# Build brief — a focused alternative to SharpAPI

> **Verdict:** Partly, if you narrow it · **Buildability:** 63/100 · **Category:** Dev Tools
> **Source:** https://www.canitbevibecoded.com/sharpapi
> Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from SharpAPI. Verify current pricing and capabilities before acting.

## Context

**SharpAPI** — Ready-made AI API endpoints for HR, e-commerce, content, and finance workflows. It currently costs $50/mo.

Any single endpoint here, summarize a text, categorize a product, parse a resume, is one LLM call with a decent prompt and a JSON schema, so the piece you actually need is often an evening of work. What you will not rebuild in a focused implementation is the catalog: 30+ endpoints with tuned prompts, response contracts that survive model upgrades, an async job queue with polling, and maintained SDKs. Rebuild the two or three endpoints you use, pay when you need the shelf.

This brief describes a focused, single-operator replacement for the part of SharpAPI that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest.

## What you are building

Wrap OpenAI or Anthropic structured outputs in a small local API: one prompt file and one JSON schema per task, a SQLite job table for async processing, and a worker that retries failures.

- Build the focused developer workflow you use repeatedly, with local configuration.
- A responsive interface with real empty, loading, success, and error states.

## Requirements

### Functional

- A JSON schema per task for structured outputs.
- A job queue (BullMQ + Redis, or a SQLite job table).
- Real sample inputs to calibrate each prompt against.

### Data and integrations

- OpenAI or Anthropic API key.

Each of these needs a real account, credential, or quota. Set them up before writing feature code.

### Non-functional

- Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support.
- Security: server-side secrets, validated input, and no credentials in the client bundle.
- Reliability: retries with backoff on external calls, and a clear failure state when a provider is down.
- Portability: the operator can export their data and leave without losing it.

## Implementation brief

Build me a personal AI text-workflow API to replace SharpAPI. Requirements:

- Node 22 + Express bound to localhost, better-sqlite3 for storage, no frontend.
  This is an API my other projects call with curl or fetch.
- Three endpoints to start: POST /summarize, POST /categorize against my own
  category list in categories.json, and POST /parse-resume (PDF or text in,
  structured JSON out).
- Every endpoint calls OpenAI structured outputs with a JSON schema kept in
  schemas/<task>.json and a prompt kept in prompts/<task>.md, so I can tune
  prompts without touching code. Adding a task means adding one schema + prompt pair.
- Async by default: POST returns a job id, GET /jobs/:id returns status and result.
  Jobs live in a SQLite table with created/started/finished timestamps.
- A worker loop processes jobs with concurrency 2, retries twice on 429 and 5xx
  with backoff, and stores the error text on the job instead of dropping it.
- Parse PDFs with pdf-parse before sending text to the model.
- API key from .env. No accounts, no telemetry, binds to localhost only.
- Out of scope: billing, rate-limit tiers, packaged SDKs, and the other 27
  SharpAPI endpoints. Do not build an admin UI.
- README: setup, .env keys, one example curl per endpoint, and a rough per-call
  cost note.

## Delivery standard

- Inspect the repository first, then write a short implementation plan before writing code.
- Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data.
- Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations.
- Include responsive layouts plus genuine empty, loading, success, validation, and failure states.
- Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data.
- Add structured logs around every external call and return actionable errors without leaking sensitive details.
- Write unit tests for the core logic and one automated test of the main user journey.
- Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations.

## Acceptance criteria

- [ ] A clean install starts the app using only the README and .env.example.
- [ ] The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable.
- [ ] Invalid input, missing configuration, provider failure, and an empty database each have a usable state.
- [ ] The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control.
- [ ] Tests, type checking, linting, and a production build all pass with no ignored failures.
- [ ] No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified.

## Non-goals

Do not build these, and do not claim to have replaced them:

- Tuned prompts per use case, tested against messy real-world inputs.
- Response contracts that stay stable when the underlying models change.
- The managed async queue with polling, throttling, and rate limits.
- Ready SDKs for PHP/Laravel, Node, and Python.
- 80+ language coverage tested per endpoint.

## What you still own after launch

- Secure credentials, rotate secrets, and handle provider rate limits.
- Run migrations, backups, restores, and dependency updates.
- Test the critical journey after every model, API, or hosting change.
- Monitor failures and fix the edge cases a first prompt will miss.
- Maintain every third-party integration as APIs and OAuth rules change.

## Risk

**Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost.

Editorial confidence in this assessment: medium. No reviewed project implementation is linked yet.

## Prior art

Working open-source software you can read, fork, or borrow from before starting:

- [OpenAI Node SDK](https://github.com/openai/openai-node) — official client with structured outputs, the engine behind most DIY task endpoints
- [Instructor](https://github.com/instructor-ai/instructor) — schema-validated structured extraction from LLMs, with retries on invalid output
- [BullMQ](https://github.com/taskforcesh/bullmq) — Redis-backed job queue if the SQLite job table stops being enough

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