Fabion Kauker fhk

Setting Up a Physics and Electronics R&D Lab

A practitioner's guide based on experience setting up and operating sensor development and characterisation labs at CERN, LBNL, DESY, and the University of Bergen.

Author: Magne Lauritzen

Contact: magne.lauritzen@summacogni.com

The majority of this guide applies to any lab doing experimental physics, electronics development, or instrumentation work. Sensor-specific sections are clearly marked.

Solveit Reference

What is Solveit?

Solveit is a "Dialog Engineering" web application for interactive development. Unlike ChatGPT (pure chat) or Jupyter (pure code), Solveit combines three message types in one workspace: code execution, markdown notes, and AI prompts. Users build solutions incrementally, writing a few lines, understanding them, then continuing, rather than generating large code blocks.

The AI sees the full dialog context (code, outputs, notes, prompts) when responding, but only those ABOVE the current message. Users can edit any message at any time, including AI responses; the dialog is a living document, not an append-only log.

The dialog is a running ipykernel instance. A "dialog" is like a "Jupyter notebook", and uses a compatible ipynb file format, but provides a superset of functionality (in particular, "prompt messages"). A "message" is like a "Jupyter cell", with additional attributes stored as ipynb cell metadata. Most standard jupyter functionality is supported (including cel

Extracting financial disclosure reports and police blotter narratives using OpenAI's Structured Output

tl;dr this demo shows how to call OpenAI's gpt-4o-mini model, provide it with URL of a screenshot of a document, and extract data that follows a schema you define. The results are pretty solid even with little effort in defining the data — and no effort doing data prep. OpenAI's API could be a cost-efficient tool for large scale data gathering projects involving public documents.

OpenAI announced Structured Outputs for its API, a feature that allows users to specify the fields and schema of extracted data, and guarantees that the JSON output will follow that specification.

For example, given a Congressional financial disclosure report, with assets defined in a table like this:

	model: claude-opus-4-20250514
	messages:
	- role: user
	content:
	- type: text
	text: \|
	<system-reminder>
	As you answer the user's questions, you can use the following context:
	# important-instruction-reminders
	Do what has been asked; nothing more, nothing less.

	/*
	The SQL `VALUES` statement (in DuckDB)

	DuckDB version: 0.10.2

	Bill Wallis, 2024-05-21
	*/


	select version();

	import multiprocessing
	from pathlib import Path
	from queue import Queue
	from time import sleep
	from typing import Callable

	import pyarrow as pa
	import pyarrow.parquet as pq
	from tqdm import tqdm

	COPY (
	WITH a AS (
	SELECT h3_cell_to_parent(h3_string_to_h3(SUBSTR(id, 0, 17)), 2) h3_2,
	COUNT(*) num_recs
	FROM read_parquet('s3://overturemaps-us-west-2/release/2024-05-16-beta.0/theme=places/type=place/*.parquet',
	filename=true,
	hive_partitioning=1)
	GROUP BY 1
	)
	SELECT h3_cell_to_boundary_wkt(h3_2),

	#!/usr/bin/env python3

	import argparse
	import os
	from pmtiles.reader import MmapSource, Reader, all_tiles
	from pmtiles.writer import Writer
	from pmtiles.tile import Compression
	from pmtiles.tile import zxy_to_tileid
	from tqdm import tqdm
	def merge_pmtiles(input_dir: str, output_file: str) -> None:

	#!/usr/bin/env bash

	set -euxo pipefail

	if [ "$EUID" -ne 0 ]; then
	echo "Please run as root"
	exit 1
	fi

	if ! command nvcc --version >/dev/null 2>&1; then