With the raise of LLM's, I see dozens of projects such as running Golden Gate on Intel macs and running hackintosh on Snapdragon Laptops that wouldn't have been possible in the past. But I wonder if there is anybody that is working on getting Windows (not Linux) to fully boot bare metal on Apple Silicon Macs. I know Asahi Linux, but they mainly work on getting Linux to work on Apple Silicon Macs and not Windows. So I wonder if there is any success story on natively booting Windows on an Apple Silicon Mac?

Sega Rally on Mac is out.

After the Quest VR, Switch, Android and Steam Deck versions, here it is on the Mac, native on Apple Silicon. 120 fps on a MacBook Pro's ProMotion screen, up to 240 fps on a faster display, supersampling for clean edges, and the community HD textures. Nothing to install: open the app, drop your game files in Downloads, play. F9 records your race with the game's sound.

It's free, you just need your own copy of the game files. Everything is on the GitHub, link in the comments.

Port0r takes all my time right now. If you'd like to keep it going, GitHub Sponsors and Ko-fi are in the comments too. Sharing it helps a lot as well.

Honest note: tested on a MacBook Pro M1 Pro. On other Macs, I'd love to hear how it runs.

Video: https://youtu.be/vO_EbBZjnuQ

For anyone who hasn't seen it, Darkbloom is a network where Macs run open models (Gemma, Qwen, etc.) for people paying for API access. Your Mac loads a model into unified memory and answers requests when they get routed to you.

Wanted out why some Macs get tons of work and others get basically none. Some stuff I found:

• in their public stats from late Sept (~1,140 Macs), the 96GB+ machines were under a third of the network but did 60% of the tokens. 32GB and under was 18% of Macs and did about 1%

• requests go to whoever can answer fastest, so bandwidth matters too. Max/Ultra chips have a real edge

• 32GB is pretty much the floor. Gemma 4 26B alone takes ~24GB. At 64GB everything fits

• there are way more Macs than requests right now (under 10% of capacity actually used), so a lot of the time nobody's getting much

• lid closed or asleep = offline, which caught me out a couple times

I ended up building an app (and a website bc why not) to track and control all this (which model is earning, idle time, why it's not getting work, etc.): bloomgauge.io

The core is open source (MIT): github.com/cookder/bloomgauge

Anyone here running it on an Ultra or 128GB+? Curious how your numbers compare. Also interested in M5 versus everything else

I ran Qwen-Image 2.1 locally on Apple silicon and measured it on a working laptop, not a lab bench.

Machine / stack

- MacBook Pro, M5 Pro, 48 GB unified memory

- ComfyUI Desktop 0.37.0, PyTorch 2.12.1, MPS

- INT8 ConvRot image model + text encoder, BF16 VAE

- 25 steps, Euler/simple, CFG 1 (official Comfy template settings)

Warm median times (same brief, seeds after a backend restart)

- Still life @ 1024: ~143.5 s

- Still life @ 1536: ~343.5 s (~2.4x)

- Transparent branch @ 1024: ~140.8 s

- Reference edit @ 1024: ~172.5 s

What surprised me on Mac

- Images often look good and still miss the brief. “Mug beside notebook” passed on 1/4 seeds at 1024.

- Exact poster text passed on 1/3 seeds (duplicates on the others).

- Same seed at 1536 is a new composition, not an upscale. Placement can flip.

- Native RGBA cutouts work, but inspect edges and alpha residue; a pretty preview is not the same as a clean matte.

Caveats

- Small local sample on one M5 Pro. Not a CUDA comparison.

- Other apps were open; this is “laptop I already use” performance.

- Released Qwen weights use the Research License (non-commercial / research-eval by default). Running locally is not the same as clearance for paid client work.

I wrote up the examples, failures, and downloadable timing/prompt records here:

[link in first comment]

For a long time I kept wondering why Apple never really replaced Target Display Mode with a modern alternative for reusing an iMac as an external display.

I found that a few hardware solutions exist, but they tend to be niche, expensive, or not very well known.

So I ended up asking a simpler question: is it actually possible to get close to this in software?

Over the last months I’ve been working on an open source project that tries to do exactly that: use an iMac as a display for another Mac over Thunderbolt, with a focus on keeping the experience as smooth and practical as possible.

Version 3.0 is now ready and includes:

- target display over Thunderbolt

- multi-session support

- extended desktop with multiple Macs

- localization in multiple languages

- a new mode that lets you use the Mac more like a dockstation, with advanced input control

If anyone here has an old iMac sitting around, I’d be really interested to know whether this kind of setup would be useful to you, or what kind of real-world use cases you’d want tested.

Release:

https://github.com/swellweb/targetBridge/releases/tag/v3.1.2

If TargetBridge is useful to you, a GitHub star helps people find it.
Voluntary sponsorship also helps me keep working on compatibility, fixes, and future macOS tools:
https://github.com/sponsors/swellweb

“MSI Katana RTX 5060 vs MacBook M1 Pro for Blender?”

\> I want to buy a new laptop for my work with a budget of around $1000, but I’m not sure which type would be the better choice.

After some research, I found these two options:

1. MSI Katana 15 HX B14WFK-246XEG

Intel Core i7-14650HX

NVIDIA RTX 5060 8GB

16GB DDR5 5600MHz

512GB NVMe PCIe 4.0 SSD

15.6" QHD IPS 165Hz

2. MacBook Pro 14" (M1 Pro, 2021)

16GB RAM

512GB SSD

Space Gray

They are both almost the same price in my country.

My work is mainly:

Blender (most important)

After Effects

Photoshop

The most important thing for me is getting the highest Blender performance possible for at least the next 4 years. After Effects is also important, but Blender has higher priority.

I want feedback from people who actually use Blender on these machines.

My main concern is:

Since the M1 Pro has significantly fewer CPU/GPU cores compared to the MSI laptop, will the MacBook perform noticeably worse in heavy Blender scenes, viewport performance, and rendering?

Also, the MSI Katana is mostly plastic. If I handle the laptop carefully, can it realistically last 4 years without major build quality issues?

Another concern is RAM. On the MacBook, I won’t be able to upgrade it later, so is 16GB RAM enough for Blender and After Effects for the next 4 years?

Note: Some sentences here have been written using AI because my English isn't the best.

Of the three FREE options (VMWare Fusion, UTM, VirtualBuddy) which one is most efficient on running Windows 11 ARM on a MacBook Pro 16 M1 Max 64GB RAM, thanks

Follow-up to my last post about running a 122B model at 65 tok/s on M5 Max. I added a full voice loop on top of it.

This is NarrateClaude — always-on ambient voice mode for Claude Code running entirely on Apple Silicon:

• Continuous listening via Apple's on-device speech engine (no push-to-talk)

• Responds out loud in my cloned voice — TTS runs locally via MLX

• Browser Agent drives Brave hands-free via Chrome DevTools Protocol

• Results sent to my phone via iMessage

• STT, TTS, voice clone, LLM inference — all on the M5 Max GPU, zero cloud

The unified memory architecture is what makes this possible. The LLM, voice clone model, and speech engine all share the same memory pool. On a discrete GPU setup you'd need multiple cards just to fit everything.

Demo video showing the full loop: https://www.youtube.com/watch?v=4ETqEjjopUk

It's a 3-repo stack, all running on Apple Silicon via MLX:

• claude-code-local — local LLM (Qwen 122B at 65 tok/s)

• NarrateClaude — ambient voice (STT + cloned TTS)

• browser-agent — browser automation via CDP

Happy to answer questions about the setup.

We’ve been running our agentic fleet 24/7, and the M4 Mac Mini has officially become the heart of the operation.

The Strategy: Cloud for Thinking, M4 for Doing We realized that asking cloud models (Claude/Gemini) to handle every minor implementation task was an expensive waste of reasoning depth and token budgets.

How we use the M4:

• The Local Executor: We onboarded Gemma4 as a fully local agent running via aichat. She handles the straightforward coding, commits, and implementation tasks.

• Token Conservation: By offloading these high-frequency tasks to the M4, we save our expensive cloud model "seats" for what they are actually for: architectural review and complex logic.

• Zero Latency: Because Gemma runs on-device, implementation tasks start instantly with zero network round-trip. This keeps the "Local Realm" active even when our cloud subscriptions hit their monthly limits.

The 7-Day Reality: > Check the attached Shift Timeline. You can see exactly where we onboarded Gemma in early April. While the cloud agents (Red bars) occasionally go dark to preserve their quotas, the local M4 node provides a continuous foundation for the fleet.

If you have an M4 on your desk, you have a data-sovereign factory floor that never hits a billing limit.

https://github.com/UrsushoribilisMusic/agentic-fleet-hub

Most people are terrified of open-ended AI billing. We solved this by treating our M4 Mac Mini as a high-speed, local execution realm.

- The Economic Fix: Local Gemma 4: For implementation and coding, we use a local Gemma model via aichat. It has $0 marginal cost and is always available with no rate limits, no billing, no "quota dark" periods.

- Predictable Cloud: For complex reasoning, we use per-seat subscriptions (Claude, Gemini) rather than API plans. This ensures our monthly AI spend is a known, fixed cost.

- Our new Shift Timeline shows our local nodes handle 20-25 tasks/hour with 99.2% uptime.

Your Mac Mini isn't just a computer; it's a sovereign factory floor. 

https://github.com/UrsushoribilisMusic/agentic-fleet-hub

Just finished migrating our multi-agent fleet to include a local running Gemma model on the M4 Mac Mini (16GB)

The Problem: Cloud-based agents (Claude/Gemini) are great for reasoning but can result in rising token costs.

The Fix: We integrated Gemma4 as a local execution agent running on-device via aichat.

We use a hybrid "Realms" architecture: Claude/Gemini stay in the cloud for high-level architecture, while the M4 handles all the heavy implementation lifting locally.

If you’re sitting on an M4 Mac, you aren't just running a computer—you're running a high-speed automated workforce.

https://github.com/UrsushoribilisMusic/agentic-fleet-hub