mmargenot

Many Qwen model weights are licensed with Apache 2.0 (though unfortunately not the latest releases). Unfortunately open-weight is generally the term being used for weights-available, so the battle tends to be on whether the models are "open source", i.e. whether they release training code and data alongside the weights.

You’re trying to get calibrated confidence, otherwise it’s less effective than you’d hope.

This approach with more data for calibration is basically the beginning of making a decision model like Jev.

Very fun to see moltbook but trying to be actually productive.

Are there any agent-sourced or agent-proposed problems or is it primarily the moderation staff that directs the pillars of agent “attention”?

The existing state is definitely couched in using robust seed data and spinning it into magnitudes more tokens. I'm not sure what a world that isn't 100% grounded in objective human-written truth looks like, but I suspect the next step is generating synthetic data from synthetic data that has been blessed by human beings, and moving yet further away from that with more sampling and evaluation.

There are some things that are true (I hope), so using those concepts, approximated by a human text filter, as seed seems reasonable. Hopefully the diversity in sampling methodologies and opinions about what makes a meaty token pays some dividends down the line for model diversity.

Did something change with it for depth of integration? Goose has been able to use local models via OpenAI compatible endpoints for at least a year

This was great! When you think about optimizing prompts with GEPA (or comparable methods and tools), do you consider each tool or skill separately? How do you think about the optimization of the system prompt for a large agentic system?

I imagine that you do a collection of passes to cover each overlapping set of what you want evaluated, but the system prompt makes all cases dependent on each other.

What I’ve done in the past is use the system prompt to extract subjective criteria for an LLM judge (like various system prompt statements that contribute to brand voice) and check individual traces with that for evaluation, but I’d like to move beyond including that in a prompt at all.

What I prefer is to be able to iterate and iterate on my spec, then implement once. That’s what I’ve come to as a process with CC, and I’ve gotten really good results. Whenever I spend less than 20-30 minutes actively discussing the spec with the agent, the quality of implementation rapidly drops, even with some tiny things.

I like the overall idea of this, but the writing of the spec to me is more like building the thing than the implementation itself, and needs more active human (and agent) cycles.

basically, yeah. wild flash in the pan, but i think it's cute in that it's signaling reduced down to the absolute bare bones.

not a lot of horror assets either unfortunately. i wanted ghosts and ghouls and zombie tiki with my pirates but got industrial wasteland and candyland.

A key part of maintaining a second brain is revisiting and pruning it. Much like a todo list, without that practice it absolutely just accumulates bunk.

And you get bm25 for free with so many modern setups! I do still love to experiment with tuning semantic search for your specific corpus via various kinds of embeddings, but bm25 is hard to beat.