Scanning mailbox, reading and classifying emails. Scanning a knowledge base, reading and improving individual articles, reading support interactions and creating summaries, checking and researching new leads that signed up. So much that is possible.
On paper the M4 should be roughly 1/3 of the M5, in practice it is only 1/2. With the right, optimized model like qwen3.6 35B MoE MLX you can get over 40 tok / sec on it. I run dozens of background jobs that are not time-critical on it.
I am not going to flag you, I am much OK with having good arguments.
I just purchased a Mac Mini M4 Pro 64GB for $3k - 2nd hand of course.
I am not a hater of Nvidia and I am planning on building a workstation based on RTX cards. You clearly do not seem to understand how convenient the MacMini actually IS - the form factor, how quiet it is, how durable it is, how well it integrates with other Macs, how well it works as a bridge to a personal agent like Hermes (integration with iMessage, Calendar, Reminders, iCloud, etc).
I am pretty sure I know a thing or two about computing, I have been in the trenches for many, many years and I have had machines of all kinds, shapes and colors. It just so happens that Macs are very capable, very convenient machines that happen to work great in the era of LLMs, too.
I disagree LAN connection is the bottleneck. I do even work with it remotely via Tailscale on shaky hotel WIFI and it works fine (or as fine as any other API-based model).
Just buy a Mac Mini really is good advice if you want to get into real, always-on convenient agentic work.
Soon it is going to be good even for coding using local LLMs. Until then, just run API models on it for coding, local LLMs for "knowledge" work or daily driver agent like Hermes.
My problem is I won't accept anything lower than the 96GB the RTX Pro 6000 Blackwell has. My dream is a workstation with 2x Pro 6000 to run DeepSeek v4 Flash comfortably, possibly qwen 3.6 / ornith on turbo speed.
But man, I have never purchased a computer which is more expensive than a decent family car.
Ballpark 25-30 tok / sec on the Mac Mini Pro M4 + qwen3.6 35B. The generation itself is good, prefill is known to be slow on any Apple M-chip architecture. It is really decent.
M5 Max. But I also have a MacMini M4 Pro 64GB. Qwen3.6 runs on the M4 just fine - sure the M5 is at least 2x the speed. If Apple launches a MacMini with an M5, I will be the 1st one to get it.
I love my MacBook Pro M5 128GB RAM and I love qwen3.6.
BUT DO NOT buy this MacBook if you plan on doing serious coding using local LLMs with it. The reason is simple: your fingers will burn and your head will explode from the noise.
Running any kind of sophisticated job on the very laptop you are using is just not viable. Sure you can use it in clamshell mode, but forget touching it while working with AI coding or agents.
If you want to run Qwen3.6 27B / 35B at its best, get a MacMini M4 with 64GB of RAM and put it in the basement - or at least a few meters from your desk. Connect to it over LAN or Tailscale. The MacMini will also cost you almost 1/3 of the MacBook Pro.
I have noticed that Opus and GPT 5.5 are very good at adjusting their thinking / reasoning intensity depending on the task at hand, something the open weights models are still not as good at.
In addition to that, some of the open weights models like GLM 5.2 or DeepSeek v4 Pro tend to be MUCH slower when generating tokens, which contributes to the perceived slowness. Although I wouldn't call models like GLM 5.2 slow by any means, e.g. it is currently one of the fastest models inside Notion today.
It is a combination of Hermes agent as the orchestrator and a custom extractor script (that uses qwen or any other LLM) that runs every 2 mins (on Mac via launchd). I had the code + skill written by Hermes.
The beauty of it is that Hermes itself has a cron too - every 4h Hermes will wake up and check if the email ingestion is working fine. If not, it will fix it. Funnily this is one of the most robust setups I've seen in a while, it is like having your own little DevOps waking up at night, fixing the infra when needed.
Nice. Are you working on it for "fun" or as a lab? I am looking into building a semi-professional cluster + setting up a lab, but the investment needed is beyond what I could justify as a hobby.
Yes, Brave search is one of these services I highly recommend paying for, the search they provide (similar to Exa, Tavily) is what makes an "OK LLM" become super smart.
10 years worth of Claude Max today. Also - Anthropic recently removed a model I relied on and isn't giving it back. As a non-US citizen, I would rather pay in advance but be sure, I will keep having access to inference on my own terms.