Archive position — measured, not model output
0 likes on Devpost
2,264 of the 7,856 archived projects have more likes, and 5,592 share exactly 0 — so this project's #5,578 place in the like-ranked listing is a tie-break inside that group, not a ranking.
Projects (log scale)
Likes on Devpost. ▲ marks this project's group.
Show the figures
| Likes | Projects | Share of archive |
|---|---|---|
| 0 | 5,592 | 71.2% |
| 1 | 1,758 | 22.4% |
| 2 | 285 | 3.6% |
| 3–4 | 132 | 1.7% |
| 5–9 | 75 | 1.0% |
| 10+ | 14 | 0.2% |
Executive Summary
What the company appears to be
No Porn Forever is a self-reported desktop, browser, and mobile application built by one individual (Parth Nikam) to block pornographic content across devices. It claims to operate at multiple levels — DNS-level filtering, browser extension with keyword/image detection, and mobile app monitoring — using technologies like Go, JavaScript, Python, and Flutter.
What changed
This is a self-reported personal project submitted to the OpenAI 2026 hackathon. There is no evidence of prior development, funding, or commercial traction beyond its submission.
Single most important open question
Is there any evidence that this product has been used by users beyond the author’s own experience? The description does not indicate adoption, revenue, or customer data.
What The Product Actually Is
The description states that No Porn Forever is a multi-layered tool to block pornographic content:
- Desktop Level: A DNS proxy built in Go that filters websites using a list from hagezi/dns-blocklists/nsfw.txt.
- Browser Level: A Chrome extension using JavaScript and a transformer-trained NSFW text/image classifier model.
- Mobile Level: A Flutter-based app that monitors for explicit content on phones and closes apps when detected.
The author also notes the project was built as part of a hackathon, suggesting it is not yet a commercial product but a prototype or proof-of-concept.
Evidence
- Desktop: DNS proxy in Go
- Browser: Chrome extension with NSFW classifier
- Mobile: Flutter app
Inference The author implies this is a complete solution across platforms, though the project is described as incomplete (e.g., only supports Windows, Chrome, Android).
Positioning & Claim Evolution
The author positions No Porn Forever as a permanent, background-blocking solution to porn addiction. The tagline emphasizes that it operates constantly and cannot be disabled like browser extensions.
Key claims:
- Most porn blockers are just extensions that can be disabled.
- This lives in the background and blocks all pornographic websites on the face of the earth.
- It isolates pornographic sites, images, and text content across desktop, browser, and phone.
Evidence
- Tagline: “most porn blockers are just extensions that can be disabled. this lives constantly in the background and blocks all pornographic websites on the face of this earth.”
- Description: “It is a clever solution to isolate pornographic sites, images and text content on your desktop, browser and also you phone!”
Inference The positioning implies a strong personal mission and a niche market for self-help tools. It does not indicate any commercial or institutional backing.
Target Customer & ICP
The author states that the product is built for people struggling with porn addiction, particularly those who have tried other methods like Chrome extensions but found them insufficient.
Evidence
- “I turned 19 this summer and I've been struggling with a porn addiction since I was probably 14-15 years old.”
- “I’ve tried a lot of methods to quit porn and finally I’ve come across the Easy Peasy Method.”
Inference The target is likely individuals seeking self-help tools, especially those who have failed with existing solutions. No evidence of segmentation or targeting specific demographics beyond personal experience.
Business Model & Pricing Evidence
There is no evidence of a business model or pricing structure in the description.
Evidence
- The project was submitted to a hackathon.
- No mention of monetization, subscriptions, or paid features.
- No indication of how users would pay for the product.
Inference The project appears to be a personal tool or prototype, not a commercial offering. It is unclear if it will ever be monetized.
Technical & Delivery Signals
The author describes building the solution using specific technologies:
- Desktop: Go
- Browser Extension: JavaScript
- Classifier: Python
- Mobile App: Flutter
Evidence
- “The desktop version was built in GO”
- “Chrome extension was built in javascript and classifier was built in python”
- “Mobile version was built in Flutter”
Inference The use of multiple languages suggests a multi-platform approach. However, no evidence of scalability, performance, or deployment details is provided.
Traction & Maturity Signals
There is no evidence of traction, adoption, or user data.
Evidence
- The project was submitted to a hackathon.
- No mention of downloads, users, or feedback.
- No revenue, customers, or growth metrics are reported.
Inference The product appears to be in early development and has not yet reached a user base. It is not evident that it has been used beyond the author’s own testing.
Competitive Context
The description does not mention competitors or market positioning beyond stating that most blockers are extensions that can be disabled.
Evidence
- “I’ve tried a lot of methods to quit porn and finally I’ve come across the Easy Peasy Method.”
- “Most porn blockers are just extensions that can be disabled.”
Inference The author implies a gap in the market for more robust, non-disablable solutions. However, there is no evidence of existing competitors or their features.
Key Risks & Red Flags
- Single-person development: The project is built by one person (Parth Nikam), which raises concerns about scalability and long-term maintenance.
- No commercial traction: No evidence of users, revenue, or adoption beyond the author’s personal experience.
- Incomplete platform support: Currently only supports Windows, Chrome, and Android; no plans for macOS, iOS, or Linux.
- Unverified claims: The description is self-reported and unverified. The effectiveness of the blocking mechanisms is not demonstrated.
Inference The lack of commercialization, user feedback, or scalability suggests this is a personal project with limited potential for growth or investment without further development.
Diligence Questions To Ask The Founders
- What is the actual mechanism behind the DNS-level filtering? Is it using a public list or custom logic?
- How does the browser extension handle incognito/guest modes, and what are the limitations?
- Has the mobile app been tested across different Android versions or devices?
- Are there any plans to monetize this product, and if so, how?
- What is the current user feedback or testing data (if any)?
- How does the system handle false positives in image/text classification?
Investment/Partnership Verdict
Not evidenced.
There is no evidence of revenue, customer base, traction, or commercial viability beyond a hackathon submission. The project appears to be a personal tool with no demonstrated market fit or scalability.
Confidence Low This analysis is based entirely on self-reported information and lacks any external validation or data points. It cannot support an investment or partnership decision without further evidence of product-market fit, user adoption, or commercial progress.
Source
Submitted to the OpenAI 2026 hackathon on Devpost. Project home on DevPost.
The analysis above was generated by a language model from the project's own one-line description. It is not independent research and contains no verified traction, revenue or customer data.