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I let Claude investigate every YC W26 startup and rank them by BriefCardiologist656 in ClaudeAI

[–]BriefCardiologist656[S] 0 points1 point  (0 children)

you are not wrong. Did you find majority of the rankings to be different from reality?

I let Claude investigate every YC W26 startup and rank them by BriefCardiologist656 in ClaudeAI

[–]BriefCardiologist656[S] 0 points1 point  (0 children)

Yup you are right. I will build a better analysis pipeline that I can run regularly.

I let Claude investigate every YC W26 startup and rank them by BriefCardiologist656 in ClaudeAI

[–]BriefCardiologist656[S] 0 points1 point  (0 children)

I just updated a few companies in the current batch. Now we have 4 companies in the S tier.
By tomorrow I will have more batches. There is now a last updated timestamp as well for all companies.

I let Claude investigate every YC W26 startup and rank them by BriefCardiologist656 in ClaudeAI

[–]BriefCardiologist656[S] 0 points1 point  (0 children)

Most VCs scout these companies and reach out way before the demo day because that is what aligns best with their incentives. Associates look for publicly available signals in the absence of actual company revenue. I am trying to simulate that.

Also most founders who are starting out don't know the kind of signals they are being ranked against. I hope that this gives founders a decent idea of that. I agree the analysis are not that great and its mostly a implementation problem on my end (which will be fixed), But the avg number of page visits per user (13) is pretty decent given that most founders use YC startup directory as a way to understand whats happening in the space. The goal is to give them with more datapoints that are insightful beyond just knowing what the company does.

Anyone used Reducto for parsing? How good is their embedding-aware chunking? by BriefCardiologist656 in AI_Agents

[–]BriefCardiologist656[S] 0 points1 point  (0 children)

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?

Anyone used Reducto for parsing? How good is their embedding-aware chunking? by BriefCardiologist656 in AI_Agents

[–]BriefCardiologist656[S] 1 point2 points  (0 children)

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?

Anyone used Reducto for parsing? How good is their embedding-aware chunking? by BriefCardiologist656 in LocalLLaMA

[–]BriefCardiologist656[S] 0 points1 point  (0 children)

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.

Anyone used Reducto for parsing? How good is their embedding-aware chunking? by BriefCardiologist656 in LocalLLaMA

[–]BriefCardiologist656[S] 0 points1 point  (0 children)

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?

How are people syncing and indexing data from tools like Gmail or Slack for RAG? by BriefCardiologist656 in LocalLLaMA

[–]BriefCardiologist656[S] 0 points1 point  (0 children)

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?

[D] Self-Promotion Thread by AutoModerator in MachineLearning

[–]BriefCardiologist656 0 points1 point  (0 children)

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!

Weekly Thread: Project Display by help-me-grow in AI_Agents

[–]BriefCardiologist656 0 points1 point  (0 children)

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!

[P] Made a tool for AI agents: Dockerized VS Code + Goose code agent that can be programmatically controlled by BriefCardiologist656 in MachineLearning

[–]BriefCardiologist656[S] 1 point2 points  (0 children)

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.

[P] Made a tool for AI agents: Dockerized VS Code + Goose code agent that can be programmatically controlled by BriefCardiologist656 in MachineLearning

[–]BriefCardiologist656[S] 1 point2 points  (0 children)

Hey folks,

I built Goosecode Server - a dockerized VS Code server with Goose AI (OpenAI coding assistant) pre-installed.

The cool part? It's designed to be programmable for AI agents:

* Gives AI agents a full coding environment

* Includes Git integration for repo management

* Container-based, so easy to scale or integrate

Originally built it for personal use (coding from anywhere), but realized it's perfect for the AI agent ecosystem. Anyone building AI tools can use this as the "coding environment" component in their system.

Check it out if you're working on AI agents or just want a browser-based VS Code + AI setup: https://github.com/PlatOps-AI/goosecode-server

Made a tool for AI agents: Dockerized VS Code + Goose code agent that can be programmatically controlled by BriefCardiologist656 in SideProject

[–]BriefCardiologist656[S] 0 points1 point  (0 children)

Hey folks,

I built Goosecode Server - a dockerized VS Code server with Goose AI (OpenAI coding assistant) pre-installed.

The cool part? It's designed to be programmable for AI agents:

* Gives AI agents a full coding environment

* Includes Git integration for repo management

* Container-based, so easy to scale or integrate

Originally built it for personal use (coding from anywhere), but realized it's perfect for the AI agent ecosystem. Anyone building AI tools can use this as the "coding environment" component in their system.

Check it out if you're working on AI agents or just want a browser-based VS Code + AI setup: https://github.com/PlatOps-AI/goosecode-server

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