John's AI blog

Introduction ChatGPT has a lot of people excited, but it deserves this level of excitement. It has a number of faults which I list below - some with workarounds - but in general it is amazingly good. It is absolutely not for having fun chatting to a robot, it's really more like a brilliant coworker who is incredibly stupid at some quite mundane tasks, but incredibly good at more tricky tasks. Summary of findings so far.  1. API vs Interactive Version - a caution 2. Coding Strengths and Weaknesses - be very careful but very useful anyway 3. Shell Scripting - amazingly good, probably what it's best at 4. Text Summarisation - fiddly but useful 5. Spreadsheet Analysis - annoying, maybe do by hand 6. Multi-Step Instruction Handling - very annoying 7. Spellchecking and writing style/grammar - ok overall but fiddly 8. File Compatibility and Formatting - not great 9. APA References from DOIs - good 10. Reference Accuracy - bad, double-check its work 11. Tone and Style Adaptations - ex...

 import os import requests import csv from datetime import datetime # Configuration REPO = 'username/reponame'  # Repository name START_DATE = '2023-09-03T00:00:00Z'  # Start of the date range END_DATE = '2023-11-13T23:59:59Z'  # End of the date range GITHUB_TOKEN = os.getenv('GITHUB_TOKEN')  # Retrieve GitHub token from environment variable headers = {     "Authorization": f"token {GITHUB_TOKEN}",     "Accept": "application/vnd.github.v3+json" } output_file = 'github_report.csv' def fetch_commits():     """Fetch commits within the specified date range."""     url = f'https://api.github.com/repos/{REPO}/commits'     params = {         'since': START_DATE,         'until': END_DATE,     }     response = requests.get(url, headers=headers, params=params)     commits = response.json()          commit_data = []    ...

 If you get an error like Access to model mistralai/Mistral-Nemo-Base-2407 is restricted. You must be authenticated to access it. Then pip install huggingface_hub huggingface-cli login it will ask you for a token go to https://huggingface.co/settings/tokens it will have an option to generate a token make sure you click the checkboxes for APIs generate the token copy it go back to the terminal at the top paste.

 So I gave ChatGPT a task of writing a CSV to ICS convertor, to take a spreadsheet of events and turn it into individual calendar file invitations. The idea is you can make a calendar with lots of event items in a spreadsheet, and then output ICS that you can just double-click to add to your calendar automatically. So here's the spreadsheet: "Event Name","Event Start Date","Event End Date","Event Start Time","Event End Time","event description or notes","URL applicable" "Meeting with John","2024-06-10","2024-06-10","09:00","10:00","Discuss project updates.","http://example.com" And here's the script ChatGPT made. It seems to get confused with things like Daylight Savings and/or Timezone but you can fix that manually. It took some debugging and I had to copy/paste my Mac's manpage for DATE into ChatGPT, but it got it eventually. The only...

 You have to test LLMs individually for hallucinations and inaccuracies. These below are attempts at summarising my first academic article. Useless! john@john-GF63-Thin-11SC:~/ai$ ./scripts/ollama_summarise_one.sh SAMPLES/hawaiiarticle.txt  Sure, here's the paragraph you requested: >The problem with some of the analyses of Libet is that they make it look  like the details were complicated. However, I do believe I agree with  McCrone that Libet's data is sloppier than people often realize. Glynn  made that point a long time ago and it was the main point of my plots in  the Tucson III Proceedings. john@john-GF63-Thin-11SC:~/ai$ ./scripts/ollama_summarise_one.sh SAMPLES/hawaiiarticle.txt  Sure, here is the paragraph you requested: >Libet showed that a thalamic stimulus requires a duration of about 250  msec to be felt whereas a 20 msec skin stimulus is adequate. This shows  that the intensity of the stimulus plays an important role in dete...

Do this: curl -fsSL https://ollama.com/install.sh | sh ollama create llama3  It will then download the llama3 model, about 4.7 gb to run it: ollama run llama3  if you want it to read and summarise a textfile called textfile.txt: ollama run llama3 "Read the text file $(cat textfile.txt) and summarise the findings in one paragraph of no more than 300 words"  And here's a gui for it: https://github.com/amithkoujalgi/ollama-pdf-bot The system runs as a service (daemon) on Linux. CPU usage is high but not bad and response time much better than localGPT. It stores the model files (large LLM blobs) in  /usr/share/ollama/.ollama/models The models can be dowloaded from huggingface.io or https://ollama.com/library