Tag: HBOT

Hi, I recently posted in here regarding my latest project utilizing ai. Essentially, you can upload any type of advertisement (display, billboard, mobile billboard, LED, etc) and you will receive a score out of 100% in 3 categories (recall, contrast, and copy engagement). It will then give you specific feedback and suggestions on how to improve the ad based on today’s best marketing practices in order to increase conversion. We currently have a trial version that will end on Oct. 1st and I would love to get as many of you to try and give feedback. It’s pretty fun to mess around with and upload your ads or even other companies ads to see how you compare. Looking forward to hearing your thoughts and I will leave url in comments 🙂 submitted by /u/germanshepherd77 [link] [comments]

Ok, so this time I am hoping to convey what my vision was originally for this application that was misunderstood in the first post. So for months I have been working on trying to create an integration of an LLM with a journal so that you can get feedback on written work. That work can be anything including coding projects. I wanted to be able to get the same kind of insight you would get when you have people from various perspectives other than your own comment on content. That is what I used to like about Reddit. Now it is just these trolls that follow my account that are just mean. The main thing about the development that I thought would be helpful to other developers is the stack I used to host everything entirely for free. Instead of using OpenAI or Anthropic I use a local instance of Llama3.1. Instead of paying for hosting I use a static site generator like Jekyll to host for free on Netlify with easy content upgrades through pushing markdown files. I wanted the functionality of a database but there are all of these great solutions now to having to pay for hosting that I have recently discovered and this was the one combination that worked and that was actually free. I still have a lot of work to do on the app. There is so much that I want to do but I have to teach myself. Thankfully I have access to LLMs to teach me how to do everything. I have learned how to test what they output a lot faster which really helps speed up the process. The main thing slowing me down now is the processor on my local machine I am running the LLM on to generate comments. The next main thing I want to work on is developing the personas to be much more detailed and editable or more functional. People did not like the fact that I used an LLM to write my last Reddit post so I am making sure to limit LLM content to the procedural parts of the guide that I used to help with deploying and troubleshooting the installation script. So you will have to deal with my abrasive personality and tendency to narcissistically wander off topic which I usually used and LLM to spare people reading from having to endure, my prompts are often much longer than the generated content, but y'all whined and complained about using an LLM saying that I don't know how to think nor write or such but that doesn't matter much to me because I am just doing this to annoy you now. So here is the guide on how to build this app I thought was interesting in that it is entirely free to deploy and that it is a quick way to create a new website hosted for free. Before we start, make sure you have the following installed: Ruby brew update brew upgrade rbenv ruby-build rbenv install 3.3.5 --force rbenv global 3.3.5 sudo chown -R $(whoami) ~/.rbenv Jekyll Git Ollama (for AI-generated comments) ​ sudo apt-get install nodejs npm Install Netlify CLI npm install netlify-cli -g Step 1: Initial Setup First, let's create a new Jekyll site and set up the basic structure: jekyll new insight-journal cd insight-journal git init git add . git commit -m "Initial commit" Step 2: Configure for Netlify CMS Now, let's add Netlify CMS to our Jekyll site: Create an admin folder in your project root. Add config.yml and index.html files in the admin folder. config.yml: backend: name: git-gateway branch: main media_folder: "assets/images" public_folder: "/assets/images" collections: - name: "journal" label: "Journal Entries" folder: "_posts" create: true slug: "{{year}}-{{month}}-{{day}}-{{slug}}" editor: preview: false fields: {label: "Layout", name: "layout", widget: "hidden", default: "post"} {label: "Title", name: "title", widget: "string"} {label: "Publish Date", name: "date", widget: "datetime"} {label: "Categories", name: "categories", widget: "list", required: false} {label: "Tags", name: "tags", widget: "list", required: false} {label: "Body", name: "body", widget: "markdown"} admin/index.html : <!doctype html> <html> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /> <title>Content Manager</title> </head> <body> <script src="https://unpkg.com/netlify-cms@\^2.0.0/dist/netlify-cms.js"></script> </body> </html> Update your _config.yml to include Netlify CMS settings. Insight Journal settings title: Insight Journal description: A journal for insights and reflections author: Your Name Netlify CMS include: - admin Build settings markdown: kramdown theme: minima plugins: - jekyll-feed - jekyll-admin Exclude from processing exclude: - Gemfile - Gemfile.lock - node_modules - vendor/bundle/ - vendor/cache/ - vendor/gems/ - vendor/ruby/ Step 3: Customize Your Journal Let's add some custom layouts and pages: Create a _layouts folder and add a post.html layout for journal entries. post.html layout: default <article class="post h-entry" itemscope itemtype="http://schema.org/BlogPosting"> <header class="post-header"> <h1 class="post-title p-name" itemprop="name headline">{{ page.title | escape }}</h1> <p class="post-meta"> <time class="dt-published" datetime="{{ page.date | date\_to\_xmlschema }}" itemprop="datePublished"> {%- assign date_format = site.minima.date_format | default: "%b %-d, %Y" -%} {{ page.date | date: date_format }} </time> {%- if page.author -%} • <span itemprop="author" itemscope itemtype="http://schema.org/Person"> <span class="p-author h-card" itemprop="name">{{ page.author }}</span>< /span> {%- endif -%} </p> </header> <div class="post-content e-content" itemprop="articleBody"> {{ content }} </div> {%- if site.disqus.shortname -%} {%- include disqus_comments.html -%} {%- endif -%} <a class="u-url" href="{{ page.url | relative\_url }}" hidden></a> </article> Update the index.md file to display your journal entries. layout: home Welcome to Insight Journal This is where you can view all your journal entries. Click on an entry to read more. <ul> {% for post in site.posts %} <li> <h2><a href="{{ post.url }}">{{ post.title }}</a></h2> <p>{{ post.date | date: "%B %d, %Y" }}</p> <p>{{ post.excerpt }}</p> </li> {% endfor %} </ul> Step 4: Set Up AI-Generated Comments This is where it gets interesting! We'll add a Python script to generate comments using Ollama: Create generate_comments.py and personas.py in your project root. generate_comments.py import os import random import requests import frontmatter from personas import PERSONAS def generate_comment(post_content, persona): url = "http://localhost:11434/api/generate" prompt = f"As a {persona['name']} ({persona['description']}), comment on this post:\n\n{post_content}" data = { "model": "llama3.1", "prompt": prompt, "stream": False } response = requests.post(url, json=data) return response.json()["response"] def generate_comments_for_post(post_content, num_comments=3): if not post_content.strip(): raise ValueError("Post content is empty") selected_personas = random.sample(PERSONAS, num_comments) return [{ "persona": p['name'], "comment": generate_comment(post_content, p) } for p in selected_personas] def get_posts(posts_dir): posts = [] for filename in os.listdir(posts_dir): if filename.endswith('.md'): posts.append(filename) return posts def select_post(posts): print("Available posts:") for i, post in enumerate(posts): print(f"{i + 1}. {post}") selection = int(input("Enter the number of the post you want to generate comments for: ")) - 1 return posts[selection] def append_comments_to_post(post_path, comments): with open(post_path, 'r', encoding='utf-8') as file: content = file.read() # Find the end of the frontmatter frontmatter_end = content.find('---', content.find('---') + 3) + 3 # Split the content into frontmatter and body frontmatter = content[:frontmatter_end] body = content[frontmatter_end:].strip() # Append comments comments_section = "\n\n## Comments\n" for comment in comments: comments_section += f"\n### {comment['persona']}\n{comment['comment']}\n" # Combine everything new_content = frontmatter + '\n' + body + comments_section # Write the new content back to the file with open(post_path, 'w', encoding='utf-8') as file: file.write(new_content) def main(): posts_dir = '_posts' # Update this to your Jekyll posts directory try: posts = get_posts(posts_dir) if not posts: print(f"No .md files found in {posts_dir}") return selected_post = select_post(posts) post_path = os.path.join(posts_dir, selected_post) print(f"Reading file: {post_path}") with open(post_path, 'r', encoding='utf-8') as file: raw_content = file.read() print(f"Raw file content (first 500 characters):\n{raw_content[:500]}") post = frontmatter.loads(raw_content) if not post.content.strip(): print(f"The content of '{selected_post}' is empty after parsing frontmatter.") print("Frontmatter:", post.metadata) return print(f"Post content (first 500 characters):\n{post.content[:500]}") comments = generate_comments_for_post(post.content) print("Generated comments:") for comment in comments: print(f"{comment['persona']}: {comment['comment'][:100]}...") # Print first 100 chars of each comment append_comments_to_post(post_path, comments) print(f"Comments have been added to {selected_post}") except Exception as e: print(f"An error occurred: {str(e)}") import traceback traceback.print_exc() if __name__ == "__main__": main() personas.py PERSONAS = [ { "name": "Critical Thinker", "description": "Analytical and skeptical, always questioning assumptions." }, { "name": "Empathetic Listener", "description": "Focuses on emotional aspects and personal experiences." }, { "name": "Devil's Advocate", "description": "Presents counterarguments to challenge ideas." }, { "name": "Optimistic Visionary", "description": "Sees potential and positive outcomes in every situation." }, { "name": "Pragmatic Planner", "description": "Focuses on practical implications and next steps." } Install required Python packages: pip install requests python-frontmatter Step 5: Writing and Commenting on Posts Now you're all set to start journaling: Write your posts as Markdown files in the _posts directory. To add AI-generated comments, run python3 generate_comments.py and select your post. Step 6: Local Development and Preview To work on your journal locally: Run bundle install to install dependencies. Use bundle exec jekyll serve to preview your site locally. Step 7: Deploy to Netlify Finally, let's get your journal online: Push your repository to GitHub. Connect your GitHub repo to Netlify. Configure Netlify to use the main branch for deployment. Set up Netlify Identity for authentication. You can write entries directly in your favorite text editor, push them to GitHub, and even add AI-generated comments for some interesting perspectives on your thoughts. Ok so that is the application I have been trying to make using a locally hosted LLM to generate insights to journal entries. Next I want to add more features such as formatting and styling, etc. This guide was written with the help of LLMs, but I also went through it all and tested it and then pieced together this guide so that it includes only a guide that works. If it does not work for you, then you should try to debug it with an LLM, that is what I do and it helps me a lot to find the issue. People dog on LLMs all the time but they are just so helpful. You still have to learn and do the thinking a lot of the time and develop strategies to condense your codebase. I write these guides or posts to social media just to create ways to encode the codebase in a way that I can feed to an LLM to help me further develop a project or debug issues. TLDR: So the idea behind this journal was that I was tired of sharing stuff to reddit and getting rude comments so I made a journaling blog that generates comments and insights from a locally hosted LLM all hosted for free. Mostly I just wanted to share what I learned and how to develop and deploy static serverless projects for free. I tried my best to document this in a way that it will work. I tested it and it worked for me but I might have forgotten to include something, thus the importance of learning to use LLMs to debug projects, or at least that is what has helped me the most. Anyway, let me know if any of this is helpful. I think it might be a good quickstart to developing with a local LLM with Ollama. submitted by /u/KonradFreeman [link] [comments]

submitted by /u/Lord--Shadow [link] [comments]

I have only had a few days' experience messing about with the software, and I have no technical knowledge about what I'm doing. I'm just trying out different settings and judging with my ears what sounds best. Currently I just load up a song in Ensemble Mode, select everything to get an idea of what each individual model does to a given song. I often ignore the "final" ensemble output at this stage because I don't expect it to be its best since there's no thought put into the combination of models. I'll then rerun Ensemble Mode, but this time only the models that I deem to give good results. I think doing it that way for two reasons: (1) One combination of models that works for one specific song, or even a specific genre, might not work for another. One moment I get clean results with rock 'n roll, then the next I get garbage with acoustic ballads. (2) I'd like to get instrumental tracks in two distinct styles: one with the backing vocals appropriate for personal karaoke covers, and true instrumentals, where there are no human voices to be heard in the final output at all. The group of models I need for these two jobs are not the same. I plan to do this for my favourite artist's full discography comprising hundreds of individual tracks, hence why I'd like a more efficient way to do things. The only thing I can think of to maximize my time is to open up the individual models' outputs altogether in one instance of Audacity so that I can properly compare and contrast without just switching songs in a music player app. submitted by /u/ignoremesenpie [link] [comments]

Sharing an easily digestible and smaller version of the main updates of the past week in the world of AI. Meet Pixtral 12B - Mistral's new Multimodal AI Model- Mistral has released a new AI model that can process images and texts. The model might transform tasks like image captioning and object counting. OpenAI debuts O-1 models boasting PhD-level performance- o1-preview and o1-mini can reason through complex tasks, solving problems by dedicating more time to thinking and refining, similar to PhD students. Google AI Studio launches model comparison mode- The feature lets users easily view how different AI models and parameters affect outputs and evaluate model progress and speed differences. Meta Connect 2024: What to expect?- The two-day event will feature the latest Meta AI, including its Orion AR glasses, a Quest-3 variant, and, reportedly, RayBan smart glasses. Luma drops Dream Machine API just hours after Runway- AI wars escalate as Luma’s API lets users quickly build applications and services on Dream Machine following Runway’s API launch. OpenAI’s new ‘Safety Board’ can halt model releases- The independent Board oversight Committee will oversee major model releases, exercise oversight over launches, and delay launches over safety concerns. And there was more… Google’s new robots - Aloha Unleashed and DemoStart, demonstrated impressive dexterity, performing tasks like tying a shoelace, hanging a shirt, and cleaning a kitchen. Salesforce released Agentforce, a suite of low-code tools to build autonomous AI agents that can perform reasoning for sales, marketing, and commerce-related tasks. Hume AI introduces EVI-2, a voice-to-voice model that can emulate diverse personalities, accents, speaking styles, and multiple speaking rates. Adobe Firefly will receive video-generation features like Generative Extend, Text to Video, and Image to Video by the end of 2024. Amazon has started experimenting with ads in its Rufus chatbot. Based on Amazon search and conversational context, the ads may soon appear for users in the US. A new UK study led by a team of firefighters, engineers, and scientists reveals that AI-piloted drones may be able to prevent wildfires by spotting and putting out flames. SambaNova Systems has offered a high-speed, open-source alternative to OpenAI’s o-1 model, boasting fast processing capabilities via a Hugging Face demo. Microsoft launches new Copilot features like Python support in Excel, a narrative builder in PowerPoint, text summaries for Teams, and improvements in Word and OneDrive. Slack unveils AI-powered note-taking tool capable of summarizing meetings with AI or Google Meet. AI startup World Labs plans to build AI models with spatial intelligence designed to generate, perceive, and interact with 3D environments and navigate physical spaces. More detailed breakdown of these news and innovations in the newsletter. submitted by /u/RohitAkki [link] [comments]

Hi, May I ask what AI voice person is this? I tried ElevenLabs detection tool but it says it is not from them. Any ideas? https://www.youtube.com/shorts/KXvobbCxeRQ Thanks submitted by /u/bentraje [link] [comments]

Title. In my job I get a lot of unstructured e-mail and other documents from clients. I would like a tool that converts them to PDF and sort them by date in a folder automaticly. Is such a tool available? submitted by /u/eliasknutsen [link] [comments]

https://drive.google.com/file/d/1eRBXFti_nlGBs2n5fuynP19sESbP5kKC/view?usp=sharing submitted by /u/alexander_vladimir [link] [comments]

OpenAI has acknowledged that their newest AI models have significantly raised the risk of artificial intelligence being used to create biological weapons. Article :https://medium.com/@sadozye86/openai-admits-their-new-models-raise-the-risk-of-being-exploited-to-create-biological-weapons-b3ef7225f5ea?sk=v2%2Ff6351073-796b-4385-b1d3-db47467cb3f9 submitted by /u/zain017 [link] [comments]

I've been creating some really interesting sketches in MidJourney and would like to use the output from MJ in other applications. Is there a way to produce vector based images with transparent background? Happy to use other AI image generators, just don't know who to achieve this perplexing problem! submitted by /u/TonightIsNotForSale [link] [comments]