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Glaze is the easiest way to go from an idea to a Mac app. Describe what you want, and it builds a real app that lives in your dock, launches instantly, works offline, and taps into the full power of your computer. Software that's finally personal, shaped around you. From the makers of Raycast.

Claude-real-video is a new capability that enables any large language model to process and understand video content by watching and analyzing it. This advancement extends LLM capabilities beyond text to include video comprehension.

Jamesob provides a comprehensive guide for running state-of-the-art large language models locally on personal hardware. The guide covers setup, optimization, and best practices for deploying LLMs without cloud dependencies.

Most AI dev tools just read your code and guess. Osloq actually runs it. Connect your GitHub, pick an issue, and an AI agent spins up a real sandbox, clones your repo, runs it, and tries to reproduce the bug the way a developer would. You get a report backed by real evidence. What happened, the steps it took, and whether the bug is real, not a hallucinated guess. No local setup, no "works on my machine." It handles the tedious reproduction step so you jump straight to fixing.

nxt is the AI task manager you talk to like a human assistant. Brain-dump your thoughts in plain language - nxt reads between the lines, extracts tasks, infers priorities, and files everything automatically. It understands what you mean, not just what you say. nxt learns your personal context, so your tasks flex around your life. When you're ready to act, nxt cuts through the noise and gives you one clear task, one reason why. No scrolling, no paralysis, no overwhelming list to wade through.

Vox is a GitHub Copilot CLI extension: run /vox and a reactive listening orb opens in its own window. Speak your turn, hear the agent reply. Voice in, voice out — on Windows, macOS, and Linux.

Safari MCP server is introduced as a tool enabling web developers to integrate Safari browser capabilities into their development workflows. It provides a bridge for developers to access web browsing functionality programmatically.

Lightning Memory-Mapped Database Manager (LMDB) has reached version 1.0, marking a stable release of this high-performance key-value database library.

Manticore Search rebuilt its ONNX path implementation to enable faster embedding generation and improved performance. The optimization work focuses on accelerating vector embedding calculations for enhanced search capabilities.

Conventional reinforcement learning strategies for visual generation typically employ sample-wise reward functions, yet this practice frequently results in reward hacking that degrades image diversity and introduces visual anomalies. To address these limitations, we present a novel framework that finetunes generative models using distribution-wise rewards, ensuring better alignment with real-world data distributions. Unlike rewards that evaluate samples individually, distribution-wise reward acc...

Vision-Language-Action (VLA) models are fundamentally bottlenecked by the scarcity of expert demonstrations -- triplets of observations, instructions, and actions that are costly to collect at scale. We argue that this bottleneck stems from conflating two distinct learning objectives: acquiring physical competence (how to move) and acquiring semantic alignment (what to do). Crucially, only the latter requires language supervision. Building on this Decomposition Hypothesis, we propose Task-Agnost...

Continual post-training enables foundation models to acquire new knowledge while preserving existing capabilities. Recent work suggests that on-policy learning can mitigate forgetting, with on-policy self-distillation emerging as a particularly attractive approach. In this work, we revisit this optimistic view through self-distillation policy optimization (SDPO). Our experiments show that SDPO can accelerate in-domain specialization when teacher signals are stable and well aligned, but it strugg...

Foundation models are routinely released to the public, yet the data recipes used to train them -- such as domain mixture weights that determine how different sources are sampled -- are rarely disclosed. This creates an access asymmetry: researchers study the resulting models but lack visibility into the training distribution that produces them. Prior works for inferring training data, such as membership inference, detect at the level of individual samples and thus cannot characterize the global...

We elucidate the design space of Representation Distribution Matching (RDM), our name for the paradigm that trains a one-step image generator by matching generated and reference feature distributions under frozen pretrained encoders. We identify two design axes, how the distributions are compared and the representations they are compared in, and controlled studies along them yield three findings. First, the classical MMD, which could not train convincing generators a decade ago, becomes a strong...

Evaluating LLM agents on benchmarks like SWE-Bench and GAIA can be expensive, time-consuming, and requires complex infrastructure. A single evaluation can cost thousands of dollars and take days to complete. In contrast, non-agentic LLM benchmarks that test individual capabilities (e.g., reasoning, code generation) are fast and cheap to run. In this paper, we investigate whether performance on expensive agentic benchmarks can be accurately predicted by the performance on a small, carefully selec...

Skills are becoming a reusable operational layer for LLM agents, encoding SOPs, domain rules, tool workflows, scripts, and validation routines. In realistic skill repositories, overlapping skills make reliable skill-use difficult. Final verifier success is too coarse for both evaluation and training, since an agent may pass through trial and error while selecting distractor skills, skipping required steps, composing workflows incorrectly or omitting final checks. We introduce SkillCoach, a self-...

Hardware-agnostic strategies for accelerating text-to-image diffusion, such as timestep distillation and feature caching, can reduce inference time without custom kernels or system-level optimization. Among them, multi-resolution generation strategies have recently received broad attention, attaining more than 5x speedup without any training. However, the design of performing upsampling in the latent space, together with the selective modification of partial regions, causes these methods to exhi...

Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see. The simplest contract appends past observations, tool calls, and reflections to every prompt, which makes prior context easy to access but also turns it into a jumbled mixture in which the effect of any single memory component is hard to isolate. We introduce and instrument an alternative bounded contract: every decision is made from a fresh user message assembled by typed retrieval, with no raw ...

Vein recognition is a secure biometric technology often constrained by limited annotated data and imaging variations. While data augmentation mitigates this, strategies designed for natural images may disrupt the fine-grained topology and textures essential for identity discrimination. We present AGVBench, which evaluates 30 representative augmentation strategies on five public palm- and finger-vein datasets with seven backbone architectures, covering classic CNNs, vision transformers, and vein-...

We present WorldDirector, a highly controllable video world model framework designed for persistent dynamic object memory and unrestricted viewpoint exploration. Unlike existing world models that entangle physical dynamics with pixel rendering and rely on continuous visual observation to sustain motion, our framework explicitly decouples semantic motion orchestration from visual generation. By leveraging an LLM to coordinate 3D trajectories with camera movements and subsequently employing these ...

Representation alignment has become an effective way to accelerate diffusion transformer training and improve generation quality. Recent self-alignment methods, such as SRA and Self-Flow, further remove the dependency on external pretrained encoders by constructing alignment within the diffusion model itself. However, the mechanism behind the improvement from SRA to Self-Flow, dual-time scheduling, remains under-examined: Self-Flow attributes its gain to interactions between tokens at different ...

Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society. Automating this process is essential to reducing labor-intensive efforts for data scientists and enabling scalable data-driven applications. Recently, large language model (LLM)-based data agents have emerged as a promising solution to automate data science workflows. However, the field lacks comprehensive benchmarks to rigorously evaluate t...

Autonomous agents are increasingly expected to improve executable policies through feedback, yet existing evaluations often collapse this process into a final score or confound it with open-ended software-engineering progress. We introduce Autonomous Policy Evolution, a controlled evaluation setting in which a harness-model agent repeatedly edits an executable policy system under a fixed interaction budget. We instantiate this setting in EvoPolicyGym, a benchmark built from compact interactive R...

Vision-Language Models (VLMs) have demonstrated immense promise in Spatio-Temporal Video Grounding (STVG). However, current evaluation protocols are largely confined to zero-shot assessments on general, daily-life benchmarks. This creates a critical disconnect from real-world applications in specialized fields, where models inevitably encounter rare visual concepts and complex spatio-temporal dynamics. Since exhaustive pre-training across infinite data distributions is infeasible, the ability to...

Google will discontinue Gemini Code Assist on July 17, ending support for this AI-powered code completion service.

Alibaba reportedly plans to ban Claude Code from workplace environments due to concerns about alleged backdoor security risks in the tool. This reflects growing corporate scrutiny of third-party AI coding assistants.

Mark Zuckerberg suggests that Meta's recent workforce reductions failed to achieve their intended objectives, implicitly admitting the ineffectiveness of the layoff strategy. The statement raises questions about the company's personnel management decisions.

Research indicates that AI tools save approximately 3% of working hours, but this productivity gain rarely translates into actual financial benefits for workers. The analysis highlights a disconnect between theoretical efficiency and real-world economic outcomes.

The 'short leash' AI coding method presents a constraint-based approach to outperform Fable's cost efficiency. The technique involves tightly controlling AI model behavior to achieve better results and reduced expenses.

An article discussing the importance and benefits of having local control over AI intelligence systems. It explores the concept of right to local intelligence as a principle for users and organizations.

The article critiques AI confidence theater, warning against overestimating the reliability and capabilities of AI systems without proper scrutiny.