Cainew

huihui-ai/Huihui-gemma-4-12B-coder-fable5-composer2.5-v1-abliterated is a specialized large language model optimized for coding and composition tasks. This fine-tuned model combines multiple capabilities for enhanced performance in software development and content creation.

This is a quantized GGUF format version of the Gemma 4 12B model configured for agentic capabilities, combining multiple specialized components for enhanced performance. The model is optimized for efficient deployment while maintaining strong reasoning and task execution abilities.

Datalab-to/lift appears to be a dataset or model resource, though specific details about its purpose and functionality are limited without additional context. It may relate to data processing, model lifting, or transfer learning applications.

GLM-5.2 has achieved the top ranking among open-weight models on Artificial Analysis, demonstrating strong performance in AI benchmarking metrics.

Apple has developed foundation models as part of its AI strategy to power features across its ecosystem. The models represent Apple's effort to create competitive, on-device AI capabilities.

DeepSeek has introduced vision capabilities to its AI models, enabling them to process and analyze images alongside text. This multimodal expansion allows DeepSeek models to perform tasks requiring visual understanding.

GLM-5.2 is a language model release from zai-org that represents advances in general-purpose language understanding and generation capabilities.

SubQ 1.1 Small is a new compact version of the SubQ model offering improved efficiency for smaller-scale deployments.

Qwen-Robot Suite introduces a comprehensive foundation model suite designed to enhance physical world intelligence and robotics capabilities.

DeepSeek V4 Pro offers significantly lower costs compared to Claude while maintaining competitive performance, demonstrating how improved efficiency and optimization can narrow the gap between different AI models. The achievement highlights the importance of cost-effective approaches in making advanced AI more accessible.

Learn how GPT-5.5 Instant improves ChatGPT’s health and wellness responses with stronger reasoning, better context, clearer communication, and physician-informed evaluations.

Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.

Community model zoo + knowledge base for Apple Core AI (iOS/macOS 27): Qwen3.5 & Gemma 4 converted end-to-end, verified on-device (iPhone 17 Pro GPU/ANE), conversion gotchas, custom Metal kernels, Swift runner

Reverse engineered Windows Copilot into an OpenAI-compatible API. Access GPT-4 and GPT-5 models through a simple REST interface without API keys or billing.

Agent 37 is managed hosting for persistent agents like Hermes, OpenClaw and ClaudeCode. So you don't need to run them on Mac minis or VPS yourself. One API call gives each of your customers their own always-on agent, from $3.44/mo. Founders use it to ship vertical agents to their own clients without babysitting servers.

A personal knowledge base that lives in this folder. Drop content in, have it organized automatically, ask questions, and get sourced answers — either through Claude Code slash commands or a local web dashboard.

ccMesh is a lightweight forwarding layer for Claude Code. It intercepts Claude protocol traffic and routes it to either Anthropic Claude endpoints or OpenAI-compatible APIs — switch backends without touching your client config.

Work with an AI Agent directly in your Word documents. The add-in enables a conversation-based agent sidebar that can search the web, update paragraphs, or build professional documents. Simply type what you want into the conversation pane and let the Agent handle research and execution. Ask it to rewrite paragraphs, summarize long reports, improve clarity and tone, generate outlines, create tables, or expand ideas, all without leaving Word.

Poolside is a foundation model company bringing intelligence to everywhere work gets done. Their mission is to drive abundance for humanity by creating artificial general intelligence.

An AI E-2 Visa Agent interviews, reads their evidence (bank statements, leases, invoices), checks the core E-2 eligibility standards, and produces a submission-ready plan with real 5-year financials, charts, and use-of-funds — as a designed PDF and editable Word doc. Outside vendors charge ~$2,000 and paralegals spend a week on this. Plansera gives you a strong first draft to review in ~30 minutes. Flat $100 per plan, no subscription. (Built for U.S. immigration professionals.)

🧭 Architecture-first system design: 26 bilingual tutorials, 25 architecture templates, and 6 end-to-end cases covering distributed systems, AI-native systems, RAG, coding Agents, and production trade-offs.

🐹 A free, open-source, native macOS GUI for the Mole CLI (mo): clean, uninstall, optimize, analyze disk, and watch live status. Plus long-range history + an MCP server for AI agents. Coming to Windows

Interactive AI General Education Course — 30 Lessons, Zero Math

A local control plane for AI agents — see what they do, approve what matters, keep secrets out. Rust + Tauri + Chrome MV3.

A performant markdown library for iOS that supports streaming

GBase — Recursive Self-Improvement Agent Framework. Memory, evolution, quality gates, identity system, and 40+ auto-registered tools.

Semiclassical gravity theory offers an efficient computational approach to solving NP-complete problems, suggesting novel methods for addressing computationally difficult challenges.

Motion forecasting is central to visual intelligence: agents must anticipate how objects will move in order to plan actions, reason about physical interactions, and synthesize realistic futures. We argue that 3D points in world coordinates provide a general representation that is class-agnostic, view-stable, compact, and directly useful for downstream tasks. We formalize the task of goal-conditioned 3D point motion forecasting: given a short visual history, a set of 3D query points on an object ...

Current AI-driven game development has made substantial progress in asset generation, gameplay design, and web-based game coding, yet project-level code engineering on professional game engines remains largely unexplored due to the absence of large-scale datasets and deterministic evaluation methods. We present JamSet and JamBench, the first project-level game code framework dataset and benchmark built on a professional game engine. Our key insight is that Game Jam competitions, community events...

Test-time reasoning is increasingly used as a serving-time control knob, but extra reasoning is not uniformly valuable: it can repair failed attempts, waste compute on already-correct answers, or introduce harmful answer changes. We study this as a deployment allocation problem rather than a new-verifier problem. We introduce \sevra, Selective Verification for Reasoning Allocation, a serving-layer controller that decides whether to preserve a frozen solver's initial answer or invoke active verif...

Conditional diffusion and flow models routinely fail to satisfy the very constraints that define their task. For instance, a depth-conditioned model often produces images whose re-extracted depth disagrees with the input, even though the forward operator--the depth predictor defining the constraint--is available during both training and inference. Existing approaches generally fall into two categories: supervised models that treat the conditioning signal as a static cue and ignore alignment info...

Reinforcement Learning with Verifiable Rewards algorithms like GRPO have emerged as the dominant post-training paradigm for complex reasoning in LLMs, yet commonly suffer from policy entropy collapse during training. We conduct a first-order gradient analysis of token-level entropy dynamics under GRPO and identify a token-level credit assignment mismatch: the per-token entropy variation decomposes into the product of the trajectory-level advantage and an entropy sensitivity function over the nex...

This technical report introduces VibeThinker-3B, a compact dense model with 3B parameters developed to investigate how far verifiable reasoning can be pushed within a strictly small-model regime. Building upon the Spectrum-to-Signal post-training paradigm, we systematically enhance the model through an optimized pipeline that includes curriculum-based supervised fine-tuning, multi-domain reinforcement learning, and offline self-distillation. Experimental evaluations demonstrate that VibeThinker-...

Multi-turn LLM serving accumulates dialogue history whose Key-Value (KV) cache grows with every turn and every user, quickly exceeding the model weights themselves and making memory -- not compute -- the binding constraint on throughput. Non-uniform KV compression, which allocates heterogeneous budgets across attention heads, preserves accuracy far better than uniform schemes, yet remains impractical: modern serving stacks assume identical KV lengths across heads, so heterogeneity traps freed me...

On-policy self-distillation (OPSD) has proven effective for post-training large language models (LLMs), yet its application to diffusion LLMs (dLLMs) remains unexplored. Existing OPSD methods are inherently autoregressive-centric. They inject privileged information via left-to-right prefix conditioning with token-level divergence supervision, a design that fundamentally conflicts with the arbitraryorder generation of dLLMs. We introduce d-OPSD, the first OPSD framework tailored for dLLMs. Our ap...

Video generative models ( VGMs) have become a new frontier that can be used not just for video generation but for a multitude of downstream tasks, including world modeling. To advance these tasks, a good video model must understand the physical reality of the world. Evaluating this understanding is an emerging field and has led to the Physics-IQ benchmark, which quantifies this explicitly by comparing model-generated videos to real-world videos of physical experiments. In this work, we present a...

Pixel-space diffusion models are trained on full-bandwidth noisy images, yet the useful signal available to the denoiser is strongly frequency dependent. Under rectified-flow diffusion and natural-image power-law spectra, the per-band data-to-noise contour k^{*}(t) = (1-t)^{-2/α} separates a signal-bearing low-frequency region from a noise-dominated high-frequency region at each time t. We show that this implicit coarse-to-fine structure is not merely descriptive: it induces a capacity-allocati...

Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bottleneck in the pursuit of general physical intelligence. Although emerging coding agents can generate code to automate algorithm search, their successes remain largely confined in digital environments. We conjecture that the missing abstraction to automate robotics research is a repeatable feedback loop for real-world policy improvement: reset the s...

Reinforcement learning (RL) has become a representative post-training paradigm for LLMs, enabling strong reasoning and agentic capabilities. However, rollout generation remains a dominant latency bottleneck because autoregressive sampling decodes responses sequentially and a small number of long-tailed generations often determine completion time. Speculative decoding (SD) offers a natural way to address this bottleneck, as it is a well-established technique for serving fixed LLMs that reduces la...

Real-world spatial intelligence requires reasoning over a continuous and evolving 3D world, yet existing VLMs and tool-augmented agents largely remain tied to static, stateless inference from isolated visual observations. We introduce \textsc{S-Agent}, a spatial tool-use agentic paradigm for understanding and reasoning over continuous multi-view images and videos. By formulating spatial reasoning as spatio-temporal evidence accumulation rather than isolated frame-level prediction, S-Agent reshap...

On-policy self-distillation (OPSD) trains a model on its own rollouts and uses a frozen copy to provide dense token-level targets conditioned on a reference target. This works well for LLM reasoning, but a direct extension to multimodal large language models (MLLMs) can create a shortcut: the privileged target may guide tokens mainly based on the text reference target rather than the image. We propose ViGOS, a visually grounded OPSD framework for MLLM post-training. The student first writes a vi...

This resource provides guidance and best practices for developing reliable agentic AI systems that can operate independently while maintaining robustness and trustworthiness.

Renowned US scientist John Jumper is departing from Google DeepMind to join Anthropic in a significant leadership shift within the AI industry. His move reflects the competitive talent landscape among leading AI research organizations.

OpenAI's losses nearly 8x'd in 2025 with annual spending reaching $34 billion, reflecting the company's aggressive expansion and investment in AI development.

Anthropic's Claude model has been experiencing elevated error rates across multiple versions, affecting reliability for users relying on the AI assistant.

Hyundai Motor Company acquired Boston Dynamics, a leading robotics company known for its advanced humanoid and quadruped robots, strengthening Hyundai's position in robotics and automation technology.

Amazon canceled a film project featuring Sam Altman following the announcement of their partnership with OpenAI, potentially to avoid conflicts of interest or competitive concerns. The decision reflects the complex business relationships between tech giants and AI companies.

An agency was accused of stealing a bestselling author's book and using AI technology to republish it under their own brand without authorization. This incident highlights copyright and intellectual property concerns in the age of AI-assisted content creation.

Companies are reducing their AI usage as operational costs become increasingly prohibitive and strain budgets. This trend suggests a more measured approach to AI adoption as organizations reassess ROI and sustainability.

The article examines how generative AI's rapid hype cycle and inflated expectations mirror the characteristics of multi-level marketing schemes, warning of potential market disillusionment.

Amazon has launched an investigation into engineers who publicly criticized the company's AI data center expansion plans, raising concerns about employee free speech and corporate transparency.

SpaceX announced its acquisition of Cursor, a popular AI-powered code editor, for $60 billion as part of its expansion into software and AI development tools.

Salesforce has agreed to acquire Fin (formerly Intercom) for $3.6 billion to enhance its AI-powered customer service and engagement capabilities. The acquisition strengthens Salesforce's position in the customer experience software market.

This piece explores Anthropic's approach to AI safety and their competitive advantages in building safer, more reliable AI systems. The company emphasizes safety as a core differentiator in the AI market.

The US government has decided not to blacklist DeepSeek while identifying over 100 companies as potential security risks, reflecting ongoing concerns about AI technology regulation.

Amazon announced a multibillion-dollar investment in a new data center facility in Missouri to support growing cloud computing and AI infrastructure demands.

The Department of Justice has filed claims arguing that xAI's gas turbine infrastructure is critical to national and energy security interests. This legal action underscores the growing intersection between AI infrastructure development and government regulatory concerns.

An exploration of 100,000 fundamental questions about artificial intelligence, examining core concepts, capabilities, and limitations that define modern AI systems.

This piece argues that local Qwen models should not be directly compared to Claude Opus as inferior, but rather recognized as serving different use cases and purposes. Local deployment options like Qwen offer distinct advantages for certain applications despite different capabilities.

An article detailing the author's decision to discontinue using Google services, likely exploring alternative platforms and tools.

This piece discusses how large language models have become increasingly complex, making them harder to understand and control as they scale up in sophistication. The complexity of modern LLMs presents ongoing challenges for researchers and developers.

The AI industry must prioritize engineering discipline and rigorous practices rather than moving faster with less oversight to build reliable and robust systems.

A guide outlining the key strategies and best practices for founders building startups that are natively designed around AI capabilities from the ground up.

This article argues that AI capabilities are fundamentally constrained by the quality of underlying code and architecture rather than prompting techniques. It suggests that improving AI systems requires technical improvements beyond instruction engineering.

Security researchers have demonstrated that ChatGPT's image generator can be manipulated through prompt engineering to produce violent and sexual content that violates OpenAI's usage policies. This vulnerability highlights potential risks in multimodal AI safety.

As AI code reviews have become more expensive to conduct, rewrites and automated fixes have become comparatively cheaper, shifting development cost dynamics.