How did you sort out mapping python constructs to their semantic equivalents?

I hope you keep at this, you may be in the right place at the right time.

It's getting to the point where some of the LLMs are immediately just giving me answers in Python, which is a strong indication of what the future will look like with Agents.

My only gripe with this is that there's alread a term in computer science for this concept of "design by contract".

It's called a type system.

What you need for this is a more expressive type system.

Thank you to everyone for participating in the discussion and for your overall support! As I said, I didn't expect this. I'm always just an email or tweet away, so you know how to reach me. It was great talking to you all!

I love the symbol LLM first approaches.

I built a version of this a few years ago as a LISP

https://github.com/nbardy/SynesthesiaLisp

great job! it reminds me genaiscript. https://microsoft.github.io/genaiscript/

// read files

const file = await workspace.readText("data.txt");

// include the file

content in the prompt in a context-friendly way def("DATA", file);

// the task

$`Analyze DATA and extract data in JSON in data.json.`;

We've been working on some exciting things with SymbolicAI and here a few things which might interest the HN community.

Two years ago, we built a benchmark to evaluate multistep reasoning, tool use, and logical capabilities in language models. It includes a quality measure to assess performance and is built on a plugin system we developed for SymbolicAI.

- Benchmark & Plugin System: https://github.com/ExtensityAI/benchmark

- Example Eval: https://github.com/ExtensityAI/benchmark/blob/main/src/evals...

We've also implemented some interesting concepts in our framework: - C#-style Extension Methods in Python: Using GlobalSymbolPrimitive to extend functionalities.

    - https://github.com/ExtensityAI/benchmark/blob/main/src/func.py#L146

- Symbolic <> Sub-symbolic Conversion: And using this for quality metrics, like a reward signal from the path integral of multistep generations. - https://github.com/ExtensityAI/benchmark/blob/main/src/func....

For fun, we integrated LLM-based tools into a customizable shell. Check out the Rick & Morty-styled rickshell:

- RickShell: https://github.com/ExtensityAI/rickshell

We were also among the first to generate a full research paper from a single prompt and continue to push the boundaries of AI-generated research:

- End-to-End Paper Generation (Examples): https://drive.google.com/drive/folders/1vUg2Y7TgZRRiaPzC83pQ...

- Recent AI Research Generation:

    - Three-Body Problem: https://github.com/ExtensityAI/three-body_problem  

    - Primality Test: https://github.com/ExtensityAI/primality_test 

    - Twitter/X Post: https://x.com/DinuMariusC/status/1915521724092743997 

Finally, for those interested in building similar services, we've had an open-source, MCP-like API endpoint service available for over a year:

- SymbolicAI API: https://github.com/ExtensityAI/symbolicai/blob/main/symai/en...

I already created a neurosymbolic code generator. Based on bench marks it received a score of 99 percent.

not to be confused with symbolica.ai

this works like functional programming where every symbol is a pure value and operations compose into clean, traceable flows. when you hit an ambiguous step, the model steps in. just like IO in FP, the generative call is treated as a scoped side effect. this can engage your reasoning graph stays deterministic by default and only defers to the model when needed. crazy demo though, love it