Tag: llm

AI Coding Assistants: Comparing Cursor Vs Windsurf for Healthcare Enterprise Readiness

~ Harini Kannan ~ Leave a comment

Read initial Enterprise readiness and security reports for:Cursor: https://harini.blog/2025/05/07/detailed-security-and-enterprise-readiness-report-cursor-ai-ide/Windsurf: https://harini.blog/2025/07/02/windsurf-detailed-enterprise-security-readiness-report/ AI Coding Assistants — Enterprise-Readiness Snapshot for Healthcare Orgs Audience: CISO / VP-Engineering / Head of AIScope: Comparison of Windsurf™ (formerly Codeium) vs Cursor for a U.S. healthcare-regulated environment that handles PHI and must satisfy HIPAA, SOC 2, and (ideally) FedRAMP controls. 1 Executive-level takeaway WindsurfCursorOverall … Continue reading AI Coding Assistants: Comparing Cursor Vs Windsurf for Healthcare Enterprise Readiness

Detailed Security and Enterprise Readiness Report: Cursor AI IDE

~ Harini Kannan ~ 1 Comment

Prepared for: Enterprise AI Teams and AI/Security Leadership Based on: Publicly available information and Cursor's provided documentation from:https://www.cursor.com/security, https://trust.cursor.com/faq and https://trust.cursor.com/ Table of Contents Executive Summary Introduction to Cursor Core Security Architecture and Practices AI Request Processing and Data Handling Codebase Indexing: Functionality and Security Privacy Mode: Guarantees and Implementation Enterprise-Specific Features and Considerations Potential … Continue reading Detailed Security and Enterprise Readiness Report: Cursor AI IDE

Ship Code 10× Faster: Guide, Don’t Grind—With AI Coding Assistants

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1. The Productivity Cliff We’re Ignoring ? In 2025, not pair‑programming with an AI coding companion is like scrolling through Google results page‑by‑page while everyone else fires off one‑sentence queries to an AI search assistant—it technically works, but you bleed hours that snowball into months of lost velocity every year. I learned this the hard way. My … Continue reading Ship Code 10× Faster: Guide, Don’t Grind—With AI Coding Assistants

Building an AI 10-Q Analyzer: Part 3 | Evaluating Results and Insights using O1-mini -using 10Qs from Microsoft and Rigetti

~ Harini Kannan ~ 1 Comment

Read Part 1 here. Read Part 2 here. In the dynamic world of financial analysis, the ability to swiftly and accurately interpret complex quarterly filings like the SEC’s Form 10-Q is invaluable. To address this need, I developed an AI-driven pipeline leveraging the Google/flan-t5-base model, Retrieval-Augmented Generation (RAG), and Named Entity Recognition (NER). Recently, I … Continue reading Building an AI 10-Q Analyzer: Part 3 | Evaluating Results and Insights using O1-mini -using 10Qs from Microsoft and Rigetti

Building an AI 10-Q Analyzer: Part 2 | Navigating the Pros and Cons of Structured Output from 10-Q Systems

~ Harini Kannan ~ 2 Comments

Read Part 1 here. Introduction In the realm of financial analysis, structured data extraction from complex documents like SEC 10-Q filings can revolutionize how investors make decisions. The 10-Q Analyzer project leverages AI to automate this process, but like any technological solution, it comes with its own set of advantages, disadvantages, and challenges. This blog … Continue reading Building an AI 10-Q Analyzer: Part 2 | Navigating the Pros and Cons of Structured Output from 10-Q Systems

Building a 10-Q Analyzer: Part 1 | Extracting Financial Insights with AI

~ Harini Kannan ~ 2 Comments

Read Part 2 and Part 3 In the evolving landscape of artificial intelligence, combining advanced techniques like Retrieval-Augmented Generation (RAG) and Named Entity Recognition (NER) has opened new avenues for extracting and structuring information from complex documents. This blog delves into the intricacies of building a 10-Q Analyzer—a tool I designed to process SEC 10-Q … Continue reading Building a 10-Q Analyzer: Part 1 | Extracting Financial Insights with AI

Understanding Production RAG Systems (Retrieval Augmented Generation)

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1. What is RAG ? Retrieval Augmented Generation (RAG), is a method where you have a foundation model, and you have a library of personal documents – this can be unstructured data in any format. Now your goal is for answering some questions from your persona library of docs, with the help of LLM. Enter … Continue reading Understanding Production RAG Systems (Retrieval Augmented Generation)