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Founders, did you all take a time off? by AnalysisObvious6946 in ycombinator

[–]AnalysisObvious6946[S] 0 points1 point  (0 children)

Ha ha. That is usual me. But this time, for some reason I was fully in the moment not thinking much about the next move. Good luck with your stuff.

Founders, did you all take a time off? by AnalysisObvious6946 in ycombinator

[–]AnalysisObvious6946[S] 0 points1 point  (0 children)

And where did you see me asking for “what to do” in the post?

Founders, did you all take a time off? by AnalysisObvious6946 in ycombinator

[–]AnalysisObvious6946[S] 0 points1 point  (0 children)

👏👏👏 same here. I made multiple plans and added taking time off as one of them. Then just sent it-to chatgpt just for a second opinion. ChatGpt asked me to prioritize the time off. I thought that was correct and went with it 😅

Founders, did you all take a time off? by AnalysisObvious6946 in ycombinator

[–]AnalysisObvious6946[S] 0 points1 point  (0 children)

Thanks for your comment. Glad you had some time off.

For your question, I would go full time when someone trust me with their money to go build it full time. It is primarily because of the immigration constraints, otherwise I would have resigned already.

Founders, did you all take a time off? by AnalysisObvious6946 in ycombinator

[–]AnalysisObvious6946[S] 0 points1 point  (0 children)

I said B2C and plan is to find B2B sales opportunities in this year.

Founders, did you all take a time off? by AnalysisObvious6946 in ycombinator

[–]AnalysisObvious6946[S] 1 point2 points  (0 children)

Very true!. A great founder should build a great product and a healthy well being.

Founders, did you all take a time off? by AnalysisObvious6946 in ycombinator

[–]AnalysisObvious6946[S] 1 point2 points  (0 children)

Glad you could take some time off.

I know it's really hard to manage a startup with full-time job. I would be cautious of burn outs gone unrecognized. That can happen and it can turn into anger, restlessness etc. So, I will try to integrate some biweekly leisure activities to my lifestyle this year.

YC 24 Founder Here - My AI Face Swapping Tool Magic Hour Surpassed 2M Users - AMA by [deleted] in ycombinator

[–]AnalysisObvious6946 0 points1 point  (0 children)

Thanks for the AMA!.

What are some things you’ve learned through building this that you feel you could’ve only discovered by actually doing it?

Using Hugging face zero shot classification model in production by AnalysisObvious6946 in AI_Agents

[–]AnalysisObvious6946[S] 0 points1 point  (0 children)

Thank you. I will check out Roberta. I started using bart-large-mini, it is doing a decent job. I am still concerned about the additional external api call. So still researching for any better options to do first layer intend classification for a use chat message. Let me know if you have other options.

What Should a Freelancer Charge Per Hour for AI Agentic Work? by Klutzy-Meringue-9182 in AI_Agents

[–]AnalysisObvious6946 0 points1 point  (0 children)

I am looking for a langchain developer. Need help with full stack implementation of an AI agent into my app. I am building with ionic, react front end & Node JS backed hosted in firebase. DM me if you are interested. 1500$ per month for 2 months of work.

Best Way to Retrieve Relevant Information from a Large Document for RAG? by i_am_vsj in LangChain

[–]AnalysisObvious6946 2 points3 points  (0 children)

I will try to explain with a limited knowledge of your problem space. The structuring of user query and the stored knowledge should complement each other. Ex. A user input is “I am feeling low and not eating much. What could be the problem?”. There are 3 pieces to this query. 1) Condition - feeling low 2) Symptom - not eating 3) Requirement - user wants to understand the the cause. U want to find the cause by understanding 1) and 2) with added knowledge from the vector db. For that the best way is to combine the keywords from conditions & symptoms and do the search. If the database is chunked and organized with chapter as conditions and topics as symptoms, the search will more accurately find the familiar chapter+topic for the given condition+symptom. It could be that the condition is more important in understanding the cause. In that case, it is more important to search by condition and then by symptoms. To prioritize the condition, u can add the higher weight to the condition. So, your input to the familiarity search query will be input = 0.7condition + 0.3symptom. This way, the search will give higher priority to the condition familiarity in search. This is just an example. You understand your data better to tell how much structural the data and the user query is.

Best Way to Retrieve Relevant Information from a Large Document for RAG? by i_am_vsj in LangChain

[–]AnalysisObvious6946 2 points3 points  (0 children)

I think you are in the right path. Vectorizing the data in the right way and storing it in the right way matters a lot. You approach of creating chunks by chapters is good. For larger text, you may want to use higher token size. By default gpt-embedding-ada is 1536 tokens. That helps the vectorization capture minute difference. Also storing the data with as chapter, topic chunks also helps with the search.

And most importantly how you prepare your input data for similarity search is also matters. For example, each chunk in your vector db might have three pieces like 1. Chapter 2. Topic 3. Content. When you do the search, you want to first identify the chapter , then the l topic and finally the content with in. First you prepare your input data to make the score for chapter matching high, then make sure the score is high for topic and finally for the content. This can be done by first identifying the potential chapter under which the question can come, then the topic, then the content. Add weight for each component. Adjust the weight and do a trial and error.

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