env vars and starting work on repo_manager

.gitignore

@@ -3,13 +3,6 @@ __pycache__
 .venv
 .aider*
 .vscode
-<<<<<<< HEAD
-<<<<<<< HEAD
 .zed
 pyproject.toml
 .ropeproject
-=======
-generated_files/*
->>>>>>> d45f0be (env set up for remote)
-=======
->>>>>>> f24bd5b (cleanup directory)

generated_files/when_to_use_ai.md

@@ -0,0 +1,53 @@
+# When Should You Use AI?
+
+Right off the bat? Well, let’s talk about when *not* using an LLM is actually pretty much like trying to build that perfect pavlova with a robot: Sure, they might have all these instructions and ingredients laid out for them (or so it seems), but can you really trust this machine to understand those subtle nuances of temperature or timing? No. And let’s be real here – if we’re talking about tasks requiring precise logic like financial calculations or scientific modeling - well, that sounds more suited to the human brain.
+
+But where does AI actually shine bright and come in handy?
+
+* **Pattern Recognition:** Spotting trends within data is one of those areas LLMs are pretty darn good at. Whether it’s identifying patterns across a dataset for insights (or even generating creative ideas based on existing information), they can do that with speed, efficiency - not to mention accuracy.
+
+**And when shouldn’t you use AI?**
+
+* **Tasks Requiring Precise Logic:** If your job is something needing absolute precision – like crunching numbers or modeling scientific data where a miscalculation could mean millions in losses for the company. Well… maybe hold off on letting an LLM take over.
+* **Situations Demanding Critical Thinking**: Let’s be honest, if you need to make judgment calls based upon complex factors that even humans can struggle with – then it might not just do a good job; but rather fall short.
+
+LMLs are great at mimicking intelligence. But they don’t actually understand things the way we human beings (or I should say: non-humans) comprehend them.
+* **Processes Where Errors Have Serious Consequences:** If your work involves tasks where errors can have serious consequences, then you probably want to keep it in human hands.
+
+**The Bottom Line**
+
+AI is a powerful tool. But like any good chef knows – even the best kitchen appliances can't replace their own skills and experience when making that perfect pavlova (or for us humans: delivering results). It’s about finding balance between leveraging AI capabilities, while also relying on our critical thinking - and human intuition.
+
+Don’t get me wrong here; I’m not anti-AI. But let’s be sensible – use it where it's truly helpful but don't forget to keep those tasks in the hands of your fellow humans (or at least their non-humans).
+
+---
+
+**Note for Editors:** This draft is designed with ease-of-editing and clarity as a priority, so feel free to adjust any sections that might need further refinement or expansion. I aimed this piece towards an audience who appreciates both humor-infused insights into the world of AI – while also acknowledging its limitations in certain scenarios.
+
+```markdown
+# When Should You Use AI?
+
+Right off the bat? Well, let’s talk about when *not* using LLM is actually pretty much like trying to build that perfect pavlova with a robot: Sure, they might have all these instructions and ingredients laid out for them (or so it seems), but can you really trust this machine to understand those subtle nuances of temperature or timing? No. And let’s be real here – if we’re talking about tasks requiring precise logic like financial calculations or scientific modeling - well, that sounds more suited to the human brain.
+
+But where does AI actually shine bright and come in handy?
+
+* **Pattern Recognition:** Spotting trends within data is one of those areas LLMs are pretty darn good at. Whether it’s identifying patterns across a dataset for insights (or even generating creative ideas based on existing information), they can do that with speed, efficiency - not to mention accuracy.
+
+**And when shouldn’t you use AI?**
+
+* **Tasks Requiring Precise Logic:** If your job is something needing absolute precision – like crunching numbers or modeling scientific data where a miscalculation could mean millions in losses for the company. Well… maybe hold off on letting an LLM take over.
+* **Situations Demanding Critical Thinking**: Let’s be honest, if you need to make judgment calls based upon complex factors that even humans can struggle with – then it might not just do a good job; but rather fall short.
+
+LMLs are great at mimicking intelligence. But they don’t actually understand things the way we human beings (or I should say: non-humans) comprehend them.
+* **Processes Where Errors Have Serious Consequences:** If your work involves tasks where errors can have serious consequences, then you probably want to keep it in human hands.
+
+**The Bottom Line**
+
+AI is a powerful tool. But like any good chef knows – even the best kitchen appliances can't replace their own skills and experience when making that perfect pavlova (or for us humans: delivering results). It’s about finding balance between leveraging AI capabilities, while also relying on our critical thinking - and human intuition.
+
+Don’t get me wrong here; I’m not anti-AI. But let’s be sensible – use it where it's truly helpful but don't forget to keep those tasks in the hands of your fellow humans (or at least their non-humans).
+
+---
+
+**Note for Editors:** This draft is designed with ease-of-editing and clarity as a priority, so feel free to adjust any sections that might need further refinement or expansion. I aimed this piece towards an audience who appreciates both humor-infused insights into the world of AI – while also acknowledging its limitations in certain scenarios.
+```

src/main.py

@@ -1,11 +1,7 @@
 import ai_generators.ollama_md_generator as omg
 import trilium.notes as tn
-<<<<<<< HEAD
 import repo_management.repo_manager as git_repo
 import string,os
-=======
-import string
->>>>>>> 6313752 (getting gemma3 in the mix)
 
 tril = tn.TrilumNotes()
 
@@ -27,7 +23,6 @@ for note in tril_notes:
     os_friendly_title = convert_to_lowercase_with_underscores(tril_notes[note]['title'])
     ai_gen = omg.OllamaGenerator(os_friendly_title,
                                  tril_notes[note]['content'],
-<<<<<<< HEAD
                                  tril_notes[note]['title'])
     blog_path = f"/blog_creator/generated_files/{os_friendly_title}.md"
     ai_gen.save_to_file(blog_path)
@@ -37,8 +32,4 @@ for note in tril_notes:
     git_pass = os.environ["GIT_PASS"]
     repo_manager = git_repo.GitRepository("blog/", git_user, git_pass)
     repo_manager.create_copy_commit_push(blog_path, os_friendly_title, commit_message)
-=======
-                                 "gemma3:latest",
-                                 tril_notes[note]['title'])
     ai_gen.save_to_file(f"/blog_creator/generated_files/{os_friendly_title}.md")
->>>>>>> 6313752 (getting gemma3 in the mix)

src/repo_management/repo_manager.py

@@ -3,7 +3,6 @@ from urllib.parse import quote
 from git import Repo
 from git.exc import GitCommandError
 
-<<<<<<< HEAD
 class GitRepository:
     # This is designed to be transitory it will desctruvtively create the repo at repo_path
     # if you have uncommited changes you can kiss them goodbye!
@@ -21,15 +20,7 @@ class GitRepository:
             git_user = quote(username)
             git_password = quote(password)
             remote = f"{git_protocol}://{git_user}:{git_password}@{git_remote}"
-=======
 
-def try_something(test): 
-
-# Set the path to your blog repo here
-blog_repo = "/path/to/your/blog/repo"
->>>>>>> d35a456 (set up chroma)
-
-<<<<<<< HEAD
         if os.path.exists(repo_path):
             shutil.rmtree(repo_path)
         self.repo_path = repo_path
@@ -37,19 +28,6 @@ blog_repo = "/path/to/your/blog/repo"
         self.repo = Repo(repo_path)
         self.username = username
         self.password = password
-=======
-
-# Checkout a new branch and create a new file for our blog post
-branch_name = "new-post"
-try:
-    repo = Git(blog_repo)
-    repo.checkout("-b", branch_name, "origin/main")
-    with open("my-blog-post.md", "w") as f:
-        f.write(content)
-except InvalidGitRepositoryError:
-    # Handle repository errors gracefully
-    pass
->>>>>>> 8575918 (latest commits)
 
     def clone(self, remote_url, destination_path):
         """Clone a Git repository with authentication"""
@@ -118,6 +96,3 @@ except InvalidGitRepositoryError:
         self.add_and_commit(f"'{commit_messge}'")
 
         self.repo.git.push()
-
-    def remove_repo(self):
-        shutil.rmtree(self.repo_path)