Hugging Face Integrations

FriendliAI serves as an advanced generative AI infrastructure platform that delivers rapid, efficient, and dependable inference solutions tailored for production settings. The platform is equipped with an array of tools and services aimed at refining the deployment and operation of large language models (LLMs) alongside various generative AI tasks on a large scale. Among its key features is Friendli Endpoints, which empowers users to create and implement custom generative AI models, thereby reducing GPU expenses and hastening AI inference processes. Additionally, it facilitates smooth integration with well-known open-source models available on the Hugging Face Hub, ensuring exceptionally fast and high-performance inference capabilities. FriendliAI incorporates state-of-the-art technologies, including Iteration Batching, the Friendli DNN Library, Friendli TCache, and Native Quantization, all of which lead to impressive cost reductions (ranging from 50% to 90%), a significant decrease in GPU demands (up to 6 times fewer GPUs), enhanced throughput (up to 10.7 times), and a marked decrease in latency (up to 6.2 times). With its innovative approach, FriendliAI positions itself as a key player in the evolving landscape of generative AI solutions.

ZenCtrl is an innovative, open-source AI image generation toolkit created by Fotographer AI, aimed at generating high-quality, multi-perspective visuals from a single image without requiring any form of training. This tool allows for precise regeneration of objects and subjects viewed from various angles and backgrounds, offering real-time element regeneration which enhances both stability and flexibility in creative workflows. Users can easily regenerate subjects from different perspectives, swap backgrounds or outfits with a simple click, and start producing results instantly without the need for prior training. By utilizing cutting-edge image processing methods, ZenCtrl guarantees high accuracy while minimizing the need for large training datasets. The architecture consists of streamlined sub-models, each specifically fine-tuned to excel at distinct tasks, resulting in a lightweight system that produces sharper and more controllable outcomes. The latest update to ZenCtrl significantly improves the generation of both subjects and backgrounds, ensuring that the final images are not only coherent but also visually appealing. This continual enhancement reflects the commitment to providing users with the most efficient and effective tools for their creative endeavors.

Thunder Compute delivers cheap cloud GPUs for companies, researchers, and developers running demanding AI and machine learning workloads. The platform gives users fast access to H100, A100, and RTX A6000 GPUs for LLM training, inference, fine-tuning, image generation, ComfyUI workflows, PyTorch jobs, CUDA applications, deep learning pipelines, model serving, and other GPU-intensive compute tasks. Thunder Compute is designed for teams that want affordable GPU cloud infrastructure with a strong developer experience, clear pricing, and minimal operational friction. Instead of dealing with the cost and complexity of legacy cloud vendors, users can deploy on-demand GPU instances with persistent storage, rapid provisioning, straightforward management, and scalable compute capacity. Thunder Compute is a strong fit for startups building AI products, engineering teams that need cloud GPUs for inference, and organizations looking for GPU hosting that is both economical and reliable. If you are searching for cheap H100s, A100 cloud instances, affordable GPUs for AI, or a RunPod alternative with transparent pricing and a simple interface, Thunder Compute provides a modern option for high-performance cloud GPU rental and AI infrastructure. Thunder Compute supports teams building and deploying modern AI applications that need dependable access to cheap cloud GPUs for both experimentation and production. From prototype training runs to large-scale inference and batch processing, the platform is designed to reduce infrastructure friction and accelerate iteration. For users comparing GPU cloud providers, Thunder Compute stands out with affordable pricing, fast access to top-tier GPUs, and a developer-friendly experience built around real AI workflows.

Bitext specializes in creating multilingual hybrid synthetic training datasets tailored for intent recognition and the fine-tuning of language models. These datasets combine extensive synthetic text generation with careful expert curation and detailed linguistic annotation, which encompasses various aspects like lexical, syntactic, semantic, register, and stylistic diversity, all aimed at improving the understanding, precision, and adaptability of conversational models. For instance, their open-source customer support dataset includes approximately 27,000 question-and-answer pairs, totaling around 3.57 million tokens, 27 distinct intents across 10 categories, 30 types of entities, and 12 tags for language generation, all meticulously anonymized to meet privacy, bias reduction, and anti-hallucination criteria. Additionally, Bitext provides industry-specific datasets, such as those for travel and banking, and caters to over 20 sectors in various languages while achieving an impressive accuracy rate exceeding 95%. Their innovative hybrid methodology guarantees that the training data is not only scalable and multilingual but also compliant with privacy standards, effectively reduces bias, and is well-prepared for the enhancement and deployment of language models. This comprehensive approach positions Bitext as a leader in delivering high-quality training resources for advanced conversational AI systems.

Portia AI is an open-source developer framework that includes optional cloud services, enabling teams to quickly create, deploy, and oversee stateful, authenticated AI agents while maintaining full visibility and control over the process. Developers initiate the process by using the SDK to generate clear, organized multi-step "plans" that integrate LLM reasoning with various tool calls, executing these plans incrementally and enhancing the plan state at each step, while also allowing for pauses to seek clarifications, whether from human users or machine inputs, when authentication or additional information is necessary. With its cohesive authentication framework and an easily customizable tool catalog, Portia automatically manages the credentials and permissions needed for remote API and MCP tool calls. Furthermore, the accompanying cloud solution provides persistent storage for plan execution states, historical log tracking, telemetry dashboards, and managed scaling, ensuring that production deployments remain dependable, traceable, and compliant with regulatory standards. This comprehensive approach not only simplifies the development process but also enhances the overall efficiency and effectiveness of AI agent deployments.

NuExtract is an advanced tool designed for extracting structured data from various document formats, such as text files, scanned images, PDFs, PowerPoints, spreadsheets, among others, while accommodating multiple languages and mixed-language inputs. It generates output in JSON format that adheres to user-specified templates, incorporating verification and handling of null values to reduce inaccuracies. Users can initiate extraction tasks by crafting a template through either specifying the fields they want or importing existing formats; they can enhance precision by including example documents and expected outputs in the example set. The NuExtract Platform boasts a user-friendly interface for template creation, extraction testing in a sandbox environment, managing teaching examples, and adjusting parameters like model temperature and document rasterization DPI. After completion of validation, projects can be executed through a RESTful API endpoint, enabling real-time processing of documents. This seamless integration allows users to efficiently manage their data extraction needs, enhancing both productivity and accuracy in their workflows.

The Vercel AI SDK is a complimentary, open source toolkit based on TypeScript, developed by the team behind Next.js, which empowers developers with cohesive, high-level tools for swiftly implementing AI-driven features across various model providers with just a single line of code modification. It simplifies intricate tasks such as managing streaming responses, executing multi-turn tools, handling errors, recovering from issues, and switching between models while being adaptable to any framework, allowing creators to transition from concept to operational application in mere minutes. Featuring a unified provider API, the toolkit enables developers to produce typed objects, design generative user interfaces, and provide immediate, streamed AI replies without the need to redo foundational work, complemented by comprehensive documentation, practical guides, an interactive playground, and community-driven enhancements to speed up the development process. By taking care of the complex elements behind the scenes while still allowing sufficient control for deeper customization, this SDK ensures a smooth integration experience with multiple large language models. Overall, it stands as an essential resource for developers seeking to innovate rapidly and effectively in the realm of AI applications.

Qwen-Image is a cutting-edge multimodal diffusion transformer (MMDiT) foundation model that delivers exceptional capabilities in image generation, text rendering, editing, and comprehension. It stands out for its proficiency in integrating complex text, effortlessly incorporating both alphabetic and logographic scripts into visuals while maintaining high typographic accuracy. The model caters to a wide range of artistic styles, from photorealism to impressionism, anime, and minimalist design. In addition to creation, it offers advanced image editing functionalities such as style transfer, object insertion or removal, detail enhancement, in-image text editing, and manipulation of human poses through simple prompts. Furthermore, its built-in vision understanding tasks, which include object detection, semantic segmentation, depth and edge estimation, novel view synthesis, and super-resolution, enhance its ability to perform intelligent visual analysis. Qwen-Image can be accessed through popular libraries like Hugging Face Diffusers and is equipped with prompt-enhancement tools to support multiple languages, making it a versatile tool for creators across various fields. Its comprehensive features position Qwen-Image as a valuable asset for both artists and developers looking to explore the intersection of visual art and technology.

NVIDIA Cosmos serves as a cutting-edge platform tailored for developers, featuring advanced generative World Foundation Models (WFMs), sophisticated video tokenizers, safety protocols, and a streamlined data processing and curation system aimed at enhancing the development of physical AI. This platform empowers developers who are focused on areas such as autonomous vehicles, robotics, and video analytics AI agents to create highly realistic, physics-informed synthetic video data, leveraging an extensive dataset that encompasses 20 million hours of both actual and simulated footage, facilitating the rapid simulation of future scenarios, the training of world models, and the customization of specific behaviors. The platform comprises three primary types of WFMs: Cosmos Predict, which can produce up to 30 seconds of continuous video from various input modalities; Cosmos Transfer, which modifies simulations to work across different environments and lighting conditions for improved domain augmentation; and Cosmos Reason, a vision-language model that implements structured reasoning to analyze spatial-temporal information for effective planning and decision-making. With these capabilities, NVIDIA Cosmos significantly accelerates the innovation cycle in physical AI applications, fostering breakthroughs across various industries.

DeepSeek V3.1 stands as a revolutionary open-weight large language model, boasting an impressive 685-billion parameters and an expansive 128,000-token context window, which allows it to analyze extensive documents akin to 400-page books in a single invocation. This model offers integrated functionalities for chatting, reasoning, and code creation, all within a cohesive hybrid architecture that harmonizes these diverse capabilities. Furthermore, V3.1 accommodates multiple tensor formats, granting developers the versatility to enhance performance across various hardware setups. Preliminary benchmark evaluations reveal strong results, including a remarkable 71.6% on the Aider coding benchmark, positioning it competitively with or even superior to systems such as Claude Opus 4, while achieving this at a significantly reduced cost. Released under an open-source license on Hugging Face with little publicity, DeepSeek V3.1 is set to revolutionize access to advanced AI technologies, potentially disrupting the landscape dominated by conventional proprietary models. Its innovative features and cost-effectiveness may attract a wide range of developers eager to leverage cutting-edge AI in their projects.

The Google AI Edge Gallery is an innovative, open-source Android application designed to showcase various applications of on-device machine learning and generative AI, allowing users to download and utilize models offline once installed. This app features a range of functionalities, such as AI Chat for engaging in multi-turn conversations, Ask Image for uploading images to inquire about objects or obtain descriptions, Audio Scribe for transcribing or translating audio files, and Prompt Lab for performing single-turn tasks like summarization and code generation. Additionally, it provides performance insights, offering metrics on aspects like latency and decode speed. Users have the flexibility to switch between compatible models, including options like Gemma 3n and models from Hugging Face, as well as the ability to incorporate their own LiteRT models while accessing model cards and source code for increased transparency. By processing all data locally on the device, the app prioritizes user privacy, requiring no internet connection for core functionalities after the initial model load, which ultimately minimizes latency and bolsters data security. Overall, the Google AI Edge Gallery empowers users to explore cutting-edge AI capabilities while maintaining their privacy and control over their data.

DeepSeek has launched DeepSeek-V3.1-Terminus, an upgrade to the V3.1 architecture that integrates user suggestions to enhance output stability, consistency, and overall agent performance. This new version significantly decreases the occurrences of mixed Chinese and English characters as well as unintended distortions, leading to a cleaner and more uniform language generation experience. Additionally, the update revamps both the code agent and search agent subsystems to deliver improved and more dependable performance across various benchmarks. DeepSeek-V3.1-Terminus is available as an open-source model, with its weights accessible on Hugging Face, making it easier for the community to leverage its capabilities. The structure of the model remains consistent with DeepSeek-V3, ensuring it is compatible with existing deployment strategies, and updated inference demonstrations are provided for users to explore. Notably, the model operates at a substantial scale of 685B parameters and supports multiple tensor formats, including FP8, BF16, and F32, providing adaptability in different environments. This flexibility allows developers to choose the most suitable format based on their specific needs and resource constraints.

Introducing DeepSeek-V3.2-Exp, our newest experimental model derived from V3.1-Terminus, featuring the innovative DeepSeek Sparse Attention (DSA) that enhances both training and inference speed for lengthy contexts. This DSA mechanism allows for precise sparse attention while maintaining output quality, leading to improved performance for tasks involving long contexts and a decrease in computational expenses. Benchmark tests reveal that V3.2-Exp matches the performance of V3.1-Terminus while achieving these efficiency improvements. The model is now fully operational across app, web, and API platforms. Additionally, to enhance accessibility, we have slashed DeepSeek API prices by over 50% effective immediately. During a transition period, users can still utilize V3.1-Terminus via a temporary API endpoint until October 15, 2025. DeepSeek encourages users to share their insights regarding DSA through our feedback portal. Complementing the launch, DeepSeek-V3.2-Exp has been made open-source, with model weights and essential technology—including crucial GPU kernels in TileLang and CUDA—accessible on Hugging Face. We look forward to seeing how the community engages with this advancement.

Tiger Data reimagines PostgreSQL for the modern era — powering everything from IoT and fintech to AI and Web3. As the creator of TimescaleDB, it brings native time-series, event, and analytical capabilities to the world’s most trusted database engine. Through Tiger Cloud, developers gain access to a fully managed, elastic infrastructure with auto-scaling, high availability, and point-in-time recovery. The platform introduces core innovations like Forks (copy-on-write storage branches for CI/CD and testing), Memory (durable agent context and recall), and Search (hybrid BM25 and vector retrieval). Combined with hypertables, continuous aggregates, and materialized views, Tiger delivers the speed of specialized analytical systems without sacrificing SQL simplicity. Teams use Tiger Data to unify real-time and historical analytics, build AI-driven workflows, and streamline data management at scale. It integrates seamlessly with the entire PostgreSQL ecosystem, supporting APIs, CLIs, and modern development frameworks. With over 20,000 GitHub stars and a thriving developer community, Tiger Data stands as the evolution of PostgreSQL for the intelligent data age.

BotDojo serves as a robust AI enablement platform tailored for enterprises, allowing companies to create, implement, oversee, and expand intelligent agents across various communication channels like chat, voice, email, and web, all through an intuitive low-code visual workflow designer that seamlessly integrates with existing enterprise data systems. It boasts a library of over 100 pre-built templates aimed at streamlining typical applications, including support automation, knowledge retrieval, sales analytics, and internal operations, while also facilitating branching logic, memory capabilities, and the orchestration of tools such as code, RPA, and web browsing. In addition, BotDojo establishes connections with essential business tools like CRMs, ticketing platforms, and databases to enhance its functionality. The platform further fosters continuous improvement and learning for agents through human feedback loops, enabling employees to mentor agents by providing feedback, embedding corrections into agent memory and responses, and assessing performance using comprehensive observability metrics, including deflection rates, first-contact resolution, and cost per interaction. Ultimately, BotDojo not only optimizes operational efficiency but also ensures that intelligent agents evolve and adapt to meet organizational needs effectively.

Ekinox serves as a visual AI automation platform that allows users to create, implement, and oversee AI-driven workflows without the need for coding; its user-friendly drag-and-drop interface facilitates the design of intelligent agents that can link to over 100 pre-existing integrations, triggering actions across numerous productivity, data, and communication applications. The platform is designed for real-time processing and encourages collaboration by offering team workspaces, version control, and immediate deployment capabilities. In addition, it boasts enterprise-level security that adheres to SOC 2 standards, features bank-level encryption, supports custom API connectors, and includes sophisticated access controls. Users benefit from the ability to monitor their workflows through comprehensive analytics dashboards, enabling them to assess costs and performance across various models and integrations while utilizing predictive auto-scaling and log retention for enhanced functionality. With setup times cut down to mere minutes, Ekinox optimizes processes ranging from straightforward task automation to more complex workflows, making it an invaluable tool. This efficiency not only improves productivity but also enhances the overall user experience.

RenderFlow AI is a cloud-based platform that generates animated videos of professional quality from simple text prompts or uploaded images, utilizing various AI models. Users are able to articulate scenes using natural language, choose their preferred style and model, and modify factors such as duration and resolution, after which the system generates a refined final product, complete with commercial usage rights. Prioritizing rapid production, it claims to deliver videos in mere minutes, contrasting sharply with the protracted processes typical of traditional editing methods, and is versatile enough to cater to different needs such as product demonstrations, animated visual content, social media posts, and educational videos. The user-friendly interface and flexibility in model selection, combined with assertions of producing high-quality results even for those without expertise, ensure that it serves as an accessible video creation solution for both industry professionals and everyday users alike. This makes it an appealing option for anyone looking to create compelling visual narratives with minimal effort.

DeepSeek-V3.2 is a highly optimized large language model engineered to balance top-tier reasoning performance with significant computational efficiency. It builds on DeepSeek's innovations by introducing DeepSeek Sparse Attention (DSA), a custom attention algorithm that reduces complexity and excels in long-context environments. The model is trained using a sophisticated reinforcement learning approach that scales post-training compute, enabling it to perform on par with GPT-5 and match the reasoning skill of Gemini-3.0-Pro. Its Speciale variant overachieves in demanding reasoning benchmarks and does not include tool-calling capabilities, making it ideal for deep problem-solving tasks. DeepSeek-V3.2 is also trained using an agentic synthesis pipeline that creates high-quality, multi-step interactive data to improve decision-making, compliance, and tool-integration skills. It introduces a new chat template design featuring explicit thinking sections, improved tool-calling syntax, and a dedicated developer role used strictly for search-agent workflows. Users can encode messages using provided Python utilities that convert OpenAI-style chat messages into the expected DeepSeek format. Fully open-source under the MIT license, DeepSeek-V3.2 is a flexible, cutting-edge model for researchers, developers, and enterprise AI teams.

DeepSeek-V3.2-Speciale is the most advanced reasoning-focused version of the DeepSeek-V3.2 family, designed to excel in mathematical, algorithmic, and logic-intensive tasks. It incorporates DeepSeek Sparse Attention (DSA), an efficient attention mechanism tailored for very long contexts, enabling scalable reasoning with minimal compute costs. The model undergoes a robust reinforcement learning pipeline that scales post-training compute to frontier levels, enabling performance that exceeds GPT-5 on internal evaluations. Its achievements include gold-medal-level solutions in IMO 2025, IOI 2025, ICPC World Finals, and CMO 2025, with final submissions publicly released for verification. Unlike the standard V3.2 model, the Speciale variant removes tool-calling capabilities to maximize focused reasoning output without external interactions. DeepSeek-V3.2-Speciale uses a revised chat template with explicit thinking blocks and system-level reasoning formatting. The repository includes encoding tools showing how to convert OpenAI-style chat messages into DeepSeek’s specialized input format. With its MIT license and 685B-parameter architecture, DeepSeek-V3.2-Speciale offers cutting-edge performance for academic research, competitive programming, and enterprise-level reasoning applications.

DeepCoder, an entirely open-source model for code reasoning and generation, has been developed through a partnership between Agentica Project and Together AI. Leveraging the foundation of DeepSeek-R1-Distilled-Qwen-14B, it has undergone fine-tuning via distributed reinforcement learning, achieving a notable accuracy of 60.6% on LiveCodeBench, which marks an 8% enhancement over its predecessor. This level of performance rivals that of proprietary models like o3-mini (2025-01-031 Low) and o1, all while operating with only 14 billion parameters. The training process spanned 2.5 weeks on 32 H100 GPUs, utilizing a carefully curated dataset of approximately 24,000 coding challenges sourced from validated platforms, including TACO-Verified, PrimeIntellect SYNTHETIC-1, and submissions to LiveCodeBench. Each problem mandated a legitimate solution along with a minimum of five unit tests to guarantee reliability during reinforcement learning training. Furthermore, to effectively manage long-range context, DeepCoder incorporates strategies such as iterative context lengthening and overlong filtering, ensuring it remains adept at handling complex coding tasks. This innovative approach allows DeepCoder to maintain high standards of accuracy and reliability in its code generation capabilities.

MiMo-V2-Flash is a large language model created by Xiaomi that utilizes a Mixture-of-Experts (MoE) framework, combining remarkable performance with efficient inference capabilities. With a total of 309 billion parameters, it activates just 15 billion parameters during each inference, allowing it to effectively balance reasoning quality and computational efficiency. This model is well-suited for handling lengthy contexts, making it ideal for tasks such as long-document comprehension, code generation, and multi-step workflows. Its hybrid attention mechanism integrates both sliding-window and global attention layers, which helps to minimize memory consumption while preserving the ability to understand long-range dependencies. Additionally, the Multi-Token Prediction (MTP) design enhances inference speed by enabling the simultaneous processing of batches of tokens. MiMo-V2-Flash boasts impressive generation rates of up to approximately 150 tokens per second and is specifically optimized for applications that demand continuous reasoning and multi-turn interactions. The innovative architecture of this model reflects a significant advancement in the field of language processing.

TranslateGemma is an innovative collection of open machine translation models created by Google, based on the Gemma 3 architecture, which facilitates communication between individuals and systems in 55 languages by providing high-quality AI translations while ensuring efficiency and wide deployment options. Offered in sizes of 4 B, 12 B, and 27 B parameters, TranslateGemma encapsulates sophisticated multilingual functionalities into streamlined models that are capable of functioning on mobile devices, consumer laptops, local systems, or cloud infrastructure, all without compromising on precision or performance; assessments indicate that the 12 B variant can exceed the capabilities of larger baseline models while requiring less computational power. The development of these models involved a distinct two-phase fine-tuning approach that integrates high-quality human and synthetic translation data, using reinforcement learning to enhance translation accuracy across a variety of language families. This innovative methodology ensures that users benefit from an array of languages while experiencing swift and reliable translations.

Liquid AI's LFM2.5 represents an advanced iteration of on-device AI foundation models, engineered to provide high-efficiency and performance for AI inference on edge devices like smartphones, laptops, vehicles, IoT systems, and embedded hardware without the need for cloud computing resources. This new version builds upon the earlier LFM2 framework by greatly enhancing the scale of pretraining and the stages of reinforcement learning, resulting in a suite of hybrid models that boast around 1.2 billion parameters while effectively balancing instruction adherence, reasoning skills, and multimodal functionalities for practical applications. The LFM2.5 series comprises various models including Base (for fine-tuning and personalization), Instruct (designed for general-purpose instruction), Japanese-optimized, Vision-Language, and Audio-Language variants, all meticulously crafted for rapid on-device inference even with stringent memory limitations. These models are also made available as open-weight options, facilitating deployment through platforms such as llama.cpp, MLX, vLLM, and ONNX, thus ensuring versatility for developers. With these enhancements, LFM2.5 positions itself as a robust solution for diverse AI-driven tasks in real-world environments.

Step 3.5 Flash is a cutting-edge open-source foundational language model designed for advanced reasoning and agent-like capabilities, optimized for efficiency; it utilizes a sparse Mixture of Experts (MoE) architecture that activates only approximately 11 billion of its nearly 196 billion parameters per token, ensuring high-density intelligence and quick responsiveness. The model features a 3-way Multi-Token Prediction (MTP-3) mechanism that allows it to generate hundreds of tokens per second, facilitating complex multi-step reasoning and task execution while efficiently managing long contexts through a hybrid sliding window attention method that minimizes computational demands across extensive datasets or codebases. Its performance on reasoning, coding, and agentic tasks is formidable, often matching or surpassing that of much larger proprietary models, and it incorporates a scalable reinforcement learning system that enables continuous self-enhancement. Moreover, this innovative approach positions Step 3.5 Flash as a significant player in the field of AI language models, showcasing its potential to revolutionize various applications.

Agent Zero is an innovative open source framework for AI agents that enables the development of autonomous assistants capable of executing intricate tasks through direct interaction with computer systems. This platform offers a unique setting where AI agents can access real system functions, empowering them to run commands, write and execute code, navigate the internet, analyze data, and oversee workflows as part of comprehensive automation solutions. Unlike a standard chat interface, Agent Zero operates within its isolated virtual environment, enabling it to engage with the operating system, install necessary tools, run scripts, and manage tasks across various components seamlessly. The framework prioritizes transparency and developer control, allowing users to monitor, adjust, and personalize agent behavior, tool accessibility, and information processing methods. With a modular architecture, Agent Zero facilitates the dynamic creation and utilization of tools, all while maintaining a consistent memory for enhanced performance. This makes it an ideal choice for developers aiming to build highly customizable and efficient AI-driven workflows.

Subconscious is a platform tailored for developers that simplifies the creation, deployment, and scaling of production-ready AI agents by automating the most challenging aspects of agent architecture. By offering a comprehensive agent system, it takes care of context management, tool orchestration, and facilitates long-term reasoning, allowing developers to concentrate on setting objectives and defining functionalities instead of dealing with intricate infrastructure setups. The platform features a cohesive inference engine that combines a jointly designed model and runtime, enabling the breakdown of complex tasks, dynamic workflow generation, and the execution of multi-step reasoning without the need for manual context management or coordination among multiple agents. In contrast to conventional methods that depend on linking various APIs and frameworks, Subconscious empowers agents to receive goals and tools and then independently plan, reason, and act with minimal human oversight. This innovation effectively results in systems that can autonomously accomplish tasks, streamlining the development process for AI applications. As a result, developers can realize their visions more efficiently and with greater ease.

Atomic Chat is an innovative conversational platform powered by artificial intelligence, designed to streamline and automate customer interactions across various messaging channels, which allows businesses to connect, qualify, and convert leads through immediate engagement. By consolidating conversations from popular platforms like WhatsApp, Messenger, Instagram, and Telegram into one comprehensive inbox, teams can efficiently oversee all customer communications while ensuring complete visibility and control. The platform employs intelligent AI agents capable of managing conversations through text, voice, and image inputs, delivering human-like responses that can address inquiries, qualify leads, schedule meetings, and conduct follow-ups automatically, around the clock. Additionally, it facilitates the automation of customer service workflows and sales strategies, such as lead scoring, re-engagement campaigns, and tailored messaging sequences, which enhance conversion rates and alleviate manual efforts. Consequently, businesses can focus more on strategic initiatives while the platform handles routine interactions seamlessly.

Better Auth is a versatile authentication and authorization solution tailored for TypeScript, enabling developers to seamlessly integrate secure login functionalities into their applications and databases. It comes equipped with a comprehensive suite of authentication tools right from the start, offering features such as email and password login, session management, email verification, password resets, and compatibility with over 40 social login options like Google and GitHub, all requiring minimal coding effort. Designed to be compatible with a broad spectrum of contemporary frameworks, including Next.js, Nuxt, SvelteKit, Astro, and Express, it empowers teams to implement authentication irrespective of their chosen technology stack while ensuring robust TypeScript support and type safety. Furthermore, Better Auth boasts sophisticated features such as multi-factor authentication, management of multi-tenant organizations, and enterprise-level functionalities including SSO, SAML, and SCIM provisioning, making it an ideal fit for both straightforward applications and complex, large-scale systems. This flexibility allows developers to focus on building their core application features while relying on Better Auth to handle security concerns efficiently.

Locally AI is an innovative application that empowers users to utilize advanced language models directly on their iPhone, iPad, or Mac without needing cloud services or an internet connection. Leveraging Apple’s MLX framework, it provides quick and efficient performance while keeping power consumption low, thus ensuring a fluid experience for chatting, creating, learning, and discovering AI capabilities across various devices. The app supports a range of open models, including Llama, Gemma, Qwen, and DeepSeek, enabling users to easily switch between them and customize outputs for various tasks. Operating entirely offline, it eliminates the need for logins and ensures that no data is collected or transmitted, thereby guaranteeing complete privacy and control over personal information. Users can engage with AI through natural dialogue, assess documents or images, and produce text within a user-friendly interface that prioritizes simplicity and responsiveness. This design fosters greater creativity and exploration, further enhancing the overall user experience.

Qwen3.5-35B-A3B is a member of the Qwen3.5 "Medium" model series, meticulously crafted as an effective multimodal foundation model that strikes a balance between robust reasoning capabilities and practical application needs. Utilizing a Mixture-of-Experts (MoE) architecture, it boasts a total of 35 billion parameters, yet activates only around 3 billion for each token, enabling it to achieve performance levels similar to much larger models while significantly cutting down on computational expenses. The model employs a hybrid attention mechanism that merges linear attention with traditional attention layers, which enhances its ability to handle extensive context and boosts scalability for intricate tasks. As an inherently vision-language model, it processes both textual and visual data, catering to a variety of applications, including multimodal reasoning, programming, and automated workflows. Furthermore, it is engineered to operate as a versatile "AI agent," proficient in planning, utilizing tools, and systematically solving problems, extending its functionality beyond mere conversational interactions. This capability positions it as a valuable asset across diverse domains, where advanced AI-driven solutions are increasingly required.

Sarvam-30B is an advanced open-source large language model that serves as a comprehensive platform for real-time conversational AI and complex reasoning tasks, emphasizing its capability in multilingual settings and practical usage. This 30-billion parameter model is engineered for enhanced speed and efficiency through a Mixture-of-Experts (MoE) framework, which selectively activates a portion of its parameters for each request, thus facilitating high throughput and minimal latency while remaining suitable for environments with limited resources, including local devices and edge computing systems. It excels in various conversational applications, programming tasks, and logical reasoning, achieving impressive outcomes in over 20 Indian languages, which underscores its utility for multilingual applications and voice interaction systems. The model features a dual-tier structure, acting as a rapid and deployable "conversational workhorse," and utilizes MoE techniques to lower computational costs without sacrificing performance. This innovative model not only enhances user experience but also broadens accessibility in diverse linguistic contexts.

Sarvam-105B stands as the premier large language model within Sarvam’s open-source lineup, engineered to provide exceptional reasoning capabilities, multilingual comprehension, and agent-driven execution all within a unified and scalable framework. This Mixture-of-Experts (MoE) model boasts an impressive total of approximately 105 billion parameters, activating only a subset for each token, which allows it to maintain superior computational efficiency while excelling in intricate tasks. It is particularly optimized for advanced reasoning, programming, mathematical challenges, and agentic processes, positioning it well for scenarios that necessitate multi-step problem-solving and organized outputs rather than merely engaging in basic conversations. With the ability to process long contexts of around 128K tokens, Sarvam-105B can effectively manage extensive documents, prolonged discussions, and complex analytical inquiries, ensuring coherence throughout. Additionally, its design facilitates a diverse range of applications, providing users with versatile tools to tackle a variety of intellectual challenges.

Trismik serves as a platform for evaluating AI models, aimed at assisting teams in selecting the most suitable large language model tailored to their unique needs by utilizing actual data rather than mere assumptions or standard benchmarks. The platform emphasizes transforming the process of model experimentation into straightforward, evidence-based choices by giving users the ability to test and contrast various models directly with their own datasets, avoiding the pitfalls of public leaderboards or limited manual evaluations. Alongside this, it features innovative tools like QuickCompare, which allows for side-by-side assessments of over 50 models across essential metrics such as quality, cost, and speed, thus rendering trade-offs visible and quantifiable in practical scenarios. Additionally, Trismik employs adaptive evaluation methods inspired by psychometrics, which intelligently select the most informative test cases and automatically assess outputs across multiple dimensions, including factual accuracy, bias, and reliability, ensuring a comprehensive evaluation process. This holistic approach not only enhances the decision-making process but also empowers teams to make informed choices that align with their specific operational requirements.

Noteweave is an advanced platform designed to assist teams in transitioning from research to actionable production strategies. Its primary function is to rigorously evaluate scientific studies, convert academic papers into confirmed experiments, and accelerate research and development processes from a research-centric environment. The Deep Analysis feature critically assesses methodologies, evaluations, and their reliability, ensuring that potential failure points are identified before reaching production stages. This proactive approach aids teams in uncovering production inconsistencies in academic literature, identifying overlooked evaluations, establishing discrepancies, and spotting misleading trends in robustness more effectively. Users can explore and search through millions of academic papers, datasets, and code repositories, synthesizing this information into executable production plans backed by verifiable evidence. Additionally, Noteweave empowers users to unearth pertinent research insights from over 3 million publications in AI and machine learning, optimize their production strategies concerning constraints like GPU usage, transform theoretical academic methods into reproducible procedures, and enhance the reliability of their evaluation strategies. By integrating these capabilities, Noteweave significantly boosts the efficiency and accuracy of research application in real-world scenarios.

Ejentum serves as a structured reasoning framework tailored for agentic AI, enhancing the reliability, auditability, and discipline of LLM agents during intricate or protracted tasks. This innovative tool can be invoked by agents mid-task, facilitating precise cognitive operations tailored to the specific challenges they face, allowing for real-time corrections in reasoning rather than depending solely on static prompts. Designed to prevent AI agents from deviating, flattering, fabricating, or fixating on incorrect hypotheses, Ejentum also ensures they don’t settle for superficial answers or lose vital context over successive steps. The framework boasts 679 capabilities organized into four cognitive harnesses: reasoning, code, anti-deception, and memory. Within the reasoning harness, analytical capabilities are directed towards understanding causality, time, space, simulation, abstraction, and metacognition, which aids agents in steering clear of merely recognizing surface patterns. By integrating these diverse functionalities, Ejentum empowers AI to maintain a deeper engagement with tasks, ultimately enhancing the quality of their outputs.

Vision Agents is a versatile open-source Python framework designed for developing low-latency voice and video AI agents utilizing any model. This framework empowers developers to integrate large language models, speech recognition, and vision models from over 25 different providers, enabling the creation of real-time agents for applications such as telehealth, voice assistance, live coaching, video analysis, interactive avatars, security surveillance, sports commentary, and a variety of other multimodal uses. Its architecture is tailored to facilitate the development of agents capable of listening, speaking, seeing, processing media, accessing tools, and providing instant responses, all while operating on Stream's expansive global edge network, which ensures latency below 500ms. With just a minimal Python setup, developers can quickly create their first agent by leveraging platforms like Gemini Realtime, OpenAI, Deepgram, ElevenLabs, Stream, or other compatible providers. Furthermore, Vision Agents accommodates both real-time speech-to-speech models and tailored speech-to-text, language processing, and text-to-speech pipelines, allowing teams to either rapidly deploy a functional voice agent or exercise complete control over the components involved in speech recognition, language reasoning, and text-to-speech functionalities. Overall, this framework not only simplifies the process of building sophisticated AI agents but also enhances flexibility and performance across diverse applications.

Laguna XS.2 represents Poolside’s innovative open-weight coding model, distinguished as the lightest and quickest member of the Laguna series. This model features a total of 33 billion parameters in a Mixture of Experts setup, with 3 billion parameters activated, and has been meticulously trained in-house using 30 trillion tokens. As the latest generation model accessible to the public, it embodies a second-generation architecture and marks Poolside’s inaugural open-weight offering, drawing from insights gained during the training of Laguna M.1 with synthetic data and reinforcement learning techniques. Specifically designed to enhance agentic coding workflows, Laguna XS.2 excels in coding, acting, and rapidly iterating, particularly within Poolside’s coding agent environment. This model is particularly advantageous for developers and teams seeking a lightweight, efficient coding solution rather than a more cumbersome frontier system. Released under the permissive Apache 2.0 license, it empowers the community to assess, fine-tune, quantize, and build upon its weights, fostering a collaborative development atmosphere. In essence, Laguna XS.2 not only provides a robust platform for agentic coding but also encourages innovation and experimentation among its users.

Laguna M.1 stands out as Poolside's most proficient model for agentic coding, meticulously developed in-house specifically for enhancing software development workflows. This model features a total of 225 billion parameters, utilizing a Mixture of Experts architecture with 23 billion activated parameters, and has been trained entirely within the organization on a dataset consisting of 30 trillion tokens, leveraging the power of 6,144 interconnected NVIDIA H200 GPUs. Poolside undertook the task of training Laguna M.1 from the ground up, employing its proprietary data, dedicated training codebase, and an asynchronous on-policy reinforcement learning approach within its agent framework, all tailored for agentic coding applications. The design of the model ensures optimal performance within Poolside's coding agent, enabling it to effectively reason through software tasks, interact with various tools, edit code, execute tests, and facilitate extended autonomous development sessions. Specifically crafted for developers and teams tackling intricate coding challenges, Laguna M.1 offers enhanced capabilities in reasoning, architectural comprehension, terminal operations, and multi-step execution, surpassing what lighter models can achieve. Ultimately, its robust feature set positions it as an essential asset for those engaged in demanding software projects.

Energent.ai serves as an autonomous AI analyst that streamlines essential business operations such as analysis, reconciliation, engineering support, and overall operations. Engineered to handle large data volumes and diverse real-world information, it seamlessly processes inputs from spreadsheets, PDFs, scans, images, documents, web pages, and even 2D/3D files, transforming chaotic data into actionable insights without the need for complex formulas, manual mapping, or extensive technical configuration. Users can effortlessly upload files or links, bypassing traditional blank spreadsheets to receive immediate deliverables with a single command, including rapid analyses, unexpected insights, visually appealing graphics, exportable spreadsheets, polished narratives, and professional reports in the form of dashboards, PowerPoint presentations, or PDFs. Energent.ai is meticulously designed to ensure high accuracy whether working with a single file or vast datasets, completing document processing within minutes, adeptly managing inconsistent real-world data, and upholding precision at scale. Furthermore, its user-friendly interface empowers organizations to harness the full potential of their data, facilitating informed decision-making and enhancing productivity across various business functions.

AI Workers are independent agents crafted specifically for your organization; these advanced AI entities function like genuine colleagues rather than mere chatbots awaiting instructions. Each comes equipped with its own professional identity, including an email, phone number, and presence on platforms like Slack and Teams, demonstrating initiative and the capacity to operate around the clock without needing prompts. Rather than dictating every detail of their tasks, you simply define the objectives, and they autonomously develop the necessary workflows, accommodating a variety of tasks such as generating daily reports, conducting weekly follow-ups, managing CRM updates, handling client interactions, performing research, overseeing content creation, executing finance responsibilities, coordinating HR activities, supporting design projects, and assisting with development operations. This system includes a diverse range of specialized AI Workers tailored for functions in marketing, development, design, HR, finance, and more, with each worker crafted for a specific role and practical applications. Furthermore, AI Workers can integrate seamlessly with over 3,000 tools, such as Slack, Microsoft Teams, Gmail, Notion, HubSpot, Salesforce, and various other business applications, ensuring they fit effortlessly into your existing workflows. Their versatility and adaptability make them an invaluable asset for enhancing productivity and streamlining processes in any business environment.

AICosts.ai serves as a comprehensive platform for managing AI-related expenses, consolidating billing and usage information from over 50 different providers into a single dashboard. Users can easily upload invoices and data exports in various formats such as PDF, CSV, or JSON, or they can utilize the developer API to send usage events, with the platform efficiently parsing this information into a standardized format without needing any proxy setups or alterations to production requests. It accommodates a wide array of services including OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Vertex AI, Cohere, Groq, Hugging Face, Pinecone, RunwayML, Make, Zapier, and n8n. Daily insights break down expenditures by platform, model, and billed units, which encompass tokens, operations, characters, and other specific metrics from providers, enabling users to compare different services and understand the origins of their charges. Additionally, users can set budgets that may either encompass the entire AI landscape or focus on specific platforms or features, while also receiving email notifications whenever their rolling 30-day expenses surpass predetermined thresholds, ensuring they stay informed and within their financial limits. This level of detail and control empowers teams to manage their AI costs more effectively.

StackSpend is an advanced cost management platform leveraging cloud and AI technologies, designed to offer engineering, finance, and FinOps teams a consolidated daily overview of their contemporary AI infrastructure. By establishing read-only connections to a variety of providers such as AWS, Google Cloud, Azure, Snowflake, and others, it seamlessly imports historical billing information and standardizes expenditure across different services. The platform features comprehensive dashboards and exploration tools that dissect costs by various dimensions, including provider, service, model, project, user, team, feature, and customer, thereby aiding teams in analyzing AI COGS, cost per request, and profit margins at the product level. Additionally, it provides insights into budgets and projected spending trends, while its same-day anomaly detection feature identifies unexpected cost spikes triggered by factors such as traffic surges, prompt errors, model adjustments, deployment activities, or specific user actions. Notifications and daily indicators, categorized as green, amber, or red based on spending levels, can be dispatched through communication platforms like Slack, Microsoft Teams, email, or webhooks, ensuring teams remain informed about their spending patterns. Ultimately, StackSpend empowers organizations to maintain a firm grip on their AI expenditures, fostering enhanced financial accountability and strategic decision-making.

The AI Knowledge Platform. The Pinecone Database, Inference, and Assistant make building high-performance vector search apps easy. Fully managed and developer-friendly, the database is easily scalable without any infrastructure problems. Once you have vector embeddings created, you can search and manage them in Pinecone to power semantic searches, recommenders, or other applications that rely upon relevant information retrieval. Even with billions of items, ultra-low query latency Provide a great user experience. You can add, edit, and delete data via live index updates. Your data is available immediately. For more relevant and quicker results, combine vector search with metadata filters. Our API makes it easy to launch, use, scale, and scale your vector searching service without worrying about infrastructure. It will run smoothly and securely.

Introducing the ultimate data annotation tool that offers unparalleled flexibility and ease of installation. Users can create customized user interfaces or opt for ready-made labeling templates tailored to their specific needs. The adaptable layouts and templates seamlessly integrate with your dataset and workflow requirements. It supports various object detection methods in images, including boxes, polygons, circles, and key points, and allows for the segmentation of images into numerous parts. Additionally, machine learning models can be utilized to pre-label data and enhance efficiency throughout the annotation process. Features such as webhooks, a Python SDK, and an API enable users to authenticate, initiate projects, import tasks, and manage model predictions effortlessly. Save valuable time by leveraging predictions to streamline your labeling tasks, thanks to the integration with ML backends. Furthermore, users can connect to cloud object storage solutions like S3 and GCP to label data directly in the cloud. The Data Manager equips you with advanced filtering options to effectively prepare and oversee your dataset. This platform accommodates multiple projects, diverse use cases, and various data types, all in one convenient space. By simply typing in the configuration, you can instantly preview the labeling interface. Live serialization updates at the bottom of the page provide a real-time view of what Label Studio anticipates as input, ensuring a smooth user experience. This tool not only improves annotation accuracy but also fosters collaboration among teams working on similar projects.

Elevate your web security testing experience with BurpGPT, a Burp Suite extension that seamlessly incorporates OpenAI's advanced models for in-depth vulnerability assessments and traffic analysis. This tool also accommodates local LLMs, including custom-trained versions, thereby prioritizing data privacy and delivering results tailored to your specific requirements. Integrating Burp GPT into your security testing processes is straightforward, thanks to its comprehensive and user-friendly documentation. Crafted by specialists in application security, Burp GPT stands at the forefront of web security innovations. It evolves continuously by incorporating user feedback, ensuring it adapts to the shifting landscape of security testing demands. With Burp GPT, you benefit from a powerful solution designed to enhance both the accuracy and efficiency of application security evaluations. Its advanced language processing features, coupled with an easy-to-navigate interface, make it accessible for both novices and experienced testers. Moreover, BurpGPT enables you to tackle complex technical challenges with confidence and precision. As such, it represents a significant advancement in the toolkit of any security professional.

We offer a comprehensive AI-driven IT workforce solution that is fully automated, scalable, and ready for payment integration. Our goal is to make it easier for U.S. businesses to tap into nearshore talent without incurring hefty vendor fees or facing security challenges. With our platform, you can forecast talent expenses and assess the availability of qualified professionals throughout the LATAM region, aligning with your business objectives. You will have immediate access to a highly skilled senior recruitment team that possesses a deep understanding of both the talent landscape and your technological requirements. Our specialized engineering managers evaluate and rank technical skills through video-recorded tests, ensuring optimal candidate alignment. Additionally, we streamline your onboarding experience for various roles across multiple countries in LATAM. We take care of procuring and setting up dedicated devices, guaranteeing that all personnel are equipped with the necessary tools and resources from their first day, allowing them to start working effectively right away. Furthermore, we enable you to quickly identify high performers and those eager to enhance their skill sets. By leveraging our services, you can transform your workforce strategy and drive innovation in your organization.

Eliminate uncertainty in prompt engineering through recommended modifications, prompt rephrasing, and the automatic creation of test scenarios. We conduct numerous evaluations of your prompts and associated data to uncover weaknesses and implement enhancements. Pinpoint prompt-related problems and opportunities for improvement with ease. Let AI take the reins in reworking prompts to address any deficiencies. Stop spending valuable time crafting test cases for your prompts; we produce high-quality examples that will evaluate your prompts and assist in refining them. Discover various strategies for enhancing your prompts and allow AI to automatically revise them for better performance. Generate a wide range of test cases to confirm any adjustments and facilitate continuous improvement. Leverage your refined prompts across different models and platforms for optimal results, ensuring a seamless experience in various applications. By streamlining this process, you can focus more on creativity and innovation in your work.

ONTEC AI delivers a secure and dynamic AI ecosystem built for enterprises tackling large, complex, and sensitive data challenges. Our solutions combine a powerful AI platform, custom AI developments, expert consulting, and advanced data engineering to transform how businesses operate. With ONTEC AI, organizations can automate intricate workflows, elevate knowledge management, and achieve razor-sharp enterprise search – all while ensuring GDPR compliance and maintaining full data sovereignty. Purpose-built for industries where precision and security are non-negotiable – including banking, finance, insurance, publishing, media, and production – ONTEC AI redefines adaptability and control. Key features such as enterprise search, automated workflows, private GPT, and seamless integration with tools like Confluence and fileshares empower businesses to streamline operations and unlock their data’s full potential. ONTEC AI’s model-agnostic architecture eliminates vendor lock-in, offering the freedom to integrate multiple LLMs and adapt to evolving needs. With industry-leading accuracy, source traceability, and advanced hallucination suppression, ONTEC AI doesn’t just process data – it delivers actionable, trustworthy insights that drive innovation and give enterprises a competitive edge.

Featherless is a provider of AI models, granting subscribers access to an ever-growing collection of Hugging Face models. With the influx of hundreds of new models each day, specialized tools are essential to navigate this expanding landscape. Regardless of your specific application, Featherless enables you to discover and utilize top-notch AI models. Currently, we offer support for LLaMA-3-based models, such as LLaMA-3 and QWEN-2, though it's important to note that QWEN-2 models are limited to a context length of 16,000. We are also planning to broaden our list of supported architectures in the near future. Our commitment to progress ensures that we continually integrate new models as they are released on Hugging Face, and we aspire to automate this onboarding process to cover all publicly accessible models with suitable architecture. To promote equitable usage of individual accounts, concurrent requests are restricted based on the selected plan. Users can expect output delivery rates ranging from 10 to 40 tokens per second, influenced by the specific model and the size of the prompt, ensuring a tailored experience for every subscriber. As we expand, we remain dedicated to enhancing our platform's capabilities and offerings.

CometLLM serves as a comprehensive platform for recording and visualizing your LLM prompts and chains. By utilizing CometLLM, you can discover effective prompting techniques, enhance your troubleshooting processes, and maintain consistent workflows. It allows you to log not only your prompts and responses but also includes details such as prompt templates, variables, timestamps, duration, and any necessary metadata. The user interface provides the capability to visualize both your prompts and their corresponding responses seamlessly. You can log chain executions with the desired level of detail, and similarly, visualize these executions through the interface. Moreover, when you work with OpenAI chat models, the tool automatically tracks your prompts for you. It also enables you to monitor and analyze user feedback effectively. The UI offers the feature to compare your prompts and chain executions through a diff view. Comet LLM Projects are specifically designed to aid in conducting insightful analyses of your logged prompt engineering processes. Each column in the project corresponds to a specific metadata attribute that has been recorded, meaning the default headers displayed can differ based on the particular project you are working on. Thus, CometLLM not only simplifies prompt management but also enhances your overall analytical capabilities.