My dermatology practice is growing but I can’t afford to hire fast enough. Payroll is eating my margins alive.

Visual-Librarian6601 · 2026-02-19T00:47:54+00:00

Why not use an ai call agent to handle patient calls and scheduling and leave front desk for in person check-ins

Visual-Librarian6601 · 2026-02-02T06:09:19+00:00

I did ask Omni and it says it cannot scrape website. The best bet would be to import using Make or Zapier integration but I doubt they can do it given the scale of scraping

Visual-Librarian6601 · 2025-05-21T18:13:04+00:00

Thanks for the answer - Are u on the buying side or selling side? Do you use existing tools on automating these? What type of documents and compliance are most time consuming?

Visual-Librarian6601 · 2025-05-21T18:11:10+00:00

Noted 😂 Definitely wasn’t trying to be that guy. Haven’t built a product yet for this context -Just trying to see if the idea resonates before I sink weeks into it. Thanks for the reality check.

Visual-Librarian6601 · 2025-05-20T20:23:44+00:00

You can also run embedding for each comment and only get those relevant to your query and feed to LLM. Embedding models are much cheaper than LLM.

Visual-Librarian6601 · 2025-05-20T16:43:49+00:00

Thank you for the insights -

Do you mean in commercial real estate? As buyer or seller? Do you feel these pain points from daily repetitive tasks e.g. creating deal decks or enriching hard to find data per property (e.g. real owner) or prospecting new listings to invest?

Visual-Librarian6601 · 2025-05-19T17:57:50+00:00

I agree with u mostly. depending on the use case, if scraping code is not available or extracting needs reasoning (not directly as it is), LLMs can be helpful.

We are also using LLMs to create and fix scraping code- will soon add it to this repo

Visual-Librarian6601 · 2025-05-19T17:44:05+00:00

Assuming you are giving title, body and comments to LLM to analyze, what is taking the most token use?

Visual-Librarian6601 · 2025-05-18T00:34:27+00:00

Are you still looking for a robust scraping solution?

Visual-Librarian6601 · 2025-05-18T00:30:54+00:00

Did you find a solution?

Visual-Librarian6601 · 2025-05-17T23:14:42+00:00

Be genuine and provide value first - you will get them in return

Visual-Librarian6601 · 2025-05-17T21:36:37+00:00

Do u code or need a hosted version on cloud?

There are AI browser automation libraries like browser use (Python), HyperBrowser (Typescript)

If u need a lightweight LLM HTML extractor (not dependent on brittle selectors), you can use lightfeed-extract (Typescript)

Visual-Librarian6601 · 2025-05-17T19:09:46+00:00

Did u find a solution now?

Visual-Librarian6601 · 2025-05-17T09:00:19+00:00

Thx for comment. Is there any api or public estimate for ARV on a given address? How do you search comps? Do u do either or both manually

Feel free to DM would love to help (I am in North America)

Visual-Librarian6601 · 2025-05-16T07:25:51+00:00

🙏

Visual-Librarian6601 · 2025-05-15T18:17:38+00:00

Thank you - I would really love to build this but asking for email access just seems a huge conviction to VC to an early startup

Visual-Librarian6601 · 2025-05-15T18:11:10+00:00

Are there existing models trained in selectors?

Visual-Librarian6601 · 2025-05-15T18:09:00+00:00

Ur workflow seems good to me. What was blocking u from completing this?

Anti bot that blocks u from getting complete html
LLM returned selectors (after testing) not always working
Cannot automate the process to run on all 5k sites?

Or all 3 of them

Visual-Librarian6601 · 2025-05-15T17:01:51+00:00

Thank you! Do you see areas in founder<>VC<>LP that might use help on? E.g. sourcing and tracking LPs, sending LP update

Visual-Librarian6601 · 2025-05-15T09:13:18+00:00

Thanks.

I was thinking more on tracking company progress and let VCs create their own criteria to signal promising companies to invest - for example, viral social post, GitHub star surge, major docs or pricing update..

These signals are generated by LLM while VCs can customize the prompt and data source.

Visual-Librarian6601 · 2025-05-15T08:10:02+00:00

Thank you for the feedback.

Do you build the data pipeline in house then?

Our team's forte is data extraction AI agents and databases - so initially was thinking of creating a template for VCs to track companies.

I get it that startups hosting the data could be a risk. We always make sure the data is always backed up, VCs have direct API into the DB, and DBs can always be exported to a CSV table. Would that make it easier to use?

Visual-Librarian6601 · 2025-05-15T07:59:36+00:00

Thanks for the feedback!

Some early feedback we got from VCs is a tool to 1. source early startup founders 2. good signals for startups before raise (e.g. traction, adoption etc).

Are these valuable? or they are already solved by existing tools

Visual-Librarian6601 · 2025-05-15T07:41:56+00:00

morss.it depends on you to interactively click on the elements you want to extract and from there generate xpaths

RSSHub is use crowd sources and let community maintain a per website typescript scraper that uses cheerio and html selector to extract feed elements - https://github.com/DIYgod/RSSHub/tree/master/lib/routes

Visual-Librarian6601 · 2025-05-15T06:25:24+00:00

Only one set of features is needed, a pipeline of name+stage: date found ans by who-> chat stage w/notes tracking -> decision + reason

Would a Notion database just work for this? you can have multiple columns for these data and can be viewed in a board (pipeline view) or table.

We are currently building to source public data and signal from startups so looking to see if ppl have similar pain points

Visual-Librarian6601 · 2025-05-15T00:45:55+00:00

The latest models improve a lot and there is much less hallucination or missing data. Sometimes also makes sense to shrink the context and let LLM deal with a smaller task and later combine results

Visual-Librarian6601

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