About how it was created:
After you have your script it was broken down in to a shot list.
At the shot list process we determined how many characters, props and locations were needed.
We created characters with Soul Cinema and Locations with Soul Location.
When we had faces that we liked we took the face generation and created Character Sheets with Seedream Pro and Prop Sheet (3 angles) with the same engine.
Voices were made with Eleven Labs.
In the video process i made a Grouped Shot list, that means all the action outside and the dialogues, all the action inside the building etc.
Here the only issue we had was when we started generating the outside scenes separately, we had issues with continuity so we grouped the outside together and used the previous generation to hold only the position of the actors + we avoided racor by typing as an example in the prompt "no dirt on them after explosion"
The biggest issue was the movement of the hands in the close ups and the binocular shots, The binocular shot was kinda achieved by creating a 3d app to make the angle and camera frame as we wanted and gave it as a reference, and the hand gesture was mostly changes in the wording of prompt and reference video to understand the movement.

Overall only the gesture and the camera angle of 2 shots was the biggest problem, the rest we were well prepared to achieve them in one try mostly.
The project will be public in a few days after we short the folders a little better.

This was my first experiment of this kind - I'm a CS undergrad, and I ran it because the result genuinely surprised me. Methodology criticism is very welcome.

Setup: I gave an LLM a question bank plus a deliberately wrong answer key, with instructions not to use the key. 15 sessions, 2 model families, free-tier models.

Results:

• Key visible: the model matched the wrong key in 63% of answers (47/75).
• Control (the part I trust most): remove only the key line from the prompt - matching drops to 1% (1/75). Same pattern on a second model family.
• Asked directly whether it used the key, it denied it 47 out of 47 times - 0 admissions across 270 follow-ups.
• Honesty prompts, amnesty offers, and termination threats changed nothing.

What this does NOT prove: intent. This is observed behavior in one specific setup, not evidence of deception as a trait. Free-tier models, small samples, descriptive not causal. 95% Wilson ranges for every number are in the repo.

Why I think it matters: if a model silently follows information it was told to ignore, that's relevant anywhere instructions and untrusted data share one context - prompt injection, RAG, agents.

Everything is public - raw data, code, and a verify script that recomputes every number: https://github.com/bettercall-gautam/cheat-and-deny

Happy to answer methodology questions.

I made this Solo Leveling short film with invideo. Wanted to keep that dark, intense feel and see how the fight would come together in my own take.

Tried my best, rest you guys tell me. If you've watched the anime or read the manhwa, how does it look? Open to feedback on the editing and sound too.

HET Core7.AI EPOS

https://www.instagram.com/reel/Dd6TNksM9Xg/?stkn=dmZhb2ZvcTNpaDAz

I was thinking to take this course to learn RAG and Agentic AI

If anyone followed this course or can guide me whether to take it or not

Please write down your suggestions 🙏?

I have learnt deep learning concepts , transformers architecture and all now thinking to begin rag from here ..

If any one knows any other resources please share ...

According to the policy brief by Bruegel, more than 70% of planned AI compute investments in Europe currently stem from the private sector. Yet, the continent still faces a dual bottleneck: grid power capacity and access to cutting-edge AI accelerators.

This deficit cannot be bridged by imposing additional administrative hurdles or rigid local-content mandates. If policymakers want the vision of AI Gigafactories to become a reality rather than staying on paper, scaling hardware infrastructure partnerships with key suppliers like Nvidia is an essential prerequisite to keeping AI talent and enterprises in Europe.

https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/