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We are building an ai agent for a dental firm. So while building we face this issue of what the agents should actually see. If anyone built similar systems or worked on this would you help like. I mean any insights or learnings. The issue is actually there is no granular enforcement of what the agents should access. I even thought of building an standalone tool for this. But, need to complete the given current work fast. Comments URL: https://news.ycombinator.com/item?id=48814323 Points: 1 # Com

<p> 1.6T MoE trained entirely on AI ASICs </p> <p> <a href="https://www.producthunt.com/products/longcat?utm_campaign=producthunt-atom-posts-feed&utm_medium=rss-feed&utm_source=producthunt-atom-posts-feed">Discussion</a> | <a href="https://www.producthunt.com/r/p/1189943?app_id=339">Link</a> </p>

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If we imagine future it is clear that there would be millions of ai models. then how we would navigate them? Comments URL: https://news.ycombinator.com/item?id=48814163 Points: 1 # Comments: 1

Open source AI has shown how quickly developers can innovate when models, data and tools are shared. Robotics has the same opportunity, but advancements in physical AI development can still be gated by costly and fragmented resources, from large datasets and robot foundation models to simulation, compute and validation tools. NVIDIA and Hugging Face are […]

Share price down sharply, apparently amid fears the bubble is getting bigger

<p><strong>Release:</strong> <a href="https://github.com/simonw/sqlite-utils/releases/tag/4.0rc4">sqlite-utils 4.0rc4</a></p> <p>The last RC before the 4.0 stable release. Mainly <a href="https://github.com/simonw/sqlite-utils/issues/769#issuecomment-4900034150">implements feedback</a> from a detailed review by Claude Fable 5.</p> <p>Tags: <a href="https://simonwillison.net/tags/sqlite-utils">sqlite-utils</a>, <a href="https://simonwillison.net/tags/claude-mythos-fable">claude-mythos-fable</a></

I was working in a team of 10 people on a project. We kept running into inconsistency across the codebase because everyone prompted differently and got different results. We tried maintaining a skills.md file to fix it, but it wasn’t great. So I built Decispher (decispher.com). Its main goal is to automatically capture engineering context from across different platforms and feed it to AI agents. It also records the decisions and assumptions agents make while coding, then surfaces them back to hu

Article URL: https://kamalg2.substack.com/p/choosing-a-real-time-voice-ai-stack Comments URL: https://news.ycombinator.com/item?id=48814001 Points: 1 # Comments: 0

Article URL: https://www.fa-mag.com/news/what-history-tells-us-about-the-ai-investment-boom-87567.html Comments URL: https://news.ycombinator.com/item?id=48814000 Points: 1 # Comments: 0

Article URL: https://www.buildaslackagent.dev/ Comments URL: https://news.ycombinator.com/item?id=48813993 Points: 2 # Comments: 0

Article URL: https://somantra.ai/aeo-research/australia/insurance/may-2026 Comments URL: https://news.ycombinator.com/item?id=48813955 Points: 1 # Comments: 0

Article URL: https://hydradb.com/blog/ai-context-graph-ontology-infrastructure Comments URL: https://news.ycombinator.com/item?id=48813904 Points: 2 # Comments: 0

Article URL: https://www.uptimelabs.io/articles/jet-engine-on-a-tractor-ais-big-productivity-trap Comments URL: https://news.ycombinator.com/item?id=48813813 Points: 1 # Comments: 0

Article URL: https://github.com/Songmu/slidown Comments URL: https://news.ycombinator.com/item?id=48813802 Points: 1 # Comments: 0

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<p>As of July 8, 2026, Anthropic moved <strong>Fable 5</strong> off Pro/Max/Team subscriptions and onto usage-credit billing. The Mythos-tier model a lot of us leaned on is now a pay-per-use add-on.</p> <p>Before you top up credits, a reframe: on the long, autonomous runs where Fable 5 felt irreplaceable, the bottleneck usually isn't raw model IQ. It's that <strong>Opus drifts off-mission and loses the goal to auto-compaction</strong>. Fix those two things and Opus punches far above where it fee

<p>Our Fable Master said something that I'm still turning over in my head.</p> <p>Here's the context: we built a 37-agent film crew to produce a YouTube tutorial from scratch. Writer drafts the script. Visual designer generates slides. Audio engineer renders voiceover. Subtitle artist burns captions. Assembly engineer compresses the final cut. Seven phases, fully automated, end to end.</p> <p>After it shipped, Fable Master—our internal review system—delivered the post-mortem assessment.</p> <p>I

<p>Want to design real microchip-style logic at home without a five-figure toolchain? Here is what you would need to get started with the new <strong>Adiuvo Explorer Board</strong>: the $99 board itself, a single USB-C cable, and AMD's free-tier Vivado tools running on a modest laptop. That is genuinely it — no bench power supply, no separate JTAG programmer, no add-on debugger. For an ECE student or a school robotics team, that shopping list is the whole point.</p> <h3>What the Explorer Board a

<p>You open a job post for a senior backend role. The stack is what you would expect: TypeScript, Postgres, AWS, a bit of Go. And then, sitting in the list like it has always belonged there, one more line.</p> <p>Cursor.</p> <p>Not "we are an AI company." Not a perk buried three scrolls down the careers page. A tool, in the requirements, in the same slot where they tell you which database they run.</p> <p>A year ago the AI editor you coded with was a private preference. You found out what your n

<p>I gave Claude Code control over my article publishing pipeline. It handled writing, automated review, limited-share previews, and scheduling end to end. It worked well enough that I stopped watching closely.</p> <p>Then one afternoon I asked it to rewrite an existing post and give me a limited-share URL so I could review the changes. Instead it published the draft publicly. By the time I noticed, roughly 100 people had already read it.</p> <p>I was publishing to Qiita, where once an article g

arXiv:2607.02551v1 Announce Type: new Abstract: Video multimodal large language models have made strong progress on open-ended video understanding, but they still lack precise local spatiotemporal perception. When two videos share almost the same global semantics and differ only in a short time span or a small region, current models often fail to find the change and provide reliable evidence. We propose DELTAVID, a verifiable proxy-task framework that enhances fine-grained spatiotemporal percept

arXiv:2607.02561v1 Announce Type: new Abstract: Consumer depth sensors such as the LiDAR scanner on recent iPhones provide metric range, but their useful range is short and their returns are sparse. We present DH-Active, a lightweight, training-free geometry back-end that treats the sensor as a metric ruler rather than the sole source of depth. Near-field returns anchor the metric relative pose of two views through PnP; visually trackable samples without a valid depth return are then triangulate

arXiv:2607.02563v1 Announce Type: new Abstract: Diffusion-based text-to-image models can synthesize complex and highly structured visual content, yet the emergence and evolution of semantic structure remain difficult to interpret. Many existing workflows rely on aggregated attention or scalar summaries that separate temporal change from image-space evidence. To address this gap, we present a visual analytics framework for exploring attention dynamics in diffusion models: the step-indexed evoluti

arXiv:2607.02572v1 Announce Type: new Abstract: In multi-source image fusion scenarios, heterogeneous inputs are typically driven by distinct generative mechanisms and can be viewed as a composition of multiple causal systems. However, cross-system discrepancy (CSD) and cross-system entanglement (CSE) commonly arise during the fusion process, often leading to significant performance degradation under out-of-distribution (OOD) predictions. To address the CSD and CSE issues, we propose the additiv

arXiv:2607.02588v1 Announce Type: new Abstract: Multimodal large language models excel on short clips but struggle on hour-long videos in an online setting, where frames are processed incrementally under limited memory. Existing online methods either retain compact visual representations that lack semantic structure, or build higher-level memory stores organized around temporal proximity rather than explicit causal links, leaving multi-hop narrative reasoning to be reconstructed by the LLM at ev

arXiv:2607.02594v1 Announce Type: new Abstract: Deep learning is widely applied in medical image analysis, but up to 10% of manually labelled images may be incorrect, degrading model performance. This paper proposes an automated method to identify incorrectly labelled medical images by analyzing sequences of loss functions from deep learning classification networks over multiple training epochs. Identified images can be reviewed and relabelled by experts, improving dataset quality and model perf

arXiv:2607.02612v1 Announce Type: new Abstract: Vision Transformers achieve strong image classification accuracy but process all image regions with nearly the same computation, even when many regions are redundant or uninformative. Recent adaptive inference methods reduce this cost by selectively compressing tokens or terminating inference early, but combining these mechanisms often causes unstable intermediate representations and accuracy degradation. We introduce Fusion, a unified adaptive inf

arXiv:2607.02680v1 Announce Type: new Abstract: MLLMs have shown strong zero-shot capabilities across diverse inputs such as across images, video, audio, and text. A crucial, yet underexplored, application of these models lies in understanding and modeling animal-centric scenarios. As animals are integral to millions of households, benchmarking next-generation AI models on pet-focused tasks, ranging from recognizing distress signals to enabling responsive robotic companions, is essential for bui

arXiv:2607.02689v1 Announce Type: new Abstract: As wearable devices enable continuous first-person recording, AI assistants must reason across long time horizons to recall past experiences-a capability known as episodic memory. Current benchmarks often rely on offline evaluation with access to entire video files, failing to simulate the streaming reality of wearable intelligence. We introduce S-EMBER (Streaming Egocentric Memory Benchmark for Episodic Retrieval), a large-scale benchmark comprisi

arXiv:2607.02718v1 Announce Type: new Abstract: Recent advances in large-scale image generative models enable photorealistic scene synthesis with controllable attributes. Beyond data augmentation, their potential as diagnostic tools for trained vision systems remains unexplored in the aerial and remote sensing domains. We introduce a synthetic diagnostic framework for aerial-view vehicle detection that combines text-guided generation, attribute-controlled editing, and automated attribute verific

arXiv:2607.02886v1 Announce Type: new Abstract: Deploying AI-generated video detectors in real-world services demands an ultra-low false positive rate (FPR) on real videos to avoid falsely rejecting authentic content, a regime where standard metrics such as AUROC fail to reflect actual operating behavior. We introduce Spatial Patch-Level Incoherence and Temporal Roughness (SPLIT), a training-free detector that operates on patch tokens from a frozen vision encoder to detect both fully generated a

arXiv:2607.02909v1 Announce Type: new Abstract: Taxonomies provide key information about the semantic relationships between concepts and the inherent organization of vision and language. Despite their impressive capabilities, large multimodal models (LMMs) often lack taxonomic knowledge, leading to low hierarchical visual recognition (HVR) consistency. These models typically only rely on language modeling objectives during fine-tuning and lack explicit taxonomy-aware regularization. To address t

arXiv:2607.02927v1 Announce Type: new Abstract: Video understanding is moving beyond closed-context perception toward open-world evidence exploration, a paradigm formalized as Video Deep Research (VDR). However, existing multimodal search agents primarily target static images, and the current VDR benchmark relies on text-centric retrieval that discards crucial visual information. To address these limitations, we propose VideoSearcher, a closed-loop agentic framework that empowers Vision-Language

arXiv:2607.02952v1 Announce Type: new Abstract: Convolutional Neural Networks (CNNs) achieve strong denoising performance by exploiting spatial context from neighboring pixels. Deep Image Prior (DIP) leverages this property to restore images from a single noisy input without requiring large datasets. However, the over-parameterized architecture of DIP often leads to noise fitting during optimization. In this paper, we propose Pool-DIP, a convolution-free architecture that incorporates pooling-ba

arXiv:2607.02963v1 Announce Type: new Abstract: Dense video captioning aims to generate temporally grounded descriptions of video events, benefiting both event-level video understanding and generation. In this domain, autoregressive video large language models have emerged as a prevalent paradigm due to their strong generative and cross-modal modeling capacity. However, generating dense captions under the token-by-token paradigm severely limits inference efficiency and hinders scalability as vid

arXiv:2607.02998v1 Announce Type: new Abstract: Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning. We present CONFLUX, a latent diffusion model for chest computed tomography (CT): a 3D variational autoencoder compresses each volume, and a rectified-flow transformer generates in the latent space. Generation is conditio

arXiv:2607.03012v1 Announce Type: new Abstract: Video Diffusion Transformers (VDiTs) have demonstrated significant capabilities in high-fidelity video generation. However, their ability to produce long-duration videos is fundamentally constrained by the quadratic complexity of the self-attention mechanism. Recent clustering-based sparse attention methods improve the quality-speed trade-off by grouping semantically similar tokens, but their practical efficiency remains limited by two bottlenecks:

arXiv:2607.03013v1 Announce Type: new Abstract: Images captured by consumer electronic devices, such as mobile phones and digital cameras, often suffer from low-light degradation due to sensor limitations and imaging pipelines, which degrades visual quality and affects downstream vision tasks. Existing methods based on Convolutional Neural Networks (CNNs) and Transformers have dominated current low-light image enhancement (LIE) due to their excellent ability to model hierarchical features. Howev

arXiv:2607.03103v1 Announce Type: new Abstract: Clinical cardiac imaging pipelines currently deploy separate models for each dataset and modality, incurring redundant training costs and precluding knowledge sharing across anatomically related tasks. Consolidating semi-supervised learning, unsupervised domain adaptation, and domain generalisation into one model is therefore a practical necessity, yet naive joint training exposes a fundamental barrier: conflicting label semantics between datasets