Search Results for "no coding" - Page 62
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Showing 1607 open source projects for "no coding"
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DJIndent is a open source pure java pretty printer, or beautifier. What it does is to allow java file(s) to be converted to a uniform coding convention dictated by an xml file. The xml file dictates such things as { location, whitespace usage, mandatory
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HTMWriter is a Java package that I *hope* will aid in generating HTML output in Java Servlets. I have started this in the hopes of reducing the amount of debugging I do. I am new to Java programming and encourage constructive input on my coding/design
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pgn2pgc converts text Portable Game Notation (PGN) chess files into highly compressed Portable Game Coding (PGC) chess files. This is the only implementation of PGC as described in the PGN standard. Code is also useful for writing chess software.
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The Cryptix ASN.1 Kit aims at facilitating the task programmers face in coding, accessing and generating java-bound, both types and values, defined as ASN.1 constructs, or encoded as such.
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To provide webmaster and other people with a PHP based chating system. This chat system is based on textfile using standard HTML coding, so access throught different interfaces (such as flash, WAP) is easy.
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Moebius is a mud server, written in python using (currently) MySQL as a back-end. It's design goals are to be extensible without taking the engine down, to be distributable via the database, and to allow a complete python coding/learning env online. It
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ncurOS is a very secure OS, which is based on an UNIX (maybe linux) kernel. Many system tools are integrated in the kernel. All of these tools are written with ncurses. All ncurOS-developers follow a coding standard.
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Ornith-1.0
Open reasoning model for agentic coding and tool workflows
Ornith-1.0 is a large open-source reasoning model from DeepReinforce, built for agentic coding, tool use, and complex software engineering workflows. It is part of the Ornith 1.0 family, which includes dense and MoE models post-trained on Gemma 4 and Qwen 3.5. The model focuses on coding-agent performance across benchmarks such as Terminal-Bench, SWE-Bench, NL2Repo, OpenClaw, and ClawEval. Its training uses a self-improving reinforcement learning framework that optimizes not only solution attempts but also the scaffolds that guide those attempts, helping the model discover better search paths and produce higher-quality solutions. ... -
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Mistral Small 4
Model that fuses instruct, reasoning and agentic skills
The Mistral Small 4 collection is a set of open-weight large language models developed by Mistral AI that aim to unify multiple capabilities, including instruction following, reasoning, and coding, within a single efficient architecture. These models are part of the broader Mistral Small family, which is designed to deliver strong performance across a wide range of everyday AI tasks while maintaining relatively low latency and efficient deployment requirements. The collection reflects an evolution toward hybrid mixture-of-experts architectures that dynamically activate subsets of parameters during inference, allowing large models to remain computationally efficient. ... -
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Kimi K3
Powerful, native multimodal AI agentic model
Kimi K3 is an open-weight, multim is an open-weight, multimodal agentic AI model from Moonshot AI designed for advanced coding, research, reasoning, and knowledge work. Builtodal agentic AI model from Moonshot AI designed for advanced coding, research, reasoning, and knowledge work. Built with 2.8 trillion total parameters and a sparse mixture-of-experts architecture, it activates 104 billion parameters per token to with 2.8 trillion total parameters and a sparse mixture-of-experts architecture, it activates 104 billion parameters per token to deliver frontier-level performance more efficiently. ... -
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Leanstral
Open-source code agent designed for Lean 4
Leanstral is an open-weight large language model developed by Mistral AI and specifically designed as a code agent for the Lean 4 proof assistant, enabling advanced interaction with formal mathematics and program verification systems. The model is built to understand and generate Lean 4 code, which is used to express complex mathematical constructs as well as formal software specifications. By focusing on theorem proving and formal reasoning, Leanstral represents a specialized direction... -
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Nemotron 3 Nano
LL model providing reasoning and conversational capabilities
...This architecture allows the system to maintain strong reasoning capabilities while improving throughput and reducing the computational cost associated with large context processing. The model is designed as a general-purpose language system capable of handling tasks such as chat interaction, coding assistance, document analysis, and instruction following. -
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Nemotron 3 Super
Open language model developed by NVIDIA as part of Nemotron-3 family
...Its architecture combines Transformer attention layers with Mamba state-space components to balance long-context reasoning, memory efficiency, and high-quality language generation. The model is optimized for building AI agents that must perform complex tasks such as planning, tool usage, coding assistance, and multi-step reasoning. -
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LongCat-2.0
Trillion-parameter MoE model for coding and million-token reasoning
...LongCat-2.0 introduces LongCat Sparse Attention and extensive 1M-context training, enabling native processing of million-token inputs for long-document analysis, repository-scale coding, and complex multi-step reasoning. Dedicated post-training further strengthens coding and agent performance, producing competitive benchmark results against leading proprietary models. -
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Kimi K2.7 Code
Coding-focused Kimi model for long-horizon agent workflows
Kimi K2.7 Code is a coding-focused agentic model built on Kimi K2.6, designed for long-horizon software engineering, autonomous coding workflows, and complex tool-based execution. It improves end-to-end task completion across real-world programming scenarios while reducing thinking-token usage by about 30% compared with K2.6. Architecturally, it uses a 1T-parameter Mixture-of-Experts design with 32B activated parameters, 61 layers, 384 experts, a 256K-token context window, and a MoonViT vision encoder. ... -
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SuperGemma4
Fast uncensored Gemma model optimized for local chat and coding
...A neutral chat template is embedded to prevent prompt misrouting issues, ensuring consistent responses without unintended shifts into coding or tool-use modes. -
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Kimi K2.6
Multimodal agent model for coding, orchestration, and autonomy
Kimi K2.6 is an open-source native multimodal agentic model built for advanced autonomous execution, long-horizon coding, and large-scale task orchestration. It is designed to handle complex end-to-end software workflows across multiple languages and domains, including front-end development, DevOps, performance optimization, and coding-driven design. Beyond coding, it can transform prompts and visual inputs into production-ready interfaces and lightweight full-stack outputs with structured layouts, interactivity, and polished visual detail. ... -
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Qwen3.6-35B-A3B
Open multimodal model for coding, agents, and long-context tasks
Qwen3.6-35B-A3B is an open-weight multimodal model built for real-world coding, agent workflows, and long-context reasoning. It combines a causal language model with a vision encoder, supports text, image, and video inputs, and is optimized for frameworks such as Transformers, vLLM, SGLang, and KTransformers. The model emphasizes stability, responsiveness, and practical developer productivity, with major improvements in agentic coding, frontend generation, and repository-level reasoning. ... -
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ZAYA1-8B
Efficient MoE reasoning model for coding and math workloads
...The model was trained entirely on AMD infrastructure and refined through supervised fine-tuning and multi-stage reinforcement learning focused on reasoning and coding. -
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Qwen3.6-27B
Dense multimodal Qwen model for coding, agents, and long context
Qwen3.6-27B is an open-weight multimodal model built to deliver strong real-world coding, agent, and long-context performance in a dense 27B-parameter architecture. It combines a causal language model with a vision encoder and supports text, image, and video inputs, making it suitable for both software workflows and broader multimodal tasks. The model emphasizes stability and practical developer utility, with major improvements in agentic coding, frontend generation, and repository-level reasoning. ... -
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Devstral Small 2
Lightweight 24B agentic coding model with vision and long context
...With 24B parameters and FP8 instruct tuning, it delivers strong instruction following while remaining lightweight enough for local and on-device deployment. The model achieves competitive performance on SWE-bench, validating its effectiveness for real-world coding and automation tasks. It introduces vision capabilities, enabling image understanding alongside text for more versatile development workflows. Devstral Small 2 supports a 256k context window, allowing it to reason across large repositories, long diffs, and extended technical contexts. Its architecture improves generalization across diverse prompts and coding environments while leveraging advanced attention scaling techniques. -
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Inkling
Frontier multimodal MoE model for coding and AI agent workflows
Inkling is Thinking Machines Lab’s first open-weight flagship multimodal Mixture-of-Experts model, designed for advanced reasoning, coding, and autonomous agent workflows. It contains 975B total parameters with 41B active parameters per token, balancing frontier-level capability with efficient sparse inference. The model natively processes text, images, audio, and video within a unified architecture and supports an exceptionally large 1 million token context window for long-document reasoning, repository-scale coding, and agentic execution. ... -
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Laguna XS.2
Open agentic coding model optimized for local deployment
Laguna XS.2 is Poolside’s first open-weight Mixture-of-Experts model designed specifically for agentic coding and long-horizon software engineering tasks. The model contains 33B total parameters with only 3B activated per token, allowing it to deliver strong coding performance while remaining efficient enough to run locally on modern consumer hardware. It uses a hybrid attention architecture that combines Sliding Window Attention and global attention layers, reducing memory requirements and improving inference speed. ... -
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Gemma 4
Google’s flagship dense multimodal model for coding and reasoning
...Gemma 4 31B supports native function calling, structured outputs, and more than 140 languages, making it suitable for enterprise assistants, coding agents, document analysis, and multilingual applications. Google positions it as a frontier-level model that can run on consumer GPUs and workstations while achieving leading results across reasoning, mathematics, coding, and multimodal benchmarks. -
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Devstral 2
Agentic 123B coding model optimized for large-scale engineering
Devstral 2 is a large-scale agentic language model purpose-built for software engineering tasks, excelling at codebase exploration, multi-file editing, and tool-driven automation. With 123B parameters and FP8 instruct tuning, it delivers strong instruction following for chat-based workflows, coding assistants, and autonomous developer agents. The model demonstrates outstanding performance on SWE-bench, validating its effectiveness in real-world engineering scenarios. It generalizes well across diverse prompts, languages, and development environments, making it adaptable to a wide range of coding workflows. Devstral 2 supports a 256k context window, enabling deep understanding of large repositories, long diffs, and extended technical discussions. ...
