Sword Launches Dawn, Bringing World-Class AI Mental Health Support to Everyone, Instantly - Yahoo Finance
<a href="https://news.google.com/rss/articles/CBMihwFBVV95cUxPdnVtb3dMR01jYVVsaEZzNmZvZWhwS25pZTF4TVRLLVd4Y2x2bFdhY19HaXE3MkZGZGd2LXdtMV9TSjNhdWdjUFpYRGtOdGxHaXJ0SU1iZUxnUktQNnotRy1GOUlGYTB4NEMtRDQxOHdsU19XOGhMdG16eWVQVWcxWUQwVDAzcFE?oc=5" target="_blank">Sword Launches Dawn, Bringing World-Class AI Mental Health Support to Everyone, Instantly</a> <font color="#6f6f6f">Yahoo Finance</font>
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Perplexity launches Secure Intelligence Institute to advance AI security, privacy, and safety research - Moneycontrol.com
<a href="https://news.google.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?oc=5" target="_blank">Perplexity launches Secure Intelligence Institute to advance
How I Got 33K Google Impressions in 2 Weeks with Programmatic SEO
<p>Two weeks ago I shared how I built a <a href="https://dev.to/christina_sanchez_f16f40a/how-i-built-a-10000-page-seo-site-with-nextjs-and-postgresql-3ipp">10,000+ page SEO site with Next.js and PostgreSQL</a>. Today I'm back with the results.</p> <p><strong>TL;DR:</strong> 33,467 impressions, 70 clicks, pages ranking on page 1 of Google — all within 14 days of deploying to Vercel. No paid ads. No backlinks. Just programmatic SEO done right.</p> <p>Here's exactly what worked, what didn't, and what I'd do differently.</p> <h2> The Numbers (first 14 days) </h2> <div class="table-wrapper-paragraph"><table> <thead> <tr> <th>Metric</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Total impressions</td> <td>33,467</td> </tr> <tr> <td>Total clicks</td> <td>70</td> </tr> <tr> <td>Average CTR</
We Were About to Buy an Automation Platform. We Already Had One.
<p>When Cowork launched in January, I didn't understand the hype.</p> <p>We'd deployed Claude Enterprise at Nayax. Hundreds of employees now had access to a tool most of them had only dreamed about — connected to Jira, Outlook, Teams, Confluence, Snowflake. People were doing things that were simply out of reach before. That felt like the win.</p> <p>Cowork existed. I knew it. But the initial version was a local file-management tool — point it at a folder, let it sort your downloads. Useful, but not what I needed. And Chat, combined with the connectors we'd spent months carefully plugging in, was already doing incredible things. So Cowork stayed on the shelf.</p> <p>Then the automation requests started coming in.</p> <h2> The Death Spiral </h2> <p>Every department had something they wanted
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Available Careers with a Master’s Degree in Business in AI - Boston University
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MELT: Improve Composed Image Retrieval via the Modification Frequentation-Rarity Balance Network
arXiv:2603.29291v1 Announce Type: new Abstract: Composed Image Retrieval (CIR) uses a reference image and a modification text as a query to retrieve a target image satisfying the requirement of ``modifying the reference image according to the text instructions''. However, existing CIR methods face two limitations: (1) frequency bias leading to ``Rare Sample Neglect'', and (2) susceptibility of similarity scores to interference from hard negative samples and noise. To address these limitations, we confront two key challenges: asymmetric rare semantic localization and robust similarity estimation under hard negative samples. To solve these challenges, we propose the Modification frEquentation-rarity baLance neTwork MELT. MELT assigns increased attention to rare modification semantics in mult
ENEIDE: A High Quality Silver Standard Dataset for Named Entity Recognition and Linking in Historical Italian
arXiv:2603.29801v1 Announce Type: new Abstract: This paper introduces ENEIDE (Extracting Named Entities from Italian Digital Editions), a silver standard dataset for Named Entity Recognition and Linking (NERL) in historical Italian texts. The corpus comprises 2,111 documents with over 8,000 entity annotations semi-automatically extracted from two scholarly digital editions: Digital Zibaldone, the philosophical diary of the Italian poet Giacomo Leopardi (1798--1837), and Aldo Moro Digitale, the complete works of the Italian politician Aldo Moro (1916--1978). Annotations cover multiple entity types (person, location, organization, literary work) linked to Wikidata identifiers, including NIL entities that cannot be mapped to the knowledge graph. To the best of our knowledge, ENEIDE represents
PRISM: PRIor from corpus Statistics for topic Modeling
arXiv:2603.29406v1 Announce Type: new Abstract: Topic modeling seeks to uncover latent semantic structure in text, with LDA providing a foundational probabilistic framework. While recent methods often incorporate external knowledge (e.g., pre-trained embeddings), such reliance limits applicability in emerging or underexplored domains. We introduce \textbf{PRISM}, a corpus-intrinsic method that derives a Dirichlet parameter from word co-occurrence statistics to initialize LDA without altering its generative process. Experiments on text and single cell RNA-seq data show that PRISM improves topic coherence and interpretability, rivaling models that rely on external knowledge. These results underscore the value of corpus-driven initialization for topic modeling in resource-constrained settings
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