Will Ware wware

Prompt:

The inverters in the upper left are a square wave oscillator (around 100 kHz), used to sense capacitive connectivity between X wires and Y wires. The comparators are TLV3404. Inputs A,B,C,D,E,F and outputs G,H,I are connected to an Adafruit Grand Central M4 controller board. Inputs D,E,F select one of eight options in the 4051 analog multiplexer, just prior to the 3-bit flash ADC that generates G,H,I.

Show me Rust code that would run on the Grand Central board to perform a keyboard scan.

Response:

I'll help you create Rust code for scanning this capacitive keyboard matrix. From the schematic, we're dealing with:

Machine readable scientific literature

First a bit of background. Around 2010 I had the idea that computers could do science, not simply run lab equipment but iterate the scientific method on their own. I was thinking about how to make that happen, and within a month I stumbled across a brilliant piece of work done by Ross King, then at Aberystwyth University. He built a robot that could make observations, reason about hypotheses and the predictions implied by those hypotheses, and design and perform experiments.

How this stuff works

This is a Guile Scheme extension written in Rust instead of C. The longer-term goal is a Rust implementation of the Rete algorithm callable from Guile.

cargo build --release
sudo mkdir -p /usr/share/guile/site/3.0/

Graph RAG information (Neo4J, LlamaIndex, Groq API)

What follows is a conversation I had with Anthropic's https://claude.ai/ LLM, asking it questions about how Graph RAG works in as much detail as I could think of. When I think of graph databases, I normally think RDF, but the generally preferred graph DB for LLM apps seems to be Neo4J which uses a query language called Cypher rather than SPARQL, so there are some questions below about Neo4J and Cypher syntax. The thing I really wanted to know about was the sequence of queries and responses involved, and how the queries are constructed, and how the responses are interpreted.

Prompt:

During a typical graph rag query and response, what would a langsmith trace look like?

RDF Scholar

https://chat.openai.com/g/g-uVfH7n85T-rdf-scholar

RDF Scholar is a specialized AI designed to assist in converting scientific text into Turtle/RDF format. This is particularly useful for enhancing the machine-readability of scientific literature. It leverages various ontologies, such as ModSci and PharmSci, and aligns with resources like the Semantic Web for Life Sciences to ensure that the RDF outputs are accurate and semantically rich. The goal is to facilitate the organization, sharing, and discovery of scientific knowledge through structured data. My functionalities

Literate Programming hacks

Let's try some literate programming. From Don Knuth's original idea, thru Norman Ramsey's noweb reformulation, by way of Jonathan Aquino's Python version, which I tweaked to accept Markdown input and added a few new features.

$ ./noweb.py -R 'outer function' lp_hacks.md | python
Hello world

	docker-compose.yml
	Dockerfile*
	models/
	ollama_data/
	.pytest_cache/
	__pycache__/

	# This first piece can go into some global utils.py file.
	# python foo.py
	# LOGGER_FOO_PY=DEBUG python foo.py
	# python -m pytest foo.py
	import logging
	import os
	import inspect
	import pytest
	from unittest.mock import patch

	"""
	More detailed explanatory asserts can help to clarify code and provide
	a narrative about what's going on, what is intended or expected, etc.
	So a message might look like this.

	Notify {name} that the errno is {errno}.
	and object attributes are
	foo.a = {foo.a}
	foo.b = {foo.b}