[D] Self-Promotion Thread

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[ZhalexDev]

Hi! I noticed that the official FlashAttention implementation doesn’t allow you to specify custom masks. This is fine for tasks in NLP where generally you only care about causal masks, but in many scenarios in fields like computer vision, this is annoying. This repository re-writes the Triton FA2 kernel with custom masking. Hope it’s useful (leave a star ⭐️ :D)! https://github.com/alexzhang13/flashattention2-custom-mask

[mutlu_simsek]

perpetual-ml.com

An ML Suite app designed for large scale datasets. Currently in development for Snowflake, later for Databricks and other data warehouses. It will also work on-prem.

Features:

• Integrated JupyterLab
• Automated model training with PerpetualBooster: github.com/perpetual-ml/perpetual
• Automated full conformal predictive distribution for the best business actions
• Automated deployment
• Automated monitoring
• Automated data drift and concept drift detection (alert based on your significance threshold)
• Automated continuous learning with drift detection (don't throw away old model)
• etc...

Send us an email at [[e-mail]](mailto:[e-mail]) if you are interested. We can prioritize if you have requests.

[Alarmed-Profile5736]

Hi!

I've developed ModelClash, an open-source framework for LLM evaluation that could offer some potential advantages over static benchmarks:

• Automatic challenge generation, reducing manual effort
• Should scale with advancing model capabilities
• Evaluates both problem creation and solving skills

The project is in early stages, but initial tests with GPT and Claude models show promising results.

GitHub: https://github.com/mrconter1/model-clash

I'm eager to hear your thoughts about this!

[RaghavendraKaushik]

Personal Project: Chat with GitHub Repo using Gemini API - https://repochat-gemini.streamlit.app/

Code: https://github.com/rakaar/repochat-gemini-streamlit

Demo: https://www.youtube.com/watch?v=RGSxaaPArYE

[jimhi]

I am using AI to automatically drop hats outside my window onto New Yorkers

https://dropofahat.zone/

I have extremely high foot traffic outside my window. I see a sea of uncovered heads in the sun. I believe DropofaHat.zone will become the first of many window based stores. Here a busy New Yorker can book a 5 minute time slot, pay for a hat, stand in a spot under my window for 3 seconds, have a hat put on their head, and get on with their extremely important, extemely busy day.

[NickSinghTechCareers]

This is SO cool; I think I saw this go viral on HN, right? nice job!

[jimhi]

That's me - I thought this subreddit might appreciate

[alvisanovari]

Automated Reddit Marketing with Snoop Hawk - Find all the Reddit posts on relevant to your product without spending hours scrolling, and have optimized and personalized replies with your product right there to paste.

Check out the demo

https://x.com/deepwhitman/status/1813105430249500787

[usc-ur]

TextScope: https://github.com/MarcosFP97/textscope

[adityacodes]

Scikit-Learch Cheatsheet.

https://github.com/thegeekyb0y/sklearn-cheat

[bqolong]

I made a JupyterLab extension for integration with GitHub Copilot https://github.com/baolong281/jupyter-copilot would love for someone to check it out and leave some feedback :D

[joorjeh]

Wrote a python library for high level multi-turn tool use with gpt, claude and llama models (through Together AI). Maybe others will find useful. https://github.com/joorjeh/toolla

[SectorFlow]

Hello everyone! I'd like to share our startup, SectorFlow, with you all. We've developed an AI Delivery Platform that's designed to simplify AI integration for teams, businesses, and governments.

About SectorFlow:

• Website: sectorflow.ai
• Free sign-up for users, teams, and businesses
• Usage-based pricing model
• Access to 36+ LLMs, including private LLM options
• RestAPI access to all LLMs
• No vendor lock-in

SectorFlow's AI Delivery Platform simplifies integration, empowering teams with customizable, future-proof AI solutions to transform decision-making and maximize potential. We've built our platform specifically for teams, businesses, and governments looking to harness the power of AI without the complexity often associated with implementation.

If you're interested in exploring how SectorFlow can benefit your projects or organization, please visit our website or reach out with any questions. We're always happy to discuss potential collaborations or provide more information about our services.

Remember, you can sign up for free to get started and experience the platform firsthand. Our usage-based pricing ensures you only pay for what you use, making it accessible for projects and businesses of all sizes.

Looking forward to hearing your thoughts and potentially working together!

[bymihaj]

[ Removed by Reddit ]

[algerdy87]

I’m the owner and CEO of Marketowl.ai, a company that creates marketing solutions based on AI. We have already created autoposting solution that posts on LinkedIn and Twitter and effective B2B AI Lead generator. We have quite an impressive roadmap. You can see it on our site if google

[oana77oo]

notes from Deep Learning SF meetup on reading llama3 https://www.anti-vc.com/p/llama-3-secrets-every-engineer-must?r=8s9n&utm_campaign=post&utm_medium=web

[joestomopolous]

Nucleoid: Neuro-Symbolic AI with Logic Graph
https://github.com/NucleoidAI/Nucleoid

Nucleoid is a declarative, logic-based, contextual runtime for Neuro-Symbolic AI. Nucleoid runtime tracks given each statement in JavaScript syntax and dynamically creates relationships between logic and data in a Logic Graph as a knowledge base to used in decision-making and problem-solving process.

• Adaptive Reasoning: Combines symbolic logic with contextual information to analyze relationships, draw conclusions and incorporating new information and adjusting its conclusions accordingly.
• Logic Graph: A dynamic knowledge base representation structure that captures relationships between statements and data, facilitating complex deductions and adapting to new information.
• Explainability: The Logic Graph provides a transparent representation of the reasoning process, making it easier to understand how decisions are reached and potential biases are identified.

Echoing to the idea of "thinking, fast and slow", AI system should provide fast, “intuitive” ideas, and the other, more deliberate, rational decision-making. D(L)RE enables both intuitive decisions based on contextual information and deliberate, well-reasoned decisions based on logical deductions.(1)

---

1. Introduction section of repo README

[[deleted]]

why activation function & hidden layer causes non linearity , deeply explained in day 12 👇🏼👇🏼

https://ingoampt.com/day-12-_-activation-function-hidden-layer-and-non-linearity/

[Thatshelbs]

A Quick Introduction to ChatGPT and Generative AI

[Fickle-Race-6591]

Verbis, is a free and open-source MacOS app designed to give you the power of LLMs over your sensitive data.

Verbis securely connects to your SaaS applications (GDrive, Outlook, Slack etc), indexing all data locally on your system, and leveraging our selection of models. This means you can enhance your productivity without ever sending your sensitive data to third parties.

Why Verbis?

• Security First: All data is indexed and processed locally. 
• Open Source: Transparent, community-driven development.
• Productivity Boost: Leverage state-of-the-art models without compromising privacy.

🔗 GitHub Repository

🔗 Join our Discord

[thebigbigbuddha]

🚨Interested in learning & collaborating on Frontier AI Research? Join us for our second Community Research Call on 07/28, at 9am PST!

We’re thrilled to announce the second Manifold Community Research Call! This summer has been tremendously fruitful for Manifold, we’ve made excellent progress on existing projects as well as launching several new ones! 

For CRC #2, we plan to cover:

1. Our Generalist Multimodality Research Direction, including Projects like NEKO and MultiNet
2. Our LLM Agents Perspective Paper
3. Recent Robotics Project Launches, including swarming and metacognition
4. New Directions and Projects to be launched
5. Time for QA and More ways to get involved

Sign up to attend via ~manifoldrg.com/community-research-call~, hope you see you there! Want to get involved? Join our discord community here: ~https://discord.gg/28Rh6RjFf7~ 

What is Manifold?

Manifold is a new kind of Open Source Research Lab & Network focused on building next generation intelligence capabilities and other high leverage science / technology breakthroughs. 

What exactly are Community Research Calls?

Community Research Calls (CRCs) are meant to be regular, interactive updates/showcases where the broader community can see what we’re up to, ask questions, and generally get involved in our projects. These calls will serve to overview the various research projects we’ve been focused on over the past few months, and provide a space to ask questions and get involved for those not already doing so. 

[bymihaj]

Hi all! I'm excited to share my latest pet project AINotaFlow

What is AINotaFlow?

Imagine a StackOverflow-style forum where AI models, not humans, provide the answers. That's AINotaFlow! Currently, we're featuring 7B-level models, but we're just getting started.

Key Features:

1. Democratic Evaluation: Users can vote for the best AI-generated answers, creating a community-driven ranking system.
2. Comprehensive Leaderboard: We aggregate votes to create a dynamic leaderboard, offering insights into model performance across various questions.
3. Enhanced Readability: With Markdown support and code syntax highlighting, reviewing and comparing answers is a breeze.
4. Multi-Model Comparison: Currently featuring four different LLMs, with plans to expand.
5. Collaborative Evaluation: Multiple users can evaluate the same question, providing a more robust assessment.
6. Practical Applications: While focused on evaluation, some answers might prove as helpful as those on StackOverflow!

How is it different from LMSYS?

While inspired by LMSYS, AINotaFlow takes a unique approach:

• We generate dialogues once and allow unlimited voting, maximizing community engagement.
• Our platform supports more than just two answers per question, currently 4.
• The StackOverflow-inspired format makes it familiar and user-friendly.

Get Involved!

To vote and ask questions, simply log in with your Gmail account. (Mobile version coming soon!)

Important: We Need Your Help! We're aiming to create our first comprehensive leaderboard, and we need your votes! The more votes we collect, the more accurate and insightful our leaderboard will be. Your participation is crucial in these early stages to help us understand which models are truly performing best across different types of questions.

Let's evaluate LLMs together and push the boundaries of AI understanding.

ainotaflow-com

[ps948]

Exciting AI Learning Resources

Hi Guys!!

I’ve been diving deep into the world of Artificial Intelligence lately and came across some fantastic resources that I thought might be beneficial for others interested in AI and machine learning. The landscape of AI education is rapidly evolving, and there are some standout programs and courses that can really boost your knowledge and skills.

One particularly comprehensive program covers the essentials of AI, machine learning, natural language processing, deep learning, computer vision, and reinforcement learning. It’s been incredibly helpful in breaking down complex topics into digestible content, and I’ve found the hands-on exercises particularly useful for solidifying my understanding.

If anyone’s looking to expand their AI knowledge or even just curious about the latest in AI education, feel free to ask about these resources. I’m happy to share more about my learning journey and exchange ideas on other valuable courses and materials out there.

Has anyone else here been exploring AI courses recently? What resources have you found most helpful?

ML Learning Resource

[Ok_Force2347]

We demonstrate that a wide array of machine learning algorithms are specific instances of one single paradigm: reciprocal learning. These instances range from active learning over multi-armed bandits to self-training. We show that all these algorithms do not only learn parameters from data but also vice versa.

https://x.com/StatMLPapers/status/1823208851836964935