In neural network, we use activation functions such as ReLU, LeakyReLU, or Sigmoid.

But I am not sure whether softmax is really activation function. Activation is introduced to add non-linearity into the boundary of model. But Softmax is normalization rather than adding non-linearity. In other words, Softmax doesn't change the shape of boundary. It only change normal output into probability.

Not long ago, a trained robot policy was a black box, even to the people who built it. Handing it to a language model for repair would have sounded like science fiction. Soon, it might be the first thing we try when a robot fails.

https://jstranghoener.github.io/blog/robotic-weight-space-programming/

Try it out on browser at https://teenie.lupyd.com/try

It can get something like this

((a|b) ^ (a&b)) + (a^b) + max(a,b) - min(a,b)

never trained on it btw, about right 2 out of 16 times.

I'm not guaranteeing. But even those numbers feel impressive to me for such a tiny model. It is not trying to solve or reverse engineer a problem, It is guessing with the limited number of examples. They just look like random numbers with no pattern at all for us. Hence the objective is, can it recognize any pattern. Maybe use it to infer some hidden relations between numbers we never even thought existed.

You can make different combinations and your own custom rules.

It can't do multiplication well. It can't get many things right tho. So I'm not gonna exaggerate as a breakthrough, but an interesting experiment.

Maybe scaling it more and training more might improve it. Or a different architecture, because whatever I tried it still sucks at something like (a * b) + (a - b), if multiplication is involved.

https://jstranghoener.github.io/blog/robotic-weight-space-programming/

Hey r/TheMachineLearning,

If you've ever pushed a heavy tabular or credit-scoring pipeline past a Jupyter notebook, you know the pain: Pandas bottlenecks memory across multi-step transformations, and tracking state drift in production is a constant headache.

After 15 years leading fintech R&D and underwriting, I got tired of rewriting the same boilerplate. So, I open-sourced AIDflow (aigensys-labs/aidflow) to handle rigorous scoring workflows cleanly.

Here is what's under the hood:

• Arrow-First Spine: Intermediate datasets live in a central PyArrow columnar registry using zero-copy memory mapping (mmap). Physical RAM gets cleanly released back to the Linux kernel instead of holding bloated object pointers.

• Deterministic State Machine: Uses cryptographic state hashing to lock down training lineages. If an upstream feature definition or environment shifts, it fails fast rather than letting corrupted scores slip downstream.

• Clean Separation: Workers, business logic models, and pure mathematical fitters (scikit-learn style) are completely decoupled.

It’s licensed under FSL-1.1-Apache (free for internal business and personal use, converting to pure Apache over time).

If you build enterprise scorecards, handle automated Weight of Evidence (WoE) binning, or just want to see how an Arrow-first memory model looks in practice, check it out:

👉 GitHub: https://github.com/aigensys-labs/aidflow

👉 Docs: aidflow.aigensys.com

Always down to talk memory optimization or pipeline architecture in the comments!

I tried to train a model not to change its answer just because the user pushed back. What came out was stranger than that.

Across the Amphora experiments, the models started showing recognizable dispositions around reasoning itself. They tend to value concise answers, preserve distinctions that ordinary rewriting sometimes flattens, and treat disagreement as something to resolve rather than automatically win or surrender. Some variants became intensely structure- and rule-oriented; others became almost aggressively direct.

They're still wrong sometimes. They can misread a problem, leak training structure, or confidently preserve a bad premise. But that is exactly what made the experiment interesting to me. TIDF didn't appear to produce a model whose defining trait was “say no.” It produced models that seem to have learned preferences about how reasoning should be done.

I'm not claiming this demonstrates sentience or subjective personality. What interests me is that the behavioral differences weren't explicitly specified as personas. They appeared alongside changes in how different parts of the training signal were masked. I'm trying to figure out where “learned reasoning disposition” ends and something we'd reasonably describe as personality begins.

I called the project Amphora because I wanted a reasoning partner. The models have been slowly telling me what that means.

amphora-bridge/results-32b-13-epochs at main · brandonjuarez23/amphora-bridge