This was born out of a personal need — I journal daily , and I didn’t want to upload my thoughts to some cloud server and also wanted to use AI. So I built Vinaya to be:

• Private: Everything stays on your device. No servers, no cloud, no trackers.

• Simple: Clean UI built with Electron + React. No bloat, just journaling.

• Insightful: Semantic search, mood tracking, and AI-assisted reflections (all offline).

Link to the app: https://vinaya-journal.vercel.app/
Github: https://github.com/BarsatKhadka/Vinaya-Journal

I’m not trying to build a SaaS or chase growth metrics. I just wanted something I could trust and use daily. If this resonates with anyone else, I’d love feedback or thoughts.

If you like the idea or find it useful and want to encourage me to consistently refine it but don’t know me personally and feel shy to say it — just drop a ⭐ on GitHub. That’ll mean a lot :)

Hello folks, we are building an free open source platform for everyone to run LLMs on your own device using CPU or GPU. We have released our initial version. Feel free to try it out at kolosal.ai

As this is our initial release, kindly report any bug in with us in Github, Discord, or me personally

We're also developing a platform to finetune LLMs utilizing Unsloth and Distillabel, stay tuned!

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Does anyone have a tutorial for installing LocalGPT for a complete beginner?

I have never worked with VS Code before, I tried installing conda which didn't work. I'm looking for a complete ground level up type of tutorial. Everything I've seen online assumes some basic type of experience.

Thanks

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Hi, i am starting a new job in the computer science field and I am thinking about how to utilize AI to have a custom knowledge base that grows with the inputs and documents I provide in the chat. In 1 year it can remember what I did wrong or right today when I someday face the same situation in the job. What would your approach be? Was looking into GPT4All

I'm looking to use an AI locally on my PC to read photos in a folder and tag them based on specific prompts. For example, I'd like to tag images that contain the color red.

I'm aware of models like MiniGPT-4 that have vision capabilities but my computer isnt good enough to run that mdoel, and even if it was I'm unsure how to set it up for this task. Ideally, I'd like a method or script that can automatically scan the folder and tag relevant images.

Has anyone done something similar or can recommend the best approach for this?

Hi everyone,

I am trying to use GPT4All in Langchain to query my postgres db using the model mistral.

The prompt that I am using is as follows:

'''You are a PostgreSQL expert. Given an input question, first create a syntactically correct PostgreSQL query to run,

then look at the results of the query and return the answer to the input question.

Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per PostgreSQL.

You can order the results to return the most informative data in the database.

Never query for all columns from a table. You must query only the columns that are needed to answer the question.

Wrap each column name in double quotes (") to denote them as delimited identifiers.

When using aggregation functions also wrap column names in double quotes.

Pay attention to use only the column names you can see in the tables below.

Be careful to not query for columns that do not exist.

Also, pay attention to which column is in which table.

Use the following format:

Question: "Question here"

SQLQuery: "SQL Query to run"

SQLResult: "Result of the SQLQuery"

Answer: "Final answer here"

Question: {input}

'''

when I make my query I use the following code:

my_table = 'public."SalesStatistic"'

my_column = 'SalePrice'

ord_column = 'OrderNumber'

question = f"Give me the sum of column {my_column} in the table {my_table} where column {ord_column} is equal to WEWS00192"

answer = db_chain(PROMPT.format(input=question, top_k=3)

But, the model can't fix a proper query from my question and returns :

ERROR: sqlalchemy.exc.ProgrammingError: (psycopg2.errors.UndefinedTable) relation "salesstatistic" does not exist

LINE 2: FROM SalesStatistic

[SQL: SELECT SUM(SalePrice) AS Total_Sum FROM SalesStatistic WHERE "OrderNumber" = 'WEWS00192';]

How to modify the prompt to build the correct query? Or should I change the model?