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Just in case you haven't, you should look at the entire history of Superhuman (which just got bought by Grammarly this past week).

You can even start here -> https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...


note: I didn't build the game, I used the title the person who submitted this game on /r/dataengineering did.

Post on /r/dataengineering: https://www.reddit.com/r/dataengineering/comments/1lagh5z/i_...


As in to buy, to sell to, something else?


I am interested in a listing of all new companies started in USA. The interest is company name, business type, location, which products and services they provide to customers, and what they need from vendors.


You might enjoy https://whatsin.space/

Which describes itself as "What's in Space is a realtime 3D map of objects in Earth orbit, visualized using WebGL"


https://www.mermaidchart.com/#pricing

comes from https://mermaid.js.org/

Maybe you could start with something similar?


I don't even realize mermaid has a paid plan, thx for pointing that.


have you looked through

https://www.reddit.com/r/AskDocs/

https://www.reddit.com/r/medicine/

to see what doctors are saying of your competitors or already existing solutions?

This could give you a good idea of what doctors care about


What document type is the RFP?

Happy to help here, or hit me up at sebgnotes@gmail.com


You could build an AI-powered RFP response assistant using a combination of document automation, retrieval-augmented generation (RAG), and a structured workflow.

I'm thinking something like:

1. Extract and Parse the RFP

Use an LLM for summarization and section extraction.

Leverage OCR tools (e.g., Tesseract, AWS Textract, or Azure Form Recognizer) if the RFPs are PDFs with scanned text.

Convert the RFP into structured data using LangChain’s document loaders or Unstructured.io.

2. Automate Reusable Content Insertion

Maintain an answer bank (e.g., .txt, .md, Notion, Airtable, Google Docs, or even a vector database like Weaviate or Pinecone).

Implement retrieval-augmented generation (RAG) to pull relevant answers dynamically.

LlamaIndex or LangChain can help fetch the closest responses.

Use Embeddings (OpenAI, Cohere, or Ada models) for semantic search.

If a response needs tweaking, allow the AI to highlight differences and suggest edits.

3. Prompt for Missing Information

If the AI detects missing information:

Generate a draft response using context from existing data.

Provide a structured form/UI (e.g., Google Forms, Typeform, or an internal dashboard) for manual input.

4. Generate the Final RFP

Use LLMs to rewrite responses for coherence and consistency.

Automate formatting with Markdown-to-PDF tools (Pandoc, LaTeX) or Microsoft Word templates.

Consider using Zapier or Make.com to integrate responses into a shared document repository. Low-Code/No-Code Approach

If you want minimal coding:

Microsoft Copilot (Word + Excel) or Google Gemini AI could automate drafting and inserting responses.

Zapier + OpenAI + Google Docs can build a pipeline to parse, retrieve, and generate answers. Notion AI can assist in structured knowledge retrieval.

Would love to hear more about your constraints (e.g., compliance needs, integration with existing tools) to refine this further.


Thanks for the response here -- 0 constraints. Lots of PDFs in and a final PDF out. Questions vary per RFP but we have 90% of cases covered in a google doc answer bank. I'd be curious how this could be within a template -- so maybe Copilot would be the right tool here. Raw output of text to then format would still be 20% of the work without that.


Got it—no constraints, mostly PDFs in → PDFs out, with 90% of answers covered in a Google Doc answer bank.

Given that, here’s a streamlined approach:

1. Extract RFP Content

Azure Form Recognizer, AWS Textract, or Adobe Acrobat API can extract text and structure from PDFs.

If RFPs follow a standard layout, LlamaIndex or LangChain can help chunk and categorize sections.

2. Retrieve and Insert Answers

Use Google Docs API to pull relevant responses based on keyword or semantic search.

OpenAI Assistants API or Claude 3 can rephrase and tailor the responses dynamically.

3. Automate Formatting with a Template

Microsoft Copilot (Word/Excel + SharePoint):

Copilot can fetch Google Doc answers and insert them into a predefined template.

If you standardize the output format in Word, you can generate a PDF-ready final draft.

Alternative: Python + Pandoc + Jinja2 templates if you want a more controlled workflow.

4. Output Final PDF

Convert the formatted document to PDF using Word’s built-in save-to-PDF, Pandoc, or LibreOffice headless mode.

** Low-Code Implementation

If you're looking for minimal coding:

Microsoft Power Automate + Copilot → Automatically extract, populate, and format RFP responses.

Zapier + OpenAI + Google Docs → Retrieve answers, format them, and push to a doc/PDF.

Would Copilot’s Word integration work within your workflow, or do you need more automation for final formatting?

If you want a completely hands-off approach, a small workflow automation script could eliminate the remaining 20% of manual work.


Enjoyed the blog post and great job getting it done by the 28th.

How did you choose what you were going to penalize yourself with?


I took something easy to do and that my friend would remind me to do => sending him cash, and selected an amount that was not so big that I'd resent him, but big enough that I didn't want to give it away!


It really depends on how much your son wants to do math.

As you can imagine, there is a whole world of kids like your kid who love math and want to do nothing more than math.

If you're interested I can chat with you or recommend resources here if you decide to help your kid do more math.


Great video - thanks for sharing!


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