Stop leaking user data to OpenAI/Claude/Gemini(risk-mirror.vercel.app)
risk-mirror.vercel.app
Stop leaking user data to OpenAI/Claude/Gemini
https://risk-mirror.vercel.app
7 comments
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Stop burning $20/mo on Claude credits. I unlocked the Prompt Optimizer for free.
If you're hitting the message cap on Claude/Cursor, you're sending too much fluff. "Please", "Thank you," and verbose context contexts are eating 30% of your token budget.
I built Risk Mirror to mathematically compress prompts (removing filler, preserving logic).
It’s usually a Pro feature, but is completely free for now while I benchmark compression rates.
Free Tools Included:
* Prompt Optimizer (Save 40% tokens)
* Safe Share (Redact PII from LOGS/Text instantly)
* Risk Scanner (Check prompts before pasting)
* Clarity Analyzer (Fix vague inputs)
Grab it before I have to close the free tier
If you're hitting the message cap on Claude/Cursor, you're sending too much fluff. "Please", "Thank you," and verbose context contexts are eating 30% of your token budget.
I built Risk Mirror to mathematically compress prompts (removing filler, preserving logic).
It’s usually a Pro feature, but is completely free for now while I benchmark compression rates.
Free Tools Included:
* Prompt Optimizer (Save 40% tokens)
* Safe Share (Redact PII from LOGS/Text instantly)
* Risk Scanner (Check prompts before pasting)
* Clarity Analyzer (Fix vague inputs)
Grab it before I have to close the free tier
[deleted]
[deleted]
If your Cursor/Claude credits vanish fast, it’s probably prompt bloat.
Polite filler + repeated context + messy JSON = wasted tokens.
Risk Mirror compresses prompts 20–40% without changing meaning.
More credits. Same results.
Try free: https://risk-mirror.vercel.app
Polite filler + repeated context + messy JSON = wasted tokens.
Risk Mirror compresses prompts 20–40% without changing meaning.
More credits. Same results.
Try free: https://risk-mirror.vercel.app
Every time you paste a stack trace into ChatGPT, you might be leaking:
- User session tokens
- Database connection strings
- API keys from env variables
I built Risk Mirror to scan and redact sensitive data BEFORE it touches any AI.
It's deterministic (no AI used for scanning because that would defeat the purpose).
Feedback welcome !
- User session tokens
- Database connection strings
- API keys from env variables
I built Risk Mirror to scan and redact sensitive data BEFORE it touches any AI.
It's deterministic (no AI used for scanning because that would defeat the purpose).
Feedback welcome !
I'm the creator of the PII Firewall Edge API (currently on RapidAPI).
I saw a lot of devs struggling to implement safety guardrails correctly—most were just using basic regex or heavy LLMs that hallucinate.
So, I decided to package my API into a full-featured UI/Toolkit called Risk Mirror.
What it does: It sits between your users and your LLM (OpenAI/Anthropic) and strips out sensitive data before it leaves your server.
The Tech (Zero AI Inference): Instead of asking an LLM "is this safe?", I use:
152 PII Types: My custom engine covers everything from US Social Security Numbers to Indian Aadhaar cards and HIPAA identifiers. Shannon Entropy: To detect high-entropy strings (API keys, passwords) that regex misses. Deterministic Rules: 100% consistency. No "maybe." Why use this?
It's Tested: The underlying API engine is already battle-tested. It's Fast: <10ms latency.
Includes a 'Twin Dataset' generator for Data Scientists (redact CSVs securely). Feedback welcome!"