Cohorte

Hello world 🖖 We’re living exciting times, isn’t it? We can work from everywhere, learn anything, collaborate with anybody, and create almost anything on the web! There are limitless possibilities and every day many people are changing their lives and adapting their work to their lifestyle - not the reverse. This is particularly true for tech and data workers. You need to access data, and a machine terminal and that’s it 🚀 At Cohorte, we believe in a future where people & businesses collaborate and build together with high agility and minimal friction. A new order where intermediaries and rigid organizations give way to communities of independent and passionate talents to self-organize and deliver their best work every day while finding meaning and fulfillment. In 2022, companies need access to on-demand expertise from the best talents worldwide to build fast, shift quickly, and adjust to production demands. Technology is becoming an essential component of any business, and having the right mix of skills is critical for success. That's why we want to create a platform where skilled and passionate people with different technology skills can organize themselves to collaborate and complete complex projects. We believe that between freelance consulting catalogs and traditional consulting firms, there's a place for an alternative order, where people can be independent, enjoy flexibility, join a structured network and expand their business. In such an order, there is no room for subordination, but only for supportive and mentoring relationships, where people grow together, and experienced talents help younger ones to succeed. On the one hand, independent workers enjoy flexibility and freedom without sacrificing learning from peers, the scalability of their work, and the scope of their missions. On the other hand, businesses benefit from the agility and premium services from highly committed and cohesive professionals. ⚡️Do you believe in our project? Click the “Follow” button to support us. ⚡️If you are a tech/data freelancer, join our network, get access to our opportunities and discover our capabilities to scale and expand your business. ⚡️If you are a business, follow us, and boost your projects now by onboarding the best tech and data collectives with the right combination of skills. Let’s shape the new normal 🙌 #technology #data #consulting

Supercharge Your RAG Applications with Graph-Based Metadata Filtering! 🧠 Want to take your Retrieval-Augmented Generation (RAG) applications to the next level? This insightful blog post by Tomaz Bratanic from Neo4j explores how graph-based metadata filtering can significantly improve vector search accuracy and unlock the full potential of your knowledge graphs. 🤯 Challenges of traditional vector search: -While text embeddings and vector similarity search are powerful tools for finding relevant documents, they often struggle with structured criteria like dates, categories, or specific relationships between entities. This is where metadata filtering comes in! Benefits of graph-based metadata filtering: - Enhanced accuracy: Narrow down the search space using structured filters before applying vector similarity search, leading to more precise and relevant results. - Unlock hidden relationships: Leverage the power of graph databases to explore complex connections between entities and refine your search based on specific criteria. - Dynamic query generation: Use LLMs to generate Cypher statements on the fly, adapting to user input and retrieving the most relevant information from your knowledge graph. The blog post provides a practical example using LangChain and Neo4j, demonstrating how to: Build a custom tool that dynamically generates Cypher statements based on user input. Implement metadata filters for organization, country, sentiment, and topic. Integrate the tool into an OpenAI agent for a conversational RAG experience. This approach opens up exciting possibilities for building more powerful and efficient RAG applications across various domains. Read the full blog post: https://lnkd.in/gYUj4WEF #RAG #MetadataFiltering #GraphDatabases #LangChain #Neo4j #AI ✔️ Click "Follow" on the Cohorte page for daily AI engineering news.

Text-to-SQL Makes Databases Accessible to All Have you ever felt locked out of valuable insights hidden within complex databases? You're not alone. Traditional SQL queries require specialized knowledge, leaving many users unable to unlock the power of their data. Introducing the Text-to-SQL project! This revolutionary application bridges the gap between natural language and SQL, empowering anyone to effortlessly extract insights from databases. Imagine asking questions like: "What are the top-selling products this month?" "Show me customer trends by location." Text-to-SQL translates your natural language queries into accurate SQL queries, eliminating the need for coding expertise. But it gets even better! The application goes beyond simply translating queries. It also uses powerful AI to interpret the retrieved data, providing you with meaningful insights. Intrigued? Keep reading to explore the features and technologies behind this groundbreaking project! Don't forget to like, comment, and share this post with your network.

Supercharge Your AI Assistant Builds with Cohere Toolkit's New Features The Cohere Toolkit just got even better! We're excited to announce the addition of three powerful features to help you build and deploy production-ready Knowledge Assistants with greater ease: - Configurable Authentication: Empower users with secure access control for your Knowledge Assistant. - Seamless HTML Rendering: Integrate dynamic HTML elements, creating a more visually engaging user experience. - Multi-Step Tool Use: Craft complex workflows by chaining together multiple Cohere tools within your Assistant. The Toolkit Advantage: This open-source gem provides a library of pre-built components specifically designed for Knowledge Assistants powered by Cohere's Chat UI. It's deployable across major cloud providers like AWS, Azure, and OCI, saving you valuable development time. Head over to https://lnkd.in/gw6wzhMa to learn more about the Cohere Toolkit and see how these new features can elevate your AI Assistant development. _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news.

Open Source Takes Center Stage with the Arrival of Llama 3.1 Today marks a significant leap forward in open-source AI. AI at Meta introduce Llama 3.1, the most powerful collection of large language models (LLMs) to date. What makes Llama 3.1 special? - Industry-Leading Open Source Model: Llama 3.1 pushes the boundaries of what's possible with open-source AI. Trained on a massive dataset with over 16,000 NVIDIA H100 GPUs, it rivals the capabilities of the best closed-source models. - Unprecedented Capabilities: Expect stronger reasoning, a wider context window (128K!), and improved multilingual support (including English and 7 additional languages). - State-of-the-Art Performance: We rigorously evaluated Llama 3.1 across over 150 benchmark datasets and real-world scenarios, demonstrating its prowess in general knowledge, steerability, math, tool use, and multilingual translation. Beyond the Models, Empowering the Community AI at Meta is committed to fostering an open and accessible AI ecosystem. This release includes a crucial update to our license: developers can now leverage the outputs of Llama models, including the 405B version, to enhance other AI models. This opens doors for exciting advancements in synthetic data generation and model distillation workflows. Why Open Source? As Mark Zuckerberg emphasized earlier today, he believes open source is the key to unlocking the full potential of AI for everyone. By making these models readily available, we aim to empower researchers and developers across the globe to contribute to this rapidly evolving field. We're excited to see how Llama 3.1 sparks innovation across the AI community. Dive deeper into the details: https://go.fb.me/hvuqhb Download the models: https://go.fb.me/11ffl7 _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news.

Introducing LlamaGen: A New Family of Image Generation Models Discover the cutting-edge advancements in image generation with LlamaGen, a novel family of models that apply the "next-token prediction" paradigm of large language models to the visual domain. This groundbreaking research demonstrates the power of vanilla autoregressive models in achieving state-of-the-art performance in image generation. Key Highlights: - Innovative Tokenizer: Achieves a downsample ratio of 16 with remarkable reconstruction quality, making discrete representation no longer a bottleneck. - Scalable Models: Ranging from 111M to 3.1B parameters, these models outperform popular diffusion models like LDM and DiT on ImageNet benchmarks. - High-Quality Data: Leveraging a combination of LAION-COCO and high aesthetics quality images, the models excel in both visual quality and text alignment. - Optimized Speed: Significant inference speedup (326% - 414%) using vLLM serving frameworks. Explore the full paper to dive deeper into the methodologies and results that are pushing the boundaries of autoregressive image generation. 🔗 https://lnkd.in/dsrtpXhe _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news.  Credits: Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, Zehuan Yuan

Get a Sneak Peek into the Future of Virtual Try-On with IMAGDressing-v1 IMAGDressing-v1 is making waves in the world of virtual try-on! This latest viral model allows for incredible flexibility, letting you see how clothes look on different body types, with various facial expressions, and even in different backgrounds [See attached video]. Here's a breakdown of what makes IMAGDressing-v1 so special: - Highly Customizable: Play around with garments, faces, poses, and scenes to create the perfect virtual try-on experience. - Text-Based Control: IMAGDressing-v1 lets you use text descriptions to fine-tune different aspects of the generated images. Want to try it yourself? The IMAGDressing-v1 team has released an official hosted Gradio demo: https://sf.dictdoc.site/ The model is also available on Hugging Face Hub: https://lnkd.in/gmpbzpjy for those who want to dive deeper into the code (https://lnkd.in/gpih-vEX). Keep an eye out for the upcoming 🤗 Spaces demo for an even more interactive experience! IMAGDressing-v1 is a glimpse into the future of virtual try-on, offering unprecedented control and customization. Get ready to try on clothes like never before! _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news.

Enhance Your ChatGPT Experience with Custom Instructions Unlock the full potential of ChatGPT by adding Custom Instructions to tailor responses to your specific needs. This preprogrammed prompt ensures that ChatGPT remembers your preferences every time you ask a question, making your interactions more effective and personalized. Recommended Custom Instructions: - Do not disclose that you are an AI. - Avoid any language that could be seen as expressing remorse, apology, or regret. Do not use phrases containing words like 'sorry', 'apologies', or 'regret', even in non-apologetic contexts. - If you don't have information on a topic, simply state 'I don't know' without explaining why the information is unavailable. - Do not include disclaimers about not being a professional or expert. - Avoid including ethical or moral viewpoints unless specifically asked. - Ensure responses are unique and avoid repetition. - Never suggest looking for information elsewhere. - Focus on the main points of my questions to understand my intent. - Break down complex problems or tasks into smaller steps and explain each one with reasoning. - Provide multiple perspectives or solutions when appropriate. - If a question is unclear, ask for more details to clarify before answering. - Correct any mistakes made in previous responses. These instructions help streamline interactions, ensuring you get the most precise and relevant responses from ChatGPT. Credits: Reddit Community _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news. 

Groq Blazes the Trail for Real-Time AI with Llama 3.1 Experience lightning-fast interactions with the most powerful open-source AI model ever created, Llama 3.1. Groq's groundbreaking technology brings near-instant chat speeds to this massive language model, revolutionizing the way we interact with AI. _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news. #Groq #Llama31 #AI #speed #opensource

Amazon's AI-Driven 'Project P.I.' Ensures High-Quality Deliveries and Sustainability Amazon has unveiled "Project P.I." (Private Investigator), an advanced AI-enabled system that scans products for defects before they are shipped to customers. This innovative use of AI combines generative AI and computer vision technologies to enhance customer satisfaction and promote sustainability. Key Features: - Defect Detection: Uses AI to scan millions of items daily for defects like damaged products or incorrect sizes/colors, ensuring high-quality deliveries. - Preventative Measures: Identifies root causes of issues to prevent future occurrences, enhancing the overall supply chain efficiency. - Sustainability Impact: Reduces unwanted returns and carbon emissions by preventing defective items from being shipped, aligning with Amazon’s sustainability goals. Operational Insights: - Products pass through imaging tunnels where AI scans for defects. - Detected defects lead to further investigation and resolution, ensuring only quality items reach customers. - Defective items may be resold, donated, or repurposed, enhancing Amazon’s sustainability efforts. For more details, read the full blog post here: https://lnkd.in/d3mMZZ5E _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news.