Search Results for "frontier" - Page 3
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Showing 70 open source projects for "frontier"
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Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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The long term goal is to create an Elite or Frontier style space trading game. It is based on Chris Laurel`s Celestia, MySQL, Lua, plib and freeglut.
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Frontier Data System is a collection programs for data acquisition and data mining. It uses MySQL and PHP to monitor process variables and performance of shop floor equipment. Produces information on issues such as cycle time, downtime and SPC.
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Crimson space is a Space Combat/Trader in the veins of Frontier: Elite2, and Frontier: First encounters, with engine that can generate an entire gaming universe from a single seed number.
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TNF2 is based loosely on the original game TNF (The New Frontiers) http://www.geocities.com/TimesSquare/Castle/2979/ written and designed by Jerry Revell Jerry@starchase.demon.co.uk. Simply put the game is about space colonization. Each player runs a
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AI-powered service management for IT and enterprise teamsGive your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.
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PCBoard compatible BBS. run/compile/decompile PPEs from your favorite OldSkooL board ! This project's goal is to have a cross platform (if possible), portable PCBoard compatible BBS, including FIDO/QWK/etc support for offline readers (hopefully).
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The suite is an extension to the UserTalk language that brings classes and objects, like in C++ and Java, to Frontier. Docs are at http://iapp.dat.it/simoni/
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GF is/will be a space exploration, trading & warfare game. It is/will be played in a single unified game space distributed among numerous servers. Proceeds from registration of the name & address space will be distributed among contributing developers.
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Mistral Large 3 675B Base 2512
Frontier-scale 675B multimodal base model for custom AI training
Mistral Large 3 675B Base 2512 is the foundational, pre-trained version of the Mistral Large 3 family, built as a frontier-scale multimodal Mixture-of-Experts model with 41B active parameters and a total size of 675B. It is trained from scratch using 3000 H200 GPUs, making it one of the most advanced and compute-intensive open-weight models available. As the base version, it is not fine-tuned for instruction following or reasoning, making it ideal for teams planning their own domain-specific finetuning or custom training pipelines. ... -
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OzoneFarm
FLOSS community-building tools and context chain technologies
Liberation Front for A Community in Cyberspace adapting to and following A Declaration of the Independence of Cyberspace by the Electronic Frontier Foundation "with no greater authority than that with which liberty itself always speaks." [url:https://www.eff.org/cyberspace-independence] FLOSS community-building tools are similar to "open source" software but more aligned with the Free Software Foundation definitions than not. [url:http://www.fsf.org/] Context chain technologies are similar to block chain technologies and cryptocurrencies but generate, aggregate, store and exchange value according to their semantic contexts rather than cryptic non-human-readable elements and randomly generated keys. ... -
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Software, content, and collaborative support for the Cube Engine community, including Assaultcube, Sauerbraten, and Blood Frontier.
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Gemopus
Stable fine-tuned Gemma model for structured, clear responses
...While not intended as a production-ready system, it serves as a high-quality local assistant for structured writing, summarization, and coding tasks. Limitations include potential hallucinations in complex domains and weaker performance compared to larger frontier models. -
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Hermes 4
Hermes 4 FP8: hybrid reasoning Llama-3.1-405B model by Nous Research
Hermes 4 405B FP8 is a cutting-edge large language model developed by Nous Research, built on Llama-3.1-405B and optimized for frontier reasoning and alignment. It introduces a hybrid reasoning mode with explicit <think> segments, enabling the model to deliberate deeply when needed and switch to faster responses when desired. Post-training improvements include a vastly expanded corpus with ~60B tokens, boosting performance across math, code, STEM, logic, creativity, and structured outputs. ... -
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DeepSeek-V4-Pro
Flagship MoE model for advanced reasoning, coding, and agents
DeepSeek-V4-Pro is a flagship open-weight Mixture-of-Experts language model designed for high-performance reasoning, coding, and agent-based workflows at scale. It features approximately 1.6 trillion total parameters with around 49B activated during inference, enabling strong efficiency while maintaining frontier-level capability. The model supports an ultra-long context window of up to 1 million tokens, making it highly suitable for long-document reasoning, large codebases, and complex multi-step tasks. Architecturally, it introduces optimizations to reduce compute and memory costs while improving stability across long sequences. DeepSeek-V4-Pro is positioned as the high-end variant of the V4 family, outperforming most open-source models in areas such as agentic coding, STEM reasoning, and world knowledge, and approaching the performance of leading closed-source systems. ... -
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Mistral Large 3 675B Instruct 2512 Eagle
Speculative-decoding accelerator for the 675B Mistral Large 3
...It works alongside the primary 675B instruct model, enabling faster response times by predicting several tokens ahead using Mistral’s Eagle speculative method. 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. ... -
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Mistral Large 3 675B Instruct 2512 NVFP4
Quantized 675B multimodal instruct model optimized for NVFP4
Mistral Large 3 675B Instruct 2512 NVFP4 is a frontier-scale multimodal Mixture-of-Experts model featuring 675B total parameters and 41B active parameters, trained from scratch on 3,000 H200 GPUs. This NVFP4 checkpoint is a post-training-activation quantized version of the original instruct model, created through a collaboration between Mistral AI, vLLM, and Red Hat using llm-compressor. -
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Ministral 3 14B Reasoning 2512
High-precision 14B multimodal model built for advanced reasoning tasks
Ministral 3 14B Reasoning 2512 is the largest model in the Ministral 3 series, delivering frontier-level performance with capabilities comparable to the Mistral Small 3.2 24B model. It pairs a 13.5B-parameter language model with a 0.4B vision encoder, enabling strong multimodal reasoning across both text and images. This version is specifically post-trained for reasoning tasks, making it highly effective for math, coding, STEM workloads, and complex multi-step problem-solving. ... -
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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 adherence. ... -
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Mistral Large 3 675B Instruct 2512
Frontier-scale 675B multimodal instruct MoE model for enterprise AIMis
Mistral Large 3 675B Instruct 2512 is a state-of-the-art multimodal granular Mixture-of-Experts model featuring 675B total parameters and 41B active parameters, trained from scratch on 3,000 H200 GPUs. As the instruct-tuned FP8 variant, it is optimized for reliable instruction following, agentic workflows, production-grade assistants, and long-context enterprise tasks. It incorporates a massive 673B-parameter language MoE backbone and a 2.5B-parameter vision encoder, enabling rich multimodal...
