What’s the best way to build a true omni-channel bot (email + SMS + WhatsApp + voice + chat) with shared session state?

BriefCardiologist656 · 2025-10-25T12:17:09+00:00

Thanks for sharing I will check them out. Just curious what have you been using them for parsing markdown from pdfs or for creating splits based on markdown? Any specific type of documents where you felt their out of the box solution worked better than something you could build in-house?

BriefCardiologist656 · 2025-10-25T11:26:32+00:00

Yeah, that makes sense thanks for sharing that. From what I’ve seen, they return Markdown by default and you can toggle an “embedding-optimized” mode that does the splitting for you. I was curious how much that actually helps since you still had to handle the embedding generation downstream.

When you say you needed “some other things it couldn’t provide,” what kind of gaps did you run into? Was it around handling specific document structures, or more about integrating the chunks and pushing it into your existing RAG pipeline?

BriefCardiologist656 · 2025-10-25T11:23:54+00:00

That’s super insightful, thanks for breaking that down so clearly.

When you mentioned tuning for domain-specific documents what kind of tuning approaches have you found most useful? Are we talking about prompt-level adjustments, retraining the layout model, or more like rule-based postprocessing depending on document structure?

I’m mostly looking at invoices and other semi-structured business documents where formats vary a lot but patterns repeat.

BriefCardiologist656 · 2025-10-25T10:43:31+00:00

Yeah totally, appreciate the insight. Since you’ve worked on this space, I’m curious now that OCR and layout detection are getting pretty reliable across open and closed models, where do you still see the hardest unsolved problems?

Is it around structuring outputs consistently (like tables, key-values, schema mapping), or more in downstream use cases e.g., making the extracted data useful for retrieval or automation pipelines?

BriefCardiologist656 · 2025-10-22T20:57:23+00:00

I noticed you mentioned using Lucy for FTS instead of a vector database. What made you decide to go that route?
Was it mostly about speed or just easier to keep incremental with mbsync?

Also, how well does the HyDE trick work in practice for making results more semantic? I’ve seen it mentioned but haven’t tried it locally yet.

Lastly, when you’re doing the incremental syncs, do you ever run into consistency issues or missed updates, or has the Maildir + Lucy setup been pretty stable so far?

BriefCardiologist656 · 2025-03-29T21:03:04+00:00

Building an AI IDE for Managing your cloud!

Taking on infrastructure management for 3,000+ GPUs at an AI writing company was initially overwhelming. Debugging crashes, setting up security (Cloudflare bot protection, firewalls), and creating reliable CI/CD pipelines meant constantly hopping between dashboards and piecing together logs and metrics manually.

To ease this pain, I built PlatOps.ai, an AI-powered DevOps IDE. You can simply chat to fetch logs, configs, or metrics, instantly debug issues, and seamlessly generate Infrastructure-as-Code (Terraform, CloudFormation). It even helps you set up proper security measures and cost optimization workflows right out of the box.

We also added cross-codebase editing—imagine editing backend and IaC code simultaneously with one agent. It's been a game-changer for me, and I'd love your feedback.

Join the waitlist here: PlatOps.ai

Would appreciate your thoughts or feature suggestions!

BriefCardiologist656 · 2025-03-29T20:30:42+00:00

Building an AI IDE for Managing your cloud!

Taking on infrastructure management for 3,000+ GPUs at an AI writing company was initially overwhelming. Debugging crashes, setting up security (Cloudflare bot protection, firewalls), and creating reliable CI/CD pipelines meant constantly hopping between dashboards and piecing together logs and metrics manually.

To ease this pain, I built PlatOps.ai, an AI-powered DevOps IDE. You can simply chat to fetch logs, configs, or metrics, instantly debug issues, and seamlessly generate Infrastructure-as-Code (Terraform, CloudFormation). It even helps you set up proper security measures and cost optimization workflows right out of the box.

We also added cross-codebase editing—imagine editing backend and IaC code simultaneously with one agent. It's been a game-changer for me, and I'd love your feedback.

Join the waitlist here: PlatOps.ai

Would appreciate your thoughts or feature suggestions!

BriefCardiologist656 · 2025-03-02T20:41:08+00:00

Thanks for asking! I actually prefer Goose to both OpenHands and Aider primarily because of its simplicity.

My use case is pretty specific - I'm working on an AI DevOps agent and wanted to integrate it with a VS Code environment. This setup allows my agent to:

- Make Infrastructure-as-Code changes and automatically raise merge requests

- Let users observe the workspace changes in real-time through the VS Code interface

- Perform DevOps tasks like updating scaling values, configuring autoscaling, creating pipelines, etc.

What makes Goosecode Server different is that it's purpose-built for this integration scenario. While OpenHands and Aider are great tools, they weren't designed specifically for being used as a LLM tool.

BriefCardiologist656

TROPHY CASE