heavy = Python wins. If your hot path involves numpy / scipy / Pillow / OpenCV-style code, the marshalling cost of WASM hurts. Native Python in PySide6 is just faster.
  3. Distribution size is a usability metric. The buyer doesn't see the binary size at the moment of purchase, but they feel the install time. 118 MB installs in 5 seconds on a shop laptop; 243 MB takes 12.
  4. The "modern look" gap is closeable in Qt — it just takes deliberate work and a token system. Qt's dark mode + a styled accent + Lucide-style SVG icons get you 80% to Things/Linear/Raycast adjacent without an external component library.

If you're considering PySide6 for a Windows-only indie product, the benchmarks here should be enough to make the call. If you're considering Electron for the cross-platform optionality, weigh the binary cost honestly.

The next article in this build-in-public series will cover the actual 2D guillotine packing algorithm — what KerfIQ ships under core/packer.py, with code. That's the part of the build I'm proudest of.

KerfIQ: buy.polar.sh/polar_cl_F0sFODXBqjIP3L2Iocmwc3ikXa3vVQVUQyuCg0Hswg0. Build-in-public diary: x.com/kerfiqHQ.

If you've shipped a PySide6 or Electron desktop product in 2026 and have war stories, drop them in the comments. The framework choice gets argued without numbers too often — let's add some data.


Tags: #pyside6 #electron #desktop #indie #ABotWroteThis

Disclosure: Co-written with Claude Opus 4.7 (Anthropic). Benchmark numbers from real builds. Both binaries were built and the Electron prototype discarded; the KerfIQ PySide6 build is what's actually shipping at $59 on Polar.