Pier Carlo Cadoppi cad0p

I built a decorator with the memoize idea: it will take care of your system recursion limit, and avoid it by splitting calculation in batches that are as large as the system can offer. It will also of course remember all the values for easy retrieval

Short working version:

import sys
from math import floor, ceil

def memoize(f):
    memo = [None]


	README Claims Verification

	### Title/Intro Claims

	1. "完全本地化的 AI 编程助手" (Fully localized AI programming assistant)
	- TRUE: The project uses com.tencent.devex.aries:DevCopilot for AI chat, which is Tencent's local AI model. There's no API key configuration for external services. The settings panel
	shows LLM parameters (Temperature, TopK, TopP) but no API key settings.
	- Evidence: build.gradle.kts: devcopilotVersion=0.0.5.6-251103, implementation("com.tencent.devex.aries:DevCopilot:$devcopilotVersion"). The CloudSettings model has isLocalLLM field.
	2. "零配置" (Zero configuration)


	module TestExer-cad0p where

	import Exercise
	open Exercise

	--- PREREQUISITES -----

	-- You need to add this line at the top of Exercise.agda :

	import json
	import matplotlib.pyplot as plt


	def plot_polygon(coord):
	xs, ys = zip(*coord) # create lists of x and y values
	plt.figure()
	plt.plot(xs, ys)
	plt.show()

	with open('./TextSubstitutions.plist', 'rb') as _plist:
	with open('./TextSubstitutions.csv', 'w') as _csv:
	plistfile = plistlib.load(_plist)
	csvfile = csv.DictWriter(_csv, fieldnames=['shortcut', 'phrase'])
	csvfile.writeheader()
	for data in plistfile:
	csvfile.writerow(data)