<a href="https://news.google.com/rss/articles/CBMizgFBVV95cUxPc2lpNm9MdjBiVVI4blRiMjcydmVPcElmaDl1aWVicllRbGVSWUJGTDlSUGdWZnhZaTFUaWsyOXFkc1JMWWJRLXBadks3QWtZalAwb3RDRm15aGRYdGFscllwRTFacklOYTA4VW1JVWJfSTFSaHF6UFNGSzRLTnNybkFRcWZFX2hZQ0wtWHc1LVZKeE1CQzF6S3dPbzZUNU9xaWxhNzNoUmJvR0dkWmZYbHItQzNNVUF3bG5QbU1jdmpJSTh3bEFtcWVDZnhoQQ?oc=5" target="_blank">Ema raises $77M in funding to deploy AI employees across enterprise HR, IT and finance departments</a>  SiliconANGLE<

The Worst Spam Emails: Inside iLands' AI Agent Hustle

Article URL: <a href="https://tedium.co/2026/09/11/ilands-agents-email-spam-kaixin-tang/">https://tedium.co/2026/09/11/ilands-agents-email-spam-kaixin-tang/</a> Comments URL: <a href="https://news.ycombinator.com/item?id=49671159">https://news.ycombinator.com/item?id=49671159</a> Points: 66 # Comments: 36

Beyond the API Wrapper: Sovereign AI Demands a New Breed of Developer - HackerNoon

<a href="https://news.google.com/rss/articles/CBMilgFBVV95cUxNYVk0MkJ4ZDhUYmFPSGdRRkdtTUpJVnk5ZEdJd196Q3BsLWc0cXY5U19EREJ2OE1NQ3ZuQ2hRZFRZNDMxcGJkNm4xeWJHSVhORnRnbHZvY2VNdWZpTGlrZ252eEhiN3VVMllpcXNKOEVqLUlCU3ItaENUMHZaR1R5U1I3cW1PLXpFMk9JZU94azMtdlJQZ3c?oc=5" target="_blank">Beyond the API Wrapper: Sovereign AI Demands a New Breed of Developer</a>  HackerNoon

Can Microsoft Stock Surge to a New All-Time High by the End of 2026 as Azure Cloud Demand Accelerates? - The Globe and Mail

<a href="https://news.google.com/rss/articles/CBMijAJBVV95cUxPOVZocUQzX0xRQXdEQ3psbzc4TFhJMXlMTy01cGR3WWJSM2ljUzZ0NUlselRGQnBkdy12U3lTYUxJODFlR19RQmlQTTRCZ25lN1BHSE0waTJnUFBWTGI1MFk1U1pkWDRrcW92alN3Z0RJZUswZ2NQTmN2bC1RckcwUHRhU1BHWmJld3UxLU5Mc0VUMUZraUktT2IxTUpvZVFndDA5dllXVm5qbFZhZEp6c3NEREtIOVZQcWdNeW5YcXpVWFZQbUNVRk0za3BUTDNMazhoN0YxSElPNGxiaF96VnpxRk1YVlhneW5RYmJOVDdhOGJSY0Y0bnA2QUdEYUFMd0x0T3plMGhfY05v?oc=5" target="_blank">Can Microsoft Stock Surge to a New All-Time High by the End of 2026

3 AI Stocks Backed By Cloud Demand And Data Center Growth - simplywall.st

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Google Rations Gemini To Meta As Demand Finally Outruns Supply - Yellow.com

<a href="https://news.google.com/rss/articles/CBMickFVX3lxTE5LQmp5RWlpbDFiUXo2TTFYNFBlT20zajBkeGdYYVdob25QR3A5blU2S09iMUh1cDVxbjlyekM2VkNxZlg2NW1IQmlqa0N0WFJ2OTVGdE5kNkZldzRteXRJZWxKVHBuYXZjb2dpTmZwMUpwdw?oc=5" target="_blank">Google Rations Gemini To Meta As Demand Finally Outruns Supply</a>  Yellow.com

Escaping Data Gravity and Infrastructure Debt: Why the AI Era Demands an Agentic Data Cloud - DevOps.com

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I do historical swordfighting and noticed AI struggles to track it. I’m building an open dataset to help fix this. Does my schema make sense? [P]

<div class="md">Hi everyone, I’m a historical swordfighter (HEMA practitioner), and while I’m not a computer vision engineer or a roboticist, I’ve been reading a lot about the current bottlenecks in embodied AI, specifically around the Sim2Real gap and thin-object tracking. It occurred to me that high-level swordfighting is basically a perfect nightmare scenario for computer vision. We move at maximum athletic output, we shift our weight rapidly in non-linear ways

People · 5

Kaheer Suleman

Kaheer Suleman — researcher or builder tracked in the Angestrom contributor network.

Oussema Dhaouadi

Oussema Dhaouadi — researcher or builder tracked in the Angestrom contributor network.

Oliver Niggemann

Oliver Niggemann — researcher or builder tracked in the Angestrom contributor network.

Johannes Hagemann

Johannes Hagemann — researcher or builder tracked in the Angestrom contributor network.

Kemal Oksuz

Kemal Oksuz — researcher or builder tracked in the Angestrom contributor network.

Papers · 6

Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrieval

Generative Retrieval (GR) has emerged as a promising paradigm by mapping queries directly to Semantic IDs (SIDs) with powerful representation capabilities for candidate items. However, existing SIDs derived solely from item content create a semantic gap, failing to align dynamic query intents with static item representations. Furthermore, current generative paradigms rarely model user behavior sequences and are always bottlenecked by the high inference latency of beam-search autoregressive decoding. To address these challenges, we propose $\textbf{C}$ross-component $\textbf{H}$ierarchical sema

PyKEEN-NSX: A Modular Framework for Static, Dynamic and Schema-Aware Negative Sampling in PyKEEN

Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embedding models are trained relying on both positive and negative samples of triples. However, since KGs generally contain only positive assertions, negative samples are artificially generated through negative sampling strategies, ranging from simple random corruption to more sophisticated approaches that exploit structural, semantic, or embedding information. The design and implementation of advanced negative samplers remains challenging, as most pop

How Much is Left? LLMs Linearly Encode Their Remaining Output Length

Large language models generate one token at a time, yet their responses show remarkably consistent length structure: step-by-step solutions converge in predictable token counts, retrievals stop after a few sentences, retractions extend responses by measurable amounts. We ask whether the model carries an internal estimate of how much response remains. Training minimal-capacity linear probes on frozen hidden states of three open-weight 7-8B models across seven completion-style datasets, we find three converging pieces of evidence. First, total response length is linearly decodable from the promp

CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition

Forecasting in large-scale e-commerce marketplaces is increasingly required to support planning: merchants need to evaluate sales outcomes under future action sequences such as budget schedules, rather than passively predicting what happens next. However, most existing time series forecasting (TSF) approaches remain inherently passive. Even when incorporating operational decisions as auxiliary covariates, they typically optimize for correlation-based extrapolation under historical policies. This design suffers from autoregressive inertia and conflates endogenous market evolution with decision-

MECoBench: A Systematic Study of Multimodal Agent Collaboration in Embodied Environments

Recent multimodal large language models (MLLMs) have strong potential as embodied agents, but their ability to collaborate in visually grounded environments remains underexplored. To address this gap, we introduce MECoBench, a multimodal embodied cooperation benchmark with an evaluation platform spanning diverse real-world tasks, two cooperation structures, and three collaboration modes. Through extensive experiments across various MLLMs, we summarize three key findings: (i) Collaboration generally improves embodied task completion, but its benefits depend on balancing collaborative gains agai

Learning Late, Guiding Early: Timestep-Decoupled Semantic Guidance for Fair Face Generation

Demographic imbalance in synthetic face generation can propagate to downstream face recognition systems, making fairness an important consideration when diffusion models are used for data generation. Existing fairness-aware generation approaches often require model retraining, architectural modifications, or repeated guidance throughout the reverse diffusion process. In this work, we introduce Semantic Boundary Predictor (SBP), an inference-time framework that performs demographic guidance through a one-shot intervention during reverse denoising. Our approach is motivated by the observation th

Ema

Investors · 4

Sequoia

Google

Roo Capital and Grand Ventures

Microsoft

News · 27

Ema raises $77M in funding to deploy AI employees across enterprise HR, IT and finance departments - SiliconANGLE