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Article URL: https://www.sailresearch.com/blog/sailboxes-general-access Comments URL: https://news.ycombinator.com/item?id=48916864 Points: 1 # Comments: 0

<p> The easiest way to build AI agents for all your tasks </p> <p> <a href="https://www.producthunt.com/products/rerun-2?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/1196885?app_id=339">Link</a> </p>

Article URL: https://www.sandordargo.com/blog/2026/07/15/how-ai-kills-flow Comments URL: https://news.ycombinator.com/item?id=48916798 Points: 2 # Comments: 0

<p> Empower your AI to directly edit documents in real time. </p> <p> <a href="https://www.producthunt.com/products/tiptap?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/1196883?app_id=339">Link</a> </p>

<p>Despite Anthropic’s claims, Claude is no more likely to achieve sentience than a simulation of a weather system is likely to generate a real hurricane</p><p>For centuries, humans have been fascinated by the prospect of creating artificial beings in our own image. Of developing synthetic minds and artificial bodies that not only think but also feel, and are both intelligent and conscious. For the vast majority of this time, this prospect seemed very distant; a topic for science fiction and phi

Article URL: https://twitter.com/EdgarDobriban/status/2077082912021786660 Comments URL: https://news.ycombinator.com/item?id=48916764 Points: 1 # Comments: 0

<pre style='white-space:pre-wrap;width:81ex'>fix(github-copilot): reject unsupported OAuth enterprise domain before refresh and model routing (#105584) * fix(github-copilot): reject unsupported OAuth enterprise domain before refresh and model routing Legacy github-copilot OAuth credentials can carry a non-github.com enterpriseUrl. The token-refresh path templated it into the endpoint and sent the bearer refresh token there with no allowlist, and the model routing path derived a base URL from the

Article URL: https://gizmodo.com/meta-sued-for-allegedly-using-discriminatory-ai-in-layoff-decisions-2000785427 Comments URL: https://news.ycombinator.com/item?id=48916609 Points: 3 # Comments: 1

<pre style='white-space:pre-wrap;width:81ex'>refactor(agents): split agent session responsibilities (#107948)</pre>

<pre style='white-space:pre-wrap;width:81ex'>refactor(auto-reply): split agent runner execution (#107985) * refactor(auto-reply): split agent runner execution * style(auto-reply): satisfy promise executor lint * fix(ci): align refactor baselines and test types * fix(ci): prune stale max-lines baseline</pre>

<p>If you are building an app that edits product photos, regenerates UI mockups, or converts assets into a new visual style, the hard part is usually not the prompt. It is getting reliable inputs and outputs into a repeatable API workflow.</p> <p>This guide walks through a practical image-editing pipeline using <code>gpt-image-2</code> through Ace Data Cloud's OpenAI-compatible Images Edits API. The goal is simple: send one or more reference images, describe the edit, and receive a generated ima

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Article URL: https://www.pcgamer.com/software/ai/george-lucas-says-rejecting-ai-is-like-rejecting-cars-in-favour-of-horses-theres-nothing-you-can-do-about-it-its-the-future/ Comments URL: https://news.ycombinator.com/item?id=48916503 Points: 1 # Comments: 1

<pre style='white-space:pre-wrap;width:81ex'>fix(discord): prevent presence wake floods after reconnects (#107969) * feat(discord): throttle online-presence events after gateway reconnects After a Discord gateway (re)connect the presence replay burst emitted one system event per member, waking the agent each time. Add a per-account emission gate: a post-reconnect suppression window (default 5 min), a sliding-window burst limit (default 8/60s, logged once per episode), and a configurable per-user

<p>I've spent enough time in production environments to know that 'access control' is usually where automation goes to die.</p> <p>You want the magic of an AI agent—you want Claude to act as a concierge, handling signups or managing memberships—but the moment you give an LLM access to your WordPress REST API with broad permissions, you've essentially handed a loaded gun to someone who might hallucinate under pressure.</p> <p>The fear isn't just that the AI will make a mistake. The fear is that a

Article URL: https://www.sogni.ai/unlimited Comments URL: https://news.ycombinator.com/item?id=48916461 Points: 1 # Comments: 0

Tender calls for providers to power free service with local LLMs, government to supply some GPUs

Article URL: https://medium.com/@chatbrat.ai/how-i-talk-to-my-grandparents-who-passed-in-a-car-accident-using-chatbrat-ai-a75b54ccffda Comments URL: https://news.ycombinator.com/item?id=48916407 Points: 2 # Comments: 0

Article URL: https://canopywave.com/events/unlimited-token-plan-200m Comments URL: https://news.ycombinator.com/item?id=48916377 Points: 2 # Comments: 0

<h1> Did Codex Overtake Claude Code? The 7M-User Question </h1> <p>Codex hit 7 million active users on July 13, 2026 — up from 600,000 at the start of the year. That is a 10x surge in six months, with the last million arriving in a single day following <a href="https://thenewstack.io/gpt-5-6-codex-user-surge/" rel="noopener noreferrer">GPT-5.6's launch on July 9</a>. Latent Space ran the headline everyone was thinking: <a href="https://www.latent.space/p/ainews-codex-usage-up-10x-in-6-months" re

<h2> Introduction: The Unseen Gap Between Courses and Real Projects </h2> <p>Today, I stumbled upon a realization that’s both humbling and transformative: <strong>building real-world projects teaches more than finishing courses ever could</strong>. Over the past few months, I’ve immersed myself in Python, completing tutorials, acing quizzes, and feeling confident in my theoretical grasp. But every time I thought I was “ready,” a real project humbled me. It wasn’t the syntax or algorithms that tr

<p> AI schedules your whole week to take action </p> <p> <a href="https://www.producthunt.com/products/nudge-26?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/1196852?app_id=339">Link</a> </p>

<p> A markdown editor you and your agent co-edit </p> <p> <a href="https://www.producthunt.com/products/openmarkdown?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/1196845?app_id=339">Link</a> </p>

<p> Analytics that lives inside your AI assistant </p> <p> <a href="https://www.producthunt.com/products/amami?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/1196837?app_id=339">Link</a> </p>

Article URL: https://www.hamo.ai/blog/taking-the-clinical-decision-out-of-the-llm/ Comments URL: https://news.ycombinator.com/item?id=48916167 Points: 3 # Comments: 1

arXiv:2607.11941v1 Announce Type: new Abstract: Computed tomography (CT) is a critical imaging modality for clinical diagnosis, but reducing radiation dose inevitably introduces severe noise and structured artifacts that degrade image quality. Existing deep learning-based low-dose CT (LDCT) reconstruction methods are typically optimized for fixed dose levels or specific anatomical regions, limiting their robustness and generalization in realistic clinical settings. We propose GenDiff, a generali

arXiv:2607.11986v1 Announce Type: new Abstract: Perineural invasion (PNI) is associated with poor prognosis in cholangiocarcinoma (CCA). However, its detection from 3D MRI remains challenging due to the subtle and spatially heterogeneous imaging signatures at the tumor periphery. Capturing such spatially sparse cues necessitates volumetric analysis of 3D MRI, but existing deep learning approaches incur prohibitive computational costs on volumetric medical images, limiting their clinical deployme

arXiv:2607.11987v1 Announce Type: new Abstract: Perineural invasion (PNI) is associated with poor postoperative outcomes in intrahepatic cholangiocarcinoma, but it is confirmed by surgical pathology. Existing preoperative imaging models often rely on radiologist-defined variables, contrast-enhanced imaging, or manual annotations. We propose an anatomy-privileged teacher--student framework for patient-level PNI prediction from T2-weighted MRI. During training, the teacher uses MRI with tumor and

arXiv:2607.12000v1 Announce Type: new Abstract: Current visual generation models are capable of producing high-quality content, yet they lack a coherent perception of the spatial structure. Existing generative novel view synthesis methods typically introduce explicit geometry priors, which enforce spatial consistency but inherently restrict generalization in large view changes. In contrast, recent interactive generative methods favor implicit scene modeling, offering greater flexibility at the c

arXiv:2607.12042v1 Announce Type: new Abstract: Visual generation is increasingly ubiquitous in diverse domains, from text-to-image/video synthesis to multimodal interactive creation. Yet prevailing monolithic models remain fundamentally constrained by their inability to learn cumulatively and evolve autonomously, which is a limitation we term the "perpetual novice" problem. They lack mechanisms for structuring experience into reusable knowledge and therefore rely on brittle, "from-scratch" reas

arXiv:2607.12048v1 Announce Type: new Abstract: Deploying medical visual question answering (MedVQA) systems in real-world clinical settings requires models that adapt to new clinical tasks without forgetting previously acquired knowledge. Continual learning (CL) provides a practical framework for this setting. Despite rapid progress in medical vision-language models, the behavior of CL methods when training these models across heterogeneous MedVQA tasks remains underexplored. This work presents

arXiv:2607.12052v1 Announce Type: new Abstract: Synthetic image attribution aims at identifying the generator responsible for a given AI-generated image. Training-free reference-based attribution methods are easily scalable, since newly emerging generators can be incorporated by adding source-specific references rather than retraining a task-specific classifier. Their performance depends on two coupled factors: the representation space used for comparison and the way source-specific references a

arXiv:2607.12110v1 Announce Type: new Abstract: Ground segmentation is a fundamental prerequisite for autonomous navigation, environmental perception, and object detection in ground mobile platforms. To address the under-segmentation of ground points caused by sparse long-range point clouds, ground undulations, and interference from non-ground structures in complex road scenarios, this paper proposes a two-stage ground segmentation method based on the Adaptive Concentric Zone Model. First, an Ad

arXiv:2607.12165v1 Announce Type: new Abstract: Recently, the societal implementation of high-performance image classification models has expanded rapidly. While these models require vast amounts of training data to improve performance, securing sufficient real images is often impractical. As a means to compensate for this shortage, the use of synthetic data is becoming widespread. However, synthetic images are not necessarily equivalent to real images for training purposes. This study systemati

arXiv:2607.12171v1 Announce Type: new Abstract: In rectified-flow-based generative models, the neural network can be trained to predict two different targets, such as the instantaneous velocity or the data endpoint, to perform denoising. Although prior work shows that these parameterizations lead to different empirical behaviors, the mechanisms underlying their respective advantages remain to be underexplored, and how to combine them effectively is still unclear. In this work, we analyze how lea

arXiv:2607.12175v1 Announce Type: new Abstract: X-ray tomography enables nondestructive characterization of material microstructures, while advances in micro-CT imaging have accelerated volumetric data acquisition and reconstruction. However, rapid interpretation remains limited by image segmentation, which often requires manual thresholding, user prompting, or material-specific model training. We present a zero-setup framework for multi-phase segmentation of synchrotron X-ray tomography data th

arXiv:2607.12214v1 Announce Type: new Abstract: Reconstructing 4D driving scenes in the wild (e.g., internet and AI-generated videos) is critical for diverse autonomous driving simulation. While recent Gaussian Scene Graph (GSG) methods achieve impressive visual quality, they heavily rely on precise priors, such as accurate camera poses and LiDAR depth, or manual annotations. When initialized with noisy priors estimated from in-the-wild videos, existing GSG methods suffer from optimization ambig

arXiv:2607.12254v1 Announce Type: new Abstract: Large language model (LLM) agents can increasingly plan, use tools, maintain memory, and execute long-horizon tasks. These advances motivate two linked questions: how can an agent improve the mechanisms by which it learns and acts, and how can that improvement increase the durable capabilities of its user rather than only the software itself? This paper proposes a governed multi-agent architecture for recursively self-improving agents and introduce

arXiv:2607.12278v1 Announce Type: new Abstract: Recent vision-language models (VLMs) for computational pathology report striking zero-shot performance on whole-slide image (WSI) visual question answering (VQA) benchmarks. We audit these claims and find them fundamentally compromised by data leakage at two hierarchical levels: patient-level leakage, where slides from the same case appear in both training and test folds, and institutional-level leakage, where different cases nonetheless share stai

arXiv:2607.12292v1 Announce Type: new Abstract: Crack segmentation is essential for infrastructure inspection and structural health assessment, but existing high-performance methods typically require task-specific pixel-level annotations and training. Text-promptable vision foundation models enable zero-shot deployment, yet their final mask proposals are poorly suited to thin, fragmented, and low-contrast cracks, whose evidence may be suppressed, truncated, or over-expanded during mask generatio