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I wanted to see if Qoder could turn a real World Development Indicators CSV into something verifiable, not just a pretty summary. I opened a new task, selected the QoderCSVReport workspace, left the model on Auto, set permissions, and gave Qoder the CSV. The file had 1,045 rows, 14...

139,779 views • 5 days ago •via X (Twitter)

7 Comments

AI StackLab's profile picture
AI StackLab5 days ago

Try Qoder free with 600 Credits 👇 300 from the Pro trial + 300 with code XK66ZB. 👉

JAYDEN™'s profile picture
JAYDEN™5 days ago

Going from a 1,045-row dataset to a complete interactive dashboard plus verification script in one workflow is seriously impressive.

Aaliyah | AI's profile picture
Aaliyah | AI5 days ago

Handling missing values correctly and validating everything against the original CSV shows how much more reliable these workflows are becoming.

Don Quijote de la IA's profile picture
Don Quijote de la IA5 days ago

New tool for me Friend ✍️

AI with MERLIN's profile picture
AI with MERLIN5 days ago

This goes way beyond a pretty AI summary — structured artifacts, a dashboard, and a verifier make the output genuinely practical.

Miguel Ángel | GptZone's profile picture
Miguel Ángel | GptZone5 days ago

Very interesting, 600 Credits total: 300 from the trial and 300 from code.

Anna K Clinton : AI's profile picture
Anna K Clinton : AI5 days ago

The independent verification step is what really stands out. Turning raw CSV data into something you can actually inspect and trust is incredibly useful.

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Nelly;

159,743 views • 5 days ago

Karpathy said something you'll regret ignoring: "We have to keep the AI on the leash. I'm still the bottleneck. I have to make sure this thing isn't introducing bugs and that there's no security issues." He said it at YC talk last year, when the worry was reliability. The models hallucinated and made mistakes no human would, so the leash implied keeping yourself in the loop and checking the output before trusting it. The models are far better now, and the line still holds, for a reason he was not focused on back then. Even a model that writes flawless code today still has no idea who is allowed to run it. Correctness and authorization are different problems, and only correctness improves as the model improves. A perfect agent still hands a tool where anyone can do anything, because permission was never part of the task. I actually tested this in practice with Claude Code. I asked it to build a small internal tool with a button that issues account credits. It worked first try, and running it locally, the credit applied the instant I clicked. Nothing decided who was allowed to click it. The agent wrote the right logic and displayed a success notification. It never checked whether the caller had the right, whether it should pause for a human, or whether anything was logged. And this is not a bug a smarter model can outgrow because the leash was never in the code. Identity, permissions, and audit live in the system that runs the app, not in what the agent generates. To solve this, I took the exact same bundle and hosted it on Retool. The credit write that fired silently on my laptop now stopped at an approval gate, resolved to a real identity through SSO, and landed in an audit log. I wrote none of it. The app inherited the entire boundary the moment it was deployed, and the video shows the before and after. You can try it yourself here: I also wrote a detailed breakdown of the whole thing in my recent article, and I worked with the team to put this together. It walks through the build, the exact moment the credit write went through on my laptop with nobody checking, and then what changed when the same app ran on Retool. It also covers why this is a property of the runtime and not something a better model fixes, which is why devs typically miss this. The article is quoted below.

Akshay 🚀

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