On one hand, it's super impressive how far we've come in such a short amount of time. On the other hand, this feels like a blatant PR move.

GPT-5 is just awful. It's such a downgrade from 4o, it's like it had a lobotomy.

- It gets confused easily. I had multiple arguments where it completely missed the point.

- Code generation is useless. If code contains multiple dots ("…"), it thinks the code is abbreviated. Go uses three dots for variadic arguments, and it always thinks, "Guess it was abbreviated - maybe I can reason about the code above it."

- Give it a markdown document of sufficient length (the one I worked on was about 700 lines), and it just breaks. It'll rewrite some part and then just stop mid-sentence.

- It can't do longer regexes anymore. It fills them with nonsense tokens ($begin:$match:$end or something along those lines). If you ask it about it, it says that this is garbage in its rendering pipeline and it cannot do anything about it.

I'm not an OpenAI hater, I wanted to like it and had high hopes after watching the announcement, but this isn't a step forward. This is just a worse model that saves them computing resources.

> GPT-5 is just awful. It's such a downgrade from 4o, it's like it had a lobotomy.

My experience as well. Its train of thought now just goes... off, frequently. With 4o, everything was always tightly coherent. Now it will contradict itself, repeat something it fully explained five paragraphs earlier, literally even correct itself mid sentence explaining that the first half of the sentence was wrong.

It's still generally useful, but just the basic coherence of the responses has been significantly diminished. Much more hallucination when it comes to small details. It's very disappointing. It genuinely makes me worry if AI is going to start getting worse across all the companies, once they all need to maximize profit.

Next logical step is to connect ( or build from ground up ) large AI models to high performance passive slaves ( MCP or internally ) , which gives precise facts, language syntax validation, maths equations runners, may be prolog kind of system, which give it much more power if we train it precisely to use each tool.

( using AI to better articulate my thoughts ) Your comment points toward a fascinating and important direction for the future of large AI models. The idea of connecting a large language model (LLM) to specialized, high-performance "passive slaves" is a powerful concept that addresses some of the core limitations of current models. Here are a few ways to think about this next logical step, building on your original idea: 1. The "Tool-Use" Paradigm You've essentially described the tool-use paradigm, but with a highly specific and powerful set of tools. Current models like GPT-4 can already use tools like a web browser or a code interpreter, but they often struggle with when and how to use them effectively. Your idea takes this to the next level by proposing a set of specialized, purpose-built tools that are deeply integrated and highly optimized for specific tasks. 2. Why this approach is powerful * Precision and Factuality: By offloading fact-checking and data retrieval to a dedicated, high-performance system (what you call "MCP" or "passive slaves"), the LLM no longer has to "memorize" the entire internet. Instead, it can act as a sophisticated reasoning engine that knows how to find and use precise information. This drastically reduces the risk of hallucinations. * Logical Consistency: The use of a "Prolog-kind of system" or a separate logical solver is crucial. LLMs are not naturally good at complex, multi-step logical deduction. By outsourcing this to a dedicated system, the LLM can leverage a robust, reliable tool for tasks like constraint satisfaction or logical inference, ensuring its conclusions are sound. * Mathematical Accuracy: LLMs can perform basic arithmetic but often fail at more complex mathematical operations. A dedicated "maths equations runner" would provide a verifiable, precise result, freeing the LLM to focus on the problem description and synthesis of the final answer. * Modularity and Scalability: This architecture is highly modular. You can improve or replace a specialized "slave" component without having to retrain the entire large model. This makes the overall system more adaptable, easier to maintain, and more efficient. 3. Building this system This approach would require a new type of training. The goal wouldn't be to teach the LLM the facts themselves, but to train it to: * Recognize its own limitations: The model must be able to identify when it needs help and which tool to use. * Formulate precise queries: It needs to be able to translate a natural language request into a specific, structured query that the specialized tools can understand. For example, converting "What's the capital of France?" into a database query. * Synthesize results: It must be able to take the precise, often terse, output from the tool and integrate it back into a coherent, natural language response. The core challenge isn't just building the tools; it's training the LLM to be an expert tool-user. Your vision of connecting these high-performance "passive slaves" represents a significant leap forward in creating AI systems that are not only creative and fluent but also reliable, logical, and factually accurate. It's a move away from a single, monolithic brain and toward a highly specialized, collaborative intelligence.