axefrog

Example of using jsondiffpatch to maintain the evolving state of a model defined in terms of a TypeScript interface. Several example properties, one with a nested interface, and at least one very large string property where diffs would be warranted (as opposed to just storing the entire string each time a change is made)

To demonstrate how jsondiffpatch can be used to maintain the evolving state of a model defined in terms of a TypeScript interface, let's consider an example. We'll define a TypeScript interface for a user profile that includes nested interfaces and a very large string property.

First, let's define our TypeScript interfaces:

interface Address {
  street: string;
  city: string;

axefrog

PS C:\Users\Nathan> ollama run glm-4.6:cloud Connecting to 'glm-4.6' on 'ollama.com' ⚡

Example of using jsondiffpatch to maintain the evolving state of a model defined in terms of a TypeScript interface. Several example properties, one with a nested interface, and at least one very large string property where diffs would be warranted (as opposed to just storing the entire string each time a change is made)

Thinking... This is an excellent request that combines several interesting aspects of TypeScript development:

1. Working with TypeScript interfaces and type safety
2. State management/diffing with jsondiffpatch
3. Handling nested objects
4. Efficient handling of large text properties

axefrog

const defaultIfZero = (a, x) => x === 0 ? a : x;
const isDefined = x => x !== void 0;
const isUndefined = x => x === void 0;

const NONE = Symbol('NONE');

const chalk = require('chalk');
const val = x => (x === NONE ? (x = 'none', chalk.bold.grey) : chalk.cyan)(String(x));
let highlightActiveNode = null;
let highlightedEdges = [];

axefrog

My process for designing systems and solving problems is fairly simple, and in a way it seems like an obvious approach, but the degree of effectiveness of the technique is greater than what you'd intuitively expect. When most developers write notes and try to work out how to tackle some kind of architectural design challenge, it usually takes the form of a few whiteboard notes, some scribbles in a notepad, etc. Usually this is fine because the problem is small and constrained to a manageable level. If you want to tackle something bigger and more challenging though, or if you want to design a much more effective system in the first iteration, rather than two-to-three years down the track after several deployments finally lead you to the conclusions you'd like to reach up front, then this process works really well. It does feel like you're committing to a chore if you haven't done it before, and the reason it works isn't itself obvious, which is part of the reason I get pushback from a lot of developers before

axefrog

import {Stream, Sink, Scheduler, Disposable} from '@most/types';
import {disposeOnce, disposeAll} from '@most/disposable';

export type ActivateExtension<A = any> = (source: Stream<A>) => Stream<A>;
export type DeactivateExtension = null;
export type ExtensionAction<A = any> = ActivateExtension<A>|DeactivateExtension;
export type ExtensionLifecycle<A = any> = Stream<ExtensionAction<A>>;

export function extensible<A>(source: Stream<A>, extensions: Stream<ExtensionLifecycle<A>>): Stream<A> {
  return new ExtensiblePipeline(extensions, source);

axefrog

function identity<T>(value: T): T {
  return value;
}
  
let wrap = identity;

export function inspect<T>(fn: (value: T) => T): void {
  wrap = fn;
}

axefrog

/*
  Preprocessor Directives
  =======================

  // --- OMIT A SINGLE LINE ----------------------------------------------------

  log("This line will be excluded from the build"); // ## DEV ##

  // --- OMIT A WHOLE BLOCK ----------------------------------------------------

axefrog

/*
  # CriticalSection
  "A stream-flavoured critical section implementation for isolating access to a stream subgraph"
  
  Events entering a stream graph fenced off by a critical section will cause all other entry points to switch into
  blocking mode, with events received at a given entry point being retained in that entry point's internal buffer,
  and the entry point buffer enqueued for subsequent processing. Each time an event flows through an exit point, the
  next entry point in the queue will be dequeued and its next buffered event emitted into the inner stream graph.
  When no data is buffered at any entry point, the critical section unblocks, allowing direct dispatch of the next
  future data item to arrive, at which point it will switch back into blocking mode. Errors ignore critical section

axefrog

function copyArray<T>(values: T[]): T[] {
  switch(value.length) {
    case 0: return [];
    case 1:  return [value[0]];
    case 2:  return [value[0], value[1]];
    case 3:  return [value[0], value[1], value[2]];
    case 4:  return [value[0], value[1], value[2], value[3]];
    case 5:  return [value[0], value[1], value[2], value[3], value[4]];
    case 6:  return [value[0], value[1], value[2], value[3], value[4], value[5]];
    case 7:  return [value[0], value[1], value[2], value[3], value[4], value[5], value[6]];

axefrog

import {using} from 'proxy-abstraction';

function render() {
  // The `using` function takes a callback function. It looks at the functions `.length` property
  // and generates a proxy for each. You then return a tuple containing the value you want to
  // persist, and fulfillment streams for each of the referenced proxies. The using function will
  // automatically complete the proxies internally and return the first tuple argument.
  return using((click$, hover$) => {
    const view = div(......);
    return [