AI News Archive: July 22, 2026 — Part 9
Sourced from 500+ daily AI sources, scored by relevance.
- What Does AI Cost When We Skip the Work?
This Week with EdSurge podcast examines what holds up when artificial intelligence moves fast.
- Chonnam National University researchers develop new AI system for highly accurate mapping and modeling of orchards
Chonnam National University researchers develop new AI system for highly accurate mapping and modeling of orchards EurekAlert!
- New Markdown rival: Open-source DGML format aims to turn docs into data that AI (and humans) can trust
Docugami, the Kirkland, Wash., startup led by XML co-creator Jean Paoli, is open-sourcing DGML, a format that turns business documents into structured, verifiable data for AI. The company is releasing it under an open-source license and partnering with Inveniam and Mantra to anchor the data on a blockchain. Read More
- Plant.Digital partners with Quest Global to expand AI-led asset management initiatives in GCC
Plant.Digital has announced a strategic partnership with Quest Global to accelerate the deployment of AI-driven asset reliability and integrity solutions for asset-intensive industries across the Gulf Cooperation Council (GCC) region. The post Plant.Digital partners with Quest Global to expand AI-led asset management initiatives in GCC appeared first on Express Computer .
- I Built a Multi-Agent AI Data Analyst on Google Cloud That Turns Natural Language into Business…
I Built a Multi-Agent AI Data Analyst on Google Cloud That Turns Natural Language into Business Intelligence “What was our highest revenue product category last quarter?” It sounds like a simple business question. Yet in many organizations, answering it still requires a chain of dependencies: Business Team → Data Analyst → SQL Query → Dashboard → Report → Decision. Somewhere along that chain, valuable time is lost. I wondered, What if the database itself became conversational? What if someone from sales, marketing, or leadership could simply ask questions in plain English and receive not only numbers, but charts, strategic recommendations, and even business-ready reports? That question eventually became the foundation of my Multi-Agent E-commerce Data Orchestrator , a cloud-native AI system built entirely on Google Cloud. The Journey Didn’t Start With an AI Project Interestingly, this project wasn’t born because I wanted to build “another AI application.” It began because I wanted to understand Google Cloud properly. Earlier this year, I participated in the Google Cloud Gen AI Academy — Cohort 1 Hackathon , organized with Hack2Skill. Rather than treating it as just another competition, I saw it as an opportunity to understand how modern AI applications are actually built. The academy wasn’t only about prompting large language models. It introduced the complete cloud ecosystem surrounding AI applications: Generative AI fundamentals Vertex AI Google Cloud services Networking Security IAM API management Cloud-native deployment Along the way, I completed learning paths and earned skill badges including: Introduction to Generative AI Google Cloud Computing Foundations: Networking & Security Build a Secure Google Cloud Network What fascinated me most wasn’t any single technology. It was how each Google Cloud service solved a different piece of the engineering puzzle. Instead of learning them individually, I decided to combine them into one complete system. That became this project. The Problem: The “Data Bottleneck” Businesses today collect enormous amounts of data- orders, customers, products, revenue, inventory, marketing metrics. Ironically, the biggest challenge isn’t collecting data anymore. It’s making it accessible. In the current e-commerce landscape, data is useless if business development and sales teams cannot easily access or understand it. Most business users do not know SQL, and static dashboards often fail when faced with non-predefined questions. I wanted to remove this bottleneck. I didn’t want just another AI chatbot that summarizes data; I wanted an AI that could “think” through business problems, execute queries, and generate actionable insights. For example: “Find our highest and lowest performing product categories, calculate the revenue gap, recommend whether the lowest performer should be discounted, and save the recommendation as an executive report.” That’s no longer a simple database lookup. It’s reasoning. Why Google Cloud? Building within the Google Cloud ecosystem allowed me to focus on application logic rather than infrastructure management. Each service solved a very specific engineering problem. Vertex AI & Gemini: Core reasoning, orchestration and natural language understanding. Google BigQuery: Serverless analytical engine utilizing public datasets. Cloud Run & Docker: Scalable, containerized deployment. Streamlit: Responsive, Interactive, user-facing frontend. Model Context Protocol (MCP): Standardizing tool binding and agent communication. That is one of the biggest strengths of modern cloud-native development. Why BigQuery Instead of Building My Own Database? One of the earliest design decisions involved choosing the data source. I could have created my own database, generated synthetic records, or imported CSV files. But since the project was built on Google Cloud, I wanted the entire stack to stay within the Google ecosystem, that's when I explore one of its most underrated resources — BigQuery Public Datasets . Its a collection of publicly accessible datasets covering domains such as healthcare, finance, transportation, weather, geospatial analytics, machine learning, GitHub activity, Stack Overflow, and e-commerce. These datasets are maintained within BigQuery itself, allowing developers to explore production-scale data without spending time collecting, cleaning, or importing datasets. Storage is hosted by Google, and you only pay for the queries you run (with a generous free usage tier for experimentation). For this project, I selected TheLook E-commerce dataset, a fictional online clothing retailer developed by the Google Looker team. Although the business itself is fictional, the dataset is intentionally designed to resemble the relational data model of a real e-commerce company. It contains information about customers, products, orders, inventory, logistics, distribution centers, website events, and digital marketing activity, making it an excellent playground for analytics, SQL practice, business intelligence, and AI applications. Unlike many sample datasets that consist of a single CSV or flat table, TheLook models how production databases are actually structured. Data is distributed across multiple related tables, requiring joins, aggregations, filtering, and relationship mapping to answer business questions, making it much closer to a production database. This also meant the AI agent had to understand relationships between tables rather than simply querying one spreadsheet. This made it an ideal benchmark for building a Text-to-SQL multi-agent system capable of answering real business questions. Side Note: One of the hidden gems of Google Cloud is its extensive collection of BigQuery Public Datasets which are updated periodically. If you’re looking to build cloud or AI portfolio projects, they’re a fantastic starting point. You can explore the available datasets in the official BigQuery Public Datasets documentation and browse the TheLook E-commerce dataset for yourself. Simplified Dataset Structure The complete TheLook E-commerce dataset consists of seven interconnected tables . However, for this project, I focused on the three tables required for product and revenue analytics: products — Stores product information such as category, brand, and pricing. orders — Contains order-level information for each purchase. order_items — Connects products to orders and records the sale price of each purchased item. Products ▲ │ product_id │ Order_Items ▲ │ order_id │ Orders Although the complete dataset is much richer, these three tables were sufficient for demonstrating end-to-end business intelligence workflows such as revenue analysis, category comparisons, and sales recommendations. Like “Which product category generated the highest revenue?” or “Compare revenue between Jeans and Sweaters.” Why One AI Agent Wasn’t Enough Initially, it might seem reasonable to let a single LLM handle everything. After all, modern language models can write SQL. They can summarize results. They can generate reports. So why introduce multiple agents? Because different responsibilities require different behavior. To achieve reliable performance, I moved away from monolithic workflows and adopted an “Agent-as-a-Tool” architecture. I wanted one AI to think like a business manager , while another focused purely on database operations. This separation makes the system significantly more reliable. Instead of mixing planning, SQL generation, visualization, and reporting into one enormous prompt, each agent has a clearly defined responsibility. Meet the Two Agents: 👨💼 Manager Agent The Manager is the only AI that communicates with the user. Its responsibilities include: Understanding business intent Breaking problems into smaller tasks Delegating database work Interpreting numerical results Creating business insights Drawing charts Saving reports Importantly — The Manager never writes SQL directly. 👨💻Data Specialist Agent The Specialist exists for one purpose. Working with data. Its responsibilities include: Translating English into SQL Querying BigQuery Handling SQL syntax Auto-correcting query errors Returning structured results It does not perform business reasoning. It simply answers database questions accurately. This separation follows a common software engineering principle: Single Responsibility Principle Each component should do one job well. Exactly the same philosophy applies to AI agents. Architectual User Diagram │ ▼ Streamlit Interface │ ▼ Manager Agent (Reasoning & Planning) │ Delegates Database Task │ ▼ Data Specialist Agent (Text → SQL Generation) │ ▼ Google BigQuery │ SQL Query Results │ ▼ Manager Agent │ ┌───────────┬─────────────┐ ▼ ▼ ▼ Business Charts Reports Insights |───────────|─────────────| ▼ Streamlit UI Rather than relying on one enormous AI prompt, the application becomes an orchestrated workflow. From Question to Insight: A Workflow Example Let’s look at a typical interaction. If a user asks: “Find the revenue for Jeans, find the revenue for Sweaters, and compare them in a chart” The application doesn’t simply guess. Reasoning: The Manager Agent breaks the request into two separate data tasks. Delegation: It prompts the Data Specialist to query the order_items and products tables for both categories. Execution: The Specialist generates and executes SQL on BigQuery. Synthesis: The Manager Agent receives the raw numbers, selects the appropriate visualization, and renders the comparison chart in the UI. Revenue Bar Chart “Identify our best and worst performing product categories. Calculate the revenue difference and recommend what we should do.” Rather than returning only numbers, the Manager produces a business recommendation explaining whether discounts, bundling strategies, or inventory optimization may improve performance. Strategic Insight Report “Find our most expensive product and draft a three-sentence cold outreach email targeting VIP customers.” The Manager first retrieved the product information, then transformed raw database values into marketing-ready copy. That’s where multi-agent reasoning becomes much more interesting than traditional dashboards. Final Thoughts If you’ve made it this far, thanks for sticking around. From the outside, this looks like a chatbot that talks to a database. Behind the scenes, it’s prompt engineering, agent orchestration, cloud infrastructure, SQL generation, debugging sessions at unreasonable hours, and a surprising number of redeployments. The screenshots don’t show the times the agents confidently generated SQL for columns that didn’t exist, Cloud Run deployments that failed for reasons that seemed completely unrelated, or me wondering why I thought giving AI multiple agents would somehow make my life easier. It eventually worked. Mostly. And that’s the part I intentionally skipped in this article. This was the “how it works” story. The next one is the “how I got it to work” story — covering prompt engineering, Text-to-SQL generation, Cloud Run deployment, IAM permissions, debugging bizarre agent behavior, and all the engineering decisions that transformed a cool prototype into something reliable enough to demonstrate. See you in Part 2. Hopefully with fewer hallucinations… from both the AI and me. 😄 I Built a Multi-Agent AI Data Analyst on Google Cloud That Turns Natural Language into Business… was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
- AI Mania to Fuel Australia Deals, Top Underwriter Macquarie Says
The investor demand for exposure to companies along the artificial intelligence supply chain is set to accelerate capital-market activity in Australia, according to the nation’s top dealmaker.
- AI reduces founders’ need for capital, says Revolut Business
AI is reducing the amount of capital startups need to build global businesses, shifting the battle for founders away from funding and towards which countries offer the best environment to scale, according to the head of Revolut Business. Speaking to City AM, James Gibson said AI had dramatically lowered the cost of building and operating [...]
Score: 41🌐 MovesJul 22, 2026https://www.cityam.com/ai-reduces-founders-need-for-capital-says-revolut-business/ - Charli XCX’s Viral Marketing Take Exposed a Problem AI Just Made Much Worse
The pop star’s anti-marketing post drew 5 million views and industry pushback. It also raised one question brands should take seriously.
- AI Gateway now supports streaming transcription
AI Gateway now supports streaming transcription . Previously, transcription required a complete audio file and returned the full transcript in a single response. Now you can stream audio in as it's captured and get transcript updates back as the model produces them, keeping latency low for uses like live captioning and voice input. Streaming transcription is in beta and available through the AI SDK 's streamTranscribe function with any streaming-capable transcription model. The example below streams raw PCM audio to openai/gpt-realtime-whisper and prints each transcript delta as it arrives. The result stream also carries partial and final transcripts: The same code works across providers: swap the model string to use xai/grok-stt or any other streaming-capable transcription model . Streaming transcription also makes it easy to add a voice mode to an agent. Stream the user's speech to a transcription model and pass the live text to your agent. The agent itself does not change: it still receives text, so this works with any text-based agent. For agents that speak back, pair it with speech generation, or use realtime voice for full two-way conversation. For more information on streaming transcription on AI Gateway, see the documentation . Read more
Score: 40🌐 MovesJul 22, 2026https://vercel.com/changelog/ai-gateway-now-supports-streaming-transcription - Oil prices rise another 2%, while AI stocks get back to falling
Oil prices rise another 2%, while AI stocks get back to falling Boston Herald
Score: 40🌐 MovesJul 22, 2026https://www.bostonherald.com/2026/07/22/oil-prices-rise-another-2-while-ai-stocks-get-back-to-falling/ - South Korea's Lee to visit South America and Germany, attend AI summit in US
South Korea's Lee to visit South America and Germany, attend AI summit in US Reuters
- What Features Should You Look for in an AI-Powered Laptop or Copilot+ PC?
AI-powered laptops and Copilot+ PCs are becoming more relevant because the way people use laptops has changed significantly over the last few years. Modern routines are now built around multitasking, cloud collaboration, video conferencing, streaming, and productivity tools that remain active throughout the day. Most professionals are no longer using laptops only for documents and […]
Score: 40🌐 MovesJul 22, 2026https://www.digitaltrends.com/brc/what-features-should-you-look-for-in-an-ai-powered-laptop-or-copilot-pc/ - AI Is Delivering on Its Promise. It's Also Uncovering an Uncomfortable Truth That Companies Can No Longer Avoid.
AI Is Delivering on Its Promise. It's Also Uncovering an Uncomfortable Truth That Companies Can No Longer Avoid. entrepreneur.com
- Kobie establishes Bengaluru tech hub to expand AI engineering capabilities
Kobie has established a technology hub in Bengaluru as part of its strategy to expand AI-led product development and engineering capabilities for its global loyalty technology business. The post Kobie establishes Bengaluru tech hub to expand AI engineering capabilities appeared first on Express Computer .
- UPES Runway Powers AI-Native Startups at The Pitch 3.0
UPES Runway, the startup incubator at UPES, hosted the New Delhi leg of The Pitch 3.0, an initiative designed to identify and support AI-native and AI-driven startups across North, Central and Southern India. The first leg, held in New Delhi, brought seven shortlisted startups before investors, mentors and UPES leaders, following more than 20 applications from founders working at the […] The post UPES Runway Powers AI-Native Startups at The Pitch 3.0 appeared first on CXOToday.com .
- Kobie Establishes India Tech Hub as Core to AI-Driven Business Growth and Innovation
Kobie today announced the establishment of a Tech Hub in Bengaluru as a cornerstone of its AI-enabled product strategy. The expansion reflects Kobie’s belief that artificial intelligence is not a parallel initiative, it is central to how the company delivers business value and drives growth for its clients worldwide. The Kobie India team has grown […] The post Kobie Establishes India Tech Hub as Core to AI-Driven Business Growth and Innovation appeared first on CXOToday.com .
- Announcing AIXI Labs
We are starting AIXI Labs, an AI safety org focused on algorithmic information theory (AIT), continual reinforcement learning (RL), and in particular the eponymous AIXI. We aim to strengthen the technical case that developing artificial super intelligence (ASI) poses an X-risk while (in parallel) developing and prototyping theoretically-founded mitigations. (Already convinced? View open positions here. ) Overview From the website : https://www.aixi.uk/ AIXI is the leading mathematical model of artificial superintelligence, representing the maximum theoretical limit of AI capabilities. Alongside the name’s many previous meanings , AIXI now also stands for the AI X-risk Initiative , though we usually just call ourselves AIXI Labs. We model AI risk factors and safety mitigations in terms of AIXI variants, and develop the means to translate them to real AI agents. This enables rigorous testing of both the risk factors and the safety mitigations. Why this approach Most AI research today sits in one of two categories: methods that are fast and mathematically clean in narrow settings, and methods that are practical for frontier applications but opaque to safety analysis. Our core focus is the third intersection: methods that are general enough to describe powerful agents and amenable to mathematical analysis to support rigorous safety claims. Aligning a hypothetical superintelligence requires a concerted effort in this historically neglected direction. Thus, we formally examine behaviors, capabilities, risks and mitigations for idealized agents in fully general environments. We aim to port the most promising methods to modern LLM-based agents, and test principled hypotheses on these agents. Since our analyses are based on a very general model of superintelligence, we gain confidence that any experimentally validated conclusions will generalize and scale to future AI systems. Strategy Our timelines to the arrival of ASI are highly uncertain, and we aim to reduce X-risk across a broad range of possible scenarios. [1] Therefore, we will dovetail a synergistic portfolio of strategies. We believe that rigorous technical work to strengthen the scientific consensus on X-risk is a top priority. This type of work is valuable in worlds where alignment is knowably very hard, so that ordinary scientific progress will eventually lead to a solid case for a pause in AGI development. In these worlds, our counterfactual impact is to make the case strong sooner. We aim to demonstrate the risks to top scientists by building a rigorous learning theory that actually explains modern ML, captures loss of control risks, and is backed by (a priori surprising) empirical evidence. For example, this type of work includes no-free-lunch style arguments, training models of misalignment, and otherwise red-teaming safety proposals in theory and practice. To be clear, our role is research and not (direct) policy advocacy. Our models of ML and decision theory predict that many existing dangers can be made more legible with additional research, so that our work bolsters the case for X-risk in expectation, [2] but we will also publish any unexpected (and potentially contrary) findings. We aim to prototype mitigations that actually make AI agents safer and drive the adoption of these mitigations at frontier labs. Our approach is heavily inspired by Michael Cohen's research on safety guarantees for conservative AIXI variants. We hope to develop these agents to more closely approximate ideal corrigibility, and in parallel to roll out practical implementations. The latter will require somewhat novel ML science to get right. Success seems plausible only under slightly longer timelines and in worlds where alignment is easy. Unfortunately, the current science of deep learning seems impoverished, [3] and we cannot be sure that either of these conditions hold. Implementing safer agents can still be valuable in several ways. Obviously, it can provide feedback loops for our research and show legible progress, both of which can unfortunately be seductive incentives for safety labs to (over)optimize. But it can also allow us to red-team our own mitigations, providing evidence of more sophisticated failure modes than we could otherwise exhibit. Also, weakly corrigible agents may be helpful for hardening the world in various ways. Over the longer term, if a longer term is granted to us, AIXI Labs will contribute to humanity's ongoing deconfusion around intelligence, corrigibility, and alignment. For example, our interest in robust learning algorithms for unrealizable environments goes beyond specific known applications to AI safety. This theory of change is not so different from MIRI's (historical) research mission. However, we are more optimistic about openly publishing our research, largely because timelines appear unlikely to be long enough for this to accelerate capabilities (see their 2018 Update for comparison). Philosophy Concepts from AIT quantify fundamental information-theoretic relationships between data, models, and behavior. Unfortunately, academia has largely abandoned AIT research in recent decades, and this may well be the reason for academic learning theory's failure to say very much of practical relevance to modern deep learning (see our position paper ). We believe that long-horizon continual RL agents converge towards some version of AIXI as they become increasingly powerful. However, this motivating claim needs to be made more rigorous. We are not sure about either the trajectory or the fidelity of convergence; for example: How to best model embedded agents reasoning about themselves as they improve? How do computational bounds and model size interact with the simplicity bias? How do ideal predictors normalize the universal distribution to a probability measure? Are there (other) limit-computable predictors superior to Solomonoff induction? We don't need to settle all of these questions to believe that there needs to be a safety lab focused on AIXI. While Aram has been investigating the basis for Solomonoff induction and AIT as ideals, Cole provisionally adopts the "modest stance" that AIXI is the minimal model rich enough to capture the problems of ASI alignment. We can only pose the alignment problem in the setting of highly general agents. The central conceptual question is how to teach an agent to act competently and (somewhat) autonomously on our behalf without corrupting the ongoing teaching process (roughly “robustifying RL ”). The ergodicity assumptions of textbook RL ignore (catastrophic) traps, and therefore totally fail to engage with the central question, which hinges on generalization around traps. AIT provides the right language to talk about competent generalization, and in the AIXI paradigm we continue to make (difficult but steady) conceptual progress on this question. If we had efficient, provably correct algorithms for these environment classes, we would study them instead of AIXI, but the requirement of worst-case tractability is too restrictive. Historically, artificial intelligence is exactly the discipline that takes advantage of learned heuristics to solve worst-case intractable problems, and today’s AIs already look more similar to AIXI than to textbook RL methods. It seems infeasible to build a theory of AI safety directly on top of efficient algorithms; we would basically need to replace deep learning in the process. Current Work Expect to see a lot more AIXI research over the next few months! We are currently working with our research fellows Gabriel Leuenberger and Yegon Kim, as well as PIBBSS fellows Mikhail Mironov and Damini Kusum, on topics spanning agent foundations and RL theory. Starting with the next fellowship round (see below), we will be more focused on empirical work and engagement with the broader AI safety research community. Get Involved The UAI community hub is a good place to casually follow AIXI research. We hold regular research meetings listed on the calendar here: uaiasi.com We are organizing a symposium at Oxford: more info The fellowship program is a good way to test your fit at AIXI Labs: more info We are hiring ML research scientists: more info Acknowledgements Our work is supported by grants from the UK AISI's Alignment Project and AISTOF. Cole is also funded by CG. AIXI Labs is fiscally sponsored by PrincInt. ^ Cole is increasingly convinced that timelines may be quite short, and is now pivoting to focus more heavily on direct AI safety research over curiosity-driven agent foundations. One factor has been that his previous model of LLM (limitations) has overall not paid rent in correct predictions. ^ Our posteriors on X-risk may be a martingale, but the legibility of X-risk to the scientific community is a submartingale. ^ Even if we understood neural network generalization performance perfectly, we would still need to select the right generalization behavior, which is a key focus for AIXI Labs. Progress on the science of deep learning has obfuscated the lack of progress on core problems of controlling a superintelligence. However, once the core problems are solved, the heuristic nature of deep learning may become the biggest remaining obstacle. Discuss
- How Thinking Machines Lab’s Inkling performs on agentic knowledge work
Inkling, a new open‑weights model from Thinking Machines Lab, is evaluated on agentic knowledge tasks, showing strong performance.
Score: 40🌐 MovesJul 22, 2026https://artificialanalysis.ai/articles/how-thinking-machines-lab-s-inkling-performs-on-agentic-knowledge-work - The Complete Technical Guide to Running LLMs Locally in 2026
Hardware math, quantization tradeoffs, five inference engines benchmarked, and two real case studies where the numbers met reality on my… Continue reading on Towards AI »
- 7 AI disclosures from IndiaMART’s Q1FY27 Earnings Call
IndiaMART is expanding AI across content moderation, supplier ranking, buyer verification and calls, while disclosing little about how these systems make decisions or offer recourse to affected users. The post 7 AI disclosures from IndiaMART’s Q1FY27 Earnings Call appeared first on MEDIANAMA .
Score: 40🌐 MovesJul 22, 2026https://www.medianama.com/2026/07/223-ai-disclosures-indiamarts-earnings-call/ - South Korea's tech rally isn't over, but 2 markets stand out as better AI bets, a research firm says
South Korea's tech rally isn't over, but 2 markets stand out as better AI bets, a research firm says Business Insider
Score: 40🌐 MovesJul 22, 2026https://www.businessinsider.com/kospi-stock-index-sky-hynix-samsung-nikkei-japan-taiwan-tsmc-2026-7 - YouTuber Gossip Goblin Will Debut AI Film ‘Gods Don’t Give Gifts’ In U.S. Theaters
YouTube creator Gossip Goblin brings AI-assisted feature "Gods Don’t Give Gifts" to U.S. theaters, testing whether online fandom can drive box office.
Score: 40🌐 MovesJul 22, 2026https://www.forbes.com/sites/maureenkerr/2026/07/22/youtuber-gossip-goblin-will-debut-ai-film-in-us-theaters/ - Hyundai claims humanoid robot plan is not part of talks with striking workers
Union previously warned automaker that any robot deployment must be negotiated.
- Bebuzee Unveils Gigbuz: A Global AI-Powered Freelance Marketplace Designed to Transform How the World Works
Bebuzee Unveils Gigbuz: A Global AI-Powered Freelance Marketplace Designed to Transform How the World Works USA Today
- 3 Years of Graph Engineering with LangGraph
Highlights three years of advancements in graph-based agent architecture using LangGraph, showcasing new capabilities and use cases.
Score: 40🌐 MovesJul 22, 2026https://blog.langchain.dev/blog/3-years-of-graph-engineering-with-langgraph - I gave Perplexity's agentic AI 5 complex tasks to run on my Mac - and I'll do it again
Perplexity's Mac app offers its own agentic AI, Personal Computer, which can handle multi-step tasks on your computer from start to finish. See why the results impressed me.
Score: 40🌐 MovesJul 22, 2026https://www.zdnet.com/article/perplexity-personal-computer-agentic-ai-complex-tasks-mac/ - How to Integrate Legacy Systems with Next-Gen Enterprise AI Applications
Guide on bridging old systems with modern AI apps for seamless enterprise workflows.
Score: 40🌐 MovesJul 22, 2026https://www.typeface.ai/blog/how-to-integrate-legacy-systems-with-enterprise-ai-applications - The One Reason Your AI Isn't Producing a Return on Investment - Automotive News
The One Reason Your AI Isn't Producing a Return on Investment - Automotive News Automotive News
Score: 39🌐 MovesJul 22, 2026https://www.autonews.com/sponsored/webinars/one-reason-your-ai-isnt-producing-return-on-investment/ - India's MSMEs power the economy, but most still aren't using AI
India's MSMEs power the economy, but most still aren't using AI YourStory.com
Score: 38🌐 MovesJul 22, 2026https://yourstory.com/2026/07/fynd-ragini-varma-ai-for-msmes-autonomous-retail-msme-sparks-2026 - AI designs are taking over city storefronts
Small businesses are turning to AI-generated signs and menus to save money. It’s not going over well with customers.
- Breakingviews - Consultants confront AI ‘heal thyself’ moment
Breakingviews - Consultants confront AI ‘heal thyself’ moment Reuters
Score: 38🌐 MovesJul 22, 2026https://www.reuters.com/commentary/breakingviews/consultants-confront-ai-heal-thyself-moment-2026-07-22/ - New AI Features Help HR Convert Wellness Investments into Employee Action
Vantage Fit has announced three solutions: an AI Program Assistant that answers admin questions, helps in executing routine tasks and extrapolates data and insights, a consolidated real-time analytics and reporting module, and an Audience Builder that targets employees using live behavioural signals instead of static fields like department or age. The update responds to a […] The post New AI Features Help HR Convert Wellness Investments into Employee Action appeared first on CXOToday.com .
- Make Long-Running NVIDIA TensorRT Engine Builds Observable and Cancelable in Python or C++
A TensorRT engine build can take seconds to many minutes. Large strongly typed models, deep tactic search, and a cold timing cache on a brand-new GPU SKU can...
- Octobotics’ robots find hidden damage before ships and rail tracks fail
Octobotics’ robots find hidden damage before ships and rail tracks fail YourStory.com
- The case for the channel in an AI-driven security market | ChannelPro
The case for the channel in an AI-driven security market | ChannelPro IT Pro
Score: 38🌐 MovesJul 22, 2026https://www.itpro.com/security/the-case-for-the-channel-in-an-ai-driven-security-market - How AI automation can fit into construction workflows: McKinsey
As artificial intelligence continues to shake up the building industry, choosing how to build versus how to buy solutions is important, said an expert for the consulting firm.
Score: 37🌐 MovesJul 22, 2026https://www.constructiondive.com/news/ai-report-construction-mckinsey-engineering-build-buy/825927/ - Transync AI Brings Real-Time Translated Voice to Online Meetings Beyond Captions
Transync AI Brings Real-Time Translated Voice to Online Meetings Beyond Captions USA Today
- Systems Thinking Is Now The Skill That Decides Who Wins In AI
Systems thinking is now the most valuable skill in AI hiring. Netflix, Gartner, DORA and a $5 billion Temporal round all point the same way.
- Science Corporation’s vision-restoring chip wins EU approval
"The thing that the space needs is a company making $100 million a year of revenue," Science Corp. CEO Max Hodak said.
Score: 35🌐 MovesJul 22, 2026https://techcrunch.com/2026/07/22/science-corporations-vision-restoring-chip-wins-eu-approval/ - GameSquare's TubeBuddy Launches New AI-Powered Creator Tool to Deliver Personalized, Data-Backed Video Ideas
GameSquare's TubeBuddy Launches New AI-Powered Creator Tool to Deliver Personalized, Data-Backed Video Ideas USA Today
- CEO Interview: OrangeAI
Will Kastroll, CEO of Orange AI, tells CB Insights how they view the market, customer needs, and their company. How do you define your market and where does your company fit into that space? There are over 39,000 independent agencies … The post CEO Interview: OrangeAI appeared first on CB Insights Research .
- CEO Interview: ScyAI
Bernhard Rannegger, CEO at ScyAI, tells CB Insights how they view the market, customer needs, and their company. How do you define your market and where does your company fit into that space? Insurance runs on trust. The policy pays, … The post CEO Interview: ScyAI appeared first on CB Insights Research .
- Neill Blomkamp irks fans with his new AI-short film
Neill Blomkamp's AI-generated short film, Nightborne, is facing backlash for being derivative and boring.
- Here’s how to ask Gemini Live for help with anything you see.
Have you ever struggled to describe something you’re looking at? Whether it’s a complex manual, a blinking error code, or a unique object, sometimes you just need an exp…
Score: 35🌐 MovesJul 22, 2026https://blog.google/products-and-platforms/products/gemini/gemini-live-camera-how-to/ - iCubesWire launches iQ, an AI-powered platform for creator marketing intelligence
Influencer marketing and digital media company iCubesWire has launched iQ, a conversational AI platform designed to help brands and marketers navigate creator marketing through natural language interactions and data-driven insights.Positioned as India's first Conversational AI Platform for Creator Marketing Intelligence, iQ enables marketers to discover creators, plan campaigns, evaluate performance, track competitors, generate insights and receive AI-powered recommendations through a single conversational interface.Built on iCubesWire's existing technology ecosystem, the platform integrates capabilities across creator discovery, campaign management, creator relationship management, content workflows, analytics and competitive intelligence. The company said iQ acts as an intelligence layer that turns complex marketing data into actionable business recommendations.According to iCubesWire, marketers can use the platform to address a range of creator marketing challenges, from selecting the right influencers and optimising campaign strategies to measuring effectiveness and identifying new growth opportunities.Commenting on the launch, Nishant Sharma, Co-Founder and CEO, Influencer Business at iCubesWire, said creator marketing has become an increasingly important growth channel for brands, but marketers often spend significant time navigating multiple dashboards instead of focusing on decision-making."With iQ, we've built an intelligence layer that allows marketers to simply ask questions and receive strategic recommendations backed by data, technology and years of campaign expertise," Sharma said.The launch reflects the company's broader efforts to expand its technology offerings within the creator economy. Over the years, iCubesWire has developed proprietary tools covering various aspects of influencer and creator-led marketing, and the new platform is designed to unify those capabilities under a conversational AI framework.Sahil Chopra, Chairman, iCubesWire, described iQ as a significant step in the company's technology roadmap."We've invested for years in building technology across every aspect of creator marketing. iQ is the intelligence layer that sits on top of this ecosystem, enabling brands to move beyond campaign execution and make faster, smarter and more informed marketing decisions," Chopra said.The launch also advances iCubesWire's vision of building a Creator Growth OS, an integrated platform that combines technology, intelligence and AI to support the next phase of creator-led marketing.
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