Tell that to my partner's grandfather, who managed to find and install malware chrome extensions on his chromebox.

    Prediction Stats:
      Stop Reason: eosFound
      Tokens/Second: 21.10
      Time to First Token: 1.827s
      Prompt Tokens: 42
      Predicted Tokens: 187
      Total Tokens: 229

Otherwise, if you have a GPU with more than like 4GB of VRAM, there are better models. Gemma4 and Qwen3.6 (or Qwen3.5 if you need the smaller dense models that haven't yet been released for 3.6) are a good place to start.

The geometric mean rule of thumb for MoE models is that the intelligence level of an MoE model with T total parameters and A active parameters is roughly equivalent to that of a dense model with sqrt(A*T) parameters. For qwen3.6-35B-A3B, that equivalent size is 10.24B, spitting distance of an 8B model. Good training can make up the 28% difference in size.

1. https://unsloth.ai/docs/models/gemma-4#llama.cpp-guide

For something paid at regular intervals like RSUs, you really should never be looking at the total value of the grant, and instead think of it in terms of how many shares per paycheck/month/quarter/year you vest.

If you've got a cliff coming up, that's different. I'd be pissed if a company laid me off 11.5 months into a 12 month cliff or a few weeks before an annual bonus and didn't accelerate the vesting / bonus.

I still maintain this is why macOS is the best OS for terminal work -- all the common keybindings for GUI tools use a different modifier key, so e.g. ⌘C and ⌘W work the same in your terminal as they do in your browser.

(Lots of the readline/emacs-style editing keybindings work everywhere in macos as well -- ^A, ^E, ^K, ^Y, but not ^U for some reason)

1. https://www.youtube.com/watch?v=UQuIVsNzqDk

but the expression given is sqrt((x-1)/(x+1))(x+1), which for x=2 would be sqrt(1/3)*3 = sqrt(3)

did you mean Sinh[ArcCosh[-2]]?

I haven't tried on a raspberry pi, but on Intel it uses a little less than 1s of CPU time per second of audio. Using https://github.com/NVIDIA-NeMo/NeMo/blob/main/examples/asr/a... for chunked streaming inference, it takes 6 cores to process audio ~5x faster than realtime. I expect with all cores on a Pi 4 or 5, you'd probably be able to at least keep up with realtime.

(Batch inference, where you give it the whole audio file up front, is slightly more efficient, since chunked streaming inference is basically running batch inference on overlapping windows of audio.)

EDIT: there are also the multitalker-parakeet-streaming-0.6b-v1 and nemotron-speech-streaming-en-0.6b models, which have similar resource requirements but are built for true streaming inference instead of chunked inference. In my tests, these are slightly less accurate. In particular, they seem to completely omit any sentence at the beginning or end of a stream that was partially cut off.

(In terms of intelligence, they tend to score similarly to a dense model that's as big as the geometric mean of the full model size and the active parameters, i.e. for GPT-OSS-20B, it's roughly as smart as a sqrt(20b*3.6b) ≈ 8.5b dense model, but produces tokens 2x faster.)

You're either underestimating how big cloud instances can get or overestimating how much it costs to rent a cloud instance that would beat an M1 Max at any multi-core processing.

According to Geekbench, the M1 Max macbook pro has a single-core performance of 2374 and multicore of 12257; AWS's c8i.4xlarge (16 vCPUs) has 2034 and 12807, so relatively equivalent.

That c8i.4xlarge would cost you $246/mo at current spot pricing of $0.3425/hr, which is, what, 20% of the cost of that M1 Max MBP?

As discussed recently in https://news.ycombinator.com/item?id=47291906, Geekbench is underestimating the multi-core performance of very large machines for parallelizable tasks -- the benchmark's performance peaks at around 12x single-core performance. (I might've picked a different benchmark but I couldn't find another benchmark that had results for both the M1 Max and the Xeon Scalable 6 family.)

If your tasks are _not_ like that, then even a mid-range cloud instance like a 64-vCPU c8i.16xlarge (which currently costs $0.95/hour on the spot market) will handily beat the M1 Max, by a factor of about 4. The largest cloud instances from AWS have 896 vCPUs, so I'd expect they'd outperform the M1 Max by about 50-to-1 for trivially parallelizable workloads. Even if you stay away from the exotic instances like the `u7i-12tb.224xlarge` and stick to the standard c/m/r families, the c8i.96xlarge has 384 vCPUs (so at least 24x the compute power of that M1 Max) and costs $3.76/hr.

As far as I understand, any reasoning tokens for previous answers are generally not kept in the context for follow-up questions, so the model can't even really introspect on its previous chain of thought.

    - translating (relatively) literally from one language to another would be ~1:1.
    - automatic spelling/grammar correction is ~1:1
    - Using an LLM to help you find a concise way of expressing what you mean, i.e. giving it extra content to help it suggest a way of phrasing something that has the connotation you want, would be <1:1

(expansion is perfectly fine in a coding context -- it often takes way fewer words to express what you want the program to do than the generated code will contain.)