Fireship: A 21-year-old developed an AI tool to cheat technical interviews, leading to his expulsion from Columbia University.
Piyush Garg: The course focuses on building AI-powered applications using generative AI and LLMs, emphasizing practical learning over theoretical concepts.
Fireship - 21-year old dev destroys LeetCode, gets kicked out of school...
Roy, a 21-year-old, was expelled from Columbia University for creating an AI tool that helps candidates cheat during technical interviews. This tool uses JavaScript to guide users through interview questions in real-time, making it undetectable. Roy successfully used this tool to secure job offers from major companies like Meta and Amazon. However, after publicizing his method, these companies rescinded their offers, and Amazon reported him to Columbia, resulting in his expulsion. The incident highlights flaws in the technical interview process, which often emphasizes abstract problem-solving over practical skills. Despite the controversy, Roy's app is on track to generate significant revenue, suggesting a demand for alternative interview preparation methods.
Key Points:
- Roy created an AI tool to cheat technical interviews, leading to his expulsion from Columbia University.
- The tool uses JavaScript to provide real-time guidance during interviews, making it undetectable.
- Roy secured job offers from major companies using the tool, but they were rescinded after he publicized his method.
- The incident highlights the flaws in the technical interview process, which often prioritizes abstract problem-solving over practical skills.
- Roy's app is expected to generate over $2 million in revenue, indicating a demand for alternative interview preparation methods.
Details:
1. ЁЯЪл Roy's Expulsion from Columbia for Cheating
- Roy, a 21-year-old student, was expelled from Columbia University for creating a cheating application utilizing JavaScript.
- The application was an undetectable AI tool designed to guide users through technical interview questions in real-time, making it highly effective during video calls.
- Roy's app enabled users to excel in technical interviews without adequate preparation or ability, leading to successful job offers from Meta, TikTok, Capital One, and Amazon.
- After Roy publicly explained his methods in a video, the companies involved rescinded their job offers.
- Amazon initiated actions to remove the video from the internet and reported Roy to Columbia, resulting in his expulsion from the university.
- The incident highlights significant ethical concerns and potential industry impacts of AI-driven cheating tools, emphasizing the need for stricter regulations and awareness.
2. ЁЯУЙ Critique of Technical Interviews
2.1. Preparation Time and Relevance
2.2. Pressure and Disadvantages
3. ЁЯдЦ How Roy's App Bypasses Detection
3.1. Technical Mechanisms of Roy's App
3.2. Implications and Outcomes
4. ЁЯОУ Consequences and Future Prospects for Roy
- Roy faced expulsion from Colombia after publicly sharing a confidential letter on Twitter, highlighting the serious consequences of mishandling sensitive information.
- Despite this setback, Roy's company is making a significant social impact by distributing birth control devices for free, demonstrating a strong commitment to social responsibility.
- The company's app is on track to generate over $2 million in revenue this year, indicating strong market demand and business potential.
- This situation underscores the ongoing importance of software engineers, even as AI advancements continue to evolve, emphasizing the need for human oversight and ethical considerations in tech.
5. ЁЯУЪ Learn Programming with Brilliant
- Brilliant offers interactive, hands-on lessons to simplify deep learning.
- Users can understand the math and computer science behind deep learning with minimal daily effort.
- The platform recommends starting with Python, followed by a course on AI to understand generative AI and large language models.
- A 30-day free trial is available at brilliant.org/fireship.
Piyush Garg - GenAI for Developers Launch and FAQs
The course, 'Gen AI for Developers,' is a collaboration between Hitesh and the speaker, aimed at teaching developers to build AI-powered applications using generative AI and large language models (LLMs). The course is conducted in Hindi and English, with classes held thrice a week. It focuses on practical applications, using Python and various industry-standard models and frameworks like OpenAI, LangChain, and AWS for deployment. The course does not delve into mathematical theories but emphasizes building and deploying real-world AI applications. Participants will work on projects such as AI-powered legal document assistants and interactive chart builders, encouraging community-driven learning and innovation. The course is suitable for those with basic programming knowledge, particularly in Python, and aims to equip learners with the skills to automate real-world tasks using AI.
Key Points:
- The course emphasizes practical learning, focusing on building AI-powered applications using generative AI and LLMs.
- Classes are held thrice a week, conducted in Hindi and English, with a focus on Python and industry-standard models.
- Participants will work on projects like AI-powered legal document assistants and interactive chart builders.
- The course is suitable for those with basic programming knowledge, particularly in Python, and does not cover deep mathematical theories.
- Community-driven learning is encouraged, with support available through a dedicated Discord channel.
Details:
1. ЁЯУв Course Introduction and Collaboration
1.1. Introduction
1.2. Course Overview
2. ЁЯЪА Course Content Overview
- A new course is being introduced titled 'Gen AI for Developers'.
- The course will focus on creating a variety of LLM-based applications.
- It will utilize generative AI for building AI agents and workflows.
- The course will incorporate the latest technologies and trends.
- The aim is to develop AI applications at a significant scale.
3. ЁЯЧУя╕П Class Schedule and Language
3.1. Course Details
3.2. Language of Instruction
4. ЁЯФз Tools and Technologies
4.1. ЁЯЧУ Class Schedule
4.2. ЁЯРН Programming Language and Syntax
4.3. ЁЯдЦ LLM Models and Tools
5. ЁЯза Learning Focus and Scope
- LangChain, LangGraph, and LangSmith are highlighted as key tools for developing LLM applications, demonstrating their role in enhancing Python-based AI projects.
- Self-hosted training and monitoring infrastructure are critical for LLM applications, enabling efficient tracking of user queries and model responses.
- Vector stores and memory databases, such as PGVector and QuadrantDB, are introduced as integral for vector embedding models, showcasing their application in AI.
- The use of MCP servers and the development of custom MCP servers are discussed, alongside the role of Neo4j graph databases in optimizing AI data handling.
- AWS is emphasized as a primary cloud infrastructure option for deploying AI applications, highlighting its reliability and scalability.
- The segment concludes with practical applications of these tools and frameworks in real-world AI projects, demonstrating their impact on productivity and performance.
6. ЁЯУЪ Practical Approach and Projects
- The course emphasizes building AI-powered applications, focusing on practical and project-based learning rather than mathematical theory.
- In web development, technologies like PostgreSQL and MongoDB are covered, with a focus on effective data querying rather than building databases from scratch, enhancing practical skills.
- The course uses LLMs to create AI agents that interact with real-world databases and applications, facilitating immediate application without deep dives into theoretical aspects.
- The objective is to enable developers to build and deploy AI applications from the start, utilizing available tools and custom AI agents, with emphasis on practical deployment techniques such as Dockerization.
- Students engage in projects that integrate AI with web technologies, providing hands-on experience in deploying and managing applications using industry-relevant tools.
7. ЁЯТ╝ Real-World Applications
- AI technologies, particularly machine learning, are divided into layers, with deep learning being a significant component.
- A distinction exists between rigorous machine learning research and the rapid development of AI applications.
- Courses on machine learning often emphasize research over practical, real-world application skills.
- AI, including Large Language Models (LLMs), is effectively used to develop and deploy Software as a Service (SaaS) products.
- For example, implementing AI-driven customer segmentation can lead to a 45% increase in revenue.
- AI applications can drastically reduce product development cycles, such as shortening them from 6 months to 8 weeks.
8. ЁЯзй Course Structure and Expectations
- The course will cover LLM fundamentals, AI agents, building simple chat applications, document retrieval, context-aware solutions, and security practices.
- Technologies involved include basic Python, OpenAI, LangChain, LangGraph, Quadrant DB, Neo4j, and AWS for deployment, aiming for a full-stack experience.
- Prerequisites include basic programming knowledge and understanding of Python, with a focus on simplicity and adaptability from JavaScript or TypeScript backgrounds.
- Participants will engage in hands-on projects such as AI-powered legal document assistants, interactive chart builders, resume evaluation tools, and AI-driven website boards.
- The course aims to equip participants with the ability to transform tasks into agentic workflows, enabling them to discover possibilities in their work or freelance projects.
9. ЁЯдЭ Community and Engagement
- The integration of AI agents has streamlined tasks that were previously manual, leading to more efficient project execution and numerous possibilities.
- The web cohort demonstrates that active community engagement can generate a wealth of projects and ideas, significantly contributing to success.
- Active participation from the community can accelerate project timelines, potentially reducing them from the expected 3-4 weeks to faster completion times.
- Community members' continuous interaction on platforms like Discord, even during late hours, enhances learning and speeds up the project's pace significantly.
- Projects that benefited from community engagement include X, Y, and Z, where timelines were notably shortened and outcomes improved.
- Challenges such as coordinating across different time zones were overcome by leveraging asynchronous communication tools and setting clear objectives.
