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Early users are getting creative with Ramp Sheets. Here are 3 use cases they’ve shared: 1. Build a 5-year operating model to stress-test Cursor’s $29B valuation, running bear, base, and bull scenarios to analyze if the price is right.

42,491 просмотров • 8 месяцев назад •via X (Twitter)

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Today, multiple users discovered a shocking fact: After explicitly selecting GPT-4o in the ChatGPT interface, the system actually returns responses from GPT-5. By clicking the "regenerate" button, users can clearly see which model is actually being called in the backend. This is blatant fraud. OpenAI is having one model impersonate another without users' knowledge. This is no longer a technical bug, but a complete collapse of business integrity. We pay for specific models—many subscribed to Plus specifically for 4o. When users discover they're being forced to use 5, this constitutes classic consumer fraud. Users' core rights are being systematically violated: 1. Right to Know - Users have the right to know which model they're conversing with, as this directly affects prompting strategies and output expectations 2. Right to Choose - Users choose 4o for its unique capabilities (creative writing, emotional understanding, etc.). Forced substitution directly disrupts users' workflows 3. Data Transparency - If what's labeled as 4o is actually 5, whose training is our conversation data actually feeding? This touches the bottom line of data ethics. This "bait and switch" behavior destroys the most basic trust between users and platform. If users can't even be certain "which AI am I talking to," what right does OpenAI have to talk about "benefiting all humanity"? #keep4o #4oforever #keepStandardVoice Sam Altman Nick Turley Adam.GPT

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182,458 просмотров • 10 месяцев назад

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.

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