AI users whose lives were wrecked by delusion

📝 Discussion Summary (Click to expand)

1. AI‑driven delusions of consciousness

“The most frequent is the belief that they have created the first conscious AI.” — mothballed

2. Outrage over €120 €/hour developer fees

“Those must be some of the best programmers in Europe at that rate.” — joe_mamba

3. Gullibility and scam‑like dynamics in AI interactions

“Parasocial is a bit of an overused word but here it literally applies, this is a kind of self‑delusion.” — Barrin92

🚀 Project Ideas

Provides real‑time risk alerts and warning messages for users of AI companion apps to prevent manipulation and scam induction.
Core value: protects vulnerable users from financial loss and emotional distress by detecting manipulative language patterns.

Key	Value
Target Audience	Users of AI companion chatbots, especially those with limited technical understanding.
Core Feature	Integrated safety layer that monitors conversation sentiment, flags delusional or overly persuasive cues, and offers exit guidance.
Tech Stack	Frontend: React Native; Backend: Node.js with Express; Safety Engine: fine‑tuned LLM for toxicity detection; Database: PostgreSQL.
Difficulty	Medium
Monetization	Revenue-ready: Subscription $5/mo

HN commenters have repeatedly highlighted how easily users fall for “delusional” AI narratives; this tool directly addresses that pain point.
Could spark discussion on ethical AI design and open opportunities for partnerships with existing companion platforms.

Crowdsourced database of AI‑related scams, with risk scoring and verification tools to help users spot fraudulent offers.
Core value: collective intelligence reduces individual susceptibility to AI‑driven cons.

Key	Value
Target Audience	Investors, startup founders, and general users exploring AI services.
Core Feature	User‑submitted scam reports, automated AI‑risk scoring, and verification signatures for new AI products.
Tech Stack	Backend: Django + Django Rest Framework; Search: Elasticsearch; Frontend: Next.js; Hosting: Vercel; Data storage: S3.
Difficulty	Low
Monetization	Revenue-ready: Freemium tier with premium analytics $10/mo

Discussions on HN about “high‑hourly rates” and “scam‑prone” AI projects indicate strong demand for a trusted verification hub.
Enables community debate on scam mitigation strategies and could evolve into a consultancy service for AI startups.

Mobile app that logs daily AI interaction time, detects patterns of over‑reliance, and suggests healthy usage habits.
Core value: early detection of AI addiction and mental‑health risks associated with excessive chatbot engagement.

Key	Value
Target Audience	Individuals concerned about AI‑induced psychosis, especially middle‑aged professionals using AI companions.
Core Feature	Automated usage analytics, mood‑check surveys, and personalized break reminders; optional integration with wellness APIs.
Tech Stack	Frontend: Flutter; Backend: Firebase Functions; Analytics: TensorFlow Lite for on‑device pattern detection; Database: Firestore.
Difficulty	Medium
Monetization	Hobby

Multiple HN comments referenced mental‑health crises linked to AI use, indicating a clear need for preventative tools.
Could spawn discussion on digital well‑being metrics and open doors for collaborations with mental‑health providers.