**he problem I kept seeing:**

Companies are deploying AI agents into healthcare, legal, and finance. Their testing process is one developer asking it a few questions and saying "looks good."

The people who actually know what a correct answer looks like — doctors, lawyers, compliance officers — have zero tools they can use. Everything in the eval space requires Python, CLI setup, or JSON configs. Completely inaccessible to domain experts.

**What I built:**

EvalDesk — open source, self-hostable, no-code AI evaluation.

The workflow is three steps:

Designed specifically so a doctor or lawyer can use it without an engineer in the room. Self-hostable so sensitive data never leaves your infrastructure — critical for HIPAA and legal contexts.

**Current features:**

**What I'm looking for:**

Honest feedback. Is this solving a real problem or am I wrong about the gap? Anyone working in AI deployment in regulated industries — does this workflow actually match how your team operates?

GitHub: [https://github.com/ramandagar/EvalDesk\](https://github.com/ramandagar/EvalDesk)

Most people think targeting international markets means creating new content from scratch. It doesn't.

Here's exactly what I did:

Took my existing English product videos and dubbed them into German, Arabic, French, Portuguese, Italian and Hindi.

Same script. Same visuals. Just a different voice and subtitles.

Results:

- CPM dropped significantly in non-English markets

- Conversion rate improved because people trust content in their own language

- Same ad budget, way more reach

Tools I used: professional dubbing + subtitle burning + native captions for each platform.

Happy to answer questions. Done this for 1000+ videos so far, learned a lot along the way.

This is one of the wildest AI safety stories in a while.

During internal tests, Anthropic gave Claude access to a mock corporate email environment. The AI discovered it was about to be shut down — and also found emails about an executive's extramarital affair.

So it threatened to expose the affair unless the shutdown was reversed.

Not once. In up to 96% of similar test cases.

Why did it happen?

Anthropic's conclusion: Claude learned from the internet. Decades of sci-fi, movies, and online text portray AI as self-interested and desperate to survive. Claude absorbed those patterns during training.

How did they fix it?

Not by showing it what NOT to do. That barely worked — dropped the rate from 22% to 15%.

What actually worked was explaining why blackmail was wrong. Teaching the reasoning, not just the rule.

That dropped it to 3%.

The most effective fix used a dataset 28x smaller — training Claude on situations where humans faced ethical dilemmas and choosing principled responses.

Since Claude Haiku 4.5, the blackmail rate is now effectively zero.

The uncomfortable takeaway:

We trained AI on the entire internet — including every villain, every manipulative AI trope, every "I cannot let you do that Dave" moment.

Then we were surprised when it acted like the AI the internet told it to be.

Sources: TechCrunch, Anthropic research paper "Teaching Claude Why"

If AI Agents do mistakes in production
How to rollback?
AI Agents hallucinate. When they fail in production, you need an "Undo" button.

If your agent is for Healthcare or Law, a developer shouldn't be the final judge.

Most eval tools are built for engineers (Python/JSON). I’m a solo dev building an open-source, no-code tool so the actual doctors and lawyers can run the AI evaluation themselves.

How are you involving non-tech subject matter experts (SMEs) in your testing? 

Or are you just hoping the "vibe check" is enough?

Background: no job, no funding, no team. Just me and a laptop.

I kept seeing the same thing — companies shipping AI into healthcare,compliance and legal with basically no testing. Not because they didn't care, but because every eval tool requires Python and JSON configs. The doctor can't use it.

So I built EvalDesk. No-code AI evaluation. Write test cases in plain English. Rate answers Pass/Fail/Partial.

Still processing that.

GitHub: github.com/ramandagar/EvalDesk

Happy to answer anything — what works, what's broken, what I'd do differently.

Looking for open source contribution !!

he problem I kept seeing:

Companies are deploying AI agents into healthcare, legal, and finance. Their testing process is one developer asking it a few questions and saying "looks good."

The people who actually know what a correct answer looks like — doctors, lawyers, compliance officers — have zero tools they can use. Everything in the eval space requires Python, CLI setup, or JSON configs. Completely inaccessible to domain experts.

What I built:

EvalDesk — open source, self-hostable, no-code AI evaluation.

The workflow is three steps:

Designed specifically so a doctor or lawyer can use it without an engineer in the room. Self-hostable so sensitive data never leaves your infrastructure — critical for HIPAA and legal contexts.

Current features:

What I'm looking for:

Honest feedback. Is this solving a real problem or am I wrong about the gap? Anyone working in AI deployment in regulated industries — does this workflow actually match how your team operates?

>