11–20 of 66 posts
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
This is the voodoo that excites me. Examples I found interesting: Semantic map lambdas S = Symbol(['apple', 'banana', 'cherry', 'cat', 'dog']) print(S.map('convert all fruits to vegetables')) # => ['carrot', 'broccoli', 'spinach', 'cat', 'dog'] comparison parameterized by context # Contextual greeting comparison greeting = Symbol('Hello, good morning!') similar_greeting = 'Hi there, good day!' # Compare with specific…
That's gonna be a very, very, long answer. What's funny is that not much changed since 2022 (eoy) when the projected started; the models just got better, but we had a good chunk of the primitives since gpt-3. What's more recent is the DbC contribution which I think is unique. It literally solved anything agent related I've thrown at it -- especially because I can chain contracts together and the guardrails propagate…
"The scope of contracts extends beyond basic validation. One key observation is that a contract is considered fulfilled if both the LLM’s input and output are successfully validated against their specifications. This leads to a deep implication: if two different agents satisfy the same contract, they are functionally equivalent, at least with respect to that specific contract.
This concept of functional equivalence through contracts opens up promising opportunities. In principle, you could replace one LLM with another, or even substitute an LLM with a rule-based system, and as long as both satisfy the same contract, your application should continue functioning correctly. This creates a level of abstraction that shields higher-level components from the implementation details of underlying models."
// 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.`;
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.`;
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.
https://deepwiki.com/dubprime/mythral/3.2-genome-system
Or feel Emotion? https://deepwiki.com/search/how-do-emotives-work_193cb616-54...
Have you read Marvin Minsky’s Society of Mind?
Nice! But have you considered a Neurosymbolic AI that can Evolve? https://deepwiki.com/dubprime/mythral/3.2-genome-system Or feel Emotion? https://deepwiki.com/search/how-do-emotives-work_193cb616-54... Have you read Marvin Minsky’s Society of Mind?
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.`;
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.`;
Thank you! I'm not familiar with that project, will take a look
https://reference.wolfram.com/language/guide/FreeFormAndExte...
It can (in theory) do very similar things, where natural-language input is a first class citizen of the language and can operate on other objects. The whole thing came out almost a decade before LLMs, I'm surprised that they haven't revamped it to make it really shine.
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
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
- SymbolicAI API: https://github.com/ExtensityAI/symbolicai/blob/main/symai/en...