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A retrieval index passes its freshness, lineage, quality, and access limits straight into the research agent reading it. I’m documenting how we connect that dependency to versioned prompts, tool outputs, and review decisions so every generated claim has a recoverable path. This is the kind of auditable AI-search workflow we help companies ship. Which proof would make you trust an AI-researched page first: a current index, a full tool trace, or a recorded review decision?
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ChatGPT for Academic Researchers: Engineering Guide
ChatGPT for Academic Researchers brings GPT-5.6 Sol Pro and Codex to scientists. Learn how to build reproducible, observable research workflows safely.
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Rocky, the Watchmaker
A cinematic tribute piece inspired by Rocky, the alien engineer from Project Hail Mary. I imagined Rocky’s problem-solving mind applied to something delicate and human: a Swiss tourbillon movement, rendered in stone-textured, alien anatomy.
Fully AI-generated, 46 seconds, closing with a full diagnostic scan sequence built from custom technical-overlay prompting (no template scan effects, no stock VFX). Includes a short workflow breakdown showing how the piece was built, start to finish.
This project pushed multi-shot creature consistency, material physics on organic-vs-mechanical surfaces, and a from-scratch diagnostic HUD language, all without traditional 3D or CGI tools.
I have nothing more to say. All I can say is, "Amazing!" 😛
Most AI tools look powerful and feel impossible.
The gap between those two is design quality, and it's the whole reason people churn.
$8,000 to close it.
Spot on Mahmudul, Great design truly bridges that gap it transforms complex tech into an experience people actually want to stick with. 💡✨
I built WITNESS — customer friction, captured before it becomes churn
Customers rarely submit perfect support tickets. They leave voice notes, screenshots, incomplete explanations—or simply leave.
So I built WITNESS: a focused web app that turns a customer’s statement, intent, and private evidence into one structured Witness Packet.
Customers can speak or type what happened, attach files, and receive a private tracking reference. Teams can review the packet, assign ownership, update its status, and resolve it without exposing internal notes or raw evidence.
WITNESS uses Base44 for data, authentication, storage, permissions, backend functions, and deployment. ElevenLabs handles voice transcription, while AI creates summaries, suggests severity, scores likely spam, and drafts customer updates for human review.
The product feels premium because it is restrained, clear, and built around trust. Instead of another crowded support dashboard, it uses an editorial case-file interface where the customer’s words stay central and every important action leaves a visible record.
https://witness-c1b4526a.base44.app
Built for the Base44 Dev Build-Off.
Very interesting project. For the voice transcription how well does the AI perform when giving severity scores if the voice recording is messy or with a lot of disturbance?