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B2B sales teams are facing an unexpected challenge: despite the efficiency of AI-driven outreach tools, response rates are dipping and buyers are developing communication fatigue. The reason is simple – technology can help you reach more people, but it can’t make them care. In business sales, using AI to be fast and efficient is just the bare minimum; it won’t make you stand out. The real advantage goes to whoever can build the deepest trust.

The B2B sales playbook has undergone its most radical transformation since the invention of the CRM, with artificial intelligence moving from a futuristic luxury to the operational backbone of modern revenue teams. AI can automate outreach at an unprecedented scale and velocity, drafting personalized emails in seconds, identifying ideal customer profiles through predictive data, and orchestrating multi-channel sequences across thousands of prospects simultaneously.

However, as pipelines flood with algorithmically clean messaging, B2B organizations are hitting a wall. The cost per lead drops, but the volume of outbound activity skyrockets, creating a new problem – business communication now feels cheap and generic. Prospects can spot an AI-generated compliment from a mile away, making personalization feel synthetic rather than genuine.

The limits of AI in B2B sales become prominent when every sales team has access to the same tools, resulting in outreach that sounds remarkably similar. A buyer does not just purchase software or a service; they wager their professional reputation on the vendor’s ability to deliver. An algorithm can state a value proposition, but it cannot assume accountability – it cannot look a client in the eye and say, ‘I will ensure your team successfully adopts this platform.’ This fundamental limitation explains why human trust matters in AI-driven sales.

Trust is incredibly valuable because it smooths out complex business deals, especially when multiple people are involved. Where there is high trust, sales cycles shorten; objections disappear; and price sensitivity is substantially reduced. In contrast, when there is low or no trust in an agreement, every item has been scrutinized under the microscope of contract language before an agreement can be reached.

Psychologists generally classify trust into two categories: capability (assurance that the agent has the proper skills to do the work) and benevolence (assurance that the agent cares about their well-being). When combined, these two elements constitute the basis of all modern-day B2B agreements. The prospect asks two questions – ‘Can this tool do the job?’ And does this team actually care about my business?

Custom Power Apps builds are now putting Microsoft’s Copilot and AI agents to work inside the systems teams already use, not in a standalone chatbot. This marks a significant shift from having these tools operate independently to being integrated directly into existing workflows. The integration is designed to make it easier for businesses to leverage the capabilities of Copilot and AI agents without requiring users to switch between multiple applications or interfaces.

According to Russell Kommer, founder and CEO of eSoftware Associates, which has been building custom Power Apps and Microsoft 365 systems since 2006, ‘Fix your permissions and data model before you buy a single extra license, and that groundwork is what makes Copilot deliver.’ This emphasis on preparation highlights the importance of having a solid foundation in place before implementing these tools. Kommer’s company has completed over 200 migrations with zero data loss, demonstrating their expertise in this area.

The difference between an AI assistant and an agentic AI agent lies in its ability to take action within a live system. While assistants can provide answers and suggestions, agents can read records, trigger workflows, update fields, and stay within user permission boundaries. This distinction is crucial for businesses looking to automate processes and streamline their operations.

Inside Power Apps, the agent can interact with Dataverse records, power Automate workflows, and update relevant fields while adhering to user permissions. This level of integration enables agents to perform real work, such as drafting next steps or updating systems when tasks are completed. The move from assistant to agent has become a clear change in how enterprise software is built this year.

Agentic AI is moving from being a rare feature to a standard expectation in enterprise software. However, many organizations struggle with scaling their AI initiatives and measuring the impact on profitability. A key challenge lies in implementing robust risk controls and governance frameworks that allow agents to safely interact with live business data.

Copilot stalls without a solid data foundation. It can read whatever a user can already open, making loose permissions a live exposure as soon as it’s switched on. If deployed over a messy permission model, Copilot can surface sensitive records to people who were never meant to see them. This highlights the need for companies to clean up their Microsoft 365 permissions and data governance before implementing these tools.

The broader pattern across the market is that many organizations have adopted AI but struggle with scaling it. Fewer still can point to a measurable profit impact. An AI readiness assessment helps fix the data and permissions before deployment, moving projects out of this holding pattern so the AI is useful and safe from day one.

Governance separates pilots from production agents. When agentic projects get canceled, the cause is usually a missing set of rules rather than broken technology. Many projects are abandoned due to rising costs, unclear value, and weak risk controls rather than by the models themselves.

The market is crowded with AI tools that can summarize, draft, and chat. However, building an agent that safely interacts with live business data while following permissions and leaving an audit trail after every action is a more challenging task. These controls are what move an agent from being a tested prototype to one that’s ready for production.

Kommer added, ‘We scope the permissions and the audit trail before we scope the agent, because a confident wrong answer in front of the wrong person is what gets a project shut down.’ This emphasis on governance highlights its critical role in ensuring agents operate safely within live systems.

For most companies, the technology to implement Copilot tools already exists. What decides the outcome now is whether their data and permissions are ready to carry real work. This part of the equation remains fully under a company’s control.

Frequently Asked Questions about Custom Power Apps reveal that running an AI readiness assessment before turning on Copilot can help determine if the environment is prepared for these tools. The assessment checks licensing, data classification, permissions, and governance since Copilot can reach anything the signed-in user already accesses.

Power Apps supports custom business systems such as CRMs, ticketing, and case management. Dataverse handles relational data while Power Automate manages workflows, inheriting Microsoft 365 security and identity in the process. This integration enables a full process like HR onboarding to be automated from start to finish using a combination of Power Apps front end, Power Automate, and an embedded agent.

Copilot and AI agents can indeed be deployed securely within compliance-sensitive businesses when they run on a governed foundation. Role-based access, data classification, and Microsoft 365 compliance controls keep the agent within set boundaries so it acts only on data cleared for the signed-in user to see.

The use of AI coding assistants is transforming software development, but many companies are still focused on the wrong question. Instead of wondering which AI model is best, they should be asking how much project context their AI has. A recent benchmark shared by a developer on X illustrates this point. The developer reported that an AI coding model performed significantly better on Convex application development tasks when it was given a structured guidelines file. Without that file, performance declined.

The broader lesson here is not about one model or one development platform. It’s about how AI coding assistants work. They perform better when they are given durable, project-specific instructions rather than a vague prompt and a blank screen. This is especially important for supply chain software, which operates inside complex enterprise environments with specialized workflows and integration logic.

Supply chain software involves transportation management systems, warehouse management systems, supply chain planning platforms, order management systems, visibility platforms, and ERP-connected applications. These systems require human developers to learn the rules of each project before they can start coding. An AI assistant faces the same challenge without context files. Without them, the model has to infer too much from vague prompts, which may lead to incorrect code generation.

Early AI-assisted development relied heavily on prompt engineering. Developers repeatedly explained the same requirements: tech stack, coding conventions, data model, API design, security requirements, testing expectations, and documentation style. However, this approach does not scale well for large projects or teams. A better pattern is emerging: persistent project guidance.

Context files provide a reusable understanding of how the project should be built. They tell the AI which frameworks to use, how database tables and APIs are structured, which coding patterns are approved, which patterns should be avoided, how errors should be handled, how tests should be written, how security and permissions should be implemented, and how documentation should be formatted.

The value of context becomes even clearer in supply chain technology. A transportation management system must reflect the operations of shippers, carriers, brokers, forwarders, warehouses, and customers. It requires a deep understanding of receiving, putaway, picking, packing, replenishment, cycle counting, labor constraints, automation interfaces, and inventory accuracy.

A warehouse management system needs to understand how goods are received, stored, picked, packed, shipped, and tracked. A planning application must account for demand signals, supply constraints, lead times, service levels, capacity, inventory policies, and scenario analysis. An AI coding assistant that lacks this context may still generate syntactically correct code but will not be operationally useful.

Persistent guidance can improve more than just code quality. It helps teams reduce rework during code review, maintain consistency across modules, onboard new developers faster, improve test coverage, generate better documentation, lower AI usage costs by reducing corrective prompts, and preserve architectural discipline as teams scale AI adoption.

This is especially important for companies moving beyond experimentation with AI in production development workflows. The more AI is used, the more governance matters. Context files are not a substitute for engineering discipline but can reduce ambiguity and make it more likely that generated code conforms to how the enterprise actually builds and runs software.

The next phase of AI-assisted software development will be defined by how well companies capture and reuse their own institutional knowledge. For supply chain software vendors, logistics service providers, manufacturers, retailers, and industrial companies, the lesson is clear: AI coding assistants need more than prompts; they need context files to provide a reusable understanding of project requirements.

The use of AI in supply chain operations is shifting from capability to execution, where context, governance, workflows, thresholds, and action pathways determine whether AI improves real decisions across planning, logistics, sourcing, fulfillment, and risk management. This requires companies to build strong project guidance into their AI development workflows for better code quality, faster delivery, lower rework, and more consistent enterprise software outcomes.

AI coding assistants are not just tools for generating code; they can also improve data analysis and visualization capabilities in supply chain operations. By providing a reusable understanding of project requirements, context files enable teams to reduce the time spent on corrective prompts and improve test coverage, leading to better decision-making across planning, logistics, sourcing, fulfillment, and risk management.

The use of AI tools for business is becoming more widespread, but companies need to focus on how they can leverage these tools effectively. This requires a shift from relying solely on prompt engineering to using persistent project guidance with context files that provide a reusable understanding of project requirements.

Historians and epigraphers now have a powerful new tool at their disposal, thanks to the launch of Google DeepMind’s Predicting the Past Skill. This innovative AI skill is designed specifically for researchers working with ancient inscriptions, allowing them to analyze and interpret these valuable historical artifacts without requiring extensive coding knowledge.

The Predicting the Past Skill connects Gemini, an interactive reasoning platform developed by Google DeepMind, with two specialist models: Ithaca and Aeneas. These models were created to restore, date, place, and contextualize ancient Greek and Latin inscriptions, but their capabilities have now been integrated into a conversational workflow that streamlines the analysis process.

According to Google DeepMind, historians and epigraphers can use this tool to query, cross-analyze, and map massive collections of ancient data as naturally as speaking with a colleague. This means they can explore complex historical questions without needing to write code or rely on manual transcription.

The Predicting the Past Skill is particularly well-suited for researchers working with fragmentary texts, uncertain dates, and unclear places of origin. It’s designed to help them navigate these challenges by providing flexible visualizations, advanced multi-text analysis capabilities, and large language models grounded in evidence and domain expertise.

Google DeepMind has been collaborating with epigraphers on this project for nearly a decade, including the development of Ithaca in 2022 and Aeneas in 2025. Thea Sommerschield, a historian and epigrapher at Durham University, has co-led Google DeepMind’s work in this area.

Ancient inscriptions are central to historical research, but many survive in damaged or partial form. They can include imperial decrees, votive dedications, everyday transactions, and personal appeals – often with missing text, uncertain dating, and disputed origin. Historians need flexible tools that can help them make sense of these complex artifacts.

Google DeepMind has identified three key barriers to AI-assisted historical analysis: researchers require flexible visualizations for individual inscriptions, more advanced multi-text analysis capabilities without specialist coding, and large language models grounded in evidence and domain expertise. The Predicting the Past Skill is designed to address these challenges by linking Gemini’s interactive reasoning with the outputs of Ithaca and Aeneas.

Rather than asking a general-purpose model to work from scratch, the skill draws on specialist inscription models and presents results in a form that historians can inspect. This approach supports restoration, attribution, contextualization, mapping, and comparison across large collections of ancient data – all without requiring extensive coding knowledge.

The Predicting the Past Skill has been tested with three case studies from the Greco-Roman world. The first focuses on Tab.Sulis 97, a curse tablet from Aquae Sulis in Roman Britain. This tablet was written by a woman named Basilia, who cursed whoever had stolen her silver ring – and it’s just one of hundreds of similar tablets found at Bath.

Google DeepMind used Aeneas to place the tablet within its proposed chronological and geographical ranges, producing an explanation that began to resemble epigraphic commentary. The model supported this conclusion using textual features, demonstrating how AI can be used to provide more nuanced historical analysis.

The second case study moves from a single inscription to a wider corpus of votive altars dedicated in 211 CE by Roman officials across the Rhine and Danube provinces. Google DeepMind analyzed regional patterns across related inscriptions and traced how religious practices spread through the movement of people across the Roman Empire – all using the Predicting the Past Skill.

The third case study uses lead oracular tablets from Dodona in northwest Greece, a sanctuary where visitors asked divine guidance on various topics. Thousands of these tablets survive, many in highly fragmentary condition. Google DeepMind used the collection to move beyond individual inscription attribution and reconstruct a wider community of people who came to the sanctuary.

This work shows how the Predicting the Past Skill can be used not just as a restoration engine but also as a tool for exploring connections across large historical datasets. Researchers can examine Dodona not just as a collection of texts, but as a network of connected individuals moving through the ancient Mediterranean – all without requiring extensive coding knowledge.

The case studies demonstrate both the potential and limitations of this new AI skill. A single inscription may need restoration and dating, while a corpus requires comparison, mapping, and pattern detection across many damaged objects. Google DeepMind is working to address these challenges by providing more advanced multi-text analysis capabilities and large language models grounded in evidence and domain expertise.

The Predicting the Past Skill draws on datasets from various sources, including Dodona Online, Ithaca’s use of the Searchable Greek Inscriptions database, and Aeneas’ training data from Epigraphic Database Roma, Heidelberg, and Clauss Slaby. This comprehensive approach ensures that the tool is grounded in specialist inscription models and evidence rather than unsupported AI output.

The Predicting the Past Skill is now available through Google Antigravity, allowing researchers to analyze patterns and produce visualizations ‘in a matter of minutes.’ By streamlining this process, historians can focus on interpreting their findings rather than spending hours transcribing or coding. This new tool marks a significant step forward in data analysis for historical research – one that could have far-reaching implications for our understanding of the past.

Graham Platner, the U.S. Senate Democratic nominee from Maine, is facing intense scrutiny after a rape allegation surfaced against him. The allegations come on top of multiple controversies that have plagued his campaign since October 2025. Despite these issues, Platner’s supporters had continued to rally behind him until now.

Jenny Racicot, one of the women who has accused Platner of rape, recounted her experience in interviews with Politico and CNN. According to Racicot, she was in a relationship with Platner on-and-off from 2021, during which time he allegedly barged into her home uninvited while intoxicated and raped her.

Racicot’s allegations are the latest in a series of controversies that have dogged Platner’s campaign. In October 2025, CNN uncovered years’ worth of deleted Reddit posts made by Platner between 2009 and 2021. The posts revealed some disturbing views held by Platner at the time.

Platner had described himself as a ‘communist,’ wrote that all police were bastards, and argued that many rural White Americans actually are racist and unintelligent. Other posts reflected how his combat experience in Iraq and Afghanistan had reshaped his politics, with Platner writing that America’s wars had left him disillusioned and significantly more left than when he enlisted.

Days after the Reddit controversy erupted, additional reporting drew scrutiny to other deleted posts, including one where Platner appeared to downplay concerns about sexual assault. In this post, Platner wrote that people should take some responsibility for themselves and avoid becoming so intoxicated that they end up in compromising situations.

There are over 2,000 posts by Platner on Reddit, which The Maine Monitor later compiled into a database. Platner distanced himself from those posts, telling CNN at the time that he was ‘f------ around the internet’ and struggling to adjust to civilian life after serving overseas in Iraq and Afghanistan.

He claimed not to want people to see him for who he was during his worst Internet comment or even his best one. However, this attempt to downplay his past views has been met with skepticism by many.

Platner’s campaign has also faced criticism over a Nazi-linked tattoo on his chest. The Totenkopf symbol is associated with the SS Death’s Head unit in Nazi Germany. Platner claimed he got the tattoo while drinking with fellow Marines in Croatia in 2007 and believed it was simply a skull-and-crossbones design commemorating surviving combat.

However, subsequent reporting questioned Platner’s claim that he had been unaware of the symbol’s meaning until his Senate campaign. Former acquaintances and past online activity suggested he may have known its association with Nazi Germany years earlier, which Platner rejected.

The controversy surrounding the tattoo intensified after Sanders brushed off concerns over Platner’s tattoo, arguing there were more important issues at hand.

Another issue that has plagued Platner’s campaign is a sexting scandal. The New York Times reported on May 30 that Platner’s wife had privately informed senior campaign officials about his exchange of sexually explicit messages with other women during the early years of their marriage.

The app Kik was used by Platner to send these messages and photos to women, raising concerns about potential political fallout. Former campaign official Genevieve McDonald said Gertner told her that her husband had been exchanging sexual messages with as many as a dozen women, while another campaign official claimed the number was lower and that the conduct had ended before the campaign launched.

The issue surfaced during an internal vetting process ahead of a high-profile Labor Day rally with Sanders. The discovery prompted outcry from Sen. John Fetterman, who called Platner a ‘creeper.’ However, Sanders and Schumer doubled down on their support for Platner, saying they believed he could defeat incumbent Sen. Susan Collins.

Days after reports about sexually explicit messages surfaced, The New York Times published interviews with six of Platner’s former romantic partners, offering sharply different accounts of their relationships with the Democratic Senate candidate.

Three women described him as kind and supportive, never making them feel unsafe. However, three others painted a far more troubling picture, alleging volatile relationships marked by heavy drinking, infidelity, and behavior they found emotionally damaging.

One former girlfriend, Lyndsey Fifield, alleged that Platner sometimes grabbed her hard enough to leave marks and during one argument twisted her arm behind her back, pushed her into a bedroom, and held the door shut until she ‘calmed down.’ Platner denied allegations of physical intimidation, but The Times could not corroborate this claim.

Fifield took fire at The New York Times for not adequately verifying her story. She claimed that she gave reporters five phone numbers, but they only reached out to two people who would be able to affirm their relationship timeline and events.

Racicot was also interviewed by the Times and alluded to the alleged sexual assault she said occurred in 2021. It was the attacks on Fifield that compelled Racicot to step forward and tell Politico the full story of her experience with Platner.

The rape allegation against Platner has now led Democratic Party leaders, including Sen. Elizabeth Warren, Senate Minority Leader Chuck Schumer, and Rep. Ro Khanna, to call for his exit from the race. Even Sanders, who had endorsed Platner just 11 days after he launched his campaign and stood by him through every major controversy, called on Platner to end his candidacy.

Republicans have questioned why Democratic leadership took so long to revoke their support despite all the controversies surrounding Platner. The latest allegation of rape has proven to be a breaking point for many who had previously defended Platner’s actions.

Meta has introduced an artificial intelligence (AI) tool called Muse Image that allows users to generate new images using public content from other accounts. This feature is enabled by default and can be accessed through the Meta AI app, which also includes WhatsApp and Instagram integration.

The tool uses advanced reasoning to blend multiple photos into high-quality creations for sharing across various platforms. Users can tag another public Instagram account on the Meta AI app to create new reels, posts, or stories that reuse part or all of a published photo, video, or reel.

According to Meta’s help document, users may be able to create content with other people’s Instagram photos using AI features at Meta. This means their reused content could become discoverable in search engine results if the settings allow for it.

In scenarios where an account has been switched from public to private and remains that way for more than 24 hours, all reels, posts, and stories using their content will be deleted from Instagram. However, existing content created by others before this change won’t be affected.

For users under 18 with public accounts, only those who follow can reuse the media if account settings allow it. Users also won’t receive notifications when their images are remixed using AI, but they will get alerts for other types of remixes and sequences.

Meta emphasizes that users have complete control over how their content is tagged for AI creation, with an option to turn off this feature entirely. To do so, Instagram users must go to their profile settings and disable the ‘Allow people to create with and reuse your content’ setting.

The company recommends public account holders switch off this setting as soon as possible since previously created content won’t be deleted even after disabling it. The AI image tool is expected to roll out in Facebook, Messenger, and Meta Advantage+ creative for advertisers soon.

This development follows a broader industry trend where tech companies are increasingly embedding AI into their products by default rather than requiring users to opt-in. Google has also rolled out similar features that allow the company to store media from signed-in users to improve its AI models.

Google’s new Search Services History option lets users view and manage saved media, including images, files, and audio recordings. This data is used to develop and improve Google’s AI services, such as generative AI models, which can be trained using user history.

In addition, Google has added a ‘Personalized Recommendations’ setting that uses an account’s profile information, Search Services History, and other saved activity across Google sites and apps to serve tailored results in search and AI responses. This feature is designed to provide users with relevant content based on their interests and location.

The introduction of these features raises concerns about user consent and control over their data. As more companies integrate AI into their products by default, it’s essential for users to be aware of the implications and take steps to manage their settings accordingly.

In India, the way people search online varies greatly depending on what they’re looking for. A recent report from YouGov sheds light on how urban Indians navigate different platforms to find information, including AI assistants and traditional search engines.

The study surveyed over 1,000 Indian adults and found that search has become increasingly fragmented across various tools. Urban Indians are using a range of platforms, such as search engines, AI assistants, maps, marketplaces, and more, each suited for specific tasks.

One key finding is that the debate around AI vs. traditional search is misguided. Instead, it’s about which tool excels in particular areas. For instance, people use AI assistants primarily for tasks like setting reminders or making voice calls, rather than complex searches.

Trust remains a significant barrier to wider adoption of AI tools for businesses and individuals alike. The report highlights that early trust in AI is fragile and may not hold up under scrutiny if users encounter issues with accuracy or bias.

The study also explores how search behavior differs across generations. Younger Indians, for example, are more likely to use AI assistants than older adults, who tend to rely on traditional search engines.

Another important aspect of the report is personalization. Consumers seem willing to accept some level of personalization in their online searches but draw the line when it comes to overly invasive or biased results.

Google’s Gemini, an artificial intelligence (AI) system, has not achieved the same level of household recognition as other popular AI models like Claude and ChatGPT. One key reason for this disparity is a phenomenon known as the innovator’s dilemma.

A new feature on Instagram is allowing users to generate AI images using public posts, raising concerns about data privacy and control. The Muse Image model, unveiled by Meta this week, enables users to create AI-generated images by tagging another person’s account in a prompt. This means that anyone with an Instagram account can use your public photos – including your profile picture – as fodder for their own AI creations unless you take action to stop them.

If your Instagram account is set to ‘public’, it will be opted-in by default, allowing others to reuse your posts and reels without needing your permission. To prevent this from happening, users must manually switch off the feature in the app’s settings menu under the ‘Sharing and Reuse’ tab. This involves disabling separate toggles for posts and reels.

The controls are only available within the Instagram app itself, which may make it difficult for some users to find or adjust these settings. Furthermore, existing AI-generated images made using your content won’t be removed from circulation, even if you opt out of this feature in future. According to Meta’s help page on the subject, users also won’t receive any notification if their content is used by others.

This development marks a significant expansion of Meta’s efforts to compete with rival image-generation tools from companies like OpenAI and Adobe. By integrating AI image creation directly into Instagram, the company aims to make this feature more accessible to its billions of users. However, this move has reignited long-standing concerns about data privacy and user control.

Privacy advocates have been critical of Meta’s approach to collecting and repurposing public posts for years, arguing that it leaves users with too little say over how their content is used. This latest development will likely fuel further debate on the issue, particularly given the potential applications of AI-generated images in business and marketing contexts.

The University of Maryland School of Dentistry (UMSOD) has been at the forefront of innovation in dental care, with experts like David George sharing their insights on how technology is transforming patient treatment.

In a recent feature, Dr. George, senior associate dean for faculty affairs and strategic initiatives, as well as chief operating officer of UMSOD’s Faculty Practice, discussed the impact of digital dentistry and AI-driven technologies on dental care.

Dr. George highlighted the benefits of these emerging technologies in improving precision during dental implant placement, which is a crucial aspect of modern dentistry.

The use of digital tools and AI algorithms has enabled more accurate diagnoses and treatment plans, ultimately leading to better long-term outcomes for patients.

As an expert in his field, Dr. George’s comments provide valuable insights into the future of dental care and the role that technology will play in shaping it.

Chinese automaker XPENG has been making waves in the electric vehicle market with its latest model, the MONA L03. The car’s design and features have sparked comparisons to the base RWD Model Y from Tesla, leading some to wonder if it could be the best competitor yet. One of the key factors that sets the L03 apart is its more restrained approach to luxury amenities. Unlike many other models on the market, which try to outdo each other with increasingly elaborate features and technologies, the L03 takes a more subtle approach.

An Italian professional football club, Como 1907, is partnering with Google’s artificial intelligence research division, DeepMind, to host a two-day hackathon focused on developing AI tools for use in the sport. The event, called Data on the Lake, aims to bring together data scientists, machine learning engineers, and sports technology professionals to build practical solutions that can support football decision-making.

The hackathon is part of Como 1907’s broader strategy to leverage its position as a professional club to drive innovation in the industry. Founded in 1907, the club has recently returned to Serie A and has established itself as a hub for football and lifestyle activities, including retail, hospitality, media, and tourism.

As part of this effort, Como 1907 operates through affiliated companies under a Multi-Club Servicing business model, providing services and expertise to other football organizations. This structure gives Data on the Lake a potential route beyond a one-off event, with successful tools potentially being tested inside the club and applied across its operations.

The hackathon will take place in Como, Italy, from July 24th to 25th, 2026, immediately before the second edition of Football on the Lake and the Como Cup. The invitation-only event is open for applications, but places are limited and subject to approval by the organizers.

Data scientists, machine learning engineers, developers, sports technology professionals, and technical founders are invited to participate in the hackathon, which will focus on developing AI tools designed for use inside a professional football club. Participants will work with a curated dataset provided by Como 1907 and build solutions that can tackle real-world problems in football.

The event is aimed at exploring how artificial intelligence can support football decision-making, particularly in areas where data collection and operational use are currently disconnected. According to Mo Dabbah, Chief Technology Officer of Como 1907 and SENT Entertainment, ‘Football produces an extraordinary amount of information, but the real opportunity lies in how clubs turn that information into better decisions.’

The hackathon will run under the theme The Future of Football Intelligence, with teams competing across two judging categories: technical innovation and practical utility. A curated dataset provided by Como 1907 will be made available to participants, along with access to tooling and focused build time.

A judging panel comprising technical, football, and business experts will evaluate submissions based on their technical strength and potential usefulness inside a professional club. The event will also include contributions from the club’s data science and football operations teams, Google DeepMind representatives, Como Ventures, and The Players Fund.

The program will cover various aspects of AI tooling, football data, startup innovation, and applied technology in sport. Participants can expect to engage with technical briefings, project presentations, networking opportunities, and sessions on AI tooling and its applications in the sports industry.

Registration for Data on the Lake requires host approval, but applications are now open for interested participants. The event is expected to provide a platform for innovators to showcase their ideas and potentially pilot or scale their solutions within Como 1907’s football ecosystem.

OpenAI has conducted an extensive audit of SWE-Bench Pro, a widely used coding benchmark, and found widespread task issues. The investigation reveals that approximately 30% of the tasks are broken, which can lead to inaccurate measurements of model capabilities and misrepresent safety cases. This is particularly concerning for OpenAI’s Preparedness Framework, where accurate evaluations are crucial for deployment and safety decisions.

The audit follows a similar review of SWE-bench Verified, another popular coding benchmark, which was found to have fundamental design and contamination issues. At the time, OpenAI recommended switching to SWE-Bench Pro as an improvement over its predecessor. However, this latest investigation has uncovered significant problems with SWE-Bench Pro.

SWE-Bench Pro was designed to test models on longer horizons and more realistic coding tasks, aiming to better track agentic coding capabilities. Tasks are sourced programmatically from public and private repositories, requiring models to implement a solution that passes new tests for a feature without breaking existing functionality. The 731-task public split saw frontier models improve from a pass rate of 23.3% to 80.3% in eight months.

OpenAI’s audit involved reviewing the dataset using a datapoint analysis pipeline, which flagged likely evaluation flaws based on model attempts at tasks, task metadata, and failure traces. Each flagged task was then assessed through multiple investigator-agent passes and independently reviewed by five experienced software engineers, with disagreements escalated for further investigation.

The issues primarily fell into four categories: overly strict tests that enforce specific implementation details not specified in the prompt; underspecified prompts that omit requirements hidden in tests or are not reasonably inferable; low-coverage tests under check the requested feature, allowing incomplete fixes to pass; and misleading prompts that point models toward incorrect behavior or contradict test requirements.

The audit’s findings highlight the difficulty of curating hard but fair benchmarks. OpenAI estimates that approximately 30% of SWE-bench Pro tasks are broken, advising model developers to carefully examine results. The aim is to ensure that task failures reflect genuine model limitations and successes represent complete and valid solutions to prompt requirements.

To assess data quality in evaluations, OpenAI created a quality assurance pipeline. This pipeline reviews the instructions given to models, their attempts at solving tasks, and tests used for grading these attempts to flag likely broken or problematic examples. An initial automated filter flagged 286 potentially broken tasks, which were then reviewed through human-supervised agent review and human annotation campaigns.

The human reviewers were more likely than investigator agents to mark tasks as broken, with some disagreement on categories between the two review paths. However, in no flagged task was ‘not broken’ the most common human label. The overlap between reviewer judgments and agent pipeline flags was 74%, indicating that both methods captured similar failure modes.

Compared to the agent pipeline, human reviewers were more likely to select multiple labels for a task, suggesting additional or overlapping issues not identified by agents. This resulted in conservative labeling: capturing broad failure modes while undercounting cases where reviewers saw extra problems. The largest difference was in low-coverage tests, which humans selected as the most common issue for 9.4% of the benchmark compared to 4.1% from the agent pipeline.

The issues identified highlight the importance of rigorously checking benchmarks. Problem descriptions and merged code often do not align due to long back-and-forths between maintainers and contributors in open-source repositories. Tests included in pull requests can be overly strict, written to validate specific changes rather than defining implementation-agnostic standards for solving tasks.

Evaluation flaws are now easier to detect with improved model capabilities allowing deeper inspection of prompts, tests, patches, traces, and edge cases. This helps surface benchmark issues previously costly or impractical to find at scale. OpenAI hopes the wider evaluation community will develop new benchmarks built by experienced software developers specifically to test model capabilities.

This approach can preserve high standards for realism while ensuring better human oversight throughout the process. Given the issues uncovered in this analysis, OpenAI retracts its earlier recommendation to adopt SWE-Bench Pro. Ultimately, evaluations should provide meaningful signal through hard-to-game, easy-to-trust benchmarks genuinely reflective of model capability or alignment.

The Association for Advancing Automation (A3) has announced the success of its Automate event, which took place in Chicago from June 22-25. The show drew a record-breaking number of attendees and exhibitors to McCormick Place, with over 50,000 registrants and 1,230 companies showcasing their latest technologies.

The event covered an impressive 425,000 square feet of floor space, featuring the latest innovations in robotics, artificial intelligence (AI), machine vision, motion control, and industrial automation. The record-setting attendance reflects the growing demand for automation solutions across various industries as companies seek to improve productivity, address workforce challenges, strengthen supply chains, and remain competitive.

According to Jeff Burnstein, president of A3, Automate 2026 was ‘the strongest show we have ever had’ in terms of both size and quality. He noted that the event’s energy was palpable throughout McCormick Place, with keynotes packed throughout the week, product launches, live technology demonstrations, and programs focused on emerging professionals drawing strong participation.

The Latin America Networking event and Women’s Empowerment Forum were particularly well-attended, underscoring the industry’s growing focus on leadership, inclusion, and talent development. The conference program also reached a new high, with over 1,600 registrants participating in more than 140 sessions focused on industrial AI, robotics adoption, workforce transformation, U.S. competitiveness, supply chain resilience, and automation beyond traditional manufacturing.

One of the major draws at Automate was the Humanoid Robot Pavilion, sponsored by NVIDIA. This pavilion gave attendees a close look at humanoid robots and the enabling technologies behind them. The new forum also featured live demonstrations and discussions on the latest advancements in this field.

The Automate Startup Challenge saw early-stage robotics and automation companies competing for $10,000 and the title of Champion. Mbodi was named the winner for its platform that allows users to teach industrial robots new tasks through natural language and simple demonstrations.

Other notable highlights from the event include the Automate Innovation Awards, which recognized new products and technologies introduced in 2025. The winners were CeiliX InfinityCrane and SkyRunner in Automation Systems; Standard Bots’ Flux AI in Vision, AI, and Software; and Synapticon ACTILINK-JD featuring POSITRON Safety AI in Components, Hardware, and Enabling Technologies.

The Joseph F. Engelberger Robotics Awards were also presented during the event, recognizing Hiroshi Fujiwara, Executive Director of the Japan Robot Association (JARA), and Robert Little, co-founder of ATI Industrial Automation, as this year’s winners.

Automate 2026 continued to expand its focus on education and workforce development through various programs aimed at students and educators. The Education Pavilion, A3 NextGen Theater, Student Challenge, A3 NextGen Student Tours, and programming focused on curriculum, career pathways, hiring, and workforce development all contributed to the event’s success.

For those unable to attend Automate 2026 in person, a digital library featuring recordings of keynote sessions, theater talks, interviews, and exhibitor spotlights is now available. This resource ensures that the wealth of knowledge shared at the event remains accessible.

The next installment of Automate will take place in Las Vegas from May 10-13, 2027, at the Las Vegas Convention Center. Companies interested in reserving exhibit space or exploring sponsorship opportunities can contact the A3 sales team for more information.

Fresno State University is responding to the growing demand for artificial intelligence skills in the workforce by launching a new minor program. The university’s Department of Computer Science has developed this 20-21 unit minor, which will be available to students across all disciplines starting this fall.

The minor is designed to equip students with a deeper understanding of how AI can be applied within their chosen fields of study. According to Dr. Alex Liu, chair of the Department of Computer Science, ‘this minor is for everybody’ who wants to gain knowledge about AI and its applications.

Fresno State already offers the necessary classes and has faculty expertise in place, making it a natural next step to formalize the program as a recognized minor. The four required courses will provide students with a solid foundation in AI principles, while one elective course allows for specialization within their chosen field.

The creation of this minor reflects growing employer demand for AI skills in the workforce. Dr. Liu noted that the Central Valley faces unique challenges and opportunities related to artificial intelligence, including agricultural automation, rural healthcare access, and workforce development.

Employers are increasingly recognizing the value of employees with AI-related knowledge and skills. By offering a formal minor program, Fresno State aims to provide students with a competitive edge in the job market. As Dr. Liu explained, ‘when it’s printed on their transcript or diploma, it makes a difference’.

The university is also responding to student interest in AI education. The new minor was developed in response to both employer demand and student enthusiasm for learning about artificial intelligence.

Fresno State officials see this program as part of the university’s broader vision for its role in emerging technologies. Dr. Liu stated that ‘we consider ourselves an AI hub of the Central Valley’, aiming to provide education, research, and solutions related to AI.

In addition to the new undergraduate minor, Fresno State offers an AI certificate program for community members who are not pursuing a degree at the university. This reflects the university’s commitment to making its expertise in AI accessible to a wider audience.

Students interested in adding the minor should meet with academic advisers early in the fall semester to determine how the coursework can fit into their academic plans.