Fireche

I did some googling but there is a lot of different information, also including lots of random tournaments. I am specifically looking for tournaments where top 20 players will attend and you can buy tickets (or not) and its spectator friendly. How do you find these tournaments?

Hello,

I built a SaaS that can connect to some popular cloud providers such as SharePoint, GoogleDrive and allow end-users to browse and manage their data through a custom UI.

I'd like to tackle organizations that do not use cloud storage providers but have their data fully on-premise, secured behind firewalls.

How would you go about that?

From my understanding, it won't make much sense to offer these organizations that are on-premise a SaaS solution but rather a on-premise solution that will have to be tailored to their infrastructure.

I know there are services such as AWS Direct Connect or Azure Express Route, allowing organizations to establish a private connection from on-premise

how do you do it? I would like the reponse to be properly formatted in markdown for example. This does work somewhat but it still uses some non-unicode formatting for example: '\( LAF,max \geq 45 \)' instead of using "≥". Even with one-shot prompting it does not follow the instructions properly.

Maybe there is another model that is trained on this task? Any suggestions? How do you solve this?

Hello,

i will slowly transition from CTO/Co-Founder position to founding engineer in the next couple of weeks and I would like to get some insights on how you would handle equity transfer, payment etc.

Founded 6 months ago, europe, B2B SaaS, two founders, CEO/CTO, pre-seed, received public funding, there is some traction but little. Iterated couple of times, getting into territory that I am less familar with (hence the transation to founding engineer and making space for CTO + current state of founder team is not easily fundable according to CEO). Have unpaid pilot + paid pilot starting in some months, more public funding outstanding, but uncertain if we receive (i'd say 50/50 to get an

I have no ML/AI background but I would like to understand on a high-level how one would go about training models which are capable of certain tasks. Aswell I am interested in how to decide which available model is best for the tasks.

• The model should be able to take an image as an input (the higher the supported resolution the better) and describe it in a certain style (that's where the fine-tuning comes into place)

The model should describe the image in detail, include all rooms, materials, descriptions, measurements etc.

The more detailed, the better.

The idea is to create an embedding for the generated description so it is possible to retrieve drawings by describing it. Does that ma

I cannot establish a connection from python code for some reason. The error shows that the name or service is not known.

(psycopg.OperationalError) [Errno -2] Name or service not known
(Background on this error at: https://sqlalche.me/e/20/e3q8)

i don't know if this is even related to supabase but maybe someone experiened something similar in the past and knows how to fix this?

from dotenv import load_dotenv
import os
from sqlalchemy import create_engine

load_dotenv()

PG_PW = os.environ.get("SP_PG_PW")
PG_USER = os.environ.get("SP_PG_USER")
PG_DB = os.environ.get("SP_PG_DB")
PG_HOST = os.environ.get("SP_PG_HOST")
`PG_PORT = int(os.environ.

I am using the managed postgres DB by supabase to store my embeddings and so far I have used the PGVector provided by langchain: https://python.langchain.com/v0.2/docs/integrations/vectorstores/pgvector/

I see that langchain also provides a vectorstore class specfically tailored to supabase: https://python.langchain.com/v0.2/docs/integrations/vectorstores/supabase/

But I can't tell what seems to be the difference except that I can pass my custom matching function to the supabase vectorstore instance.

Does anyone know?

Looking at the following tool: https://python.langchain.com/v0.1/docs/integrations/tools/google_drive/

What happens under the hood when this tool is called? For queries to find relevant results, does langchain simply make use of the public API which is based on a fullText search?

Any way to retrieve documents with a semantic search with langchain? I think this would actually be a quite neat feature, so we could pass an embedding model, db and it would create embeddings for all the documents.

Hello fellow founders,

does anyone know here an online community for CTOs? I sometimes get stuck on certain things and would love to get some outside perspective from other CTOs in similar situations.

I know of some closed communities where you need an invite to get into it which is not so easy.

Thanks!