| # Test gist that is added to all .bashrc files upon .bashrc_gist_pull.sh script | |
| echo "I am file .bashrc_gist_1.sh that was pulled off gist github" |
| # Check if the code from the gist is already into the .bashrc_gist_1.sh script. If not, add it to that file: | |
| if test -f ~/.bashrc_gist_1.sh; then | |
| echo "Code already pulled to bashrc_gist_1.sh script. Skipping the wget command..." | |
| else | |
| # Add the code from the gist to the end of the .bashrc_gist_1.sh script | |
| echo "# Pulling the bashrc_color_root_red.sh script from gist and saving it locally..." | |
| # Download the gistrc #1 and save it to a file | |
| wget https://gist.githubusercontent.com/Manamama/9b0e0ead8f96ab3937a780f119d6f4ff/raw/a0b3a1dfa0dcbde2bc3446049da8baf0f01c8bb7/bashrc_gist_1.sh -O ~/.bashrc_gist_1.sh -q | |
| # Load the old or new bashrc_gist_1.sh file |
| <!doctype html> | |
| <html lang=en> | |
| <head> | |
| <meta charset=utf-8> | |
| <title>HTML tags converter, minimal Html document</title> | |
| </head> | |
| <body> | |
| <p> |
| sentencepiece_trainer.cc(177) LOG(INFO) Running command: --input=sentences.txt --model_prefix=m --vocab_size=800 | |
| sentencepiece_trainer.cc(77) LOG(INFO) Starts training with : | |
| trainer_spec { | |
| input: sentences.txt | |
| input_format: | |
| model_prefix: m | |
| model_type: UNIGRAM | |
| vocab_size: 800 | |
| self_test_sample_size: 0 | |
| character_coverage: 0.9995 |
| # from an OWL file from here: https://github.com/kuehnram/ESTHER-Ontology/ | |
| { | |
| "classes": { | |
| "LinguisticElement": [], | |
| "RhetoricalFigure": [], | |
| "LinguisticScope": [], | |
| "LinguisticObject": [], | |
| "LinguisticPosition": [], | |
| "LinguisticGroup": [], | |
| "LinguisticEntity": [ |
| Accismus | |
| Accumulatio | |
| Acrostic | |
| Allegory | |
| Alliteration | |
| Allusion | |
| Amphiboly | |
| Amplification | |
| Anacoluthon | |
| Anadiplosis |
Official: https://developers.google.com/people/api/rest/v1/people/searchContacts?utm_source=chatgpt.com
In real life:
A practical guide to read your Google Contacts using the People API in the terminal. Assumption: You have a Google Cloud project with the People API enabled and a Desktop app OAuth Client ID ready.
bash test_gemini.sh
A. Iteration #1 B. Model name: embedding-gecko-001 C. Reply: 2025-07-04 02:32:02.034277: To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. Error: 'Unknown model: embedding-gecko-001'
A. Iteration #2 B. Model name: gemini-1.0-pro-vision-latest
