gnothi VS gpt4all

Ok, glad someone brought this up. Premium uses GPT to generate summary / themes, and it's pretty much perfect every time. Free uses a version of led-16384 trained on booksum, and can.. well.. struggle. A common mistake it will make is to hallucinate content public-domain book type content, especially if the journal entry itself wasn't "meaty" enough to work with. The solution will be to implement a local large language model https://github.com/ocdevel/gnothi/issues/160, so that free users experience similar benefits to premium, if less precise. In the mean time, I'm going to keep fiddling with the summarization model today/tomorrow to see if I can't get it just a bit less book-ish.

Website, Github. My partner u/mVadr and I just launched Gnothi (v1). We’re both psychology/personal development enthusiasts who work in tech. We started Gnothi to get us journaling more, and to leverage machine learning to optimize the process.

Website, Github. Insights & resources include GPT-powered prompt (which uses journal entries as context) behavior analysis, book recommendations, entry summaries, and recurring themes.

I created a GPT journal and am now looking into LocalLLM for the privacy-focused. I gave RedPajama-INCITE a spin, and found it soooo bad! I mean.. really bad. I should note this was about 2 months ago? I did see they released a newer version, I'm assuming there was a data-collection / training process they were waiting on. But I'm wondering: is the magic in the fine-tuning? As in, did you find the model useful before you fine-tuned, and the fine-tuning was just a cherry on top? Or did you have to fine-tune to make it work?

I built a GPT-based Journal. Not a robo-therapist, but there's so much healing in journaling. This has the benefit of maintaining context (your past entries, based on filters). Website, Github

AI Journal. Website, Github. Trying to make an ultra power-tool quantified-self platform, coming from Habitica.

I built an open source robo-journal Gnothi. Started in 2019 with summarization, recurring themes, book recommendations, behavior tracking. Soon as GPT came along, you bet your butt I integrated.

I don’t know if Ollama can do this but https://gpt4all.io/ can.

Gpt4all is a local desktop app with a Python API that can be trained on your documents: https://gpt4all.io/

The readme page is cryptic. What does 'mature' mean in this context? What is the sample text a continuation of?

Hving a gif the thing in use would be great, similar to the gpt4all readme page. (https://github.com/nomic-ai/gpt4all)

Check https://github.com/nomic-ai/gpt4all instead.

"I ran performance tests on two systems, here's the results of system 1, and heres the results of system 2. Summarize the results, and build a markdown table containing x,y,z rows."

"extract the reusable functions out of this bash script"

"write me a cfssl command to generate a intermediate CA"

"What is the regex for _____"

"Here are my accomplishments over the last 6 months, summarize them into a 1 page performance report."

etc etc etc

If you're not using GPT4 or some LLM as part of your daily flow you're working too hard.

Get GPT4All (https://gpt4all.io), log into OpenAI, drop $20 on your account, get a API key, and start using GPT4.

offline versija, nors ir ne tokia pažengus - https://github.com/nomic-ai/gpt4all ; https://gpt4all.io/index.html

Take a look at https://gpt4all.io, their docs are pretty awesome

I run one. I built an iMessage-like frontend to it using plain JS and a Python websocket backend. I mostly just use it for curiosity and playing with different prompts. I only have 16GB of RAM to dedicate to it, so I use an 8B parameter model which is enough for fun and chitchat, but I don't find it good enough to replace ChatGPT.

https://github.com/nomic-ai/gpt4all