KyroDB

Codex can get dumber and slower on long sessions. Here's the fix: 1. Run Process_narration=false This will stop Codex from showing you all the planning steps, resulting in saving a lot of output tokens. 2. Prompt: "Act as an orchestrator. Use parallel agents to do the research and execution work. Write detailed tasks for each parallel agent and force them to act, iterate, get their tasks done, and bring back an in-depth report. Your job is to deeply analyze the agents' work, provide feedback, and provide them with continuous tasks." This prompt offloads the majority of the context-burning work to agents, and each agent has its own context window. So you can utilize 5 agents (5 context windows). 3. Add this hard rule: "Measure twice, cut once policy." Debugging and patching is messy work. Force Codex to plan first, act after (don't use plan mode; it's just overcomplicated). Ask it to make a task list for every task so it can track progress and iterate better. 4. Add this hard rule: "Keep the codebase clean, no tmp files, no dead code, no dead files. Stay organized all the time. No unnecessary folders, subfolders, or files." Claude keeps most of its working files in cache as temporary files (which is bloatware, but it keeps the codebase neat), but Codex is output-heavy. It creates tons of folders and files, and your workspace can become a mess after a few sessions. As a result, this contaminates the context window and degrades performance. Force Codex to stay organized and follow the file structure. These 4 techniques can help you save 40% context every session, and performance will be a lot better. For planning, use Codex 5.5 (extra high), and after the plan is done, shift to Codex 5.5 (high) with fast mode. This works faster.

Model intelligence won't solve context. Memory/context is not a feature. It is becoming the data plane of AI. Long context windows help, but they do not eliminate the need for memory/context infra. They mostly change what the infrastructure has to do. The state of AI where we are at right now, the requirement is: How do you continuously assemble the right, fresh, permissioned, compressed, explainable context for an AI system that is reasoning, acting, remembering, and coordinating across tools? The future will not be 'one giant model that remembers everything.' The future is: Many intelligent models operating over governed, persistent, external state. That external state is where the business value lives. Models will become more interchangeable over time. Context will become more proprietary. The company’s memory, workflows, relationships, permissions, and operational data will be the moat. We need to build a system that can answer: What should the AI know right now, why should it trust it, what is it allowed to do with it, and how do we prove that later?

Give your AI context that actually works.

RAG is broken and nobody's talking about it. Stanford researchers exposed the fatal flaw killing every "AI that reads your docs" product in existence. It’s called "Semantic Collapse," and it happens the second your knowledge base hits critical mass. If you've noticed your AI getting "dumber" as you add more data, this is exactly why. Right now, companies are dumping thousands of documents into their AI, thinking it’s getting smarter. When you add a document to RAG, it converts it into a high-dimensional vector. Under 10,000 documents, this works perfectly. Similar concepts cluster together. But past 10,000 documents, the space fills up. The clusters overlap. The distances compress. Everything starts to look "relevant." It is a mathematical law called the Curse of Dimensionality. In a 1000-dimensional space, 99.9% of your data lives on the outer edge. All points become equidistant from each other. That perfect, relevant document you are looking for now has the exact same mathematical similarity as 50 completely irrelevant ones. The Stanford findings are brutal: At 50,000 documents, precision drops by 87%. Semantic search actually becomes worse than old-school keyword search. Adding more context doesn’t fix the AI. It makes the hallucinations worse. Your "nearest neighbor" search isn't finding the best answer anymore. It's finding everyone. We thought RAG solved hallucinations. It didn't. It just hid them behind math.