Repurposing One Piece of Content into a Week of Posts with an AI‑Driven Workflow

Описание и применение

Преобразование одного длинного материала в семь ежедневных постов с помощью локальной, детерминированной пайплайна, включающего разбиение, генерацию LLM, проверку и публикацию.

Задача

Turning a single long‑form asset into seven daily social‑media posts requires a repeatable, deterministic pipeline that can be version‑controlled and inspected locally before external API calls.

Результат

The workflow produces a week’s worth of social‑media posts from a single source, with deterministic, idempotent steps, local validation, and auditability, enabling scaling without hidden costs.

Рабочий процесс

• Extract source material (download audio or clone article)
• Normalize audio to 16 kHz mono WAV
• Chunk text into overlapping 1,200‑token windows with tiktoken
• Embed chunks with a 7–8 B quantized model (optional)
• Generate draft posts via LLM with strict JSON schema
• Validate drafts (schema, length, fact‑check, deduplication)
• Schedule and publish posts, recording platform IDs
• Maintain audit logs and prevent re‑posting

Доказательства

Turning a single long‑form asset — such as a conference talk, a deep‑dive blog post, or a video tutorial — into seven daily social‑media posts is a repeatable engineering problem.

Источник

All steps are scriptable, version‑controlled, and can be inspected locally before any external API is called.

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Because webhooks (or any async callback) are delivered at least once, the generator must be idempotent: include the chunk index in the output file name and skip already‑existing files.

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A tiny scheduler (cron, systemd timer, or GitHub Actions) picks the next unpublished draft in lexical order, calls the platform’s posting API, and marks the file as posted_timestamp.json.

Источник