PeaZip 11.0.0 is ready for download, read the full change log! https://peazip.github.io/changelog.html

WHAT IS PEAZIP

PeaZip is an Open Source, cross-platform (BSD, Linux, macOS, Windows) archive manager and file manager utility, written with Lazarus / FreePascal IDE, which works as a command line scripts generation engine for 7z/p7zip, Brotli, Zpaq, Zstd and other open source archiving and compression tools.

This allows either to use PeaZip as an interactive GUI application, or to save tasks as batch CLI scripts for later use - for fine tuning beyond GUI's capabilities, learning the syntax, or re-use and automation purposes.

WHAT'S NEW IN THIS RELEASE

Major release 11.0.0 consolidates the 10.x line evolution with bugfixes, code cleanup, and file / archive manager improvements.

The new release speeds up archive browsing, enhances Bookmarks and internal drag and drop (it is now possible to drag&drop items to Tabs and Breadcrumb bar), improves zoom and fractional scaling, supports alternative rendering styles for icons, adds new functions for batch archives testing and password entropy rating.

Backend are updated to 7z/p7zip 26.00 and Pea 1.30.

NOTES

Sources are compiled with Lazarus 4.x, and are still compatible with Lazarus 3.x and 2.x; please note that for building the app it is necessary to add "metadarkstyle" package to the IDE before compiling "peazip" and "pea" binaries, which can be scripted as:

lazbuild --add-package (peazip sources path)/dev/metadarkstyle/metadarkstyle.lpk

PeaZip 11 running on Linux in dark mode with Sharp rendering style, showing in-app drag&drop from file browser to address breadcrumb - Tabs and items in the left navigation bar are supported as well as drop destinations.

The problem: I have AuDHD (Autism + ADHD) and executive dysfunction. Node.js toolchains - `npm install`, broken lockfiles, PATH issues - are a cognitive load wall I hit before I can write a single line of code. I needed an IDE that was just there.

What I built: EDE (Everything Development Environment) - a browser-native IDE that runs entirely in a service worker. No npm. No terminal. No installation. Open the URL, write code, it runs.

Features:

- JSX transpilation in-browser (Babel standalone)

- Offline-first via service worker

- Spoon tracking (Q Distribution - chronic illness energy model)

- Samson's Law entropy linter (flags cognitive overload patterns in code structure)

- Atkinson Hyperlegible + JetBrains Mono - accessibility-first typography

Try it: https://p31ca.org/ede

Source: https://github.com/p31labs/andromeda (monorepo - EDE is in `04_SOFTWARE/p31ca/public/ede.html`)

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EDE is one of 12 deployed products from P31 Labs - a Georgia nonprofit I incorporated April 3, 2026 to build open-source assistive tech for neurodivergent individuals. Everything runs on Cloudflare's free tier. $0 to users. No accounts required for core function.

Three Zenodo publications (DOIs: 10.5281/zenodo.18627420, 10.5281/zenodo.19411363, 10.5281/zenodo.19416491)

670+ automated tests total. Solo founder. Built while fighting to stay housed.

Happy to answer questions about the browser-native approach, the service worker architecture, or the accessibility design decisions.

Been working on a small tool to record terminal sessions and replay them step by step.

It captures commands, outputs, and exit codes so you can reproduce what actually happened instead and share it with out relying on shell history / docs / logs / screen recordings.

Built this mainly after running into issues where I couldn’t reproduce something I fixed earlier.

Would love discussion / feedback!

BunkerM Now Supports Local LLM via LM Studio

Your Mosquitto MQTT broker now has a built-in AI assistant that runs entirely on your own hardware. Connect BunkerM to any model loaded in LM Studio and control your entire IoT setup with plain English, no internet connection required, no data ever leaving your network.

Your MQTT broker now has a built-in AI assistant that runs entirely on your own hardware. Connect BunkerM to any model loaded in LM Studio and control your entire IoT setup with plain English, no internet connection required, no data ever leaving your network.

Why This Matters

Until now, BunkerM's AI features required a BunkerAI Cloud subscription. That works well for most users, but a growing number of deployments cannot send data outside the network, whether due to compliance requirements, limited connectivity, or a preference for keeping infrastructure fully self-contained.

Local LLM mode solves this by routing all AI requests to a model running on your own machine via LM Studio, an open-source desktop app that runs models locally. BunkerM injects live broker context into every request, so the model knows your connected clients, active topics, latest payloads, and statistics, and can act on them directly.

What It Can Do

The local AI has the same execution capabilities as the cloud version for web chat. You can ask it to create clients, publish messages, delete devices, and query live broker state, all in plain English. A few examples:

• "Create 10 sensor clients with random credentials" produces 10 real entries in Mosquitto's dynamic security immediately.
• "What is the current value of home/sensor/temperature?" reads the actual retained payload and returns it.
• "Turn off the conveyor belt" publishes the correct stop payload to the right topic, based on your topic annotations.

The model receives a fresh snapshot of your broker on every message. There is no stale cache. It sees what your broker sees, right now.

I was working on a project where I was relying on a bunch of SKILL files, and I wanted to recommend them to all the contributors I work with. I needed something like Node.js's package-lock.json or VS Code'S .vscode/extensions.json, something minimal that doesn't collect analytics or usage data.

So I created Skilleton:

• Repository: https://github.com/Fcmam5/skilleton
• NPM: https://www.npmjs.com/package/skilleton

If you want to contribute, please feel free to file an issue or tackle one of the existing ones.