kpw94

> they will use that lack of response to then go and replace American workers with what is effectively foreign indentured servants

Wait but PERM process is precisely for getting that foreign person outside of the temporary "effective indentured servant" phase. It's to get a green card... Once you have it you can leave the company and work anywhere.

Where it gets maybe abused is only India/China pipelines, where they're stuck in that GC backlog for years. so it makes them in this limbo of renewable H1B until the GC is granted, which might be decades out.

Interesting project.

2 gut feeling concerns:

- Strict subset of go might be confusing to an agent actually (trying to use unavailable go features)

- So -> c11 source to source compile loop might be confusing to agent: if So compiles to c11 does it guarantee c11 program compiles. If runtime exception (segfault etc), is it going to be easy for agent to map that back to original So code?

In the context of local LLMs on limited hardware I've ran to the exact same conclusion: "tok/s" isn't the most useful metric when my personal North star metric, given my fixed hardware is: Model smart enough to execute my goals _in the minimum amount of time_.

Some models I tried (Mistral I think) had better tok/s, and roughly same billion parameters / scores on various benchmark... But they were _so_ verbose, that they generated many more tokens compared to a Qwen model of same caliber to answer the same thing.

So even though it had better generated tok/s, because so many more were generated, the clock time was longer.

And this compounds over mutli-turns: more generated token means more context used in the next turn (until some compaction or something runs)

> What it does:

>

> --jinja for tool calling support

Pretty sure this flag hasn't done anything for a while. It's enabled by default since ~November of last year

The huge spike of "lk-99" in science & frontier tech is amusing...

This is cool concept, would love a positive/negative sentiment computed for each comment that refers to a given word, so you can see trends of "cloudflare (positive)" vs "cloudflare (negative)" where first one counts comments only if sentiment confidence is greater than say 0.6 and the other one counts comments only if sentiment is less than 0.4 (assuming [0,1] sentiment score)

Thanks! Super helpful.

I do use it the same way as you're describing on personal projects at home, in a very crude manner (pasting code snippets in llama server web UI prompt. Next will attempt OpenCode)

At work I use it in similar manner with more mature tools, but the vast majority of token spend comes from a totally different workflow: "pretend the AI is a fleet of junior/intern engineer you're delegating work to", where the agent will on its own do the implementation, commit the changes etc.

It does indeed spend a lot of tokens wandering the codebase, talking to MCPs, loading skills etc.

> About the generation speed: ~100-150 t/s on the RTX 5090 and ~40 t/s on the Mac

Curious if you can share the prefill speed too?

I run locally on a crappy desktop (some AMD iGPU with Vulkan llama.cpp, 32 GB DDR4 RAM) for experimentation. I get 15 tok/s on generation for the qwen & gemma4 MoE models. I get around 150 tok/s prefill speed.

Reason I'm asking about the prefill is looking at my stats at work, I use between 20M to peaks of 300M input tokens daily. Some of those token are cached but in general, I seem to have roughly 500x more input tokens than output. So interested in prefill tok/s stats.

Huge Thank you for llama.cpp btw!!