Ollama
ollama-1b46dc70·6 events·first seen 18d agoAliases: Ollama
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Benchmarking Local LLMs for Confidential Translation Workflows
This paper evaluates locally runnable LLMs (via Ollama) for offline, privacy-constrained translation workflows targeting freelance translators and smaller language service providers. The authors expand their Reeve Foundation corpus to include German and Simplified Chinese, then benchmark local models across four language directions against commercial NMTs (DeepL, Baidu), a frontier LLM (GPT-5.2), and professional local NMT systems. Results show substantial performance variation by language direction and model size, with the best local LLMs matching or exceeding local NMT systems and the frontier LLM, though falling short of top commercial NMTs. The study supports the viability of local LLMs for confidentiality-sensitive translation use cases.
KaliGPT: agentic AI assistant for offensive security built on Gemini, ChatGPT, Ollama, and OpenRouter
KaliGPT is an open-source agentic AI tool targeting ethical hackers and offensive security students, integrating multiple model backends including Gemini, ChatGPT, Ollama, and OpenRouter. The project aims to streamline penetration testing workflows. It is gaining traction on GitHub with 494 stars and 46 added today.
Deep Eye: Multi-Provider AI-Orchestrated Vulnerability Scanner
Deep Eye is an open-source Python tool that orchestrates multiple AI providers (OpenAI, Claude, Grok, Gemini, Ollama, Groq, Mistral, and others) to generate attack payloads and scan targets for 45+ vulnerability types. It produces professional security reports with compliance mapping. The project has accumulated 1,572 GitHub stars with 42 added today, indicating growing community interest in AI-augmented offensive security tooling.
Mistral Small 3: 24B Latency-Optimized Open-Weight Model Released Under Apache 2.0
Mistral AI has released Mistral Small 3, a 24B-parameter instruction-tuned model optimized for low latency, achieving over 81% on MMLU at 150 tokens/s on a single GPU. The model is competitive with Llama 3.3 70B and Qwen 32B while being more than 3x faster on equivalent hardware, and is released under Apache 2.0 for both pretrained and instruction-tuned checkpoints. It is explicitly not trained with RL or synthetic data, positioning it as a base model for community fine-tuning and reasoning capability development. Deployment targets include local inference on consumer hardware (RTX 4090, MacBook 32GB RAM), agentic function calling, and domain-specific fine-tuning.
Andrew Ng introduces OpenCoworker, an open-source desktop AI agent harness
Andrew Ng and collaborators Rohit Prasad and Devika Verma have released OpenCoworker, a free open-source desktop agent built by extending the aisuite library to support agent harnesses. The tool allows users to connect frontier LLMs (OpenAI, Anthropic, Google) or local models via Ollama to desktop tasks including file access, messaging, and workflow automation, with privacy as a design priority. Ng frames this as a response to data-retention concerns with commercial desktop agents, citing Anthropic's Fable release as a recent example of policy opacity. The post also provides a concise overview of the current desktop agent landscape and the shift toward LLM-driven agentic loops.
Anthropic launches Claude Mythos 5 and Claude Fable 5; Andrew Ng introduces OpenCoworker desktop agent
Anthropic released Claude Mythos 5 and Claude Fable 5, two variants of the same frontier model that set new state-of-the-art results across software engineering, knowledge work, cybersecurity, and agentic coding benchmarks. Claude Fable 5 is the general-availability version with safety classifiers that restrict responses on security, biology, chemistry, and cutting-edge AI topics, priced at $10/$50 per million input/output tokens; Mythos 5 is restricted to selected partners via Project Glasswing. Separately, Andrew Ng and collaborators released OpenCoworker, a free open-source desktop agent harness built on top of aisuite, designed to give users privacy-preserving agentic workflows with their own API keys or local models. The newsletter also contextualizes the broader shift toward LLM-driven agent harnesses as frontier models have become capable enough to reliably drive next-action decisions.