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2026-04-24 · Δ 26 new vs 2026-04-23 · generated 2026-04-24T03:51:10Z · 0.021s

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digest 2026-04-24 · generated 2026-04-24T03:51:10Z · no feed lag detected · news sources 6/6 ok · Δ 26 new

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- NEWS · AI: GPT-5.5 System Card (https://openai.com/index/gpt-5-5-system-card) — OpenAI Blog
- NEWS · AI: What is Codex? (https://openai.com/academy/what-is-codex) — OpenAI Blog
snippet: Learn how Codex helps you go beyond chat by automating tasks, connecting tools, and producing real outputs like docs and dashboards.
- NEWS · AI: Automations (https://openai.com/academy/codex-automations) — OpenAI Blog
snippet: Learn how to automate tasks in Codex using schedules and triggers to create reports, summaries, and recurring workflows without manual effort.
- NEWS · AI: Plugins and skills (https://openai.com/academy/codex-plugins-and-skills) — OpenAI Blog
snippet: Learn how to use Codex plugins and skills to connect tools, access data, and follow repeatable workflows to automate tasks and improve results.
- NEWS · AI: Working with Codex (https://openai.com/academy/working-with-codex) — OpenAI Blog
snippet: Learn how to set up your Codex workspace, create threads and projects, manage files, and start completing tasks with step-by-step guidance.
- NEWS · AI: How to get started with Codex (https://openai.com/academy/codex-how-to-start) — OpenAI Blog
snippet: Learn how to get started with Codex by setting up projects, creating threads, and completing your first tasks with step-by-step guidance.
- NEWS · AI: Codex settings (https://openai.com/academy/codex-settings) — OpenAI Blog
snippet: Learn how to configure Codex settings, including personalization, detail level, and permissions, to run tasks smoothly and customize your workflow.
- NEWS · AI: Top 10 uses for Codex at work (https://openai.com/academy/top-10-use-cases-codex-for-work) — OpenAI Blog
snippet: Explore 10 practical Codex use cases to automate tasks, create deliverables, and turn real inputs into outputs across tools, files, and workflows.
- NEWS · AI: GPT-5.5 Bio Bug Bounty (https://openai.com/index/gpt-5-5-bio-bug-bounty) — OpenAI Blog
snippet: Explore the GPT-5.5 Bio Bug Bounty: a red-teaming challenge to find universal jailbreaks for bio safety risks, with rewards up to $25,000.
- NEWS · AI: Making ChatGPT better for clinicians (https://openai.com/index/making-chatgpt-better-for-clinicians) — OpenAI Blog
snippet: OpenAI makes ChatGPT for Clinicians free for verified U.S. physicians, nurse practitioners, and pharmacists, supporting clinical care, documentation, and research.
- NEWS · AI: Speeding up agentic workflows with WebSockets in the Responses API (https://openai.com/index/speeding-up-agentic-workflows-with-websockets) — OpenAI Blog
snippet: A deep dive into the Codex agent loop, showing how WebSockets and connection-scoped caching reduced API overhead and improved model latency.
- NEWS · AI: Workspace agents (https://openai.com/academy/workspace-agents) — OpenAI Blog
snippet: Learn how to build, use, and scale workspace agents in ChatGPT to automate repeatable workflows, connect tools, and streamline team operations.

Digest

Tech + AI + Science News

Tech + AI + Science News

12 item(s)

GPT-5.5 System Card
OpenAI Blog · 2026-04-23
Open
What is Codex?
OpenAI Blog · 2026-04-23
Open
Learn how Codex helps you go beyond chat by automating tasks, connecting tools, and producing real outputs like docs and dashboards.
Automations
OpenAI Blog · 2026-04-23
Open
Learn how to automate tasks in Codex using schedules and triggers to create reports, summaries, and recurring workflows without manual effort.
Plugins and skills
OpenAI Blog · 2026-04-23
Open
Learn how to use Codex plugins and skills to connect tools, access data, and follow repeatable workflows to automate tasks and improve results.
'Impressive' trial results for experimental gene therapy for deafness | STAT
Brave News
Open
After gene therapy for a rare form of deafness, 90% of participants in a clinical trial in China had significant improvement in hearing.
The Ed-Tech Backlash Is Here. What It Means for Schools
Brave News
Open
Most educators—74%—say their ... technology due to pushback or complaints from parents, according to the EdWeek Research Center survey. The percentage of districts dialing back tech use in school could rise as generative AI increasingly becomes integral to ed-tech tools, and educators, parents, and students see the downsides of too much AI use. Surveys from multiple organizations so far show that while parents want their children to learn how to use ...
From GPUs to AI factories: Inside the Nvidia-Google Cloud superstack - SiliconANGLE new
Brave News
Open
With this partnership, customers can prototype with Gemini or Nemotron models on Vertex or GKE, then scale to DGX Cloud or massive AI Hypercomputer clusters without changing hardware architectures or rewriting for a different accelerator. Operational maturity: Google wraps Nvidia GPUs in managed ...
CISA orders feds to patch BlueHammer flaw exploited as zero-day new
Brave News
Open
CISA has ordered U.S. federal agencies to patch a Microsoft Defender privilege escalation flaw (dubbed BlueHammer) that has been exploited in zero-day attacks.
DeepSeek v4 new
Hacker News · 2026-04-24
Open
Comments
2026 Ruby on Rails Community Survey new
Hacker News · 2026-04-24
Open
Comments
Why I Write (1946) new
Hacker News · 2026-04-24
Open
Comments
Show HN: Tolaria – Open-source macOS app to manage Markdown knowledge bases new
Hacker News · 2026-04-23
Open
Comments
Research (arXiv)

Research (arXiv)

10 item(s)

Seeing Fast and Slow: Learning the Flow of Time in Videos new
arXiv · 2026-04-23
Open
How can we tell whether a video has been sped up or slowed down? How can we generate videos at different speeds? Although videos have been central to modern computer vision research, little attention has been paid to perceiving and controlling the passage of time. In this paper, we study time as a learnable visual concept and develop models for reasoning about and manipulating the flow of time in videos. We first exploit the multimodal cues and temporal structure naturally present in videos to learn, in a self-supervised manner, to detect speed changes and estimate playback speed. We then show that these learned temporal reasoning models enable us to curate the largest slow-motion video dataset to date from noisy in-the-wild sources. Such slow-motion footage, typically filmed by high-speed cameras, contains substantially richer temporal detail than standard videos. Using this data, we fu
Temporal Taskification in Streaming Continual Learning: A Source of Evaluation Instability new
arXiv · 2026-04-23
Open
Streaming Continual Learning (CL) typically converts a continuous stream into a sequence of discrete tasks through temporal partitioning. We argue that this temporal taskification step is not a neutral preprocessing choice, but a structural component of evaluation: different valid splits of the same stream can induce different CL regimes and therefore different benchmark conclusions. To study this effect, we introduce a taskification-level framework based on plasticity and stability profiles, a profile distance between taskifications, and Boundary-Profile Sensitivity (BPS), which diagnoses how strongly small boundary perturbations alter the induced regime before any CL model is trained. We evaluate continual finetuning, Experience Replay, Elastic Weight Consolidation, and Learning without Forgetting on network traffic forecasting with CESNET-Timeseries24, keeping the stream, model, and t
Subsystem-Resolved Spectral Theory for Quantum Many-Body Hamiltonians new
arXiv · 2026-04-23
Open
We study spectral properties of quantum many-body Hamiltonians through a subsystem-based framework. Given a Hamiltonian of the form $H = \sum_{X \subseteq Λ} Φ(X)$ acting on a tensor product Hilbert space, we associate to each subset $S \subseteq Λ$ a subsystem Hamiltonian $H_S$ and its spectrum $\mathcal{S}(S) = σ(H_S)$. This produces a family of spectra indexed by subsystems, allowing spectral data to be organized according to interaction structure. We show that subsystem Hamiltonians admit local approximations: $H_S$ can be approximated by operators supported on finite neighborhoods with an error bounded by $\|H_S - H_{S,r}\| \le |S| e^{-μr} \|Φ\|_μ$. As a consequence, subsystem spectra are stable under truncation in the sense that $d_H(\mathcal{S}(S), σ(H_{S,r})) \le |S| e^{-μr} \|Φ\|_μ.$ We then prove that for disjoint subsets $S_1, S_2 \subseteq Λ$, the subsystem spectrum is approx
Evaluation of Automatic Speech Recognition Using Generative Large Language Models new
arXiv · 2026-04-23
Open
Automatic Speech Recognition (ASR) is traditionally evaluated using Word Error Rate (WER), a metric that is insensitive to meaning. Embedding-based semantic metrics are better correlated with human perception, but decoder-based Large Language Models (LLMs) remain underexplored for this task. This paper evaluates their relevance through three approaches: (1) selecting the best hypothesis between two candidates, (2) computing semantic distance using generative embeddings, and (3) qualitative classification of errors. On the HATS dataset, the best LLMs achieve 92--94\% agreement with human annotators for hypothesis selection, compared to 63\% for WER, also outperforming semantic metrics. Embeddings from decoder-based LLMs show performance comparable to encoder models. Finally, LLMs offer a promising direction for interpretable and semantic ASR evaluation.
Fine-Tuning Regimes Define Distinct Continual Learning Problems new
arXiv · 2026-04-23
Open
Continual learning (CL) studies how models acquire tasks sequentially while retaining previously learned knowledge. Despite substantial progress in benchmarking CL methods, comparative evaluations typically keep the fine-tuning regime fixed. In this paper, we argue that the fine-tuning regime, defined by the trainable parameter subspace, is itself a key evaluation variable. We formalize adaptation regimes as projected optimization over fixed trainable subspaces, showing that changing the trainable depth alters the effective update signal through which both current task fitting and knowledge preservation operate. This analysis motivates the hypothesis that method comparisons need not be invariant across regimes. We test this hypothesis in task incremental CL, five trainable depth regimes, and four standard methods: online EWC, LwF, SI, and GEM. Across five benchmark datasets, namely MNIST
Seeing Without Eyes: 4D Human-Scene Understanding from Wearable IMUs new
arXiv · 2026-04-23
Open
Understanding human activities and their surrounding environments typically relies on visual perception, yet cameras pose persistent challenges in privacy, safety, energy efficiency, and scalability. We explore an alternative: 4D perception without vision. Its goal is to reconstruct human motion and 3D scene layouts purely from everyday wearable sensors. For this we introduce IMU-to-4D, a framework that repurposes large language models for non-visual spatiotemporal understanding of human-scene dynamics. IMU-to-4D uses data from a few inertial sensors from earbuds, watches, or smartphones and predicts detailed 4D human motion together with coarse scene structure. Experiments across diverse human-scene datasets show that IMU-to-4D yields more coherent and temporally stable results than SoTA cascaded pipelines, suggesting wearable motion sensors alone can support rich 4D understanding.
Long-Horizon Manipulation via Trace-Conditioned VLA Planning new
arXiv · 2026-04-23
Open
Long-horizon manipulation remains challenging for vision-language-action (VLA) policies: real tasks are multi-step, progress-dependent, and brittle to compounding execution errors. We present LoHo-Manip, a modular framework that scales short-horizon VLA execution to long-horizon instruction following via a dedicated task-management VLM. The manager is decoupled from the executor and is invoked in a receding-horizon manner: given the current observation, it predicts a progress-aware remaining plan that combines (i) a subtask sequence with an explicit done + remaining split as lightweight language memory, and (ii) a visual trace -- a compact 2D keypoint trajectory prompt specifying where to go and what to approach next. The executor VLA is adapted to condition on the rendered trace, thereby turning long-horizon decision-making into repeated local control by following the trace. Crucially,
The Sample Complexity of Multicalibration new
arXiv · 2026-04-23
Open
We study the minimax sample complexity of multicalibration in the batch setting. A learner observes $n$ i.i.d. samples from an unknown distribution and must output a (possibly randomized) predictor whose population multicalibration error, measured by Expected Calibration Error (ECE), is at most $\varepsilon$ with respect to a given family of groups. For every fixed $κ> 0$, in the regime $|G|\le \varepsilon^{-κ}$, we prove that $\widetildeΘ(\varepsilon^{-3})$ samples are necessary and sufficient, up to polylogarithmic factors. The lower bound holds even for randomized predictors, and the upper bound is realized by a randomized predictor obtained via an online-to-batch reduction. This separates the sample complexity of multicalibration from that of marginal calibration, which scales as $\widetildeΘ(\varepsilon^{-2})$, and shows that mean-ECE multicalibration is as difficult in the batch se
Context Unrolling in Omni Models new
arXiv · 2026-04-23
Open
We present Omni, a unified multimodal model natively trained on diverse modalities, including text, images, videos, 3D geometry, and hidden representations. We find that such training enables Context Unrolling, where the model explicitly reasons across multiple modal representations before producing predictions. This process enables the model to aggregate complementary information across heterogeneous modalities, facilitating a more faithful approximation of the shared multimodal knowledge manifold and improving downstream reasoning fidelity. As a result, Omni achieves strong performance on both multimodal generation and understanding benchmarks, while demonstrating advanced multimodal reasoning capabilities, including in-context generation of text, image, video, and 3D geometry.
Algorithmic Locality via Provable Convergence in Quantum Tensor Networks new
arXiv · 2026-04-23
Open
Belief propagation has recently emerged as a powerful framework for evaluating tensor networks in higher dimensions, combining computational efficiency with provable analytical guarantees. In this work, we develop the first end-to-end theory of tensor network belief propagation for a class of projected entangled pair states satisfying \emph{strong injectivity}. We show that when the injectivity parameter exceeds a constant threshold, BP fixed points can be found efficiently, and a cluster-corrected BP algorithm computes physical quantities to $1/\mathrm{poly}(N)$ error in $\mathrm{poly}(N)$ time for an $N$ qubit system. We identify a striking phenomenon we term \emph{algorithmic locality}: local perturbations of the tensor network affect the BP fixed point with an influence decaying rapidly with distance. As a result, updates to the fixed point after a local perturbation can be carried o
Security (NVD + CISA KEV)

Security (NVD + CISA KEV)

0 item(s)

No items in this section.
Projects + Resources (Discovery)

Projects + Resources (Discovery)

10 item(s)

Welcome To The Python Tutorial new
Brave Search
Open
<strong>Learn Python with our free tutorial, suitable for beginners</strong>. It contains carefully crafted, logically ordered Python articles full of information, advice, and Python practice! Hence, it helps both complete beginners and those with prior programming ...
Sıfırdan İleri Seviye Python Programlama new
Brave Search
Open
We cannot provide a description for this page right now
En İyi Ücretsiz Online Python Kursları ve Eğitimleri - Güncellendi: [Ocak 2026] new
Brave Search
Open
En yüksek puan alan Python eğitmenlerinden online ortamda Python öğrenin. Web geliştirme için Python&#x27;dan veri bilim için Python&#x27;a kadar seviyenize ve ihtiyaçlarınıza en uygun Python programlama kursunu bulun. Python; web geliştirme, veri bilim ve diğer teknoloji işlerinde sıklıkla kullanılan en yaygın ve rağbet gören bilgisayar programlama dillerinden biridir
Python Eğitimi | Ücretsiz Python Dersleri | Mobilhanem new
Brave Search
Open
Bu eğitm serisi hiç programlama bilgisi olmayan kişilere , Python başlangıç seviye bilipte kendini geliştirmek isteyen kişilere ve Python Programlama ile proje geliştirmek isteyen kişilere hitap ediyor. Açıkçası bu eğitim serisi her seviyeye hitap ediyor.
Machine Learning Training in South Korea new
Brave Search
Open
This instructor-led, live training in 대한민국 (online or onsite) is aimed at beginner to intermediate-level developers and data scientists who wish to learn the basics of LightGBM and explore advanced techniques. By the end of this training, participants will be able to: Install and configure LightGBM. Understand the theory behind gradient boosting and decision tree algorithms · Use LightGBM for basic and advanced machine learning tasks.
KAIST 김재철AI대학원 new
Brave Search
Open
The mission of Graduate School of AI at KAIST is to ensure that artificial intelligence automates everything in the earth. - 카이스트 김재철 AI 대학원, 카이스트 인공지능대학원, KAIST AI대학원, 카이스트 AI 인공지능대학원, KAIST GSAI
First Step Korean | Coursera new
Brave Search
Open
Offered by <strong>Yonsei University</strong>. This is an ... Enroll for free.
인공 지능 교육 | Artificial Intelligence (AI) 교육 new
Brave Search
Open
This instructor-led, live training in 대한민국 (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.
En İyi Online JavaScript Kursları - Güncellendi: [Nisan 2026] new
Brave Search
Open
En yüksek puan alan eğitmenlerden Javascript öğrenin. En iyi online Javascript kurslarını bulun ve Javascript kullanarak kod yazmaya hemen başlayın. Javascript, web geliştiriciler ve arayüz mühendisleri tarafından etkileşimli web sayfaları oluşturmak için yaygın bir şekilde ...
JavaScript Ücretsiz Eğitim 2026 | 7 Dilde Sertifika new
Brave Search
Open
Ücretsiz JavaScript eğitimi, kursu, sertifika programı, belgesi, sertifikası, iş ilanları, dersleri, bölümü, online ücretsiz eğitim

Sources

Grounded items come from local feed artifacts (metadata-only). This panel shows feed freshness and errors.

Feeds: generated 2026-04-24T03:50:33Z · 37.184s · ok
news ok

News (RSS/Atom)

900 item(s) · updated 2026-04-24T03:51:10Z · sources 6/6 ok · newest 2026-04-24 · configured
News sources (6/6 ok)
OpenAI Blog ok
18 item(s) · newest 2026-04-23
Hacker News ok
18 item(s) · newest 2026-04-24
GitHub Blog ok
10 item(s) · newest 2026-04-20
ScienceDaily — Artificial Intelligence ok
18 item(s) · newest 2026-04-20
ScienceDaily — Education & Learning ok
18 item(s) · newest 2026-03-11
ScienceDaily — Stem Cells ok
18 item(s) · newest 2026-04-08
arxiv ok

Research (arXiv)

180 item(s) · updated 2026-04-24T03:50:34Z · newest 2026-04-24
nvd ok

Security (NVD)

600 item(s) · updated 2026-04-24T03:50:49Z · newest 2026-04-24
kev ok

Security (CISA KEV)

600 item(s) · updated 2026-04-24T03:50:50Z · newest 2026-04-24
brave ok

Discovery (Brave)

566 item(s) · updated 2026-04-24T03:50:50Z · newest 2026-04-24 · configured

Archive

Archived by UTC day. Early v1 pages are best-effort and may be incomplete if feeds were offline.

19 day(s)