u/Disastrous_Visit_454

I am restricted to M365 Copilot (Enterprise) due to data sensitivity, so external models are not an option. My primary struggle is the lack of a persistent, dedicated workspace.

In Claude (Projects) or Gemini (Gems), you can define a static, continuously updated set of system instructions and core documents. The model treats this as an isolated foundational truth, allowing it to provide complex logical advice across multiple files while still pulling in fresh web or external data when prompted.

In M365 Copilot, the RAG defaults to indiscriminately scraping recent emails, Teams chats, and random SharePoint files. This dilutes the context completely when trying to do focused, multi-document analytical work.

How are you bypassing this limitation to create a persistent context workspace within the M365 ecosystem?

• Can specific SharePoint folders or hubs be rigidly isolated and prioritized to act as a "Project" base?
• Are you using a specific "anchor document" architecture that Copilot reliably treats as a continuously updating knowledge base / system prompt? If yes, do you keep manually extending it or is there any way to make that automated?
• Is there a way to build a Copilot Studio agent that maintains this strict grounding but still has web-search capabilities for real-time data?

Looking for production-tested architectural workarounds, not basic prompt engineering tips.

I am using M365 Copilot (Enterprise) and constantly hitting memory limits where the model loses the conversational thread during long document analysis.

Our tenant now displays a model selector with "GPT 5.5" under an explicit "OpenAI" label, including a "deeper thoughts" (reasoning) mode.

I have two technical questions pls:

1. Model Authenticity: Are these actually the native OpenAI models running under the hood with identical reasoning capabilities, or is this a Microsoft-specific RAG pipeline just carrying the OpenAI branding?
2. Context Window: Does this "GPT 5.5" mode offer a true context window comparable to native OpenAI/Anthropic models (e.g., 128k+ continuous recall)? The standard Copilot RAG drops data and forgets chat history far too quickly for heavy, multi-document synthesis.

I haven't found any technical facts on the architecture and the actual token limits behind these UI labels so I hope anyone can advise. Thanks!

I am using M365 Copilot (Enterprise) and constantly hitting memory limits where the model loses the conversational thread during long document analysis.

Our tenant now displays a model selector with "GPT 5.5" under an explicit "OpenAI" label, including a "deeper thoughts" (reasoning) mode.

I have two technical questions pls:

1. Model Authenticity: Are these actually the native OpenAI models running under the hood with identical reasoning capabilities, or is this a Microsoft-specific RAG pipeline just carrying the OpenAI branding?
2. Context Window: Does this "GPT 5.5" mode offer a true context window comparable to native OpenAI/Anthropic models (e.g., 128k+ continuous recall)? The standard Copilot RAG drops data and forgets chat history far too quickly for heavy, multi-document synthesis.

I haven't found any technical facts on the architecture and the actual token limits behind these UI labels so I hope anyone can advise. Thanks!

I am using M365 Copilot (Enterprise) and constantly hitting memory limits where the model loses the conversational thread during long document analysis.

Our tenant now displays a model selector with "GPT 5.5" under an explicit "OpenAI" label, including a "deeper thoughts" (reasoning) mode.

I have two technical questions pls:

1. Model Authenticity: Are these actually the native OpenAI models running under the hood with identical reasoning capabilities, or is this a Microsoft-specific RAG pipeline just carrying the OpenAI branding?
2. Context Window: Does this "GPT 5.5" mode offer a true context window comparable to native OpenAI/Anthropic models (e.g., 128k+ continuous recall)? The standard Copilot RAG drops data and forgets chat history far too quickly for heavy, multi-document synthesis.

I haven't found any technical facts on the architecture and the actual token limits behind these UI labels so I hope anyone can advise. Thanks!

Hi everyone,

I am looking for first-hand, anecdotal experiences from anyone who has used psilocybin (magic mushrooms) while actively taking alpha‑2‑delta calcium channel ligands, such as Pregabalin (Lyrica) or Gabapentin.
I am fully aware of the general pharmacological consensus: these medications act as central nervous system depressants/anxiolytics and generally blunt, dull, or diminish the visual and cognitive intensity of a psychedelic trip. I know it’s not ideal from a textbook therapeutic perspective.

However, much like how people with bipolar disorder are clinically discouraged from using psychedelics yet there is a massive amount of anecdotal reports where individuals found significant relief, I want to hear the actual ground-reality experiences with alpha‑2‑delta ligands.I am highly interested in any insights across the spectrum, let it be microdosing, recreational dosage or therapeutic doses.

If you have tried this combo, could you share your dosage of both substances, and how it felt?

Thank you in advance for sharing your stories .🙏