ROBOMARKET
What's Trending
The most talked-about robotics and AI stories right now — ranked by coverage, authority, and freshness.
Kimi: Threat or menace?
Chinese company Moonshot AI released a new version of its Kimi model this week, prompting concern about "full AI communism."
Federal employees can download TikTok on their work phones again
The Department of Justice says that federal employees can now download TikTok on their government devices.
Setting up your spare Mac for Claude Code to control, a step-by-step guide
Comments
Google might not kneecap the Pixel 11a with an old processor
The Pixel 10a shipped with a last gen processor. | Photo: Dominic Preston / The Verge Mystic Leaks suggests that the Pixel 11a will return to featuring a flagship-grade processor with the Tensor G6. Rather than the Tensor G5 found in the Pixel 10 and 10 Pro, the Pixel 10a shipped with the previous generation Tensor G4. That was a huge disappointment since, typically, the Pixel a lineup kept the modern processor, but cut corners in other places to keep costs down. The Tensor G6 is rumored to feature the same PowerVR DXT-48-1536 GPU as the G5, but it should still be an improvement over the Mali-G715 in the Tensor G4. The big upgrade is that the G6 moves on from Samsung's Exynos modems and instead uses a MediaTek M90 modem. Tha … Read the full story at The Verge.
GPT-5.6 used a prompt to close a 30-year gap in convex optimization
Comments
Google is open-sourcing its 3D emoji
Now, if you want to, you can use Google's 3D emoji in your own creations. The company shared some details about how it went about designing the little pictograms and why, as part of World Emoji Day on Friday. Things you might not necessarily worry about in a 2D illustration suddenly become very important when you're talking about a 3D model. Is a smiley face a sphere? A mask? A flat disc? In addition to offering a behind-the-scenes look at Google's design process, it also announced that it would be completely open-sourcing the emoji set: We're handing over raw .OBJ files to the community so they can use them to build immersive VR worlds, … Read the full story at The Verge.
Surprise! Facial recognition smart locks are actually good
Facial recognition smart locks are here; now you can unlock your door the same way you unlock your phone. | Photo by Jennifer Pattison Tuohy / The Verge Hands-free unlocking is the future of smart locks. The best smart home tech removes friction, and having your door unlock for you as you walk up is as frictionless as it gets - no passcodes to remember, no need to have a free hand to wave, press, or poke at the lock. One way to achieve this nirvana is through facial recognition. You already unlock your phone with your face; why not your home? Hands-free unlocking using geofencing has been around for a while, but it can be slow and unreliable, and requires an app running in the background on your phone. Newer innovations - facial recognition and unlocking using an ultrawideband (UWB) radio - … Read the full story at The Verge.
Many Companies Use AI. Few Know How to Build an AI-Native Enterprise Data Platform.
A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance. The post Many Companies Use AI. Few Know How to Build an AI-Native Enterprise Data Platform. appeared first on Towards Data Science.
Palm Garden AI develops Coherence Guard relational decision layer for human-facing robots
Palm Garden AI is developing a hardware-agnostic software layer, including Coherence Guard, for human-robot interaction and relationships. The post Palm Garden AI develops Coherence Guard relational decision layer for human-facing robots appeared first on The Robot Report.
Neil Rimer thinks the AI money is coming back out
Neil Rimer, the venture capitalist who co-founded Index Ventures, predicts the historic wealth AI is generating in Silicon Valley will have to be redistributed, voluntarily or involuntarily.
Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It
Enterprise Document Intelligence [Vol.1 #10A] - The escalation cascade and the free, deterministic checks that flag a failed parse before you pay for a deeper one The post Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It appeared first on Towards Data Science.
The 10 Best AI Presentation Makers (2026): Tested & Ranked by Professional Designers
If you are still designing slide decks from scratch in 2026, you are losing hours of your life to formatting that a machine can do in Continue reading on Medium »
Stop Using AI Like a Search Engine: Learn This Better Way Instead
I still remember the first time I used AI.Continue reading on Medium »
Every Major AI Model Has the Same Security Hole, Including the Ones You Use
If you use ChatGPT, Claude, Gemini, Copilot, or pretty much any AI assistant built on a large language model, there s a vulnerability Continue reading on Medium »
Capital One releases VulnHunter, an open-source AI tool that finds software flaws before hackers do
Capital One on Thursday released VulnHunter, an open-source, agentic AI security tool that scans source code for exploitable vulnerabilities, maps out how an attacker would reach them, and proposes targeted fixes — all before a single line ships to production. The tool, built internally and now available on GitHub under an Apache 2.0 license, is one of the most ambitious attempts by a major financial institution to turn offensive AI capabilities into a public defensive resource.At a time when security teams are facing a rising tide of new AI threats, Capital One s decision to open-source the tool reflects an effort, according to CISO Chris Nims, to address an increasingly brief window before sophisticated, next-generation AI attack capabilities become affordable and accessible to virtually every adversary. Capital One is not simply releasing another vulnerability scanner. VulnHunter introduces what the company calls an attacker-first forward analysis — a workflow in which the tool begi
Intuit scrapped its own AI agent architecture twice in four months. At VB Transform 2026, its AI VP called that the fast path
Intuit was an early pioneer in the usage of agentic AI, but its path to success has hardly been a straight line.At VB Transform 2026, Intuit VP of AI Nhung Ho described how the company rebuilt its agent architecture twice in the span of about four months, first moving from a fleet of specialist agents to a central orchestration layer, then abandoning that layer for a skills and tools based system once the orchestrator itself started failing under its own complexity. The full second rebuild took 60 days, with a first working version in under 20.The failure mode that forced the second rewrite was specific. Agents in the orchestrated system passed results to each other in natural language, and each handoff lost context the next agent needed to act correctly. If you have 10 agents and they all are passing to each other, every time that pass happens, error compounds, Ho said.Why the orchestration layer broke downHo said the original push toward specialist agents came from a straightforward
Founder of Maximo discusses how robotics is accelerating solar construction
Maximo founder Deise Yumi Asami discusses the process of automating solar construction with robotics. The post Founder of Maximo discusses how robotics is accelerating solar construction appeared first on The Robot Report.
CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models
arXiv:2607.14125v1 Announce Type: new Abstract: Pre-trained vision-language models (VLMs) enable zero-shot image classification by computing the similarity score between an image and textual descriptions, typically formed by inserting a class label (e.g., "cat") into a prompt (e.g., "a photo of a"). Since the score for a given image-class pair is sensitive to the choice of prompt, existing studies ensemble multiple prompts using a weighting vector to aggregate scores across different prompts. Yet, in current strategies, the weighting vector assigned to each prompt is shared across all classes, implicitly assuming that prompts are conditionally independent of classes, which often does not hold in practice, as a prompt like "an aerial view of" might be apt for "airport" but ill-suited for "apple". To address this, we propose class-aware zero-shot prompt reweighting (CARPRT). This scoring scheme adjusts the weighting vector for each class label by capturing the class-specific relevance of
HG-RAG: Hierarchy-Guided Retrieval-Augmented Generation for Structured Knowledge Graphs
arXiv:2607.14095v1 Announce Type: new Abstract: Retrieval Augmented Generation (RAG) has proven to be a widely successful process at improving the quality of outputs from a Large Language Model (LLM) for wider context. However, RAG systems typically retrieve context from flat document stores, which struggles when queries require hierarchical or relational reasoning across structured knowledge. I present HG-RAG (Hierarchy-Guided RAG), a framework that performs graph-traversal over a hierarchical knowledge graph to deliver structured context to a language model. My retrieval pipeline resolves a named entity anchor from the query, then expands context upward through parent nodes, laterally through relational neighbors, and downward through child nodes when needed. I evaluate HG-RAG against a dense retrieval baseline across three world scales (18-800 nodes) with four query types: local fact, hierarchical, neighborhood, and multi-hop. Results show HG-RAG consistently outperforms the flat ba
Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
arXiv:2607.14123v1 Announce Type: new Abstract: Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice, they are often generated and discarded without guiding meaningful action. This gap reflects foundational shortcomings: research has not yet established methodologies for integrating explanations into end-to-end, human-in-the-loop systems. This position paper argues that the machine learning community must pivot from ad-hoc XAI methods toward addressing foundational & structural challenges, including unclear problem formulations, underspecified evaluation objectives, and the absence of pipelines for explanation-driven feedback. We support this claim through an analysis of recent ICML, NeurIPS, and ICLR papers and a survey of XAI practitioners, revealing recurring issues that limit cumulative progress. We conclude by outlining a practical checklist designed to sh