AI News Archive: May 6, 2026 — Part 17
Sourced from 500+ daily AI sources, scored by relevance.
- Pet2Human: Your Pet’s Human Self
See your furry friend as a cute human, powered by AI
- Yveloxy
AI chatbots & digital employees for modern businesses
- SmartBuy AI
Never get fooled by fake Amazon reviews again
- Mnemona
AI photo restoration. Give memories a second life
- Mocki
AI mock interviews with a 3-person panel that adapts to you
- TryVeo4
veo-4-ai
- Chaptrly
Turn any YouTube video into high-performing content with AI
- VintroHub
Video introductions that get you hired
- Agnixa
Your AI DevOps Engineer
- Virgo
The best AI tts extension
- ReplyGenie
Message generator
- sweepr
sweepr - the documentation janitor
- Seleen
AI recruitment exams for SMBs in Mexico
- MediNITS
AI That Grows Your Practice
- Image AI Assistant
AI photo manager. Search images with natural language
- ApiLink
One API for GPT, Claude, Gemini & every major AI model
- Microsoft reconsiders ambitious clean energy goal amid AI boom
The deliberation comes as Microsoft and other tech giants race to build new AI data centers and secure enough energy to power them.
- Microsoft’s AI data center push is colliding with its clean power goals
The push for new data centers at Microsoft is putting its key clean power goals at risk.
- NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models AI at Meta
- SpecMD: A Comprehensive Study on Speculative Expert Prefetching
Mixture-of-Experts (MoE) models enable sparse expert activation, meaning that only a subset of the model’s parameters is used during each inference. However, to translate this sparsity into practical performance, an expert caching mechanism is required. Previous works have proposed hardware-centric caching policies, but how these various caching policies interact with each other and different hardware specification remains poorly understood. To address this gap, we develop SpecMD, a standardized framework for benchmarking ad-hoc cache policies on various hardware configurations. Using SpecMD…
- Normalizing Flows with Iterative Denoising
Normalizing Flows (NFs) are a classical family of likelihood-based methods that have received revived attention. Recent efforts such as TARFlow have shown that NFs are capable of achieving promising performance on image modeling tasks, making them viable alternatives to other methods such as diffusion models. In this work, we further advance the state of Normalizing Flow generative models by introducing iterative TARFlow (iTARFlow). Unlike diffusion models, iTARFlow maintains a fully end-to-end, likelihood-based objective during training. During sampling, it performs autoregressive generation…
📄 ResearchMay 6, 2026https://machinelearning.apple.com/research/normalizing-flows-iterative-denoising - Valossa Assistant
AI Video Assistant — Like ChatGPT, But Built for Video Work
- Popsight
Check what ChatGPT says about your brand, from your desktop
- From Where Things Are to What They’re For: Benchmarking Spatial–Functional Intelligence for Multimodal LLMs
True spatial intelligence for multimodal agents transcends low-level geometric perception, evolving from knowing where things are to understanding what they are for. While existing benchmarks, such as VSI-Bench, effectively evaluate this foundational geometric stage, they fall short of probing the higher-order cognitive abilities essential for grounded intelligence. To bridge this gap, we introduce the Spatial-Functional Intelligence Benchmark (SFI-Bench), a video-based benchmark with over 1700 questions derived from diverse, egocentric indoor video scans. SFI-Bench is designed to…
- TDA for drug discovery: Cyclic molecule generation with topological guidance
TDA for drug discovery: Cyclic molecule generation with topological guidance maths.ox.ac.uk
- On the application of Large Language Models for language teaching and assessment technology
On the application of Large Language Models for language teaching and assessment technology repository.cam.ac.uk
- Uncertainty-aware learning from sparse, unlabelled, and out-of-distribution time series
Uncertainty-aware learning from sparse, unlabelled, and out-of-distribution time series repository.cam.ac.uk
- Kanwas
An open-source brain for your team
- Superset 2.0
Run 100s of coding agents on any machine from anywhere
- pay.sh
Discover, access, and pay for any API autonomously
- WOZCODE
Cut Claude Code costs by up to 50%
- Contrario
The AI recruiting platform powered by expert recruiters
- Ads in ChatGPT
Create, manage, and measure your ChatGPT ad campaigns
- Realtime TTS-2
Voice AI that feels as good as it sounds
- DevAlly
AI powered accessibility compliance for teams who ship fast
- Open Finance MCP
Access your bank data in ChatGPT & Claude via Open Finance
- moar
Your documents. AI ready.
- Ajelix AI Agent for Work
The first truly agentic AI sidebar for Google Workspace™
- Knowly 1.0
LLM Wiki + NotebookLM, in one closed-loop Proactive AI
- Pattern.io
Your personal AI content strategist for short-form video
- Clipo AI — One Video, 20 Content Pieces
One video → clips, carousels, personalized posts, img & more
- Impulse AI
From plain English to deployed ML model in an hour
- Troubleshooting Agent by Pluno
Debugs customer issues before engineers do
- AI Actions
AI that doesn't just chat, it takes actions.
- Kit 🏵️
Editor, Browser, Mail, Terminal, Agents. AI at the center.
- Platos | The runtime for Managed Agents
Your agents. Your servers. Your keys. Your model.
- Buddy
Voice First AI assistant for macOS
- Weez AI
Where Marketing Runs Itself
- Sell AI — Snap, List, Sold
Get a ready-to-post listing in sec on Vinted, Ebay & more
- MoltPe
A wallet for your AI agent. Not access to yours.