simodepth96/reddit-content-opportunity-finder
User repository on GitHub
- Forks
- 0
- Last pushed
- Updated Oct 2026
Repo stats from the GitHub API, cached Oct 2026.
Jev directory
CommunityJev Tools & CLIs
Reddit Content Opportunity Finder
A two-stage Python framework that cleans an Apify Reddit export, clusters comments with UMAP and HDBSCAN, then has Jev score each comment 0-1 for whether the brand could publish new content about it. Opportunities are flagged when the score clears 0.8 and no competitor is mentioned.
- Stars
- 3
- Language
- Python
- Reading time
- 3 min
- Published by
- Community
- Author
- simodepth96
- Last verified
- Oct 2026
- Languages
- Python
- Capabilities
- Open sourceRequires API key
Highlights
- Stage 1 cleans an Apify Reddit export: drops bots and deleted comments, strips Markdown, URLs and HTML entities, and converts timestamps to UTC.
- Stage 2 embeds comments with all-MiniLM-L6-v2, reduces with UMAP and clusters with HDBSCAN, then names clusters with Gemini and falls back to TF-IDF.
- Jev scores each comment 0-1 for whether the brand could write new, useful content about it; the model is pinned to jev-1.13.0 with 8 parallel workers.
- content_opportunity is Yes when body_score clears OPPORTUNITY_THRESHOLD (0.8) and no competitor is mentioned; the run writes a workbook and an HTML heatmap.
- Missing keys degrade gracefully: no Gemini gives TF-IDF cluster names, no TypeSafe key leaves body_score blank, and no NLP access falls back to regex matching.
Quickstart
bash
pip install sentence-transformers hdbscan umap-learn scikit-learn openpyxl typesafe-sdk numpy pandas plotly google-genai google-cloud-languageWatch out
README reviewed 2026-10-17; the 0.8 opportunity threshold is a project default that the README itself recommends replacing with a hand-labeled one, GitHub metadata lists no license, and the pipeline depends on an Apify export plus Gemini, Google Natural Language and TypeSafe keys.
Questions about this entry
Short answers drawn from the linked project or page.
A two-stage framework that finds content opportunities for a brand in scraped Reddit comments: first clean the Apify export, then cluster the comments and have Jev score each one for whether the brand could publish new, useful content about it.
Comments that do not mention the target brand get a Jev score: the probability from 0 to 1 that the brand can write new, useful content about the comment. The model is pinned to jev-1.13.0 and scoring runs with 8 parallel workers.
content_opportunity is Yes when the Jev body_score is at least OPPORTUNITY_THRESHOLD (0.8 by default) and the comment mentions no competitor, and each row keeps its score, cluster name and brand and competitor flags.
Stage 1 drops bots and deleted comments, strips Markdown, URLs and HTML entities and converts timestamps to UTC; stage 2 embeds with all-MiniLM-L6-v2, reduces with UMAP and clusters with HDBSCAN, naming each cluster with Gemini or TF-IDF.
An Excel workbook whose bodies sheet lists every comment with its score, cluster name, brand and competitor flags, Jev body_score and content_opportunity value, plus an HTML heatmap of clusters against brands, competitors and extra terms.
Google Colab or a similar Python environment with the listed packages, a TYPESAFE_API_KEY for Jev, a GEMINI_API_KEY for cluster names and Google Cloud Natural Language credentials for the entity check; missing keys degrade gracefully rather than failing.
The README says Jev answers a yes/no question so scores tend to bunch high, and recommends labeling about 100 comments and picking the threshold from precision and recall instead of trusting 0.8, or training a SetFit or logistic-regression classifier on the existing embeddings.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
Reddit: Localization QA with a decision model: meaning, terminology, and placeholders as separate checks.
Reddit: Reranking rebooking options with a decision model, ranked with reasons.
Reddit: LocalLLaMA debates whether TypeSafe's $870M raise is Jev hype.
Reddit: A developer turns Jev's pick-from-a-list output back into generated text.
More Jev resources like this
An independent open-source pipeline that gives Jev vision: it detects and tracks objects in images, video, and RGB-D camera feeds, then asks Jev a typed question about each relevant object and prints the chosen answer with its confidence.
Jev Tools & CLIs#community#github#python
Community
CharlesFeng0314/JEV_seesPython
CharlesFeng0314/JEV_seesPythonA local web app where Claude Code runs the hunt and Jev judges every competitor post, surfacing the ones most likely to work for your audience. It adds saved-post analysis, transcription, an Ask AI view and a My Studio planning page.
Jev Tools & CLIs#community#github#python
A Python script that scores an AI agent's documented security finding with TypeSafe's Jev model, returning JSON with is_vulnerability, impactful and an impact_score from 0 to 1. It accepts structured JSON or a full text or Markdown report and can be imported by another agent.
Jev Tools & CLIs#community#github#security
Community
mhaskar/JevImpactPython
mhaskar/JevImpactPythonA proof-of-concept Python scanner that maps a backend codebase's attack surface and ranks likely vulnerabilities down to the suspicious line, using Jev as the classifier for about one cent per repo. It reads Python, JavaScript and TypeScript, and a demo site shows read-only results on PyGoat and NodeGoat.
Jev Tools & CLIs#community#github#security
A 2.4 GHz WiFi site-survey instrument built on a ZimaBoard 2 and an Alfa USB adapter: Python and Scapy turn captured 802.11 frames into structured RF telemetry, and Jev reads each measurement window through OpenRouter to return coverage, congestion and optimization decisions.
Jev Tools & CLIs#community#github#wifi
Community
ronibandini/jevwifianalyzer-Python
ronibandini/jevwifianalyzer-PythonA repository described as Jev use cases; the captured README documents an Austrian hotel ranking pipeline that merges Google Maps and Booking.com data, matches the ÖHV member list from meinhotel.at and computes a 0–100 guest score, with dashboards and Excel reports.
Jev Tools & CLIs#community#github#python
Community
schilfschneider/jevPython
schilfschneider/jevPythonFrom the Jev guides
Original write-ups on what this entry is for.
From the Jev AI model community
Posts from builders shipping with Jev right now.