steipete

• Delete unused or obsolete files when your changes make them irrelevant (refactors, feature removals, etc.), and revert files only when the change is yours or explicitly requested. If a git operation leaves you unsure about other agents' in-flight work, stop and coordinate instead of deleting.
• Before attempting to delete a file to resolve a local type/lint failure, stop and ask the user. Other agents are often editing adjacent files; deleting their work to silence an error is never acceptable without explicit approval.
• NEVER edit .env or any environment variable files—only the user may change them.
• Coordinate with other agents before removing their in-progress edits—don't revert or delete work you didn't author unless everyone agrees.
• Moving/renaming and restoring files is allowed.
• ABSOLUTELY NEVER run destructive git operations (e.g., git reset --hard, rm, git checkout/git restore to an older commit) unless the user gives an explicit, written instruction in this conversation. Treat t

bouroo

/**
 * Cloudflare Worker for Server-Sent Events (SSE)
 *
 * This worker demonstrates how to set up an SSE endpoint
 * that sends an initial message and then periodic updates.
 */

// Listen for incoming requests
addEventListener('fetch', event => {
  event.respondWith(handleRequest(event.request));

howardjohn

#!/bin/bash

# Usage: tracing::init [endpoint; default localhost:4317]
function tracing::init() {
  export OTEL_EXPORTER_OTLP_ENDPOINT="${1:-${OTEL_EXPORTER_OTLP_ENDPOINT:-localhost:4317}}"
}

# Usage: tracing::auto::init [endpoint; default localhost:4317]
function tracing::auto::init() {
  tracing::init

nicolas-miari

import Foundation
import CoreImage

/**
 Based on: https://gist.github.com/SheffieldKevin/566dc048dd6f36716bcd
 Updated for Swift 5.5 (Xcode 13)
 */
class ImageDiff {

  func compare(leftImage: CGImage, rightImage: CGImage) throws -> Int {

randomsequence

//
// Thanks Kevin!
// https://gist.github.com/SheffieldKevin/566dc048dd6f36716bcd
//

import CoreGraphics
import CoreImage

extension CGImage {
    

apaszke

from graphviz import Digraph
import torch
from torch.autograd import Variable, Function

def iter_graph(root, callback):
    queue = [root]
    seen = set()
    while queue:
        fn = queue.pop()
        if fn in seen:

tokestermw

""" 
Play with saving .

Closest:
    https://github.com/tensorflow/tensorflow/issues/616#issuecomment-205620223
"""
import numpy as np

import tensorflow as tf
from tensorflow.python.platform import gfile

edgarriba

import torch
import numpy as np

import pickle

f = open('/home/eriba/software/pytorch/examples-edgarriba/triplet/nan_test.pkl', 'rb')

data = pickle.load(f)

a = torch.from_numpy(data['a']).cuda()

mrdrozdov

from tf_logger import TFLogger


""" Example of using TFLogger to save train & dev statistics. To visualize
    in tensorboard simply do:
    
        tensorboard --logdir /path/to/summaries
        
    This code does depend on Tensorflow, but does not require that your model
    is built using Tensorflow. For instance, could build a model in Chainer, then

andymatuschak

A composable pattern for pure state machines with effects

State machines are everywhere in interactive systems, but they're rarely defined clearly and explicitly. Given some big blob of code including implicit state machines, which transitions are possible and under what conditions? What effects take place on what transitions?

There are existing design patterns for state machines, but all the patterns I've seen complect side effects with the structure of the state machine itself. Instances of these patterns are difficult to test without mocking, and they end up with more dependencies. Worse, the classic patterns compose poorly: hierarchical state machines are typically not straightforward extensions. The functional programming world has solutions, but they don't transpose neatly enough to be broadly usable in mainstream languages.

Here I present a composable pattern for pure state machiness with effects,