TL;DR: MIT’s new State of AI in Business 2025 should be read as a blurry mirror. It reflects real pain points in a handful of enterprises, but it is far from a complete picture of global adoption. The lesson isn’t that “AI doesn’t work,” but that most organizations are still struggling to cross the pilot-to-production … Continue reading The MIT GenAI Report: A Blurry Mirror, Not the State of Enterprise AI
Tag: artificial-intelligence
AI Agent Frameworks: A Product-First Approach to the Problem of Plenty
The hardest part of building an AI agent platform isn't the technical complexity—it's choosing which tools to use when every option seems both perfect and overwhelming. The Ocean of Options Building an AI agent platform today means navigating an unprecedented landscape of choices. The sheer volume of available frameworks, tools, and platforms can quickly overwhelm … Continue reading AI Agent Frameworks: A Product-First Approach to the Problem of Plenty
AI Coding Assistants: Comparing Cursor Vs Windsurf for Healthcare Enterprise Readiness
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
Windsurf : Detailed Enterprise Security & Readiness Report
Prepared for: Enterprise AI Teams and AI/Security Leadership Table of Contents Executive Summary About Windsurf Certifications & Third-Party Assessments Deployment Options & Data Residency End-to-End Data-Flow Anatomy Privacy / Zero-Data-Retention Posture Enterprise Controls & Admin Tooling Full Sub-processor & Model-Provider List Risk Analysis & Recommended Mitigations Adoption Road-map 1 Executive Summary Windsurf (formerly Codeium) offers … Continue reading Windsurf : Detailed Enterprise Security & Readiness Report
Detailed Security and Enterprise Readiness Report: Cursor AI IDE
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
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 2 | Navigating the Pros and Cons of Structured Output from 10-Q Systems
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
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
Quick Outline for Designing Data Pipelines for Machine Learning Projects
As a Machine Learning Engineer, designing a data pipeline involves ensuring data flow is efficient, scalable, reliable, and optimized for the requirements of ML models. Here’s a structured outline to keep in mind: 1. Data Ingestion Sources and Types: Identify data sources (e.g., databases, APIs, logs, IoT devices) and data types (structured, semi-structured, unstructured). Batch … Continue reading Quick Outline for Designing Data Pipelines for Machine Learning Projects
Understanding Production RAG Systems (Retrieval Augmented Generation)
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)
Infinite Couch 