PH Deck

Users struggle with in-depth project understanding and efficient coding within their IDE, leading to workflow disruptions.

The current situation involves developers and data scientists facing the challenge of efficiently coding and reasoning tasks.

The drawbacks of this old situation include the need to manually handle complex code logic and reasoning issues, leading to increased development time and potential errors.

Users struggle with the cost and efficiency of previous AI models like GPT-3.5 Turbo, which are expensive and less optimized for chat preferences. The cost and performance on specific tasks are major drawbacks.

Users interested in leveraging GPT-3 for product development face challenges due to lack of coding skills or understanding of GPT-3's capabilities, leading to barriers in innovation and application development. The lack of coding skills or understanding of GPT-3's capabilities significantly hinders the ability to innovate or develop applications.

Users currently rely on separate tools for instant responses and step-by-step thinking. The drawback here is that switching between different tools can be inefficient and disrupts the workflow, leading to a fragmented experience.

The current situation involves users working with AI language models that are slow and costly, which can hinder productivity and escalate operational expenses. The drawback is primarily the inefficiency and high expenses associated with previous generation AI models.

Users need AI tools capable of handling complex tasks like reasoning, math, coding, multilingual understanding, and vision with safety and accuracy, but many existing models lack specialization, safety, and precision in these areas.

Developers often struggle with completing code efficiently and accurately across multiple programming languages due to the vast syntax and libraries involved, leading to increased debugging time and decreased productivity. Inefficiency and inaccuracy in code completion are major drawbacks.

Users previously relied on earlier AI models (e.g., GPT-4) for complex tasks, but faced less efficient reasoning, slower response times, and lower fact-checking accuracy.

Developers often struggle with understanding complex code segments, which leads to decreased productivity and increased frustration. The main drawbacks are the time-consuming nature of seeking explanations and the potential for misinterpretation or confusion.