Qwen 3.8 27B is now available on Ollama, offering substantial performance improvements for coding, professional work, research, and long-horizon agentic tasks. The model is built for agents, has multimodal capabilities, and supports long-context sessions. Performance optimizations are available on Apple Silicon and NVIDIA hardware, reaching up to 131 output tokens/s on NVIDIA hardware.
Flue 2's React-style "Agent Hooks" allow agents to be represented by JavaScript functions that "re-renders on every turn," enabling dynamic state management and real-time adaptation. 16 built-in hooks, including useSkill() and useTool(), enable developers to build dynamic agents that can attach and detach resources at runtime.
Multi-agent Systems (MAS) involve multiple autonomous agents collaborating, competing, or coexisting to tackle problems beyond the reach of a single entity. Each agent has its own perspective, goals, and capabilities, and their interactions drive sophisticated, adaptive, and often emergent behaviors. MAS thrive on decentralization, unlike monolithic AI systems built for centralized tasks.
MLX is an array framework designed for efficient and flexible machine learning research on Apple silicon, optimized for the unified memory architecture of Apple silicon. The framework features a NumPy-like API and higher level neural net and optimizer packages, allowing for the building of complex yet efficient machine learning models. MLX can run on any Apple platform, with bindings available in Swift, C++, and C. It also has documentation and examples available on GitHub.
Pi is a minimal and extensible coding agent that can be quickly set up through Ollama, launching it with the command "ollama launch pi". Pi ships with four core tools, which can be expanded through its extension system. It can also use web search and fetch tools via the "@ollama/pi-web-search" package. Pi can be configured through a configuration block in "~/.pi/agent/models.json" and "~/.pi/agent/settings.json".
Ollama is a coding agent that can be used from various tools and platforms, including coding agents, personal assistants, and editors. Integrations include Claude Code, OpenCode, DeepSeek Harness, and Hermes Agent, which offer features like vision, web search, and self-improving skills. Users can also integrate Ollama with messaging apps and everyday tasks through personal assistants like OpenClaw. Ollama models can also be used within the VS Code editor.
Agent profiling is the structured definition and ongoing interpretation of an agent's role, goals, capabilities, constraints, environment, and relevant user context. It gives the system a consistent operating identity and helps determine what it should perceive, remember, prioritize, and do in a particular situation.
AI agents store and retrieve memory through various types, including semantic, episodic, and procedural memory. Long-term memory involves explicit (declarative) memory, which stores general truths and common knowledge, and implicit (non-declarative) memory, which enables learning from past experiences. Short-term memory ensures agents stay responsive in real-time interactions, while working memory plays a vital role in multi-step reasoning and decision-making.
LLM-assisted enterprise tools often fail to verify the accuracy of their outputs, relying on qualitative review that only catches obvious errors. A systematic evaluation harness scores model output against labeled ground truth, revealing patterns that qualitative review misses, such as when a model is most confident in its wrong answers. This approach is crucial for tools that influence business decisions, as correctness is a matter of real consequence. Building a synthetic ground truth dataset is the most challenging and valuable part of this process, as it requires defining what "correct" means for a specific use case.
Mass student protests have swept India, Portugal, and Mexico, with grievances including exam paper leaks, AI cheating, and marking errors, leading to widespread unrest and calls for government resignations. In India, an exam paper leak affected millions of students and led to the education minister's resignation, while in Portugal, a failed attempt to digitize exam marking sparked a national crisis. The high stakes of good grades in a tough job market, combined with the growth of AI cheating and attempts to digitize examinations, have contributed to the volatility. Experts say the pressure on young people has changed, with a single test determining their entire future creating untenable pressure.
Fraudsters use AI to analyse holiday photos posted on Instagram or Facebook to create emails seeking bank account details, making the messages appear more legitimate. Common AI tools can accurately detect where a photo was taken more than 90% of the time, using details such as background, architecture, signage, and light. The information can be used to defraud by text message or email, adding credibility to the scam. To protect yourself, delay posting pictures until after your trip, change your settings to limit who can see them, and be wary of urgent messages and links from unknown sources.
Mathematical experiments are becoming abundant through human-machine teaming, with the use of AI systems to propose, execute, and refine mathematical experiments. Researchers have successfully attacked two open problems, one of which produced a complete proof candidate with a machine-checked algebraic core, while the other remained unsolved. The experiments used a combination of parallel agents, exact-arithmetic programs, adversarial critics, and a proof assistant.
To be a successful Data Scientist in the Vibe Coding Era, it's essential to take a deep breath and not feel overwhelmed by the rapid evolution of AI. Large Language Models (LLMs) are productivity tools, not functioning Data Scientists, and should be used to extract information and evaluate suggestions, rather than delegating the thinking process. By building your own system, being diligent, and staying adaptable, you can leverage AI to boost productivity and showcase your unique strengths.
A yaw control system for suspended payloads using reinforcement learning was developed, featuring four thrusters and an SAC policy controlling them at 3 Hz. Separate policies were trained for three different payloads, with results showing that larger target angles were not necessarily harder to control. The system's thrust usage varied between payloads, with the H-steel beam using low average thrust and the truss using higher thrust continuously.
A reinforcement learning environment and engine for Azul Duel was built from scratch in Rust, reaching an internal Elo of ~1358 against a strong baseline heuristic. The engine can play the game via a browser interface with a teacher mode providing candidate move evaluations and win-probability estimations. The current focus is to train a separate "ownership" head to consider final scoring plates, with an 8,000-game dataset generated using handcrafted heuristics.
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Wynd Kaufmyn, a 69-year-old activist, was jailed for 30 days for chaining the front doors of OpenAI's headquarters in protest against artificial intelligence. She was found guilty of multiple misdemeanor charges and said she would go to jail to "sounding the alarm" about the dangers of AI superintelligence. Kaufmyn's message to OpenAI's executives is to "regain your humanity" and stop the development of superintelligent AI, citing fears of existential threats and bioweapons. Her conviction has sparked controversy, with supporters calling her a "brave and courageous woman" and likening her to Rosa Parks.