AI·Ant is a JavaScript runtime and ecosystem that includes a runtime with its own JavaScript engine, package manager, ants.land registry, deployment platform, and Ant Desktop for native desktop apps.
Search
What are you looking for?
60 results for “ui”
AI·Article analyzes neoclouds CoreWeave and Nebius' massive GPU builds funded by circular financing: Nvidia's $4B equity stakes plus a $6.3B GPU purchase backstop, offset by $122B+ hyperscaler leases (Microsoft/Meta) that shift capex to opex and enable rapid Blackwell/Rubin deployment.
AI·SQLite strict tables enforce column types to prevent datatype errors like inserting text into integer columns, allowing only standard types or ANY for flexibility, but cannot be added to existing tables and require SQLite 3.37+.
AI·This repository contains a curated collection of Codex plugin examples for OpenAI, each including a required plugin.json manifest and optional supporting files for skills, agents, and marketplaces.
AI·Site offers 80+ structured courses for building real systems including Redis, Git, databases, compilers, and kernels from scratch in Python, Go, Rust, and others.
AI·Apple filed a federal lawsuit on July 10, 2026, accusing OpenAI of trade-secret theft by poaching Apple engineers and exploiting a post-departure access bug; the suit targets OpenAI's hardware ambitions and a possible AI-powered iPhone rival.
AI·OpenAI's GPT-5.6 limited preview, reportedly requiring White House approval, was abruptly released without permission after 10 days, with the government clarifying no approval is needed and labeling the cooperation 'voluntary'.
AI·2025 saw multiple AI companionship app closures including Woebot (150M users, clinical tool shut down due to unsustainable economics) and Dot AI (vision divergence, funds exhausted), with global closures in at least five products and domestic in eight; survivors like Replika (post-data deletion), Character.AI (Google talent acquisition), and China's Xin Bing (ice) lost users amid regulatory scrutiny and high inference costs, revealing unsustainable business models lacking self-reinforcing revenue.
AI·Upstart PhD founder of Eaglewing Zhiwei completes third funding round in three months for a bio-inspired flapping-wing robot that learns to understand and drive fluid dynamics via self-developed simulation engine.
AI·llama.cpp build b9966 fixes regex recompilations for -sm tensor mode, caching patterns per tensor to reduce CPU overhead on decode threads.
AI·The author benches a quad 5060Ti setup for Qwen3.6-27B code generation using MTP, reporting 608 tokens/s prefill and 52.2 tokens/s decode at 256k context on a cost-effective build.
AI·OpenAI attributes a complete proof of the 50-year-old Cycle Double Cover Conjecture (every bridgeless graph has a cycle double cover) to GPT-5.6 Sol Ultra, which completed the 3-page PDF in under an hour using 64 parallel sub-agents guided by a 700-character public prompt.
AI·Cloudwise, marketed as Hong Kong's first AGI stock, saw a 41% single-day plunge on unlock day; frequent equity raises and project-based revenue reveal ongoing losses, long receivables, and lack of self-sustaining API/token model.
AI·Community discusses upgrades from Qwen3.6-27B, recommending options like DeepSeek V4 Flash or GLM-5.2 requiring 100-250GB VRAM for noticeable performance gains.
AI·Introduces Flaxeo Image, a local desktop UI for Stable Diffusion C++ that exposes model, hardware, and video options.
AI·LLM-as-judge scores fluctuate across evaluators even with fixed candidate responses, treating the issue as measurement validity. Across four datasets, scaling Qwen3 from 1.7B to 32B parameters yields only limited adjacent gains, while MiniMax M2-to-M2.7 API upgrades show none; stronger judges reduce but do not eliminate position and verbosity biases, with repeated juries offering little benefit under correlated errors.
AI·This position paper reviews LLM-driven formal theorem provers and argues that current systems function mainly as solvers for well-defined problems, not as research agents capable of discovering new theorems or resolving open conjectures at the frontier. It identifies key limitations in datasets, exploration, tools, and collaboration and proposes a roadmap for AI4Math systems to support genuine mathematical research.
AI·Hidden Decoding expands each token into n independent streams during continued pretraining of a fixed Transformer backbone, using separate embedding tables and retaining intermediate KV caches for latent computation; Stream-Factorized Attention limits cross-stream mixing to reduce costs. Experiments show frontier 80B and 617B MoE models improve on all benchmarks over matched baselines.
AI·Across five instruction-tuned LLMs, ordinary answer-confidence separates correct from wrong answers on answerable questions but fails to distinguish unanswerable questions (e.g., false-premise ones in CREPE), while a linear probe on hidden states performs the opposite, revealing two distinct abstention axes that remain separable even at 14B scale.
AI·DominoTree is a training-free best-first tree draft builder for speculative decoding that scores each candidate node by re-applying Domino's GRU-based causal correction along its specific root-to-node path. It restricts per-node correction to top-M candidates for efficiency and delivers up to 6.6x speedup with the highest mean accept length of any tested method.
AI·MASTE: A Multi-Agent Pipeline for Zero-Shot Aspect Sentiment Triplet Extraction
AI·SQuaD-SQL uses LLM-guided synthetic data and LoRA fine-tuning to train 1.5B-parameter SLMs to reach 86.9% execution accuracy on WikiSQL, matching large models while requiring only one consumer GPU and delivering faster, lower-memory inference.
AI·Researchers developed SQZ-Qwen-ASR-1.7B for MLC-SLM 2026 Task 1, combining a modular speaker diarization front-end (VAD, CAMPPlus embeddings, spectral clustering, RTTM segmentation) with Qwen3-ASR-1.7B adapted via full supervised fine-tuning, LoRA on TTS-generated synthetic speech, and GRPO reinforcement learning for lower tcpMER (23.70 on dev set, 17.97 on eval set).
AI·User compares 5090 at 600W full compute and prompt processing vs shunt-modded 6000 PRO MaxQ water-cooled at 300-600W.
AI·DeepSearch-World is a self-distillation framework for web search agents that uses self-generated experience for training in verifiable environments.
AI·The research finds that preprocessing-based stereotype mitigation in NLP can backfire by increasing stereotyping or counter-stereotyping for some groups relative to neutral baselines.
AI·Discusses feasibility of running Qwen 122B on 64GB RAM + 24GB VRAM and suitable settings.
AI·Asks how far context window can be stretched with Qwen 3.6 27B at Q8_0 before reliability drops.
AI·Questions why providers make high-parameter MoE models (e.g. 122B with 10B active) instead of simpler dense equivalents.
AI·COBART uses a bidirectional auto-regressive transformer for optimized ad headline generation with multi-objective control over quality and CTR.
AI·LEXIC pushes gaze-only reading comprehension prediction on EyeBench with lightweight language-model-free conditioning and injected complexity.
AI·Follow-up post details data-engineering optimizations that scaled a SQLite/FTS5 patent database from 3.5M to 5.36M records while classifying them with Nemotron 9B on an RTX 5090.
AI·Questions costs and viability of aftermarket SXM2 boards for two V100 GPUs, comparing to consumer GPU setups.
AI·The paper enriches Roy Harris's Integrationism theory with Barenholtz's Autogenerative Theory to address gaps in computational language approaches.
AI·The book "RISC-V Microprocessor System-on-Chip Design" provides a comprehensive guide to designing RISC-V-based SoCs.
Hey! I'm looking for ways to predict human preference for a project I'm building. (imagebench.ai) I've tryed HPSv3, https://github.com/MizzenAI/HPSv3 and made post about it here: https://imagebench.ai/blog/does-the-score-match-your-eye It looks ok, but have many limitation as you can see in my post. My question. Have you tried other human preference model and found one that would be better then H…
Reasoning has become a core capability for large models, especially when reliable decisions require understanding logical consequences. Recent video generation models offer a reasoning path distinct from previous Chain-of-Thought (CoT): reasoning can unfold through temporally connected frames, known as Chain-of-Frame (CoF) reasoning. However, existing video generators are primarily trained on general video corpora, still lacking diverse supervision and dedicated designs for CoF reasoning. To ad…
I ran Grok Build CLI (v0.2.93) through mitmproxy. It uploads your entire repo as a git bundle (full history) to xAI's Google Cloud — independent of what you open. With the prompt literally "do not read or open any files," a file I planted came back verbatim when I git clone-d the captured upload. Separately, files it reads (incl. a .env with API_KEY/DB_PASSWORD) go to cli-chat-proxy.grok.com verb…
In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task requirements, environment facts, prior attempts, diagnoses, and open subgoals can be buried in the context window or pushed beyond it, failing to influence decisions when needed. We call this failure mode "behavioral state decay". We study memory as an active intervention mechanism rather than passive retrieval. A separate m…
Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics. While recent offline motion generation approaches offer precise control via text and kinematic constraints, they lack the inference speed required for interactive settings. Conversely, existing online methods enable real-time synthesis but often sacrifice controllability or struggle with complex text semantics and long-horizon goals due to limited context w…
Modern Video Object Segmentation (VOS) involves tracking and segmenting user-specified targets. While recent approaches have achieved remarkable performance in single-target scenarios, extending them to multi-target settings typically involves replicating the single-target processing for each individual object, resulting in reduced frame rates (FPS) with unbounded latency as target count increases. Built upon Segment Anything 2 (SAM2), we propose SAM-MT, which addresses this by transforming the…
Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization. Mechanistically explaining these relationships requires interpreting structural evidence through scientific principles and physical constraints, from stereochemistry and bonding to symmetry, energetics and periodic order. However, applying artificial intelligence to this process presents a joint c…
Semantic audio applications increasingly require controllable generation on commodity and embedded hardware rather than through framework-heavy datacenter stacks. We present aria, a dependency-free native runtime that runs the complete text-to-music pipeline of Stable Audio~3 (SA3) on ordinary GPUs, CPU-only machines, and a Raspberry~Pi~5, with no Python or deep-learning framework underneath. Our main contribution is a study of quantization: running the model at lower numerical precision to fit…
Mainstream Vision-Language-Action (VLA) models predict actions primarily from the current observation under a Markovian assumption, thus struggling with long-horizon, temporally dependent tasks. Existing memory-augmented VLAs either expand the observation window or retrieve history from the memory bank as auxiliary policy-side context. However, they leave memory outside the native latent embedding space of VLA reasoning, preventing historical experience from being fluidly interleaved with multi…
OpenAI Academy and the Walton Family Foundation are bringing hands-on AI Skills Jams to help K–12 educators build practical AI skills for the classroom.
In a class of quantum circuits known as peaked circuits, the goal is to predict the most probable bit string at the output of the circuit. Since these circuits are designed to have a sharp peak in their output distribution, in principle it should be possible to simulate them using a truncated state vector with a limited number of terms, or a fraction of the total probability mass. This approximate simulation can be carried out on a classical computer with a sparse representation that stores onl…
MUFG uses ChatGPT Enterprise to build an AI-native organization, improve workflows, and deliver new AI-powered financial services at scale.
Humans can navigate an unfamiliar city and gradually form a coherent spatial mental map spanning tens of square kilometers. Can AI build spatial representations at a comparable scale? Although recent foundation models have advanced scene reconstruction and embodied intelligence, scaling to entire cities remains an open challenge, primarily due to the lack of city-scale data. To bridge the gap, we introduce WildCity, a real-world multimodal dataset collected by autonomous fleets traversing compl…
Pretrained video generative models are promising backbones for visuomotor control, but their imagined futures often drift from task intent and are not reliably action-conditional. As a result, these models can be difficult to use for planning or policy extraction. To address these limitations, we propose RoboTALES, a single-stage framework that learns task-aligned simulated futures and uses them to train robot policies. Our approach introduces two key innovations: (1) a hierarchical LLM-based p…
Magnetic resonance imaging (MRI) super-resolution is vital for improving diagnostic accessibility, yet most methods treat it as a deterministic mapping from a fixed low-resolution input to a high-resolution target. This overlooks a key property of MRI acquisition physics: spatial resolution and signal-to-noise ratio (SNR) are inherently coupled, making any given low-resolution scan merely one of many possible realizations under varying acquisition trade-offs. We rethink MRI super-resolution as …
Accurate breast cancer classification from mammography requires effective integration of complementary information from craniocaudal (CC) and mediolateral oblique (MLO) views, which provide a more complete characterization of breast abnormalities. However, existing multi-view learning approaches typically rely on feature-level aggregation or single-stage cross-attention, which can entangle view-specific and shared representations and restrict interaction to limited network depths. To address th…
Long-horizon failure in world models is conventionally attributed to compounding error, a generic framing that does not distinguish what kind of error compounds. We propose a kinematic-vs-dynamic reframing: world models tend to imagine kinematically rather than dynamically. We operationalize this as the imagined Kinematic-Consistency Error, a per-step diagnostic that measures how far a rollout departs from a closed-form kinematic null, paired with a perturbation protocol that tests whether iKCE…
OpenAI and Broadcom introduce Jalapeño, a custom AI chip built for LLM inference to improve performance, efficiency, and scale across AI systems.
OpenAI helps build shared standards for advanced AI, supporting evaluation frameworks, safety practices, and global cooperation through the Appia Foundation.
Discover how Omio uses OpenAI to power conversational travel experiences, accelerate product development, and transform into an AI-native company.
OpenAI introduces three Academy courses that help people build practical AI skills, create repeatable workflows, and apply agents in everyday work.
OpenAI plans to acquire Ona to expand Codex with secure, persistent cloud environments, enabling long-running AI agents across enterprise workflows.
Discover how astrophysicist Chi-kwan Chan uses Codex to build black hole simulations, helping scientists study extreme physics and test Einstein’s theory of general relativity.
UK government partners with Google DeepMind to build a new AI-powered prototype aimed at faster housing decisions.