Boost Form Efficiency with AI-Driven Entity Extraction

Boost Form Efficiency with AI-Driven Entity ExtractionBoost Form Efficiency with AI-Driven Entity Extraction

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Tolulade Ademisoye

• 10h

Today language models can be used to take forms from users using entity extractio. This is one project that I am proud to work with.

Aldrin Caballero

pro

• 8h

Figma Make with Claude 4.6 or Gemini 3 AI is reshaping how we design products, and the tools are evolving rapidly. Figma Make, Figma’s prompt-to-code prototyping tool, now lets you choose which AI model powers your output, with Claude 4.6 and Gemini 3 as the two main options.

What is Figma Make Figma Make is an AI-powered prompt-to-code tool that is a part of the Figma ecosystem. Although it has a name “Figma” in it, Make is very different from the traditional Figma Design files. Rather than assembling static frames and components by hand, you describe what you want in plain language, and Make generates a fully interactive, high-fidelity prototype for you, complete with working React and Tailwind CSS code running underneath.

Brian Johnson

• 14h

I was asked to design a web application for college student entering the job market. The primary focus was Eng student who would leverage AI power tools to enhance their resumes, conduct mock interviews, and more. Free and premium users prospectives were designed. A strong focus on conversion and value was strategically placed across all user flows.

Web Design Product Design UI Design FigJam Figma Figma Make

ANIMESH SINGH

• 11h

Problem: Many organizations still process invoices manually by reading PDF documents and entering key details (invoice number, vendor, amount, etc.) into systems. This process is slow, error-prone, and difficult to scale, and it also makes it harder to detect duplicate invoices or incorrect totals.

Solution: This project builds an automated invoice processing pipeline that converts uploaded invoice PDFs into structured data. It uses OCR to extract text, LLMs to identify invoice fields, validation checks to ensure correctness, and Kafka-based event streaming to manage the processing pipeline. The extracted data is stored in PostgreSQL and visualized through a dashboard, enabling faster, scalable, and more reliable invoice processing.

Data Engineering Apache Airflow Kafka Python AI Engineer

Abhiram Tinguria

• 11h

Very real problem. Manual invoice processing is still surprisingly common and becomes a huge bottleneck as companies scale. Automating extraction and validation with AI can save a lot of operational time and reduce errors significantly.