Random News Feed Mock Dataset Generator

Description

Compose supplied fictional post templates and synthetic author IDs into a chronological feed with bounded code-point excerpts and explicit Unix-millisecond bounds.

An interface fixture makes content and state explicit so a consumer can test a rendering or interaction. Literal labels, modeled states, and calculated geometry remain separate from what a browser or assistive technology actually displays or announces.

Compose supplied fictional post templates and synthetic author IDs into a chronological feed with bounded code-point excerpts and explicit Unix-millisecond bounds.

When to use this tool

Use this generator to create reproducible news feed mock dataset cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply templates, authorIds, count, startMilliseconds, endMilliseconds, excerptCodePoints, suffix in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read posts. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Precompute code-point-limited excerpts of supplied fictional post templates, then sample templates, authors, and timestamps. Sort newest first while retaining full body, excerpt, and truncation flag.^[1]

Delegated operations used for this result: truncate. Their calculation or parsing conventions apply to the corresponding result fields.^[2]

Input field	Meaning and units	Accepted input
templates	Templates	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 128.
templates[].title	Title	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
templates[].body	Body	Required. Text; maximum UTF-16 length 4096.
authorIds	Author Ids	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 1; maximum items 128.
count	Requested count; the mechanics explain whether one draw expands into several returned records.	Required. Safe integer; minimum 1; maximum 128.
startMilliseconds	Start Milliseconds	Required. Safe integer.
endMilliseconds	End Milliseconds	Required. Safe integer.
excerptCodePoints	Excerpt Code Points	Required. Safe integer; minimum 0; maximum 4096.
suffix	Suffix	Required. Text; maximum UTF-16 length 128.
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
posts	Posts	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 128.
posts[].id	Literal identifier used to match records, independently of their display labels.	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
posts[].title	Title	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
posts[].body	Body	Required. Text; maximum UTF-16 length 4096.
posts[].authorId	Author Id	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
posts[].timestampMilliseconds	Timestamp Milliseconds	Required. Safe integer.
posts[].excerpt	Excerpt	Required. Text; maximum UTF-16 length 4096.
posts[].wasTruncated	Was Truncated	Required. Boolean.
randomness	Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.	Required. Object; no unknown keys.
randomness.seed	Optional numeric replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "templates": [
      {
        "title": "Kite",
        "body": "Kites drift."
      },
      {
        "title": "Garden",
        "body": "Seedlings grow."
      },
      {
        "title": "Workshop",
        "body": "A model takes shape."
      }
    ],
    "authorIds": [
      "actor-a",
      "actor-b"
    ],
    "count": 2,
    "startMilliseconds": 1000,
    "endMilliseconds": 5000,
    "excerptCodePoints": 9,
    "suffix": "…",
    "seed": 42
  },
  "output": {
    "posts": [
      {
        "id": "post-1",
        "title": "Garden",
        "body": "Seedlings grow.",
        "excerpt": "Seedling…",
        "wasTruncated": true,
        "authorId": "actor-a",
        "timestampMilliseconds": 4410
      },
      {
        "id": "post-2",
        "title": "Workshop",
        "body": "A model takes shape.",
        "excerpt": "A model …",
        "wasTruncated": true,
        "authorId": "actor-a",
        "timestampMilliseconds": 3106
      }
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Excerpts inherit the linked suffix-budget convention. Author IDs are supplied fixtures, and sampling can repeat templates or authors. No news source, account, or publishing system is accessed.
A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random News Feed Mock Dataset Generator — tool.io
Calculation or validation: text/truncate — tool.io

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