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
Tag: Data Science
Building an AI 10-Q Analyzer: Part 3 | Evaluating Results and Insights using O1-mini -using 10Qs from Microsoft and Rigetti
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
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
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)
AI Product Management in Cybersecurity: McKinsey’s Three Horizons of Growth ? Flip it for startups, keep it for mega corps
What’s McKinsey’s Three Horizons of Growth ? The McKinsey’s Three Horizons of Growth is a management framework first introduced in the book “The Alchemy of Growth” published in 2000, which has since been referenced by top product strategists to help businesses execute existing business models while simultaneously innovating and pushing the boundaries and creating new … Continue reading AI Product Management in Cybersecurity: McKinsey’s Three Horizons of Growth ? Flip it for startups, keep it for mega corps
Data Science in Venture Capital – An Overview
I have been recently doing some research on applied Machine Learning and AI in Venture Capital (VC) funds, and it's a really interesting rabbit hole to go into, especially if you yourself are interested in either startups (as a would-be founder / employee) or in VCs themselves as to how they work as data driven … Continue reading Data Science in Venture Capital – An Overview
Data Science Leadership Series – Part 2 : How to choose data projects: Core Product Vs Support Consulting Vs Research | Beware of your bottomline
The biggest challenge for data scientists / managers in decision making capacity and one with the biggest consequential outcome for both the business and the data team, I feel, is the part where you say yes / no / let’s modify - to a new data project idea from leadership, or even starting a new … Continue reading Data Science Leadership Series – Part 2 : How to choose data projects: Core Product Vs Support Consulting Vs Research | Beware of your bottomline
Understanding Multicollinearity and Confounding Variables in Regression
Multicollinearity When two or more of the predictors are correlated, this phenomenon is called multicollinearity. This affects the resulting coefficients by masking the underlying individual weights of the correlated variables. This is why model weights are not equal to feature importance. Ways to deal with multicollinearity Looking at Variance Inflation Factor (VIf), which measures the … Continue reading Understanding Multicollinearity and Confounding Variables in Regression
Infinite Couch 