AI News Archive: May 17, 2026 — Part 6
Sourced from 500+ daily AI sources, scored by relevance.
- Tooliy
80+ Free Online Tools for Image, PDF, SEO & AI
- Recally
AI search for your WhatsApp history
- claude-ticker for Claude Code CLI
Status bar for Claude Code workspace, token, limits and more
- WizBot: No-Code WhatsApp AI Chatbot
No-code AI chatbot & flow builder for WhatsApp Business
- LOADOUT
the AI command post for your PC
- PromptMed
Prompt Engineering for Healthcare Professionals
- VerveXC: Translate Video
Minimalist video translator and transcriber
- Code update - InfinityAI
Code mode is here
- Revluma
AI that recovers revenue your store keeps leaking
- CraftMyResume
Beat ATS filters with AI. Built for Indian job seekers.
- PixelMint
AI Product Photos & Marketing Visuals in Seconds
- Flowt
Lancement de l'agence Data & IA Flowt.
- MakanX
AI-powered multi-lingual global real estate marketplace
- ToolSki
Free Tools — AI ID Photo, Upscale, PDF, QR Code & More
- Awemefly AI
14 AI video & image models in one editor, one credit pool
- suitance
AI suitability reports for financial advisers in 60 seconds
- omshie
The AI receptionist with 3× more minutes per dollar
- SEO Content Architect
AI‑driven WordPress automation for SEO content
- Keyword Universe
Keyword clustering and content brief creation AI powered
- Magif.ai
Turn expertise into client ready AI agents
- Letras Para Instagram
Generate aesthetic Instagram fonts and stylish bio text.
- Support IQ
AI knowledge base that kills repetitive support tickets
- Vaaani
The speaking coach that measures your voice, not guesses it
- Eyeball — AI Links Tracker
A Smarter Saved Folder
- Phone Wallpaper Mockup Batcher
Batch-generate realistic phone wallpaper mockups in browser
- OptiHedge
AI-powered 5-day stock forecasts for S&P 500 investors
- LAC Memory Kit
Give your AI a real memory that survives sessions.
- PresciaIQ
Australia's AI Intelligence & Development Company
- Invoice Splitter
AI-Powered PDF Invoice Splitting & Receipt Cropping
- Realm.ai
Autonomous AI Medical Coding & Claim Denial Prevention
- REVO
The AI Automation Tool Built into your Workflow
- Lets Meet IQ
Schedule meetings in one click. AI does the rest.
- Outweach
AI voice agents that cold call, qualify leads, book meetings
- SimpleAct
AI governance and EU AI Act compliance made operational
- Budget AI
Stop arguing with your own bank statements.
- PHP SaaS Bundle: Shop & AI Analytics
Two PHP platforms. One price. Launch today.
- ShortsForge
Agentic AI workstation for viral video engineering. 🚀
- Hinglish to Hindi
Best hinglish to hindi app
- Text2Task
Turn Messy Client Messages Into Organized Projects
- Overweight status drives early tumor microenvironment reprogramming in pancreatic ductal adenocarcinoma: a cell-type-resolved Bayesian hierarchical modeling and interactome analysis
Background: Obesity significantly increases the risk of prognosis and clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). While research on the interactions between obesity and the tumor microenvironment (TME) is mostly confined to a few interactions at a time, leaving a gap in the comprehensive understanding of obesity-driven PDAC. We set out to develop a cell-type-resolved model of obesity-driven PDAC using bulk transcriptomic data to investigate TME changes. Methods: We conducted an integrated transcriptomic analysis of PDAC patients from the CPTAC-3 cohort (n=140) stratified by BMI. A custom immune and stromal functional gene signature database covering 65 cell types was constructed, followed by LLM-assisted review, overlap control, and validation. BayesPrism deconvolution using matched single-cell references was used to derive expression profiles for each cell type. Stabl, a machine-learning algorithm, was used to identify BMI-associated signatures. Bayesian hierarchical
- Evo 2 Predicts Cardiomyopathy-Associated Variants and Elucidates Their Underlying Mechanisms
Background: Although advances in next-generation sequencing have accelerated the identification of genetic variants in cardiomyopathy, interpreting variants of uncertain significance (VUS) remains a clinical challenge. Evo 2 is a high-resolution genomic artificial intelligence model capable of predicting pathogenicity across large sequence contexts and enabling mechanistic interpretation; however, its application in cardiovascular genetics is limited. Here, we evaluated the utility of Evo 2 for assessing the pathogenicity and underlying mechanisms of cardiomyopathy-associated variants. Methods: We used Evo 2 to predict the pathogenicity of single-nucleotide variants in cardiomyopathy-related genes listed on ClinVar. We assessed the ability of the model to identify characteristic structural features in both coding and noncoding regions using internal representation such as embeddings, and to infer the molecular mechanisms of variants within these regions. Results: Evo 2 demonstrated hig
- Cell-Type Clusters in the MICrONS Connectome Reveal Hidden Organizational Principles of the Mouse Visual Cortex and Possible Candidate Substrates for Elementary Perceptual States
How neural structure constrains cortical dynamics and perceptual experience remains a fundamental open question. The MICrONS connectomics dataset enables direct examination of this problem by combining dense ultrastructural reconstructions with functional measurements across a mesoscale volume of mouse visual cortex. Here, I test a prediction arising from field-based models of perception: that subsets of cortical neurons should exhibit rare but reproducible structural similarity and spatial clustering. Using exhaustive pairwise NBLAST comparisons across tens of thousands of reconstructed neurons, I quantified morphological similarity among excitatory populations spanning layers 2/3, 5, and 6, including extratelencephalic, intratelencephalic, and near-projecting cells. High similarity scores were exceedingly rare, occurring more than 8 to 10 standard deviations above population means. Despite this rarity, structurally similar neurons formed discrete, spatially coherent clusters across p
- A Longitudinal Clinical Foundation Model on Nationwide Veteran Health Trajectories
We present VA-LLM, a 1.62-billion-parameter autoregressive transformer pre-trained from scratch on 1.74 trillion tokens of clinical text spanning 22 years of care for 13.8 million patients in the Veterans Health Administration, with mortality outcomes confirmed through the National Death Index for 7.8 million patients. In a retrospective-prospective evaluation on 107,555 withheld patients, VA-LLM achieved higher 5-year AUPRC than Llama-2 (7 billion parameters), BioGPT _large (1.57 billion parameters), and GatorTron (3.91 billion parameters), matching GatorTron's 100,000-patient performance with only 10,000 labeled patients. In a clinical validation against the VA's operational Care Assessment Need (CAN) score on 5.5 million patients one year beyond the pre-training corpus, VA-LLM achieved a 90-day mortality AUROC of 90.00% versus 87.74% (p < 0.001) and a 45% relative improvement in AUPRC; post-hoc recalibration recovered calibration comparable to CAN (Brier 0.0091 versus 0.0093) withou
- Acute-Phase Machine Learning Prediction of 12-Month Aphasia and Discourse Recovery
Approximately 30-40% of stroke patients retain aphasia at 12 months. Early forecasting may guide rehabilitation and prognostic enrichment of clinical trials, yet machine learning (ML) prediction of language recovery has typically relied on chronic-phase data unavailable at the acute decision point. Whether acute features predict 12-month outcomes, and whether global severity and connected-speech recovery share substrates in an ML framework, is untested. We studied 73 patients with acute left-hemisphere ischemic stroke and aphasia (mean 2.8 days post-onset). Two 12-month outcomes were defined: aphasia resolution (Western Aphasia Battery-Revised Aphasia Quotient [WAB-AQ] [≥]93.8) and discourse normalization (Modern Cookie Theft content units [≥]22.1; N=61). Four ML algorithms were trained on four hierarchical feature sets (clinical, volumetric, anatomical, network-disconnection) using nested cross-validation and SHapley Additive exPlanations (SHAP) stability analysis. Acute WAB-AQ domina
- A Prospective Observational Study on a Multimodal Non-Invasive Physiological Monitoring System (Hayl): Feasibility, Signal Characterization, and Exploratory Biomarker Correlation
Chronic conditions such as Type 2 Diabetes Mellitus (T2DM) and Hypertension (HTN) remain underdiagnosed in community settings, particularly in resource-limited populations. Conventional diagnostic approaches rely on episodic measurements and laboratory-based assessments, limiting scalability for large-scale screening. Non-invasive physiological monitoring systems offer a potential pathway for accessible and rapid wellness assessment in real-world environments. This study aimed to evaluate the feasibility, signal acquisition performance, and exploratory physiological signal characteristics of a non-invasive multimodal monitoring system (Hayl) in community-based screening settings. Methods: A prospective, cross-sectional, multicenter observational pilot study was conducted across rural and urban screening camps in south India. A total of 281 adult participants were enrolled, including individuals with known T2DM, HTN, and those without known comorbidities, encompassing both symptomatic a
- A multi-modal phase plane method for constructing multivariate disease trajectories.
Understanding the sequential order and timing of different biomarkers in the progression of Alzheimer's disease (AD) is paramount for understanding the pathophysiology of the disease, leading to better staging and improved prediction of clinical progression, providing crucial knowledge for the design and timing of effective clinical therapeutic trials. This study developed and evaluated a multi-modal phase plane (MMPP) method to construct long-term multivariate disease trajectory curves from short term longitudinal data for neuro-degenerative diseases like AD. The MMPP method is an extension to a previously presented four-step method for constructing single variable disease trajectories. A novel anchoring step which uses study participants' multivariate data to infer the staging of the separate single variable progression trajectories allows multivariate disease trajectory curves to be generated. Further, the anchoring step provides disease staging at the individual level. A bootstrapp
- Researchers Claim Anthropic's Mythos Helped Crack macOS Security
Researchers Claim Anthropic's Mythos Helped Crack macOS Security PCMag Australia
- Researchers Claim Anthropic's Mythos Helped Crack macOS Security
Researchers Claim Anthropic's Mythos Helped Crack macOS Security PCMag UK
- Japan to craft cyber defense guidelines in response to Anthropic's Mythos
Japan to craft cyber defense guidelines in response to Anthropic's Mythos Nikkei Asia
- Japan to craft guidelines on using Anthropic's Mythos for cyberdefense
Japan to craft guidelines on using Anthropic's Mythos for cyberdefense Nikkei Asia