DEV Community: Rohan Arora

Building Matrix Destiny Calculator with React, Vite, and SVG

Rohan Arora — Sun, 01 Nov 2026 13:03:58 +0000

A calculator can fit its arithmetic into a few dozen lines and still need serious engineering around it.

Matrix Destiny Calculator turns a birth date into a numbered, interactive chart. Its domain is a symbolic numerology convention, but the engineering problems are familiar: preserve business rules, validate dates, explain results, support keyboard users, and avoid shipping unnecessary JavaScript.

This is a technical walkthrough of those decisions. The readings are reflection prompts; numerology is not scientifically validated. Testing the arithmetic establishes conformance to a documented convention, not predictive accuracy.

1. Put the calculation contract before the components

The stack is React 19 and Vite 7, with plain JavaScript modules for the calculation engine. The engine does not import React, access the DOM, or make network requests.

The core rule sounds simple:

Preserve positive integers from 1 through 22.
When a completed value exceeds 22, repeatedly add its digits until it fits.
Reject inputs outside the positive safe-integer domain.

Here is the implementation:

export function digitSum(value) {
  return [...String(value)].reduce(
    (sum, digit) => sum + Number(digit),
    0
  );
}

export function reduce22(value) {
  if (!Number.isSafeInteger(value) || value < 1) {
    throw new RangeError(
      'Arcana input must be a positive safe integer.'
    );
  }

  while (value > 22) value = digitSum(value);
  return value;
}

This is neither a modulo operation nor repeated subtraction of 22. For example, 28 becomes 10, while 22 stays 22.

Five anchors establish the personal chart:

A = reduce(day)
B = month
C = reduce(sum of year digits)
D = reduce(A + B + C)
E = reduce(A + B + C + D)

For 15 May 1990, those anchors are 15, 5, 19, 12, 6. The other positions are derived from completed nodes. The public methodology documents the convention and worked examples.

The useful pattern is the separation: a calculation module returns structured data; presentation components decide how to display it.

2. Reduction timing is part of the algorithm

The easiest mistake is to assume that moving a reduction cannot change the answer.

It can:

reduce22(reduce22(15 + 10) + 5); // 12
reduce22(15 + reduce22(10 + 5)); // 3

Preserving values through 22 makes the grouping meaningful. A refactor that looks algebraically harmless can change the chart.

Compatibility mode makes this especially important. It completes both personal charts first, then combines corresponding completed positions:

const pairedNodes = Object.fromEntries(
  Object.keys(first.nodes).map(key => [
    key,
    reduce22(first.nodes[key] + second.nodes[key])
  ])
);

Rebuilding every inner position from the combined outer anchors produces a different algorithm. Purpose totals also have their own documented recomputation order.

This generalizes to pricing, tax, and scoring systems: rounding, clamping, and reduction boundaries belong in the specification. They are observable behavior.

3. Validate calendar dates, not just numeric ranges

Checking that a day is between 1 and 31 does not establish that a date exists. JavaScript also normalizes overflowing date parts, so constructing a Date without checking the result can accept invalid input.

The validator accepts a strict YYYY-MM-DD string, checks the supported year range, constructs a UTC date, and compares its components with the input:

const date = new Date(Date.UTC(year, month - 1, day));

if (
  date.getUTCFullYear() !== year ||
  date.getUTCMonth() !== month - 1 ||
  date.getUTCDate() !== day
) {
  throw new Error(
    'That date does not exist. Check day, month, and leap year.'
  );
}

Future dates are rejected separately. Calendar validity uses UTC; the future-date boundary is built from the current local calendar day.

The validator also accepts an injected today argument. Tests can freeze the clock instead of depending on the day they run. That matters around birthdays, year boundaries, and future-date checks.

4. Treat an SVG chart as an interface

SVG suits this chart because positions have explicit coordinates, labels remain text, and the same geometry can be exported.

But drawing clickable circles is only part of the work. Interactive nodes need names, focus, keyboard activation, and selection state. A simplified node looks like this:

<g
  role="button"
  tabIndex={0}
  aria-label={`${label}: ${value}`}
  aria-pressed={selected}
  onClick={onSelect}
  onKeyDown={event => {
    if (event.key === 'Enter' || event.key === ' ') {
      event.preventDefault();
      onSelect();
    }
  }}
>
  <circle cx={x} cy={y} r={radius} />
  <text x={x} y={y}>{value}</text>
</g>

The reading panel exposes the selected position's formula alongside its explanation. An ordinary HTML position selector offers another way to navigate, and a polite live region announces reading updates.

The broader lesson: a visualization needs a usable interaction model as well as a visual design. Keyboard support and alternative controls should influence the component API from the beginning.

5. Keep the input local by removing the API call

The calculation is deterministic and inexpensive. It does not need a server round trip.

Birth-date fields and chart results stay in React state. They are not placed in query strings or persisted in browser storage. Readings come from editorial content keyed to calculated values; generating a chart does not call an AI service.

Downloads follow the same boundary. Text reports and SVG charts are built with Blob and URL.createObjectURL(), downloaded in the browser, and followed by object-URL cleanup.

This does not mean the whole website makes no external requests. The site uses Google Analytics for traffic measurement; birth dates and chart values are not supplied as Analytics fields. Theme preference is stored locally. The privacy policy describes those distinctions.

A useful review question for any calculator is: which feature actually requires this input to leave the browser? If none does, a backend may add complexity without improving the result.

6. Render content once; hydrate the interactive sections

The site contains calculators and educational articles. Their JavaScript needs differ.

Production builds use React's server renderer to generate HTML ahead of time. Vite produces the client and server bundles; a build script renders each route. This is build-time prerendering, rather than rendering the full React page on every request.

Interactive sections are marked with data-island. A small entry script discovers those sections, imports their components, and calls hydrateRoot() on their existing HTML.

React templates + route data
          ↓ build
Prerendered HTML
          ↓ browser
Small theme/menu script
          ↓ where required
Hydrated calculator or other interactive section

Static articles remain readable without JavaScript. Calculator interactions require it. The results component and its stylesheet are split out with React.lazy() and loaded on demand; focusing the calculator form starts preloading that component.

The build also generates responsive WebP images, includes page-specific CSS, and prepares Brotli/gzip responses. Performance checks enforce byte budgets and verify that result chunks stay out of the initial preload set.

Those are implementation checks, not a claim about a live Lighthouse score. A real deployment still needs measurement on representative devices and networks.

React's hydration documentation and Vite's SSR guide explain the underlying primitives. One constraint deserves attention: hydrated markup must match the HTML generated at build time.

7. Test examples, boundaries, and the entire supported date range

The engine uses Node's built-in test runner and strict assertions. Its tests cover:

Worked personal-chart and compatibility fixtures.
Preservation of 22 and reduction of larger values.
Invalid dates, leap-century rules, future dates, and birthdays.
Deterministic output and valid node ranges across every date in a fixed interval.

That last test visits all 46,304 valid dates from 1 January 1900 through 10 October 2026. Each date is calculated twice, and every node must be an integer from 1 through 22.

The endpoint is deliberately frozen for reproducibility. This sweep tests invariants, while the worked fixtures test specific expected answers. Neither replaces the other: an engine that always returned 1 would satisfy a range check.

During preparation of this post, the current local suite passed all 15 tests, including calculation, localization, and HTTP transport checks. That result describes the checked source, not a production load test.

What this project taught about small tools

The arithmetic is small. The contract around it is the product.

An explicit convention prevents accidental algorithm changes. Pure functions make exhaustive checks affordable. Browser-local state limits data movement. Accessible SVG makes the chart usable in more ways. Prerendered content keeps the delivery model proportional to what each page needs.

You can explore the result at matrixdestinycalculator.org, then inspect a chart position to see how its value was calculated.

If you have built a calculator or scoring tool, where did your hardest bug live: the formula, the input boundary, or the UI?

Building GTA6 Grid: React, PostgreSQL Search, and SEO Beyond Meta Tags

Rohan Arora — Sun, 01 Nov 2026 13:00:26 +0000

A character page, a vehicle catalog, and a news archive look like three different features. Underneath, they ask the same questions: What is this record? How do I find it? What should its URL return before JavaScript runs?

Those questions shaped GTA6 Grid, an unofficial GTA VI fan site I built with React, TypeScript, Vite, Express, and PostgreSQL.

The interface has the expected visual side: a cinematic homepage, location browsing, an illustrated map, trailers, and saved articles. But the more reusable engineering work sits behind it: a searchable content model, predictable HTTP responses, and a clear boundary between archived information and verified facts.

Here are the decisions behind that architecture, including a small PostgreSQL search example you can adapt to another content site.

1. Model the content before building the screens

Characters, vehicles, locations, and guides share a pages table. Each record has a stable path, a title, a category, an excerpt, and structured content. PostgreSQL JSONB columns hold blocks, facts, images, and videos.

That gives the application one browsing contract while allowing different article shapes. A character can have biographical facts; a vehicle can have a different set of attributes. Neither needs a separate search pipeline.

The model also stores source_url, published_at, scraped_at, and an evidence classification:

evidence TEXT NOT NULL DEFAULT 'Reported'
  CHECK (evidence IN ('Confirmed', 'Reported', 'Rumor'))

Those labels describe the source archive's classification. They are not independent verification by GTA6 Grid. Keeping that distinction visible matters on a fan site where an old report can otherwise look like a current announcement.

Publication time and import time also answer different questions. An article imported today may describe something reported months ago. Sorting news by import time would make old information appear new.

Content preparation runs separately from visitor requests:

Archive preparation → review → PostgreSQL import
                                    ↓
Browser → Express API → catalog or article response

Imports use ON CONFLICT (path) DO UPDATE inside a transaction. Reimporting the same paths updates their records instead of creating duplicate articles. That is useful repeatability, although changed content still needs editorial review.

2. Start search inside the database you already operate

This catalog needs ranked text search, categories, evidence filters, and pagination. PostgreSQL already provides the primitives for that workload, so the project does not need a separate search service to offer those features.

The actual schema stores a generated tsvector with three weights: title at A, excerpt at B, and article blocks at C. A GIN index supports matching against that document.

Here is a standalone, reduced example using only titles and excerpts. Run it in a scratch PostgreSQL database:

CREATE TABLE search_demo (
  path TEXT PRIMARY KEY,
  title TEXT NOT NULL,
  excerpt TEXT NOT NULL DEFAULT '',
  search_document TSVECTOR GENERATED ALWAYS AS (
    setweight(to_tsvector('english', title), 'A') ||
    setweight(to_tsvector('english', excerpt), 'B')
  ) STORED
);

CREATE INDEX search_demo_document_idx
  ON search_demo USING GIN (search_document);

INSERT INTO search_demo (path, title, excerpt) VALUES
  ('/places/harbor', 'Harbor District', 'Boats and waterfront roads.'),
  ('/guides/boats', 'Boat Guide', 'Exploring the harbor by water.');

WITH query AS (
  SELECT websearch_to_tsquery('english', 'harbor') AS value
)
SELECT path, title,
       ts_rank(search_document, query.value) AS rank
FROM search_demo
CROSS JOIN query
WHERE search_document @@ query.value
ORDER BY rank DESC, title;

The generated column keeps the search document synchronized with its inputs. Weighting gives title matches a stronger contribution than excerpt matches. websearch_to_tsquery accepts familiar search input, including quoted phrases and OR; PostgreSQL documents its behavior in Controlling Text Search.

The application additionally allows a case-insensitive substring match on titles. That is helpful when a visitor remembers only part of a name, but it introduces a separate query-planning concern: a GIN index on the text-search vector does not automatically accelerate ILIKE '%term%'.

If that branch becomes expensive, I would inspect the complete query with EXPLAIN (ANALYZE, BUFFERS) and evaluate a trigram index or a different matching policy. The decision should follow the workload, rather than the presence of an index somewhere in the schema.

One further refinement: the current body vector uses the JSON blocks cast to text. Extracting only reader-visible block text would avoid indexing JSON field names.

3. Make the first HTTP response useful

A visitor can arrive directly at an article URL. A social preview service can request that same URL. Both need the page to describe itself without depending on an earlier visit to the homepage.

Express generates route-specific titles, descriptions, canonical URLs, social metadata, JSON-LD, and visible initial content. The response includes navigation, headings, article text, and relevant links before the browser executes the React application.

The current flow is:

URL + PostgreSQL content
          ↓
Express → HTML + route metadata
          ↓
Browser loads React → interactive interface

There is an important implementation detail here: the current client uses createRoot, so React replaces the initial markup when it renders. This is not React hydration.

React's createRoot documentation explains that the first render clears existing content inside the root. hydrateRoot reuses matching server-rendered React markup instead.

A future hydration implementation would need shared rendering and initial data so the server and browser produce matching output. Swapping the API name alone would not establish that contract. The present approach provides readable initial HTML, while accepting a second rendering pass in the browser.

4. Treat URL behavior as part of the application

An article, a category page, and a search result should not all follow identical indexing rules.

GTA6 Grid uses one route metadata module to drive the server response, client navigation metadata, and sitemap eligibility. The policy distinguishes durable content from utility views:

Articles and main collections get their own canonical URLs.
Search, saved articles, and legal pages receive noindex and stay outside the sitemap.
Catalog search and filter variants receive noindex with a clean-path canonical.
Unknown routes return HTTP 404.
Trailing-slash and /index.html variants redirect to the preferred form.

For example, /vehicles?q=car currently returns:

<meta name="robots" content="noindex, follow">
<link rel="canonical" href="https://gta6grid.com/vehicles">

These directives serve different purposes. noindex excludes the utility view from search results; the canonical identifies the preferred URL. A canonical is a signal, not a guarantee that a search engine will select that URL, as Google's canonicalization documentation explains.

The filtered pages remain crawlable. Blocking them in robots.txt would prevent a crawler from seeing their noindex directive. Google documents that requirement.

Client navigation matters too. When the visitor changes routes without a full reload, the React metadata component requests /api/seo and updates the document head. It aborts obsolete requests so an earlier response cannot overwrite a newer page's identity.

5. Keep browsing responses and personal state small

The public API separates listing from detail:

GET /api/pages?category=vehicles&q=car&limit=24&offset=0
GET /api/page?path=/characters/lucia-caminos

The listing returns summary fields. The detail endpoint returns blocks, facts, and media for one article. Opening a catalog therefore does not require downloading every article body.

The API validates category and evidence values, binds user input as SQL parameters, and bounds pagination. List requests default to 24 records and cap at 60. Those boundaries make the response contract predictable for both the server and the interface.

Saved articles take a different path: they live in the browser's localStorage, alongside the theme preference. The application can offer bookmarking without an account system. The tradeoff is explicit: those saves belong to that browser, do not synchronize across devices, and disappear if the visitor clears site data.

What I would carry into another content project

The most transferable decisions were the ones that made several features agree with each other:

A stable content path supports imports, article lookup, and canonical URLs.
A shared metadata policy keeps direct requests and client navigation consistent.
Evidence and timestamps preserve context as archived information ages.
Database search combines naturally with the catalog's existing filters.

You can explore GTA6 Grid and follow a search result through to its article to see that structure in use. The project is an unofficial fan site, unaffiliated with Rockstar Games or Take-Two Interactive.

For your next content-heavy React project, which would you prioritize first: PostgreSQL search relevance, shared server/client rendering, or stronger content provenance?

Building EditPrompt: A Visual Prompt Library with React and SQLite

Rohan Arora — Sun, 01 Nov 2026 12:46:21 +0000

“Make this photo cinematic” leaves a lot of decisions to an image model. Should the background change? The lighting? The person's expression? Their clothes?

A useful editing prompt makes those decisions visible. It describes what to change, what to preserve, and what to leave out.

I built EditPrompt around that workflow: browse a visual prompt library, choose a starting point, adapt the instructions, and use them with a reference photo in an external image editor. The site also includes a generator that assembles prompt text directly in the browser.

The interesting engineering problem is keeping that experience predictable. The application uses React and Vite for the interface, a Node.js API backed by SQLite for the catalog, and a shared JavaScript module for prompt generation.

Here are the decisions behind that design, including a small generator you can run yourself.

1. Give the prompt a structure users can edit

An editing request has at least three parts:

Change: the background, lighting, framing, or another specific feature.
Preserve: facial details, hairstyle, clothing, or the existing composition.
Exclude: extra people, lettering, logos, or unwanted accessories.

For example:

Replace the background with a quiet garden. Keep the person's facial features, hairstyle, glasses, and clothing close to the reference. Match the background lighting to the light already on the face. Use a waist-up frame with realistic skin texture. Add no extra people or lettering.

Each sentence gives the user something concrete to adjust. If the background is wrong, they can change that instruction without rewriting the rest.

This also separates two kinds of correctness. Software can verify that the instructions contain the selected scene. Whether an image model follows those instructions requires a different evaluation. Requests to preserve identity remain requests; the resulting image still needs review.

2. Make the generator a pure function

EditPrompt's generator has four inputs: subject, scene, lighting, and framing. Each currently has three choices, giving 3 × 3 × 3 × 3 = 81 combinations.

That bounded space makes deterministic composition useful. The same choices produce the same text, and changing a dropdown requires no model request.

The generator lives in a shared module, separate from React. Here is a standalone, shortened implementation of the same approach. Save it as prompt-demo.mjs:

const options = {
  subject: {
    portrait: "Edit the uploaded reference of one person.",
    couple: "Edit the uploaded reference of two adults, with Person A on the left and Person B on the right.",
    child: "Edit the uploaded reference of a child.",
  },
  scene: {
    studio: "Use a plain neutral studio background.",
    garden: "Use a quiet garden with foliage clear of the face.",
    birthday: "Place a plain birthday cake on a separate table, with balloons behind the subject and no lettering or lit candles.",
  },
  light: {
    window: "soft window light",
    overcast: "overcast daylight",
    warm: "warm reflected light",
  },
  framing: {
    close: "head-and-shoulders",
    waist: "waist-up",
    full: "full-length",
  },
};

function buildPrompt(choices) {
  for (const [field, values] of Object.entries(options)) {
    if (!Object.hasOwn(values, choices[field])) {
      throw new TypeError(`Invalid ${field}`);
    }
  }

  return [
    options.subject[choices.subject],
    options.scene[choices.scene],
    `Use ${options.light[choices.light]} and ${options.framing[choices.framing]} framing.`,
    "Preserve each person's distinct facial details, hairstyle, actual age, skin tone, natural proportions, and age-appropriate clothing coverage.",
    "Retain realistic skin texture. Add no lettering, logos, new accessories, or extra people.",
  ].join(" ");
}

The validation belongs inside the function even though the interface uses dropdowns. Tests, future callers, and restored state can supply values the interface never offered.

Object.hasOwn() checks the allowed values themselves. A value such as "__proto__" cannot pass by matching an inherited property.

React only needs to hold the choices and derive the text:

const [choices, setChoices] = useState({
  subject: "portrait",
  scene: "studio",
  light: "window",
  framing: "waist",
});

const prompt = buildPrompt(choices);

There is no second state variable containing the generated prompt, so there is no extra synchronization step between the selections and the text. EditPrompt displays that text in a read-only textarea and offers a copy button, with manual selection available if clipboard access fails.

3. Test every combination, then state what the tests prove

With 81 combinations, exhaustive composition checks are small enough to run directly. Append this to prompt-demo.mjs and run node prompt-demo.mjs:

import assert from "node:assert/strict";

const outputs = new Set();

for (const subject of Object.keys(options.subject)) {
  for (const scene of Object.keys(options.scene)) {
    for (const light of Object.keys(options.light)) {
      for (const framing of Object.keys(options.framing)) {
        const prompt = buildPrompt({ subject, scene, light, framing });
        assert.ok(prompt.includes(options.subject[subject]));
        assert.ok(prompt.includes(options.scene[scene]));
        assert.ok(prompt.includes(options.light[light]));
        assert.ok(prompt.includes(options.framing[framing]));
        assert.doesNotMatch(prompt, /undefined/);
        outputs.add(prompt);
      }
    }
  }
}

assert.equal(outputs.size, 81);
assert.throws(() => buildPrompt({}), /Invalid subject/);
assert.throws(() => buildPrompt({ subject: "__proto__" }), /Invalid subject/);

console.log("81 distinct combinations checked; invalid inputs rejected.");

The project's generator tests also check subject-specific wording and birthday constraints. These checks establish that the text builder follows its contract.

They do not establish image quality or compatibility with every image model. An evaluation of generated images would need to record the reference image, model, settings, prompt, and review criteria separately. Keeping those claims separate makes the feature easier to explain honestly.

4. Let the catalog API return only what the screen needs

The browsing side has different requirements from the generator. It needs searchable records, categories, tags, previews, and full detail pages.

The SQLite schema separates posts, taxonomy terms, and assets. Join tables connect posts to terms and additional media. That keeps a prompt's text distinct from the files used to illustrate it.

The public API makes a corresponding distinction:

GET /api/prompts?category=portrait&page=1&limit=24
GET /api/prompts/:slug

The list endpoint returns summary fields and preview URLs. The detail endpoint returns the complete prompt and article content. A gallery visit therefore avoids downloading every full prompt body.

List requests default to 24 records and cap the limit at 48. Queries bind user-supplied values, and sort choices map to predefined SQL ordering clauses. A stable identifier breaks ties when publication dates match.

The web process opens the catalog read-only. Catalog preparation runs separately from HTTP requests, keeping image processing and imports outside the visitor's request path.

There are practical limits here. The current search uses LIKE against titles and prompt text, and database calls are synchronous. Query duration and event-loop delay would matter as the workload grows. If search became a bottleneck, SQLite FTS5 would be one option to evaluate for full-text indexing.

5. Treat image delivery as part of the interface

A visual library asks visitors to compare previews before opening a detail page. Image delivery directly affects how soon they can make that choice.

EditPrompt prepares WebP gallery thumbnails and supplies responsive candidates through srcset and sizes. Cards include image dimensions to reserve space while files load. Initial cards load eagerly; later cards use lazy loading.

Those choices solve different problems:

Smaller image candidates reduce the bytes needed for a small card.
Reserved dimensions help prevent the layout from moving during loading.
Lazy loading postpones requests for cards farther down the page.
Eager loading lets the first visible previews start loading immediately.

The grid and its sizes rules need to agree. A browser can only choose a suitable candidate when the declared display size is close to the space the card actually occupies.

The important measurement is whether visitors receive useful previews sooner under realistic conditions. A different file extension alone cannot establish that improvement.

6. Share the initial page between server and browser

The React application also renders HTML on the server. Its initial response includes page content and route-specific metadata, followed by the data needed for hydration.

The flow is:

Route + catalog data
        ↓
React server render → HTML + initial data
        ↓
Browser hydration → interactive page

The browser uses hydrateRoot to attach React to the existing HTML. The initial browser render must agree with the server render, including the generator's default selections.

This makes shared data and pure functions useful beyond testing: the server and browser can derive the same prompt from the same inputs. Browser-only features, such as saved prompt IDs in localStorage, still need separate handling. Those saves belong to that browser and can disappear when site data is cleared.

A useful boundary for AI product design

EditPrompt prepares an editable brief. The chosen external editor handles the reference photo and image generation under its own capabilities and access rules.

That division gives each layer a clear responsibility: the catalog helps people find a starting point, the generator turns choices into explicit instructions, and the interface makes those instructions easy to inspect and copy.

You can try the photo prompt generator and change one input at a time to see that composition in practice.

If you were extending this tool, which would you add first: more scene controls, versioned prompt templates, or an evaluation set for reviewing model outputs?

Building a Reliable Trade Calculator with TypeScript and BigInt

Rohan Arora — Sun, 01 Nov 2026 12:20:52 +0000

Imagine comparing two offers worth 98 and 100 units. Your calculator calls that fair.

Now change the first offer to 97.999999. Both comparisons can display a rounded difference of 2.00%, but only one falls inside an inclusive 2% fairness band.

That small boundary captures a lot of what makes a calculator interesting to build. The interface needs to explain a decision that depends on precise arithmetic, complete inputs, and a particular version of the data.

I built Adopt Me Value Calculator to compare bundles of virtual pets and items. The application uses Next.js, React, and TypeScript, with Payload CMS and PostgreSQL for editorial data. Visitors calculate offers against published snapshots and keep saved trades in their browser.

This post focuses on three decisions behind that design: how values are represented, when a result is allowed, and what happens when prices change. The examples below use illustrative values and simplified versions of the calculation helpers.

1. Keep decimal quotes exact from the moment they enter the system

The familiar JavaScript example is easy to reproduce:

0.1 + 0.2 === 0.3; // false

A formatted total can hide that difference. A comparison near a decision boundary still needs a defined arithmetic rule.

The calculator accepts quotes with up to six decimal places. It stores those quotes as decimal strings and converts them into scaled integers for calculation:

const SCALE = 1_000_000n;
const MAX_QUOTE = 1_000_000_000_000n * SCALE;

function parseValue(input: string): bigint {
  if (
    typeof input !== "string" ||
    !/^(?:0|[1-9][0-9]{0,12})(?:\.[0-9]{1,6})?$/.test(input)
  ) {
    throw new Error("Expected a non-negative decimal with up to 6 places");
  }

  const [whole, fraction = ""] = input.split(".");
  const value =
    BigInt(whole) * SCALE +
    BigInt(fraction.padEnd(6, "0"));

  if (value > MAX_QUOTE) {
    throw new Error("Quote exceeds the supported maximum");
  }

  return value;
}

parseValue("0.1") + parseValue("0.2") === parseValue("0.3"); // true

Here, "12.345678" becomes 12_345_678n. An offer total is the sum of each scaled quote multiplied by its validated integer quantity.

BigInt represents integer values; the decimal behavior comes from our chosen scale. That choice also defines the smallest supported increment: one millionth of a unit.

The parser rejects scientific notation, negative values, extra precision, and out-of-range quotes. Those are explicit input rules, so an importer cannot silently round a seventh decimal place away.

Keep decimal strings at JSON boundaries and convert them directly into scaled integers. Passing through Number first would lose the benefit of preserving the original decimal text. Persisted evaluation totals use strings too, since ordinary JSON.stringify cannot serialize a BigInt value directly.

2. Make the fairness decision before rounding the display

The site's published methodology defines the gap as:

gap = abs(receive - give) / max(give, receive)

An offer is fair when that gap is at most 2%. This is a product policy, so the denominator and inclusive boundary both need to be deliberate.

Two percent is 200 basis points. Cross multiplication lets us compare the gap without division:

abs(receive - give) × 10,000 <= max(give, receive) × 200

The helper becomes:

type Verdict = "win" | "fair" | "lose";

// Call only after both offers and every required quote are validated.
function classifyCompleteTrade(
  give: bigint,
  receive: bigint,
  fairnessBasisPoints = 200n,
): Verdict {
  const difference = receive - give;
  const absoluteDifference = difference < 0n ? -difference : difference;
  const larger = give > receive ? give : receive;

  if (
    larger === 0n ||
    absoluteDifference * 10_000n <= larger * fairnessBasisPoints
  ) {
    return "fair";
  }

  return difference > 0n ? "win" : "lose";
}

For 98 versus 100, the gap is exactly 2%. For 97.999999 versus 100, it is 2.000001%. The second comparison receives a Win from the perspective of the person receiving 100, even if the displayed percentage rounds to 2.00%.

That gives the interface an extra responsibility: explain close results. A rounded percentage should not make an exact verdict look arbitrary. More display precision near the boundary, or a short explanation of the cutoff, can help.

The full engine reads the fairness threshold from the selected release. The helper above accepts it as an argument so the policy stays separate from the arithmetic.

3. Give unknown prices their own state

Consider an offer containing two items. One has a reviewed quote of 20. The other has no quote.

Code such as total += quote ?? 0n would produce a total of 20 and let the application continue toward a verdict. A user could mistake a known subtotal for the value of the whole offer.

The data model distinguishes reviewed values from unpriced entries. A simplified TypeScript representation is:

type Quote =
  | { status: "reviewed"; value: string }
  | { status: "unpriced"; value: null };

The evaluation also has a status separate from its Win/Fair/Lose result:

empty: neither offer contains items.
one-sided: an offer is still missing.
incomplete: at least one selected variant has no reviewed quote.
invalid: validation failed, including a release mismatch.
complete: both offers are present and every required quote is available.

Only a complete evaluation can produce a verdict. An incomplete evaluation can show known subtotals and identify missing quotes, while leaving the comparison undecided.

A reviewed zero remains a valid value. Two populated offers whose reviewed totals are both zero can be fair; two empty offers remain an empty state. The totals alone cannot tell you which situation exists.

These rules make uncertainty visible all the way from the imported data to the interface.

4. Identify the exact variant and value system

In Adopt Me, a pet can have Normal, Neon, or Mega status, along with Fly and Ride combinations. The item name alone does not identify the selected asset precisely enough.

The engine looks up quotes using a variant identifier and a value system. Quantities refer to that exact variant.

The distinction matters because an interface should not invent a missing variant quote by multiplying another quote. Any source-specific transformations belong in the ingestion process, where their rules and provenance can be checked. The calculation engine consumes the resulting explicit records.

Value systems need the same care. RP and Shark quotes are maintained separately, so a total from one cannot be compared directly with a total from the other. For releases using a shared Shark-to-Frost benchmark conversion, the engine keeps the comparison in the underlying units and converts the display afterward. Direct Frost quote releases use their own recorded values.

Demand is separate context as well. Showing a demand label does not silently add an undocumented premium to the arithmetic.

5. Save the data release alongside the trade

A saved offer needs more context than its item list.

Suppose a comparison is fair today. Next week, new quotes make the same bundle uneven. Opening the saved trade should make it clear which data the original result used.

Each trade therefore records a releaseId, its value system, and both offers. Each published snapshot contains the item and quote records, benchmarks, and settings needed for evaluation. Saving a trade also stores its snapshot and original evaluation in the browser.

The engine rejects a trade whose release identifier does not match the supplied snapshot. Recalculating with the latest release is an explicit action.

Publication follows an ordered process:

Validate the candidate and compare it with the active release.
Check that the active release and reviewed draft have not changed.
Write a new release in a staging directory.
Verify its checksum and record the publication audit.
Move the completed release into place, then atomically replace the active manifest.

The filesystem operations run under a publication lock. On this single-host design, readers can resolve a complete old or new release while publication proceeds.

That approach fits the deployment's persistent filesystem. Moving to multiple hosts or object storage would require a publication mechanism with appropriate concurrency and consistency guarantees.

There is another boundary to reproducibility: a data snapshot preserves inputs and settings. If a future application changes the evaluation algorithm, that rule would need versioning too.

6. Keep the calculator independent from visitor accounts

The public application reads published snapshots. Editorial work happens through Payload CMS and PostgreSQL, while visitor inventories, favorites, and saved trades use IndexedDB through Dexie.

Saving a trade writes the trade and its snapshot in one browser database transaction. The application can retain the data needed for the original comparison without creating a visitor account.

Shared offers encode their selected fields as UTF-8 JSON, then base64url, in a URL fragment. The fragment is handled by the client and excluded from the HTTP request. It is still readable by anyone holding the link, so encoding provides portability rather than secrecy. Decoding enforces size limits and validates the resulting trade before evaluation.

Browser storage has a practical cost: clearing site data can remove saved records. Backup import and export matter. Cached releases help when an update request fails, but they do not establish full offline support for a first visit.

Test the boundary users cannot see

These assertions exercise the simplified helpers above:

import assert from "node:assert/strict";

assert.equal(
  parseValue("0.1") + parseValue("0.2"),
  parseValue("0.3"),
);

assert.equal(
  classifyCompleteTrade(parseValue("98"), parseValue("100")),
  "fair",
);

assert.equal(
  classifyCompleteTrade(parseValue("97.999999"), parseValue("100")),
  "win",
);

assert.equal(
  classifyCompleteTrade(parseValue("100"), parseValue("97.999999")),
  "lose",
);

The application tests also cover missing quotes, legitimate zero values, mismatched releases, exact variant selection, and historical evaluations after a newer release is published.

Exact arithmetic preserves the recorded quotes. Their quality still depends on source accuracy, review, and freshness. A calculator should expose enough context for someone to understand what its result means.

Which boundary would you test first in a pricing tool: a decimal cutoff, a missing value, or a saved calculation reopened after a data update?