D-Wave Quantum study highlights the potential for quantum optimization to create value across industries. According to the study, 46% of surveyed business leaders whose company has implemented quantum optimization or plans to do so within the next two years expect a return on investment of between $1 and $5 million, while 27% predict a return of more than $5 million in the first 12 months. A majority of the business leaders surveyed (81%) believe that they have reached the limit of the benefits they can achieve through optimization solutions running on classical computers. Against that backdrop, many are starting to explore whether quantum technologies can help. 53% are planning to build quantum computing into their workflows and 27% are considering doing so, indicating a growing recognition of quantum computing’s real-world business value. 22% are seeing quantum make a significant impact for those who have adopted it, while another 50% anticipate it will be disruptive for their industry. The results of the study show that quantum computing is gaining recognition among business leaders for its ability to potentially deliver major efficiencies in addressing complex optimization problems and operational improvements. 60% respondents expect quantum computing-based optimization to be very or extremely helpful in solving the specific operational challenges that their companies face. In fact, among those respondents most familiar with quantum, this figure rises to 73%, including nearly a quarter who describe it as “a game changer.” The areas in which business leaders expect to benefit from an investment in quantum optimization include: supply chain and logistics (50%), manufacturing (38%), planning and inventory (36%), and research and development (36%). Most respondents (88%), especially those in the manufacturing industry, believe that their company would go “above and beyond” for even a 5% improvement in optimization.
Bloomreach personalization platform offers native Snowflake integration to securely sync customer attributes, event data, and product catalogs
Bloomreach, the agentic platform for personalization, announced a new partnership with the Snowflake AI Data Cloud, empowering businesses to activate enterprise-grade data infrastructures across marketing channels. With Bloomreach and Snowflake, businesses can combine customer engagement with maximum personalization, connecting customer data stored in Snowflake with Bloomreach’s AI-powered marketing solutions. Bloomreach’s native Snowflake integration is already available to businesses today. It securely syncs customer attributes, event data, and product catalogs from Snowflake into Bloomreach. Businesses unlock the full value of their customer data, breaking down silos between storage and activation, and enabling personalization powered by AI. Additionally, this integration enables marketing teams to: Power Loomi AI agents in Bloomreach with enriched customer and product intelligence; Automate campaign triggers based on Snowflake events; Deliver timely personalization with fresh data and hyper-relevant insights; Safeguard sensitive data through selective import capabilities, transferring only the specific data required for a given use case.
Delta Air Lines is testing new AI-based dynamic (“surveillance”) pricing system that tailors fares to individual customers based on the personal data collected; with plans to expand it to 20% by the end of the year
Delta Air Lines is testing a new AI pricing system that tailors fares to individual customers, a move that could reshape how airline tickets are sold and priced. The system, developed in partnership with Israeli startup Fetcherr, is already being used on 3% of Delta’s flights, with plans to expand it to 20% by the end of the year. Personalized pricing — or surveillance pricing as the Federal Trade Commission (FTC) calls it — is pricing tailored to the individual based on the personal data collected. That’s different from dynamic pricing, which is determined by market factors such as real-time supply and demand and pricing by competitors. While the price changes, everyone sees the same price at a given time. Airlines, ride-sharing and other companies already use dynamic pricing. In a nutshell, dynamic pricing changes based on when a consumer buys. Personalized pricing changes based on who the consumer is. Delta seeks to gain a “first-mover advantage,” President Glen Hauenstein added. “We do believe that we are ahead of our competitors in terms of implementing this and in changing our business processes and rules around it.” Ultimately, this is “a full reengineering of how we price — and how we will be pricing in the future,” Hauenstein said.
The share of AI-powered chatbots in searches conducted through desktop browsers reaches 5.6% in June, up from 2.48% in June 2024 and 1.3% in January 2024
AI-powered chatbots reportedly account for a small but growing share of searches conducted through desktop browsers. The share of those searches in the United States that went to chatbots rather than traditional search engines reached 5.6% in June, up from 2.48% in June 2024 and 1.3% in January 2024. These numbers do not include search traffic from mobile browsers and apps. The share is higher among early adopters of AI-powered chatbots. Among the consumers who were already using these chatbots when Datos began tracking their behavior in April 2024, the share of desktop searches that went to AI reached 40% in June, up from 24% a year earlier. Datos CEO and co-founder Eli Goodman told that this growth marks a change that could be as significant as the introduction of Google’s web browser and the first social media platforms. This development has sparked the creation of AI-optimization startups that aim to help brands have their names show up in AI searches. It has also created an opportunity to include advertising in AI search replies, something Perplexity is experimenting with. Perplexity launched an AI-powered web browser called Comet, saying the browser lets users answer questions and carry out tasks and research from a single interface.
Latest merchandise spending data says dollar stores and Amazon both are gaining momentum while warehouse clubs such as Costco, BJ’s and Sam’s Club are slowing
Retailers face mounting pricing pressure as consumers adjust their spending habits, and value becomes a universal priority, according to a new report from Consumer Edge. The study finds high-income and young shoppers are both trading down. Walmart and Dollar General saw more new customers from the $150K-plus income bracket and 18-to-34 age group, signaling broader value-seeking behavior. Dollar stores and Amazon both gaining momentum Dollar Tree and Five Below posted double-digit growth since mid-April 2025, while Amazon maintained steady high single-digit gains and remains the dominant U.S. e-commerce player. Warehouse growth slows Growth slowed from elevated levels at warehouse clubs such as Costco, BJ’s and Sam’s Club as consumers pulled back on bulk purchases after a stock-up driven boost. Target’s new customer mix skews older and lower-income Unlike Walmart, Target’s new customer growth is driven by lower-income shoppers, suggesting it may be losing appeal among higher-income consumers managing discretionary spend. Its once-strong “cheap chic” image may no longer resonate with younger, trend-conscious buyers. Geographic shifts Dollar stores and discount chains remain dominant in rural areas with continued expansion, while urban consumers lean toward warehouse clubs and e-commerce. However, Amazon’s rapid delivery rollout could blur that divide. Retention trends Deep brand loyalty contributes to Amazon, Walmart and Costco’s strong customer retention rates. Meanwhile, dollar stores maintain lower retention rates, as their value-driven, transactional model attracts more occasional and price-sensitive shoppers.
Target’s back-to-school promo to include in-store personalization stations- shoppers can customize school and dorm essentials — from backpacks and lunchboxes to towels and pillows — with embroidery, patches, pins and more
Target Corp. is bringing back a popular in-store feature for its “Back-to-School-idays” shopping event, running July 27 – Aug. 2. The discounter will once again offer personalization stations, where shoppers can customize school and dorm essentials — from backpacks and lunchboxes to towels and pillows — with embroidery, patches, pins and more. The personalization is free with purchase. The stations will be featured in nearly 500 stores, doubling the number of locations compared to last year due to its popularity and shoppers’ request for more, the retailer said. Target will also host 100 college-focused events in stores July 26, and 400 events celebrating “back-to-school style” Aug. 2-3. The in-store events will include exclusive giveaways such as shoelace kits, school-themed patches and monogrammed bath wraps. Select stores will also feature tech demos. As part of its back-to-school shopping event, Target will offer discounts of up to 30% on key school items such as select backpacks and kids’ apparel. And as previously announced, Target is holding 2024 prices on its list of 20 must-have supplies — adding up to a total of less than $20 — and last year’s top-selling $5 backpack. The retailer is also offering over 1,000 items under $5, including $0.25 crayons and glue sticks, $2 water bottles and $5 wired headphones.
Unit21’s integration of Fingerprint’s device intelligence, which collects and analyzes over 100 signals from the browser, device, and network with its AML platform to help detect complex fraud types such as credential stuffing and geolocation spoofing in real-time
Unit21 announced its new device intelligence capabilities designed to help fintechs combat the ongoing threat of fraud. The company’s fraud-fighting platform now incorporates Fingerprint’s device intelligence, which collects and analyzes over 100 signals from the browser, device, and network to flag potential fraud patterns, such as repeated login attempts across multiple user accounts, in real time. Unit21 is the most flexible real-time fraud and AML platform that empowers fintechs to build and adapt faster than fraudsters without the need for complex coding, cumbersome reporting processes, or lengthy analyses. With access to persistent, highly accurate device IDs and real-time Smart Signals, such as Bot Detection, VPN Detection, and more, fintechs using Fingerprint and Unit21 can expand their arsenal of insights to combat bad actors. These newly added capabilities help tackle complex fraud types, including: Credential stuffing: Detects bot activity and repeated login attempts across multiple accounts from the same device. Elder & emergency scams: Identifies potentially suspicious activity such as new or unrecognized devices accessing an account and IP geolocation mismatches, which can signal scammers attempting to exploit vulnerable users. Tech support scams: Detects use of virtual machines, developer tools and abnormal device behavior, such as unusual spikes in activity, as well as new logins from unfamiliar devices or locations. Geolocation spoofing: Detects mismatched time zones, use of proxies, and other methods fraudsters use to evade detection.
Agent2.AI’s AI orchestration platform can understand user intent, break down the request into smaller, manageable steps, delegate each task to focused atomic agents and deliver real, usable outputs such as reports, spreadsheets, and presentations
Agent2.AI announced the upcoming launch of Super Agent, a breakthrough AI orchestration platform designed to coordinate intelligent work across multiple agents, APIs, and even real human collaborators. Unlike traditional AI tools that focus on generating content or answering questions, Super Agent acts as an orchestration layer — a system that understands user intent, delegates work to the right components, and delivers real, usable outputs such as reports, spreadsheets, and presentations. “We’re not building just another AI agent,” said Chuci Qin, CEO of Agent2.AI. Users can prompt Super Agent with requests and system will automatically break each request into smaller, manageable steps. Each task is broken down and handled by focused atomic agents. Each agent is built to do one specific job, such as finding information, organizing research, or creating slides. These atomic agents form a growing ecosystem inside Agent2.AI, each focused, reliable, and composable. Super Agent can also call on external tools and agents through standard protocols such as MCP or A2A, allowing the system to dynamically connect with open-source frameworks, third-party APIs, or no-code automations as needed. In some cases, tasks may require not just software, but real-world execution, such as placing an order, contacting a vendor, or managing a physical deliverable. When that’s the case, Super Agent can seamlessly coordinate with vetted freelancers or agency partners. These human contributors are not fallback options, but core participants in a flexible, multi-agent system.
A new open-source method utilizes the MCP architecture to evaluate agent performance through a variety of available LLMs by gathering real-time information on how agents interact with tools, generating synthetic data and creating a database to benchmark them
Researchers from Salesforce discovered another way to utilize MCP technology, this time to aid in evaluating AI agents themselves. The researchers unveiled MCPEval, a new method and open-source toolkit built on the architecture of the MCP system that tests agent performance when using tools. They noted current evaluation methods for agents are limited in that these “often relied on static, pre-defined tasks, thus failing to capture the interactive real-world agentic workflows.” MCPEval differentiates itself by being a fully automated process, which the researchers claimed allows for rapid evaluation of new MCP tools and servers. It both gathers information on how agents interact with tools within an MCP server, generates synthetic data and creates a database to benchmark agents. Users can choose which MCP servers and tools within those servers to test the agent’s performance on. MCPEval’s framework takes on a task generation, verification and model evaluation design. Leveraging multiple large language models (LLMs) so users can choose to work with models they are more familiar with, agents can be evaluated through a variety of available LLMs in the market. Enterprises can access MCPEval through an open-source toolkit released by Salesforce. Through a dashboard, users configure the server by selecting a model, which then automatically generates tasks for the agent to follow within the chosen MCP server. Once the user verifies the tasks, MCPEval then takes the tasks and determines the tool calls needed as ground truth. These tasks will be used as the basis for the test. Users choose which model they prefer to run the evaluation. MCPEval can generate a report on how well the agent and the test model functioned in accessing and using these tools. What makes MCPEval stand out from other agent evaluators is that it brings the testing to the same environment in which the agent will be working. Agents are evaluated on how well they access tools within the MCP server to which they will likely be deployed.
Alibaba’s Qwen3-Coder launches and it ‘might be the best coding model yet’- designed to handle complex, multi-step coding workflows and can create full-fledged, functional applications in seconds or minutes
Chinese e-commerce giant Alibaba’s “Qwen Team” has come out with Qwen3-Coder-480B-A35B-Instruct, a new open-source LLM focused on assisting with software development. It is designed to handle complex, multi-step coding workflows and can create full-fledged, functional applications in seconds or minutes. Qwen3-Coder, is available now under an open source Apache 2.0 license, meaning it’s free for any enterprise to take without charge, download, modify, deploy and use in their commercial applications for employees or end customers. It’s also so highly performant on third-party benchmarks and anecdotal usage among AI power users for “vibe coding.” Qwen3-Coder is a Mixture-of-Experts (MoE) model with 480 billion total parameters, 35 billion active per query, and 8 active experts out of 160. It supports 256K token context lengths natively, with extrapolation up to 1 million tokens using YaRN (Yet another RoPE extrapolatioN — a technique used to extend a language model’s context length beyond its original training limit by modifying the Rotary Positional Embeddings (RoPE) used during attention computation. This capacity enables the model to understand and manipulate entire repositories or lengthy documents in a single pass. Designed as a causal language model, it features 62 layers, 96 attention heads for queries, and 8 for key-value pairs. It is optimized for token-efficient, instruction-following tasks and omits support for <think> blocks by default, streamlining its outputs. Qwen3-Coder has achieved leading performance among open models on several agentic evaluation suites: SWE-bench Verified: 67.0% (standard), 69.6% (500-turn); GPT-4.1: 54.6%; Gemini 2.5 Pro Preview: 49.0%; Claude Sonnet-4: 70.4%. The model also scores competitively across tasks such as agentic browser use, multi-language programming, and tool use. For enterprises, Qwen3-Coder offers an open, highly capable alternative to closed-source proprietary models. With strong results in coding execution and long-context reasoning, it is especially relevant for: Codebase-level understanding: Ideal for AI systems that must comprehend large repositories, technical documentation, or architectural patterns Automated pull request workflows: Its ability to plan and adapt across turns makes it suitable for auto-generating or reviewing pull requests Tool integration and orchestration: Through its native tool-calling APIs and function interface, the model can be embedded in internal tooling and CI/CD systems. This makes it especially viable for agentic workflows and products, i.e., those where the user triggers one or multiple tasks that it wants the AI model to go off and do autonomously, on its own, checking in only when finished or when questions arise. Data residency and cost control: As an open model, enterprises can deploy Qwen3-Coder on their own infrastructure—whether cloud-native or on-prem—avoiding vendor lock-in and managing compute usage more directly.