# Build brief — a focused alternative to Wholana > **Verdict:** Partly, if you narrow it · **Buildability:** 45/100 · **Category:** Social Media > **Source:** https://www.canitbevibecoded.com/wholana > Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from Wholana. Verify current pricing and capabilities before acting. ## Context **Wholana** — TikTok research tool that ranks videos against each creator's own baseline and labels what they did. It currently costs $5/mo. The core idea is simple arithmetic: a video's views divided by that creator's own median. An agent can build that for a watchlist of creators you pick, over a contained effort, on top of a paid scraper API. What it cannot hand you is the corpus, hundreds of thousands of videos already scraped, deduped, and labeled against a curated craft taxonomy, which is what makes search across creators useful instead of a list of your own bookmarks. So: yes for watching 25 creators you already know, no for finding the ones you don't. This brief describes a focused, single-operator replacement for the part of Wholana 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 Scrape a watchlist of creators nightly, compute each creator's rolling median views, and surface the videos that beat their own baseline. - Draft, queue, and track content for the few networks you actually use. - A responsive interface with real empty, loading, success, and error states. ## Requirements ### Functional - SQLite. - A nightly cron job. - A scrape budget that recurs every month. ### Data and integrations - TikTok scraper API (Apify or similar, paid per run). - LLM API key for hook labeling. 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 TikTok outlier tracker to replace Wholana. Requirements: - A nightly Node script (node-cron) that pulls recent videos for up to 25 handles listed in handles.txt, using a TikTok scraper actor on Apify, token in .env. Do not scrape TikTok directly, you will be blocked inside a day. - Store videos in SQLite via better-sqlite3: handle, video id, url, caption, posted date, views, likes, comments, shares, date first seen. Upsert on video id so a re-scrape updates metrics instead of duplicating rows. - Per creator, keep a rolling median of views over their last 30 videos and score each video as views divided by that median. 3x or higher is a breakout. Skip creators under 10 videos, the median is noise below that. - Label each breakout with one LLM call (Anthropic or OpenAI, key in .env): caption plus the first 15 seconds of subtitles from yt-dlp, returning one hook type from a fixed list of 12 in hooks.json. Fixed list, not free text, or nothing groups. - A page on localhost:3000 (Express, server-rendered HTML, Chart.js): last 7 days of breakouts sorted by score, filterable by handle, each row showing score, views, hook type, and a link, plus a per-creator sparkline of views over time. - A save button per row that writes the video into a swipe collection and appends it to swipe.md, so my picks survive the database. - Localhost only. No accounts, no telemetry, everything on my machine except the Apify and LLM calls. - Out of scope: search across creators I am not already tracking, and a shared craft taxonomy. Do not build auth, multi-user workspaces, or hosting config. - README: Apify token and actor id, the cron entry, and the cost per 1,000 videos scraped. The scraper bill, not the code, is what makes people quit this build. ## 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: - The cross-creator corpus. - Search across videos you never chose to watch. - A curated craft taxonomy instead of labels you invented. - Semantic and hybrid search. - The useful dataset is owned, accumulated, or expensive to reproduce. - Reliability at the vendor's scale is an operations problem, not a prompt. ## 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. ## Risk **Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost. Editorial confidence in this assessment: medium. No independent one-shot implementation is linked yet. ## Prior art Working open-source software you can read, fork, or borrow from before starting: - [TikTokApi](https://github.com/davidteather/TikTok-Api) — Unofficial Python wrapper for TikTok's web endpoints; gets you raw metrics, not a corpus, and breaks when TikTok changes --- Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/wholana