AI News Archive: May 21, 2026 — Part 24
Sourced from 500+ daily AI sources, scored by relevance.
- AI planner
AI planner for multiple use
- TensorLinks AI FrontDesk
Agentic AI receptionist for dental practices & DSOs
- QwikIMG
Secure Browser-Based Image Processing
- Xcode .xcloc local AI localizer
Use local AI model or AI API to localize your iOS apps
- SnapShe
Find the outfit you love with a photo or video.
- Initial release of InfoSlides
Slides that update themselves
- Tracecast Open Data Apps
Open source generative data apps
- StoryBrain AI Tools Suite
50+ free AI & crypto tools. No signup required.
- ToolWise
20+ Free AI & Utility Tools Fast & Free No Signup Required
- MenlyAI
Your personal AI hairstyle assistant & tracker
- Wonder Stamp
Add intelligent watermarks to photos and videos with one tap
- Sentinelle
Autonomous offensive security, powered by AI
- Arbitrage Agent
Automated arbitrage across Polymarket & Kalshi markets
- PitchPredict
Football stats, trends & AI insights in one place
- Carmy AI is here for car owners
Drive more, Spend less. We make your car more reliable
- Inferenz
Transforming Enterprises : Data -> AI -> ROI
- AIHR
AI-powered interview practice for students and job seekers
- The Best Home Design 」 Yuzu Design AI
From Bare Shell to Dream Home in 30 Seconds.
- EssayWritingGuides
Detect AI-generated content in seconds.
- Sarah
A macOS voice assistant for dictation and AI commands
- SeedGen AI
Generate unlimited Seedance 2.0 AI videos with no credits
- Tellaflow - Free Voice Typing
You speak. We type. Nobody's server gets involved.
- GroupX — 专业的 Telegram 群管理 Bot SaaS
AI自动审核、防广告、关键词拦截、防炸群、入群验证、欢迎语、数据统计
- VoiceRix
speak human. Think Machin
- VisionAI — AI Image Generator SaaS
Launch your own AI wrapper business in 5 minutes.
- Kit IA para Negocios Mexicanos
50 Spanish prompts+Obsidian vault,Claude knows YOUR business
- Radhe Ai
Without internet or token totally free in work,upload
- The Blue Shark
AI multi-agent platform for business domination
- Novix Library
Learn, create, and earn with AI
- Thrust — AI Budgeting Without the Cloud
Your finances. Your device. Your rules.
- AutoVision AI-Vehicle Damage Assessment
Intelligent AI damage analysis that keeps claims moving
- AreYouAnAiSlave.xyz
Are you mastering AI — or just a prompt-monkey code slave?
- AssisT
Free TTS, STT and AI writing support for Canvas LMS
- Equilibrium Propagation with Predictive Learning in Leaky Integrate-and-Fire Spiking Neural Networks
Equilibrium propagation (EP) is a biologically plausible alternative to backpropagation that has demonstrated competitive performance across a range of machine learning tasks. Recent work has extended EP to spiking neural networks (SNNs), leveraging leaky integrate-and-fire (LIF) neurons and spike-based plasticity rules to improve biological realism while maintaining strong performance. In this work, we propose an EP-based SNN framework that combines LIF neural dynamics with a predictive learning rule, replacing conventional spike-timing-dependent plasticity (STDP) with a learning rule more directly aligned with predictive coding principles. We evaluate the proposed model on multiple image classification benchmarks, including MNIST, KMNIST, and Fashion-MNIST, and compare its performance with a BP-trained LIF SNN baseline. Our results show that the proposed EP-based LIF model (EP+LIF) achieves competitive accuracy across datasets, with performance approaching that of the BP-trained coun
- Antimicrobial peptide databases and prediction tools: Toward a standard evaluation framework
Antimicrobial resistance (AMR) has a profound impact on animal and human health and is associated with substantial morbidity, mortality and public health costs. There is a clear need to develop novel, effective antibiotic agents, which can overcome the current AMR crisis. Antimicrobial peptides (AMPs) may offer such a solution and have attracted growing attention for their potential to combat AMR. In parallel, the growing availability of peptide sequences in public databases has stimulated the development of numerous machine learning and deep learning tools to predict antimicrobial activity computationally. However, it remains unclear how reliably these tools can be compared, as existing studies often rely on heterogeneous datasets and inconsistent evaluation protocols that may lead to data leakage and inflated performance estimates. This raises a central question: what evaluation criteria and benchmark resources are needed to enable fair, reproducible, and biologically meaningful asse
- NanoCortex: A Unified Agentic System for Nanopore Sequencing Analysis
Nanopore sequencing has enabled various layers of information about DNA and RNA sequence isoforms and chemical modifications. Yet, the archipelago of disjoint nanopore analysis tools makes navigating among these a significant challenge for the nanopore user. We present NanoCortex, a unified autonomous agentic framework designed to bridge this shortcoming by providing end-to-end data processing which ranges from raw signal basecalling to biological interpretation. Built upon Gemini API services that incur usage-based API costs and orchestrated through the Gemini Agent Development Kit (ADK), the system utilizes a multi-agent architecture to autonomously perform task parsing, code generation, iterative code-level self-correction of code, and scientific interpretation. Following code generation, the code can be used offline. Benchmarking reveals that NanoCortex achieves significantly higher usability across complex analytical tasks compared to general-purpose large language models. The fra
- FlowFI: an interactive graphical software package for bespoke design of imaging parameters in flow cytometry to explore morphological diversity in bone marrow megakaryocytes
Background: Imaging flow cytometry (IFC) provides a high quantity of single-cell morphological data, yet the field lacks open access tools for designing interpretable, bespoke parameters. In particular, rare and atypical cell populations where well annotated data is limited, are negatively affected. Results: We present Flow cytometry Feature Importance (FlowFI), an open-source graphical software for bespoke image parameter design and analysis. FlowFI provides a suite of image parameter options combining data across multiple channels and markers, tailored digital noise reduction (reducing noise resulting from common flow cytometry ultra-high image acquisition modalities), and a scalable, unsupervised feature selection pipeline that allows experimentalists to refine image-derived parameters iteratively, with a novel ensemble subsampling approach that provides robust feature importance scoring. We validated FlowFI using data from a rare and heterogenous bone marrow cell type, megakaryocyt
- Real-time Sub-cycle Oscillatory Beta Burst Detection
Beta-band burst activity is a key biomarker of Parkinsonian pathophysiology and a control signal for adaptive deep brain stimulation (aDBS). Existing burst detection approaches rely on bandpass filtering followed by envelope extraction and thresholding, which introduces onset/offset latency due to sliding-window estimation and can be unstable under variations in signal-to-noise ratio or threshold choice. We introduce a switching state space approach for real-time burst detection that models oscillatory activity as a superposition of latent harmonic components and explicitly represents burst and non-burst regimes. The framework performs real-time inference using a set of Kalman filters and computes posterior mode probabilities using a Markov transition prior, producing sampleby-sample burst probabilities together with phase and uncertainty estimates. In simulated data with sinusoidal beta bursts embedded in both white and pink noise, the method improved burst detection accuracy, reduced
- Beyond iron concentration: iron aggregation shapes quantitative MRI in the human brain
Aceruloplasminemia (ACP) is a rare neurodegenerative disorder characterized by extreme cerebral iron overload and a shift towards larger iron aggregates, providing a unique possibility to study how iron aggregation shapes MRI contrast in vivo. We introduce a clinically feasible, multiparametric quantitative MRI (qMRI) framework that combines quantitative susceptibility mapping (QSM), R*2, and R2, to disentangle changes in total iron concentration from alterations in iron aggregation and its spatial organization at the cellular scale. Our biophysical model links the microstructure sensitive R*2/R2 ratio and the slope of the susceptibility relaxation coupling (iron) to iron aggregation size and distribution. In a 3T qMRI study of three patients with ACP and three matched controls, we observed a marked increase in R*2/R2 and a pronounced increase in the R*2 - QSM slope (iron: controls 154.09 {+/-} 52.89 s- 1ppm- 1; patients 296.68 {+/-} 57.18 s- 1ppm- 1; p = 0.016), consistent with enhanc
- Semantic map learning and externalization in an embodied neural agent: A comparison to human behavioral and neural data
All mammals can build internal cognitive maps from sensory input, supporting spatial learning and planning. While rodent studies have shown the mammalian navigation system solves the SLAM (Simultaneous Localization and Mapping) problem, its role in human spatial memory and overt recall are less understood. To investigate this, we adapted a spiking semantic SLAM algorithm for a Treasure Hunt task, where human participants navigate a 3D beach in virtual reality and later point to remembered object locations. Our agent integrates networks for bipedal locomotion, vision, memory, and arm control to enable first-person learning of place-object associations, and externalizing that knowledge by pointing and expressing confidence. Comparing model observables to human data, we replicate key behavioral and neural effects: monotonic scaling of accuracy with confidence, and recall-dependence on local field potential power observed in the left hippocampus. This work offers a mechanistic framework li
- Multinational Public Opinion on Race, Ethnicity, and Algorithmic Reform in Medicine
Importance: Several professional medical societies have removed race and ethnicity from widely used clinical algorithms with implications for millions of patients. Yet the opinions of patients and the public regarding the tensions underlying these pivotal changes have not been systematically explored. Objective: To assess global public opinion on the use of race or ethnicity in clinical algorithms, including preferences for different approaches to algorithmic reform and perceptions of alternative predictors. Design: Cross-sectional survey study. Setting: Multinational opt-in online survey conducted via Prolific in January 2026. Participants: A volunteer convenience sample with quota sampling to achieve approximately equal participation by sex at birth and across ten categories of self-identified race and ethnicity. Main Outcomes and Measures: Self-reported comfort with demographic and social predictors in clinical calculators, with net comfort defined as percentage extremely or somewha
- Google is turning the internet into a giant group chat — and websites aren’t invited
Google is turning the internet into a giant group chat — and websites aren’t invited Tom's Guide
- Alibaba introduces Qwen3.7-Max as next-gen AI agent model
Alibaba’s Qwen on Wednesday unveiled Qwen3.7-Max, its new flagship AI model designed for the agent era, with API access set to roll out soon. The company said Qwen3.7-Max is its most advanced and comprehensive agent model to date, capable of handling coding and debugging, office workflow automation, and long-horizon tasks spanning hundreds or even thousands […]
- Cohere launches open weights model Command A+, more than a year since the Command A release
Cohere releases Command A+, an open weights model and successor to Command A.
- Bildy
Your company, automated