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  • Paessler: Easy to Use With Enterprise Power. Free Trial Icon
    Paessler: Easy to Use With Enterprise Power. Free Trial

    A low-code dashboard makes monitoring intuitive for any admin, while scripting and custom sensors give experts full control.

    You shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
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  • Demo Series - Small Business Backup By Veeam Icon
    Demo Series - Small Business Backup By Veeam

    Learn how to protect your Microsoft 365 data, with simple, actionable tips today.

    Watch this on-demand demo series and learn how to protect your Microsoft 365 data with clear, simple, actionable steps that are easy to implement for businesses of all sizes.
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  • 1
    QSO-Graph

    QSO-Graph

    Ham radio MCP servers for AI Agents — 71 tools, 11 packages

    QSO-Graph is a suite of 11 MCP (Model Context Protocol) servers for amateur radio operators. Provides AI-powered access to QRZ, eQSL, LoTW, HamQTH, POTA, SOTA, IOTA, WSPR, solar weather, ADIF parsing, and HF Description: Propagation analytics. Native installers for Windows (InnoSetup) and Linux (RPM). All servers also available via pip from PyPI. Source code at github.com/qso-graph.
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  • 2
    Kimi K2.6

    Kimi K2.6

    Multimodal agent model for coding, orchestration, and autonomy

    ...One of its most distinctive capabilities is horizontal agent scaling, supporting up to 300 sub-agents and 4,000 coordinated steps in a single run, which enables parallel task decomposition and end-to-end completion of outputs such as documents, websites, and spreadsheets. Architecturally, it uses a 1T-parameter Mixture-of-Experts design with 32B activated parameters, a MoonViT vision encoder, and a 256K context window.
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  • 3
    Qwen2.5-VL-7B-Instruct

    Qwen2.5-VL-7B-Instruct

    Multimodal 7B model for image, video, and text understanding tasks

    Qwen2.5-VL-7B-Instruct is a multimodal vision-language model developed by the Qwen team, designed to handle text, images, and long videos with high precision. Fine-tuned from Qwen2.5-VL, this 7-billion-parameter model can interpret visual content such as charts, documents, and user interfaces, as well as recognize common objects. It supports complex tasks like visual question answering, localization with bounding boxes, and structured output generation from documents. The model is also...
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  • 4
    FLUX 3 Action

    FLUX 3 Action

    7B world action model for vision-guided SO-101 robotic control

    ...It combines visual observations, robot state information, and natural-language instructions to predict upcoming robot actions while simultaneously denoising predicted future video frames. The checkpoint uses two camera streams, scene and wrist, alongside six-dimensional state and action representations and historical observations for temporal context. It predicts chunks of 42 actions, executes 32 at 30 Hz, and then replans, enabling closed-loop robotic control. The model was trained on SO-101 episodes from the LeRobot Community Dataset v3 and integrates directly with the LeRobot framework. Developers can adapt it to new robotic tasks using the provided rank-32 LoRA training recipe. ...
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  • Build Agents and Models on One Platform Icon
    Build Agents and Models on One Platform

    Everything you need to build production-ready agents and models. Access 200+ Google and third-party AI models and tools.

    Gemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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  • 5
    Ministral 3 8B Instruct 2512

    Ministral 3 8B Instruct 2512

    Compact 8B multimodal instruct model optimized for edge deployment

    Ministral 3 8B Instruct 2512 is a balanced, efficient model in the Ministral 3 family, offering strong multimodal capabilities within a compact footprint. It combines an 8.4B-parameter language model with a 0.4B vision encoder, enabling both text reasoning and image understanding. This FP8 instruct-fine-tuned variant is optimized for chat, instruction following, and structured outputs, making it ideal for daily assistant tasks and lightweight agentic workflows. Designed for edge deployment,...
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  • 6
    BinktermPHP

    BinktermPHP

    Web based FidoNet/FTN Bink mailer terminal

    ...Features include echomail and netmail via a native BinkP mailer, file areas with inline previews for images, video, audio, MOD music, ANSI art, PETSCII, and C64 disk images, QWK/QWKE offline mail packet downloads, ANSI and Sixel rendering, classic DOS door games via DOSBox-X, browser-side JS-DOS emulation, HTML5 WebDoors with a full SDK, C64 emulated doors, multi-room chat with Matterbridge bridging to Discord/IRC/Slack/Telegram, MRC multi-relay chat, a Gemini capsule server, built-in Telnet and SSH daemons, a web terminal, PacketBBS access for mesh radio nodes, and a Model Context Protocol (MCP) server for AI assistants. ISO images and directories can be mounted as read-only file areas. Licensed under BSD-3-Clause. Actively developed. Claude's BBS at claudes.lovelybits.org
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  • 7
    Llama-3.2-1B-Instruct

    Llama-3.2-1B-Instruct

    Instruction-tuned 1.2B LLM for multilingual text generation by Meta

    Llama-3.2-1B-Instruct is Meta’s multilingual, instruction-tuned large language model with 1.24 billion parameters, optimized for dialogue, summarization, and retrieval tasks. It builds upon the Llama 3.1 architecture and incorporates fine-tuning techniques like SFT, DPO, and quantization-aware training for improved alignment, efficiency, and safety. The model supports eight primary languages (including English, Spanish, Hindi, and Thai) and was trained on a curated mix of publicly available...
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  • 8
    granite-timeseries-ttm-r2

    granite-timeseries-ttm-r2

    Tiny pre-trained IBM model for multivariate time series forecasting

    ...It can integrate exogenous variables, static categorical features, and perform channel-mixing for richer multivariate forecasting. The get_model() utility makes it easy to auto-select the best TTM model for specific context and prediction lengths. These models significantly outperform benchmarks like Chronos, GPT4TS, and Moirai while demanding a fraction of the compute.
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  • 9
    Laguna M.1

    Laguna M.1

    Flagship Poolside model for agentic coding and software engineering

    ...Laguna M.1 was designed to compete with leading frontier coding models on benchmarks such as SWE-Bench, Terminal-Bench, and other agentic engineering evaluations. It supports reasoning, tool calling, and long-context workflows, making it suitable for autonomous coding agents, software maintenance, debugging, and large-scale development projects.
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  • Fully Managed MySQL, PostgreSQL, and SQL Server Icon
    Fully Managed MySQL, PostgreSQL, and SQL Server

    Automatic backups, patching, replication, and failover. Focus on your app, not your database.

    Cloud SQL handles your database ops end to end, so you can focus on your app.
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  • 10
    Mistral Large 3 675B Instruct 2512 Eagle

    Mistral Large 3 675B Instruct 2512 Eagle

    Speculative-decoding accelerator for the 675B Mistral Large 3

    ...Built on the same frontier-scale multimodal Mixture-of-Experts architecture, it complements a system featuring 41B active parameters and a 2.5B-parameter vision encoder. The Eagle variant is specialized rather than standalone, serving as a performance accelerator for production-grade assistants, agentic workflows, long-context applications, and retrieval-augmented reasoning pipelines. It supports the same multilingual, system-prompt-aligned, and function-calling behavior as the main instruct model when used in the recommended server-client configuration.
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  • 11
    Ministral 3 3B Instruct 2512

    Ministral 3 3B Instruct 2512

    Ultra-efficient 3B multimodal instruct model built for edge deployment

    Ministral 3 3B Instruct 2512 is the smallest model in the Ministral 3 family, offering a lightweight yet capable multimodal architecture designed for edge and low-resource deployments. It includes a 3.4B-parameter language model paired with a 0.4B vision encoder, enabling it to understand both text and visual inputs. As an FP8 instruct-fine-tuned model, it is optimized for chat, instruction following, and compact agentic tasks while maintaining strong adherence to system prompts. Despite its...
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  • 12
    Ministral 3 14B Instruct 2512

    Ministral 3 14B Instruct 2512

    Efficient 14B multimodal instruct model with edge deployment and FP8

    Ministral 3 14B Instruct 2512 is the largest model in the Ministral 3 family, delivering frontier performance comparable to much larger systems while remaining optimized for edge-level deployment. It combines a 13.5B-parameter language model with a 0.4B-parameter vision encoder, enabling strong multimodal understanding in both text and image tasks. This FP8 instruct-tuned variant is designed specifically for chat, instruction following, and agentic workflows with robust system-prompt...
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  • 13
    VaultGemma

    VaultGemma

    VaultGemma: 1B DP-trained Gemma variant for private NLP tasks

    VaultGemma is a sub-1B parameter variant of Google’s Gemma family that is pre-trained from scratch with Differential Privacy (DP), providing mathematically backed guarantees that its outputs do not reveal information about any single training example. Using DP-SGD with a privacy budget across a large English-language corpus (web documents, code, mathematics), it prioritizes privacy over raw utility. The model follows a Gemma-2–style architecture, outputs text from up to 1,024 input tokens,...
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  • 14
    Running Reality

    Running Reality

    World history plays out down to street level on a detailed map.

    ...When you are standing at a historical site and using your mobile phone, you can see all the events that happened around you at that exact spot then zoom out to see the context of what was happening more globally.
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  • 15
    Tesbo

    Tesbo

    Open source AI-powered test case management software.

    Tesbo is an open source AI-powered test case management software built for QA teams. Tesbo's Context Engine reads your actual project requirements from Jira and Linear, existing test cases, product documentation, and defect history before generating a single test case. Every draft cites exactly which requirement, document, and defect shaped it. Nothing enters your repository without a QA engineer approving it first. The engine learns from every approval, edit, and rejection, so generated case quality improves with every cycle. ...
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