AI REWIND 2024

Last year has been a fantastic learning experience for us at Superteams.ai. 

Our work in organizing highly effective AI teams for startups, SaaS companies, and enterprises enabled us to act as an extended R&D team—creating demos and open-source integrations, educating developers through deeply technical guides, building AI APIs that integrate with SaaS platforms, and delivering solutions that seamlessly fit into business workflows.

Some key AI APIs we built in 2024:

• Call Analysis API for Customer Support: Developed using open-source AI technologies like Whisper, knowledge graphs, vector search, and advanced RAG to provide actionable insights for support teams.
• Bulk Invoice Parsing API for ESG AI Firms: Automated bulk invoice processing for ESG-focused companies, enhancing financial data management.
• Recommendation API in Retail: Built a recommendation system leveraging customer profiles and transaction data to deliver relevant suggestions.
• Report Generation API for Analysts: Created an API to automate report creation from data, improving analyst efficiency.
• Advanced Private RAG System for Communities: Designed a secure RAG system using open-source AI for a community platform.

We also collaborated with AI and cloud computing businesses to produce over 500 tutorials, demos, tech-centric PR, and developer-focused content pieces. This meant that we were learning and building in tandem with emerging AI, while helping our partnering companies drive massive visibility through content.

As 2024 unfolded, the AI landscape advanced at an unprecedented pace, driving us closer to unparalleled efficiency and technological sophistication. At Superteams.ai, we drew up a crisp timeline of the pivotal releases and updates that have defined the year.

January

•  Google’s Gemini: Google's first model release, built on enhanced Transformer decoders with efficient attention mechanisms, supported 32k context, excelling in reasoning and multimodal tasks.

February

• Google’s Gemini 1.5: Built using advanced Transformer and MoE architectures, featuring a 128K context window, revolutionized long-context understanding—a vital capability for handling complex workflows efficiently.
• OpenAI’s Sora: A groundbreaking text-to-video model, enabled seamless video creation from text inputs, generating high-fidelity videos up to a minute long and images up to 2048x2048 resolution using advanced spacetime transformer architecture.
• Stability AI’s Stable Diffusion 3: A suite of models, ranging from 800M to 8B parameters, combined diffusion transformer architecture and flow matching, enabling enhanced multi-subject image generation with superior quality and accuracy.
• LlamaIndex’s LlamaCloud: Featured LlamaParse for complex document parsing and a Managed Ingestion API to simplify data processing and retrieval for LLM and RAG applications.

March

•  NVIDIA Blackwell Architecture: Featured a second-generation Transformer engine with 208 billion transistors and is built on TSMC's custom 4NP process. Delivered real-time generative AI on trillion-parameter models, cutting costs and energy consumption by up to 25x.
•  xAI’s Grok-1: A bold entry in LLMs, with 314 billion parameter Mixture-of-Experts model showcasing xAI’s vision for high-performance reasoning.
• Anthropic’s Claude 3 Family: Introduced modularity with Haiku, Sonnet, and Opus models, optimizing use cases across domains.Supported up to 200K tokens, enabling the processing of extensive documents and conversations.
• Google’s Gemini Ultra 1.0: Packed advanced capabilities with multimodal design architecture, enabling seamless processing and generation of text, images, audio, and video. It supports a context window of up to 32,768 tokens for managing extensive inputs.

April

• Meta’s Llama 3: Llama 3 set a new standard for open-source AI, delivering enhanced reasoning and contextual understanding with a 128K-token vocabulary and improved inference efficiency via grouped query attention (GQA) in 8B and 70B models.
• Mistral Mixtral 8*22B: Introduced sparse Mixture-of-Experts (SMoE) architecture for cost-efficient AI at scale, with 39B active parameters, 64K token context, multilingual fluency, advanced math, coding skills, and native function calling capabilities.

May

• OpenAI’s GPT-4o: Featured advanced multimodal capabilities, processing text, audio, image, and video inputs while generating text, audio, and image outputs. Its unified architecture integrates text, vision, and audio, enabling seamless cross-modal understanding. With a 128,000-token context window, it efficiently handled extensive and complex inputs, enhancing interaction quality.
• EU’s AI Act Approval: Marked a milestone in ethical AI regulation, shaping global compliance standards.

June

• Apple’s “Apple Intelligence”: Signaled Apple’s AI debut, seamlessly integrating intelligence into its ecosystem.
• Anthropic’s Claude 3.5 Sonnet: Combined speed and depth, refining model efficiency with a 200,000-token context window for handling extensive documents and conversations. Offered multimodal capabilities to process and generate text and images, enabling advanced data analysis and versatile content creation.
• LangChain’s LangGraph v0.1: an open-source framework for building agentic and multi-agent applications with enhanced workflow control. Additionally, it launched LangGraph Cloud in closed beta, providing scalable infrastructure for deploying agents with tools for prototyping, debugging, and monitoring.

July

• Meta’s Llama 3.1: Meta introduced Llama 3.1 405B, a groundbreaking open-source language model with 405 billion parameters. As a significant update to the Llama 3 series, it stood as the largest open-source LLM. 

August

• EU’s AI Act Implementation: Enforced ethical standards, solidifying Europe’s leadership in responsible AI.
• xAI’s Grok-2: A refined model with superior contextual capabilities.
• Mistral-NeMo-Minitron 8B: Lightweight and multimodal, ideal for edge computing applications. Mistral-NeMo-Minitron 8B is a width-pruned version of the 12B model, fine-tuned with 127B tokens to handle distribution shifts effectively.
• AnswerAI’s answerai-colbert-small-v1: a 33M parameter model using JaColBERTv2.5 training. The compact model outperformed larger competitors while maintaining MiniLM-level efficiency, proving sophisticated optimization can achieve superior results without massive architectures.

September

• Meta’s Llama 3.2: A powerful multimodal model with advanced vision capabilities, available in 1B, 3B, 11B, and 90B sizes, optimized for visual recognition, image reasoning, captioning, and answering image-related questions.
• Flux 1.1: Elevated text-to-image generation with faster rendering and higher fidelity. Delivered 6x faster generation with enhanced image quality, prompt adherence, and diversity, and supported ultra-fast 2K high-resolution image generation.
• Pixtral 12B: Delivered native multimodal capabilities with a 400M-parameter vision encoder and 12B multimodal decoder, excelling in document analysis, image processing, and handling multiple images simultaneously with a 128K-token context window.
• YOLO11: Redefined real-time object detection with cutting-edge speed and accuracy. Supported various tasks including object detection, instance segmentation, pose estimation, and oriented object detection.

October

• Stable Diffusion 3.5: Multimodal Diffusion Transformer (MMDiT) text-to-image model designed to enhance image quality, typography, complex prompt comprehension, and overall resource efficiency.
• SearchGPT by OpenAI: Specialized in contextualized queries, transforming search efficiency.
• Microsoft’s OmniParser: Streamlined unstructured data processing, enhancing enterprise workflows with OmniParser's curated datasets and dual-model architecture, featuring detection for UI regions and captioning for functional semantics, ensuring versatile and efficient document parsing.

November

• Anthropic’s Model Context Protocol (MCP): Enabled seamless AI integration with universal dataset connectivity.
• Alibaba’s Qwen with Questions (QwQ): A reasoning powerhouse having 32.5 billion parameters with exceptional multilingual capabilities and a 32,000-token context window.
• OLMo 2 by Ai2: Fully open-source models outperformed competitors on English benchmarks available in 7 billion (7B) and 13 billion (13B) parameter configurations.
• Lightricks’ LTX Video: Democratized video generation with real-time, high-quality output on consumer devices.
• OpenGPT-X’s Teuken-7B: Advanced multilingual support across 24 EU languages, championing inclusivity in AI. With seven billion parameters, Teuken-7B incorporates approximately 50% non-English pre-training data, ensuring robust performance across multiple languages.
• Meta’s Llama 3.3: A 70B instruction-tuned text-only model, delivering superior performance compared to Llama 3.1 70B and Llama 3.2 90B for text applications, while approaching the performance of the larger Llama 3.1 405B in some tasks.

December

• OpenAI unveiled Sora Turbo, democratizing high-quality video creation with faster speeds and greater creative control for ChatGPT Plus/Pro users.‍
• Google responded with Veo 2, elevating 4K video generation and enhancing Imagen 3 for superior image manipulation.
• Google's Gemini 2.0 emerged as a breakthrough in universal AI assistance, redefining multimodal AI with native image and audio generation, advanced tool integration, and a 2-million-token context window, enabling detailed, personalized interactions and processing extensive data inputs.
• Meta AI introduced the Apollo family, groundbreaking video-focused LLMs, while ApolloBench redefined video evaluation with a remarkable 41× efficiency boost.
•  xAI expanded the Grok ecosystem with Aurora, a cutting-edge image generation model.
• Amazon's strategic entry into the frontier AI race with Nova, a sophisticated family of foundation models designed for enterprise-scale deployment. The lineup features three distinct models: some text
  • Nova Micro (3B parameters) for rapid text processing with a 128K context window.
  • Nova Lite (7B parameters) offering cost-effective multimodal capabilities with a 300K token context.
  • Nova Pro (175B parameters) delivering state-of-the-art performance in visual-text understanding and extended code processing.
• Closing the year, OpenAI launched the o1 API, achieving a 60% efficiency improvement in complex reasoning tasks, setting a new benchmark for cost-effective AI solutions.
• Wake Vision, a state-of-the-art TinyML dataset with 6M images, is 100x larger than VWW, offering high-quality annotations for efficient person detection on embedded and edge devices.

December has been a whirlwind of groundbreaking updates and announcements, and there’s likely more to come! It’s thrilling to be at the cusp of this revolutionary phase where each advancement tackles efficiency challenges, democratizes access, and pushes technical limits—shaping an AI ecosystem that’s increasingly accessible, powerful, and resource-efficient. As we reflect on the year, we can’t help but look forward excitedly to what 2025 will bring in the ever-evolving world of AI innovation.