Apple recently released https://github.com/apple/container, a CLI tool for running Linux containers natively on macOS with Apple Silicon. It's fast and lightweight, but it's terminal-only.

I built Container GUI — a native SwiftUI app that wraps the CLI and gives you a proper desktop interface for managing containers, images, volumes, networks, and more.

Features: - Containers: list, inspect, start/stop/kill/delete, live log streaming - Images: browse, inspect (config, platform, history), pull with streaming progress - Volumes & Networks: full CRUD - Builder & System: status, start/stop services, disk usage

  Tech: Pure SwiftUI + AppKit (for the log viewer), MVVM with @Observable, Swift 6 strict concurrency, zero dependencies. Wraps the CLI via Foundation.Process.

Install: brew tap FeSens/tap && brew install container-gui

Would love feedback!

Its based on the latest political and economic events. I think it may be better than many recent sets.

We are building lovable for enterprise software, and we are genuinely impressed with what it can do in a single prompt. Authentication and persistence comes out of the box.

This was mostly vibe coded with grok and o3 in two hours.

The main advantage of using a new and constant token for reasoning is that, while we would pay the full price during training, in the inference phase, we could do most, if not all, the "reasoning" in one shot, without having to feed one generation token at a time.

Cool!

Reasoning 1 vs. 3 is the number of reasoning tokens between each "text" token. The 1 reasoning token is exactly what you see in the picture explanation in the article.

The generalization comes from making the network predict a <"start reasoning token"> and end the sequence only when it predicts a <"end reasoning token">. The training dataset for the upcoming experiment contains examples like: """ Q: What is 3+2? A: 3 + 2 is equal to <start reasoning> <reasoning> ... <reasoning> <end reasoning> 5 """

Wasting two tokens on start/end reasoning seems expensive to me (a priori)

I am curious what that would yield though - in some ways that would be the most fun to analyze (when does it think a lot??)

I would also be curious to see at what point you see diminishing returns from reasoning tokens (eg a 1:10 ratio? More?)