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The Auto-Reply Agent (Drafting): Using the extracted context, the LLM crafts a highly personalized, context-aware response addressing the specific questions or needs of the sender.
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The challenge theme was “Anything you can imagine. Create something you wish existed but doesn’t.”
That got me thinking about how often we randomly wish a product, brand, or idea existed after having a dream or strange thought.
So I asked Melius to create an AI agent system that takes a dream, surreal idea, or imaginary product and turns it into a real brand.
For this project, I gave it a single prompt:
“A perfume that smells like rainy childhood evenings.”
What blew my mind was that it didn’t just generate random visuals. The agents actually started thinking like a real creative team! Interpreting emotions, building a brand strategy, deciding on art direction, creating packaging, ad campaigns, cinematic posters, commercials, social media assets, and even the overall world around the brand.
The final brand it created was called “Afterrain.” All in an instant!
What makes Melius feel different from normal AI tools is that the workflow feels collaborative and intentional. Instead of just outputting images, the agents behave more like art directors, strategists, filmmakers, and designers working together creatively. Watching the nodes evolve and influence each other honestly felt closer to working with a futuristic creative agency than using a generator.
The craziest part is that all of this is the branding, visuals, campaigns, packaging, posters, and cinematic direction, came from a single prompt.
This project explores the idea:
“What if dreams and impossible ideas could instantly become real brands?”
Try it yourself!🙂
Project Link: https://app.melius.com/projects/c34f166a-dbdd-4008-aea3-fb644a0200f2/canvas/d695e870-4e73-40ad-8439-e5839afd77c9
Social Link: https://x.com/Arjustings/status/2054868056485290045?s=20
A few more assets generated by Melius!
Can reference images really guide AI outputs effectively?
Here’s my visual exploration of retro pastel editorial aesthetics using Krea AI’s newest image model, Krea 2.
For this test, I used the Style Transfer feature together with curated references to see how well the model could maintain a consistent visual direction across multiple generations.
Full process breakdown in the comments.
Curious to hear your thoughts on the results, do you think the consistency works?
Fluffless: Deterministic AI Proposal Engine
Project Overview / TL;DR
A high-performance Next.js web application that forces LLMs to write hyper-direct, senior-level technical pitches by anchoring generation to a persistent, client-side profile vault.
The Problem:
The freelance market is flooded with generic, AI-generated proposals that read like bloated cover letters. Clients ignore them because they lack technical depth and focus on "marketing fluff" rather than execution. Additionally, existing tools create massive UX friction by forcing users to manually re-type their past work experience for every single pitch.
The Solution:
I architected and deployed Fluffless to solve both the output quality and the user friction. I bypassed standard AI wrappers to build a constraints-based engine that only generates technically sound, highly specific proposals.
Key Technical Execution:
Client-Side "Profile Vault": To eliminate repetitive data entry, I engineered a persistent LocalStorage architecture. Users input their raw technical resume once, and the application silently passes this base context to the AI for all future generations, reducing user input to just the target job description.
Heavyweight AI & Prompt Engineering: I integrated the Groq API utilizing the massive Llama-3.3-70B reasoning model. I engineered a strict, one-shot prompt architecture loaded with negative constraints to completely strip out default "AI-speak" (e.g., words like leverage, delve, seamless) and force a rigid 4-step psychological sales structure (Hook -> Proof -> Execution -> Technical CTA).
Native Streaming Architecture: When the standard Vercel AI SDK introduced silent failures due to protocol mismatches, I bypassed the abstraction entirely. I built a custom, native JavaScript fetch and TextDecoder implementation to guarantee 100% stable, low-latency text streaming directly to the UI.
My Role:
Lead Full-Stack Developer & Prompt Engineer
Tools & Technologies:
Next.js (App Router)
React
Tailwind CSS
Groq API (Llama-3.3-70B)
Vercel
Project Links:
Live Site: https://fluffless.vercel.app/
Nice work🔥👏
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