All posts tagged: context

Social context influences dating preferences just as much as biological sex

Social context influences dating preferences just as much as biological sex

Published by José Betancourt

A recent study published in Evolution and Human Behavior suggests that a person’s socioeconomic background plays a massive role in shaping what they look for in a romantic partner. The findings provide evidence that the surrounding environment and access to resources often influence dating preferences just as much as biological sex. Ultimately, this research challenges rigid stereotypes about male and female behavior, showing that human mating strategies adapt fluidly to social conditions. Historically, evolutionary psychology has focused heavily on the biological differences between men and women when it comes to choosing a partner. The standard framework suggests that men tend to prioritize physical attractiveness to maximize reproductive success, while women tend to prioritize resources to ensure stability for offspring. However, human dating behavior is highly complex and responsive to environmental pressures. The authors of the new study wanted to better understand how resource availability and social standing interact with these biological predispositions. They wanted to see if people from different socioeconomic backgrounds adjusted their romantic preferences and their self-esteem to fit their specific life circumstances. …

Qwen 3.6 Plus : 1M Context Window & Agentic Coding Tools

Qwen 3.6 Plus : 1M Context Window & Agentic Coding Tools

Published by skeptic

Qwen 3.6 Plus has arrived, bringing a host of updates tailored for developers and researchers tackling technical challenges. This latest iteration, as highlighted by Prompt Engineering, emphasizes structured problem-solving through features like agentic coding, which facilitates step-by-step refinement for intricate workflows. With its 1-million-token context window, the model can handle extensive datasets while maintaining coherence, making it particularly effective for tasks such as simulations and large-scale data analysis. While not designed for conversational AI, its specialized focus positions it as a reliable choice for users requiring precision and advanced reasoning. In this feature, you’ll explore how Qwen 3.6 Plus excels in multimodal understanding, integrating text, images and videos to address diverse technical and creative challenges. Gain insight into its real-world applications, from real-time tracking of the International Space Station to generating detailed datasets for creative projects. Additionally, the discussion will touch on its limitations, such as its dependency on specific frameworks, making sure a balanced understanding of its capabilities. This breakdown offers a comprehensive look at how Qwen 3.6 Plus can support your most demanding …

Gemma 4 Models: 31B and 26B MoE with 256K Context Window

Gemma 4 Models: 31B and 26B MoE with 256K Context Window

Published by skeptic

Google’s release of Gemma 4 introduces a new era in AI development, combining advanced capabilities with open source accessibility. As highlighted by Sam Witteveen, this family of models is designed to address a diverse range of needs, from high-performance computing tasks to lightweight, on-device applications. Notable features include its multi-modal integration, which processes text, vision and audio inputs seamlessly and its long chain-of-thought reasoning, allowing nuanced problem-solving and decision-making. With two distinct model tiers, Workstation and Edge, Gemma 4 ensures flexibility for developers working across industries and environments, whether tackling complex workflows or optimizing for constrained devices. Dive into this explainer to uncover practical insights into how Gemma 4’s capabilities can be applied to real-world challenges. You’ll gain a deeper understanding of its 256K and 128K context windows, which enhance performance for both enterprise and edge use cases and explore its licensing under Apache 2.0, which encourages customization and collaboration. Additionally, discover how its support for multi-image inputs and speech recognition opens up new possibilities for unified workflows. This breakdown offers a clear view of …

I moved my entire ChatGPT context to Claude and it finally felt like home

I moved my entire ChatGPT context to Claude and it finally felt like home

Published by skeptic

Like many, I’ve been using ChatGPT as my AI of choice for quite a while (even though some are turning away recently. But the truth is that for most things, Claude is better (like for doing real work) and in some cases save you a ton of time by helping you with work. It’s more perceptive (and can brilliantly ask you clarifying questions in order to form a deep understanding), it has vastly better coding skills than ChatGPT, and it has more capability to do work on your behalf through agentic tools like Cowork which can literally use your computer on your behalf. But how do you move from ChatGPT to Claude without losing the history you’ve had with GPT? I was worried about that, since I’ve spent over a year working with and inputting info into ChatGPT that I didn’t want to lose. The truth is that there is presently no perfect migration tool to go from ChatGPT to Claude, but there are several methods to transition to Claude so that you can give …

Imagine if your Teams or Slack messages automatically turned into secure context for your AI agents — PromptQL built it

Imagine if your Teams or Slack messages automatically turned into secure context for your AI agents — PromptQL built it

Published by skeptic

For the modern enterprise, the digital workspace risks descending into “coordination theater,” in which teams spend more time discussing work than executing it. While traditional tools like Slack or Teams excel at rapid communication, they have structurally failed to serve as a reliable foundation for AI agents, such that a Hacker News thread went viral in February 2026 calling upon OpenAI to build its own version of Slack to help empower AI agents, amassing 327 comments. That’s because agents often lack the real-time context and secure data access required to be truly useful, often resulting in “hallucinations” or repetitive re-explaining of codebase conventions. PromptQL, a spin-off from the GraphQL unicorn Hasura, is addressing this by pivoting from an AI data tool into a comprehensive, AI-native workspace designed to turn casual, regular team interactions into a persistent, secure memory for agentic workflows — ensuring these conversations are not simply left by the wayside or that users and agents have to try and find them again later, but rather, distilled and stored as actionable, proprietary data in …

Agentic commerce runs on truth and context

Agentic commerce runs on truth and context

Published by skeptic

Product truth: If the catalog is inconsistent, an agent’s choices will look arbitrary (“the wrong shirt,” “the wrong size,” “the wrong material”), and trust collapses quickly. Payee truth: Agentic commerce expands beyond cards to account-to-account and open-banking-connected experiences, broadening the universe of payees and the need to recognize them accurately in real time. Identity truth: People operate in multiple contexts (work versus personal). Devices shift. A system that cannot distinguish amongst these contexts will either block legitimate activity or approve risky activity, both of which damage adoption. This is why unified enterprise data and entity resolution move from nice to have to operationally required. The more autonomy you want, the more you must invest in modern data foundations that ensure it is safe. Context intelligence: The missing layer When leaders talk about agentic AI, they often focus on model capability: planning, tool use, and reasoning. Those are necessary, but they are not sufficient. Agentic commerce also requires a layer that provides authoritative context at runtime. Think of it as a real-time system of context that …

How xMemory cuts token costs and context bloat in AI agents

How xMemory cuts token costs and context bloat in AI agents

Published by skeptic

Standard RAG pipelines break when enterprises try to use them for long-term, multi-session LLM agent deployments. This is a critical limitation as demand for persistent AI assistants grows. xMemory, a new technique developed by researchers at King’s College London and The Alan Turing Institute, solves this by organizing conversations into a searchable hierarchy of semantic themes. Experiments show that xMemory improves answer quality and long-range reasoning across various LLMs while cutting inference costs. According to the researchers, it drops token usage from over 9,000 to roughly 4,700 tokens per query compared to existing systems on some tasks. For real-world enterprise applications like personalized AI assistants and multi-session decision support tools, this means organizations can deploy more reliable, context-aware agents capable of maintaining coherent long-term memory without blowing up computational expenses. RAG wasn’t built for this In many enterprise LLM applications, a critical expectation is that these systems will maintain coherence and personalization across long, multi-session interactions. To support this long-term reasoning, one common approach is to use standard RAG: store past dialogues and events, retrieve …

OpenClaw Persistent Memory Files & Context Management Guide

OpenClaw Persistent Memory Files & Context Management Guide

Published by skeptic

OpenClaw is an open source AI agent designed to automate tasks while prioritizing privacy and security. It integrates with advanced models like Claude and GPT and runs on private servers, making sure full control over your data. According to Parker Prompts, OpenClaw’s sub-agents are capable of handling specialized tasks such as coding, research and workflow management. Each sub-agent operates independently with its own memory, allowing multitasking without overlap or interference. Learn how to set up OpenClaw on a private server and connect it to AI models using API keys. Explore its ability to automate tasks like content creation and scheduling and discover how plugins from the Claw Hub marketplace can extend its functionality. Gain insight into how its modular design supports efficiency while maintaining data security. Setting Up OpenClaw: Security at Its Core TL;DR Key Takeaways : OpenClaw is an open source AI agent designed for task automation, integrating advanced AI models like Claude and GPT while prioritizing data privacy through private server operation. It features robust security measures, including isolated environments and daily backups, …

Nvidia BlueField-4 STX adds a context memory layer to storage to close the agentic AI throughput gap

Nvidia BlueField-4 STX adds a context memory layer to storage to close the agentic AI throughput gap

Published by skeptic

When an AI agent loses context mid-task because traditional storage can’t keep pace with inference, it is not a model problem — it is a storage problem. At GTC 2026, Nvidia announced BlueField-4 STX, a modular reference architecture that inserts a dedicated context memory layer between GPUs and traditional storage, claiming 5x the token throughput, 4x the energy efficiency and 2x the data ingestion speed of conventional CPU-based storage. The bottleneck STX targets is key-value cache data. KV cache is the stored record of what a model has already processed — the intermediate calculations an LLM saves so it does not have to recompute attention across the entire context on every inference step. It is what allows an agent to maintain coherent working memory across sessions, tool calls and reasoning steps. As context windows grow and agents take more steps, that cache grows with them. When it has to traverse a traditional storage path to get back to the GPU, inference slows and GPU utilization drops. STX is not a product Nvidia sells directly. It …

Nyne, founded by a father-son duo, gives AI agents the human context they’re missing

Nyne, founded by a father-son duo, gives AI agents the human context they’re missing

Published by skeptic

AI agents are expected to soon start making autonomous purchasing and scheduling decisions on behalf of humans. But Michael Fanous, a UC Berkeley computer science graduate and former machine learning engineer at CareRev, argues that these agents are currently missing a critical piece of the puzzle: the full context required to truly understand the people they are programmed to serve. Fanous claims that machines currently struggle to discern whether a person’s professional profile on LinkedIn, their activity on Instagram, and their public government records all belong to the same human being. To solve this, he teamed up with his father, Emad Fanous, a veteran CTO, to build Nyne, a startup aiming to become the intelligence layer that helps agents understand humans across their entire digital footprint. On Friday, Nyne announced it raised $5.3 million in seed funding led by Wischoff Ventures and South Park Commons, with participation from several angel investors, including Gil Elbaz, the co-founder of Applied Semantics and a pioneer of Google AdSense. While it may seem that Nyne is tackling an issue …