H. Chase Stevens hchasestevens

Operation	Input	Result	Notes
map	`F[A]` , `A => B`	`F[B]`	Functor
apply	`F[A]` , `F[A => B]`	`F[B]`	Applicative
(fa, fb, ...).mapN	`(F[A], F[B], ...)` , `(A, B, ...) => C`	`F[C]`	Applicative
(fa, fb, ...).tupled	`(F[A], F[B], ...)`	`F[(A, B, ...)]`	Applicative
flatMap	`F[A]` , `A => F[B]`	`F[B]`	Monad
traverse	`F[A]` , `A => G[B]`	`G[F[A]]`	Traversable; `fa.traverse(f)` == `fa.map(f).sequence`; "foreach with effects"
sequence	`F[G[A]]`	`G[F[A]]`	Same as `fga.traverse(identity)`
attempt	`F[A]`	`F[Either[E, A]]`	Given `ApplicativeError[F, E]`

How to recover lost Python source code if it's still resident in-memory

I screwed up using git ("git checkout --" on the wrong file) and managed to delete the code I had just written... but it was still running in a process in a docker container. Here's how I got it back, using https://pypi.python.org/pypi/pyrasite/ and https://pypi.python.org/pypi/uncompyle6

apt-get update && apt-get install gdb

Picking the right architecture = Picking the right battles + Managing trade-offs

User cases (description of sequences of events that, taken together, lead to a system doing something useful)
- Who is going to use it?
- How are they going to use it?

	# train_grpo.py
	#
	# See https://github.com/willccbb/verifiers for ongoing developments
	#
	"""
	citation:

	@misc{brown2025grpodemo,
	title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models},
	author={Brown, William},

	import torch
	from torchvision import models


	modules=list(models.resnet18(weights='IMAGENET1K_V1').children())[:-1]
	model = nn.Sequential(*modules)

	x = torch.rand(size=[1, 3, 640, 480])
	emb = model(x)

	from streamlit.report_thread import get_report_ctx
	from streamlit.hashing import _CodeHasher
	from streamlit.server.server import Server
	from prometheus_client.registry import REGISTRY
	from prometheus_client import Counter


	class _SessionState:
	def __init__(self, session, hash_funcs):
	"""Initialize SessionState instance."""

	import cv2, numpy as np, os

	#parameters
	working_dir = '/home/stephen/Desktop/keras_demo/'
	cap = cv2.VideoCapture(0)

	org, font, scale, color, thickness, linetype = (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (234,12,123), 2, cv2.LINE_AA
	#chromakey values
	h,s,v,h1,s1,v1 = 16,0,64,123,111,187 #green
	h,s,v,h1,s1,v1 = 0,74,53,68,181,157 #skin tone


	#Evolution Strategies with Keras
	#Based off of: https://blog.openai.com/evolution-strategies/
	#Implementation by: Nicholas Samoray

	#README
	#Meant to be run on a single machine
	#APPLY_BIAS is currently not working, keep to False
	#Solves Cartpole as-is in about 50 episodes
	#Solves BipedalWalker-v2 in about 1000

	from datetime import datetime, timedelta
	import networkx as nx

	from airflow import DAG
	from airflow.operators import BashOperator, SubDagOperator

	start_date = datetime(year=2017, month=6, day=13, hour=19, minute=0)
	schedule_interval = '0 * * * 1-5'

	default_args = {

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)