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Scientific computing is at the heart of modern research in academia and industry, but its software has struggled to keep pace with the rapid growth of data. Many widely used tools were initially developed as code accompanying research papers by small academic teams with limited engineering expertise and no time for packaging, testing, or long-term support. As a result, scientific infrastructure often relies on slow, fragile workflows that require constant maintenance, hindering the pace of discovery.

The situation is about to change thanks to AI agents like Codex and Claude Code. By reducing the costs associated with engineering work and taking over tedious implementation tasks, these agents can help researchers prototype ideas more quickly, pursue projects previously deemed impractical, and maintain software over time without breaking their stride. This shift enables scientific software to become more efficient and better maintained, freeing up researchers to focus on discovery.

The field report explores eight agent-assisted computing projects in the life sciences, five using Codex alone and three combining Codex with Claude Code. The case studies bring together contributions from teams behind each project, highlighting recurring themes and patterns. These projects range from routine maintenance and targeted optimization to large-scale language migrations and GPU-native redesigns.

One of these projects involved modernizing a widely used library for parsing genomic data called cyvcf2. GPT-5.5 replaced the legacy build and packaging system with a unified process designed to make installation, testing, and release easier. The result was a significant improvement in usability and maintainability.

Brent Pedersen noted that while agents can accelerate development, there’s still a need for expert guidance, understanding, taste, and care when it comes to scientific computing. He emphasized the importance of human oversight in validating an AI agent’s output, which often depends on human judgment.

Across case studies, agents handled specific requests effectively but struggled with judging whether their work was scientifically valid or met expectations. In fact, they sometimes expressed confidence even when containing clear errors. Human reviewers therefore needed to find reliable ways to validate the results, such as using an external reference or measurable acceptance target like exact output agreement.

Another recurring theme in these projects was that agents often produced initial implementations quickly but resolving edge cases and subtle numerical differences took much longer. Completing the ‘last mile’ of implementation required significant effort, highlighting the importance of iterative refinement rather than one-shot approaches.

The case studies suggest that agents are enabling researchers to spend less time on implementation and more on directing scientific work. People define goals, break down complex projects into manageable chunks, and judge whether results are scientifically valid. By easing engineering constraints, agents expand what researchers can build while freeing them to focus on the scientific questions and decisions that matter most.

The maintenance gap in research software has long slowed iteration and limited reproducibility and reliability. Published studies have found that published software often fails to properly install or run as documented, forcing researchers to spend substantial time on configuration and debugging. Even routine improvements can save researchers time and reduce computing demands, while performance-based refactoring and rewrites can deliver larger gains.

However, lower implementation costs also make it easier to produce many similar rewrites, fragmenting users and spreading the expert attention required to keep any one tool reliable. This makes long-term stewardship and attribution essential for mature scientific software, which carries undocumented conventions, compatibility requirements, and user trust that cannot be reproduced by translating source code alone.

The case studies illustrate several possible paths forward. Changes to MHCflurry and cyvcf2 were incorporated into their original upstream projects, while rustar-aligner moved under new community stewardship because the original project had been abandoned. Where coordination with existing maintainers is available, it should begin as early as possible. When a separate implementation is necessary, it needs a clear owner and a credible maintenance plan.

The field report highlights that coding agents like Codex can significantly lower the cost of maintenance, migration, optimization, and new implementations. Their long-term scientific value still depends on human decisions around what to build, how to verify it, and who will maintain it. The deeper change is not simply that researchers can produce more software but that they can focus more effort on defining, validating, and stewarding the tools.

These case studies show that agents can already accelerate iteration in scientific computing. As coding agents improve, researchers will be able to spend less time keeping analysis pipelines running and more time advancing their fields.

A recent breakthrough by Anthropic’s Claude AI has revealed a faster attack on the lattice-based signature scheme, HAWK-256. The research also uncovered an improved method for attacking seven-round AES-128, a widely used encryption standard. According to the findings, the new attacks are significantly faster than previous methods but still remain impractical in real-world scenarios.

The HAWK-256 attack exploits a previously unused symmetry in the lattice behind the signature scheme. This discovery was made possible by Claude AI’s ability to analyze and manipulate complex mathematical structures. The resulting attack is an end-to-end key-recovery method that can recover short vectors from the public key, allowing for the reconstruction of a secret basis.

The researchers used Anthropic’s Mythos Preview tool to derive the HAWK-256 attack. This tool largely conducted the research itself, with human input limited to project direction and occasional guidance. The entire process took approximately 60 hours in a multi-agent environment, with an estimated API cost of around $100,000.

The new attack on seven-round AES-128 targets one of the ten rounds of the encryption standard. By removing a 256-way guessing step from existing meet-in-the-middle attacks, Claude AI’s method achieves a speedup of between 200 to 800 times faster than previous methods. However, this improvement still requires an impractical number of chosen plaintexts and remains infeasible at realistic scales.

The researchers emphasized that neither the HAWK-256 nor seven-round AES-128 attack affects production systems or poses any immediate threat to security. The company stated that no changes are needed for existing software as a result of these findings. Additionally, both larger parameters (HAWK-512 and HAWK-1024) remain impractical to attack.

HAWK is the only lattice-based scheme among nine candidates advanced by NIST in its post-quantum standardization process. Its security parameter sets are HAWK-512 and HAWK-1024, with HAWK-256 serving as a challenge parameter for cryptanalytic purposes. The attack on HAWK-256 targets this smaller parameter set rather than the larger ones.

The researchers used Claude AI to find an additional automorphism in the lattice that enables the exploitation of previously discovered symmetry. This discovery was made possible by the tool’s ability to analyze and manipulate complex mathematical structures. By constructing a τ-cocycle lattice from the public key, the attack can recover short vectors before reconstructing a secret basis.

The HAWK-256 recovery process involves verifying the recovered key through signing a message with the NIST reference implementation. However, this does not reveal the original 96-byte secret-key seed but instead produces a decoded key containing functionally equivalent signing material. The company’s released code supports only HAWK-256 and rejects non-HAWK-256 inputs.

The AES result is narrower in scope than its name might suggest. While it targets seven rounds of AES-128, the attack remains impractical due to the requirement for an impractically large number of chosen plaintexts. The researchers used Claude AI’s Möbius Bridge invariant fingerprint to remove a 256-way enumeration step from existing meet-in-the-middle attacks.

The development and verification process took several hundred hours, with two researchers spending nearly a month to reach confidence in the correctness of the method. Anthropic estimated that the expected HAWK-256 key-recovery work factor falls from 264 to 238, while gate-count estimates for larger parameters decreased by around 42.

The company’s disclosures follow the release of CryptanalysisBench, a benchmark developed by researchers from ETH Zurich and other institutions. This benchmark evaluated Mythos 5, which Anthropic describes as an update to its earlier tool, Mythos Preview. The HAWK and AES disclosure specifically names Mythos Preview as the tool used in these attacks.

The public thread on NIST’s forum announcement contained no replies when checked, and there is currently no independent reproduction of Anthropic’s HAWK-256 recovery available for review.

A new app called ARTA is making waves in the creative community. It offers a suite of tools for generating AI images, videos, and more. For a limited time, users can grab a one-year Premium subscription to ARTA for just $19.97 (regularly $39.99). This affordable price point makes it an attractive option for those looking to explore the possibilities of AI-generated content without breaking the bank.

ARTA’s capabilities extend far beyond simple image generation. The app allows users to create realistic headshots, social media graphics, and stylized artwork using a range of built-in AI models. Users can also upload their own photos to experiment with different hairstyles, remove distracting objects, or generate an entirely new look without needing to open a complicated editing program.

The app’s flexibility is one of its strongest selling points. With over 45 art styles and customizable aspect ratios, users have the freedom to create images that fit their project needs rather than being limited by pre-set templates. This makes ARTA an ideal tool for professionals looking to streamline their workflow or hobbyists seeking to explore new creative possibilities.

ARTA’s Premium plan includes a generous allocation of 500 credits per week, which automatically refresh and can be used for AI images, avatar packs, videos, and edits. The app is available on multiple platforms, including iPhone, Android, and desktop. Users can start projects on their phone and finish them wherever it’s most convenient.

ARTA has earned a reputation as one of the top AI creative apps on the market. Its impressive range of features and user-friendly interface make it stand out from other options. The app also allows users to create AI-generated images, which can be used for a variety of purposes, including marketing materials and social media graphics.

A new project called NightRun allows users to run local large language models (LLMs) directly on a Raspberry Pi’s bare metal, eliminating the need for an operating system like Linux. This development is significant because it enables users to run AI-generated images and other tasks without relying on cloud services or powerful hardware.

The creators of NightRun acknowledge that LLMs deserve serious ethical consideration, but running them locally on one’s own hardware can alleviate many concerns. To achieve this, centoslinux built NightRun using Rust programming language with the assistance of Claude AI. The project runs as a UEFI application at boot, pulling the LLM from removable storage and copying it into RAM to execute.

NightRun supports various lightweight LLMs on Raspberry Pi 5 models, including Llama 3.2-1B Instruct, which can run on the 4GB model. The 8GB and 16GB versions of the device support additional LLMs such as Granite 4.1-3B and Qwen3-4B Instruct 2507. These models are capable of running more complex tasks due to their increased memory capacity.

Although NightRun is not truly running on bare metal, it operates within UEFI Boot Services. This allows the application to accept keyboard input, read from storage, and output a terminal to a display without incurring significant overhead. The creators emphasize that even lightweight operating systems supporting peripherals are more demanding than UEFI Boot Services.

Users can try NightRun by downloading the files and installer script from GitHub. The installation process involves creating a removable storage image using the provided instructions. This project demonstrates the potential for running AI-generated images and other tasks locally on single-board computers without relying on cloud services or powerful hardware.

AWS has recently made available the public preview of its Amazon GuardDuty investigation agent, a cutting-edge AI-powered security tool designed to streamline threat triage and reduce the time spent on security investigations. The tool evaluates findings, correlates historical activity, and maps threat telemetry across AWS accounts and organizations, providing a comprehensive view of potential threats.

The company’s goal is to minimize the manual overhead required for security teams to investigate alerts by synthesizing metadata from security findings, 90-day activity logs, and affected resource topologies into structured analysis reports. This capability was first announced in June as part of AI-powered investigations, with a detailed walkthrough published under the investigation agent name in July.

Threat detection services like Amazon GuardDuty continuously stream alerts regarding suspicious network or runtime behavior. However, security teams often struggle with alert fatigue and the manual overhead required to correlate findings across fragmented accounts and logs. Clarke Rodgers, who works in the Office of the CISO at AWS, framed the product thesis directly on LinkedIn: ‘Security teams don’t have a detection problem. They have an investigation problem.’

The GuardDuty investigation agent addresses this bottleneck by evaluating telemetry on demand across three distinct scopes: Finding Analysis, Account Analysis, and Organization Analysis. Each scope provides a structured result set containing an overall risk rating (ranging from Info to Critical), a confidence score, classification against the MITRE ATT&CK matrix, and actionable CLI remediation steps.

Finding Analysis evaluates a specific 32-character GuardDuty finding ID during preview, supporting all Extended Threat Detection findings as well as select foundational, S3, and Runtime findings. Account Analysis assesses the current threat posture of an individual 12-digit AWS account, while Organization Analysis evaluates threat findings across up to 100 member accounts in an AWS Organization.

Sena Yakut, a cloud security architect and AWS Security Hero, drew the distinction between the agent and GuardDuty’s existing correlation capability. She noted that Extended Threat Detection connects related findings into an attack sequence, whereas Investigation analyzes affected resources, IAM activity, and surrounding context to generate a summary with recommended next steps.

Yakut sees the strongest fit for this tool in organizations without a large security function, where initial evidence-gathering consumes a disproportionate share of analyst time. She tested it against sample findings, which returned low risk ratings on test resources as expected. Her caveat is that AI should assist investigation, not replace human validation and decision-making before taking remediation actions.

Programmatically, developers and SecOps teams can trigger investigations using standard AWS SDKs, the AWS CLI via aws guardduty create-investigation, or via EventBridge rules to automate downstream response pipelines. This integration allows for automated threat triage, streamlining the process of identifying potential threats and reducing manual overhead.

The MCP integration is a key detail for teams already running agentic tooling. Through the AWS MCP Server, an engineer can trigger a threat investigation from Claude Desktop or a custom CLI agent runner using existing AWS Identity and Access Management (IAM) credentials. This places security investigation inside the same agent surface as code and infrastructure work.

The integration also inherits governance questions such as which principal actually ran the investigation, whether the agent’s context window now holds 90 days of correlated security telemetry, and how that transcript is retained. AWS’s own Loom reference platform exists precisely to answer these types of questions for agent deployments.

To address data residency and compliance concerns associated with Large Language Models (LLMs), AWS leverages its Cross-Region Inference Service (CRIS) powered by underlying Bedrock models. While compute inference might route to another region within the same geographic boundary to optimize resource availability, investigation data and generated reports remain stored within the originating home region.

The launch puts GuardDuty in a category entered first by other hyperscalers such as Microsoft’s Security Copilot for incident summarization and guided response across Defender and Sentinel. Google offers Gemini-assisted investigation inside Security Operations. What distinguishes the GuardDuty agent is its scope, which is bounded to GuardDuty’s own finding corpus and surrounding AWS telemetry rather than positioned as a general security assistant.

The GuardDuty investigation agent is currently available in public preview across 10 commercial AWS regions: US East (N. Virginia, Ohio), US West (Oregon), Canada (Central), Europe (Frankfurt, Ireland, London, Paris, Stockholm), and Asia Pacific (Tokyo). Usage is free during the preview period, subject to a throttle limit of 10 investigations per account per day, capped at a cumulative maximum of 100 per account during the preview phase. Failed investigations do not count toward either quota.

The limits on usage sit awkwardly beside the EventBridge integration. Ten investigations per account per day, capped at 100 for the whole preview, is a budget for manual triage rather than automated response pipelines. This constraint aligns with Yakut’s caveat that the preview is scoped for analysts evaluating output, not systems acting on it.

The GuardDuty investigation agent marks a significant step forward in automating threat triage and reducing the time spent on security investigations. By streamlining the process of identifying potential threats and providing actionable remediation steps, this tool has the potential to greatly improve the efficiency and effectiveness of security teams.

A financial services company has introduced an artificial intelligence-driven feature designed to help users prepare for regular expenses. Oportun’s Smart Bills tool automatically sets aside money for recurring payments, including rent, utilities, insurance, and subscriptions. This new addition builds on the company’s existing Set & Save platform, which uses AI analysis of income and spending patterns to allocate funds towards savings goals.

Since its launch in 2015, the Set & Save platform has helped users accumulate over $12.8 billion in savings, with an average annual saving of $1,800 per user. The new Smart Bills feature takes this a step further by calculating ‘safe-to-save’ amounts and reserving funds ahead of upcoming payment dates. It can also identify recurring expenses that users may want to track.

Early data from Oportun suggests that users are taking advantage of the tool’s capabilities, with those using Smart Bills saving 48% more each month compared to those relying on Set & Save alone. The feature has collectively saved its users over $14 million so far, with car payments, rent, and utilities being among the most commonly added expenses.

The launch reflects a broader trend in consumer finance, where FinTech companies are increasingly using artificial intelligence to provide personalized budgeting tools. Rather than simply tracking spending, newer AI-powered solutions aim to help consumers anticipate expenses and make proactive financial decisions.

According to Annie Ma, Oportun’s head of savings, ‘Set & Save makes it easier for people to build healthy financial habits in a way that works for their everyday lives.’ With Smart Bills, the company is helping users connect their savings to recurring priority expenses, allowing them to better budget and plan ahead.

Military personnel at Marine Forces Special Operations Command (MARSOC) are seeking to streamline their armory operations by leveraging cutting-edge technologies. The command has partnered with the SOFWERX innovation hub on an initiative aimed at automating its armories using computer vision, among other tools.

The goal is to develop a secure and efficient system that can track and manage thousands of serialized weapons, optics, lasers, night vision devices, communications equipment, and other controlled assets. Current processes rely heavily on manual inventories, handwritten forms, repetitive data entry, and multiple independent records, which are labor-intensive, prone to human error, and consume significant man-hours.

The MARSOC armory maintains a vast inventory of serialized items that transfer custody among operators, maintenance activities, and storage locations. To address this challenge, the command wants to collaborate with innovative companies to design an automated system that relies exclusively on optical recognition and computer vision.

A key aspect of this initiative is the development of a passive, image-based inventory system where weapons, gear, and serial numbers are automatically tracked, verified, and logged by taking digital photos or scanning optical images as items enter or exit the facility. This approach will eliminate slow paper-based workflows and human-error bottlenecks while ensuring absolute property accountability.

The use of RFID, active Bluetooth, or ‘radiating’ digital transmitters is prohibited to maintain operational security. The command wants a system that guarantees absolute property accountability without exposing its facilities to electronic detection.

MARSOC and SOFWERX are scheduled to host a collaboration event in Tampa, Florida, in September where solution providers will meet with military personnel to discuss their needs. Vendors who make it past a downselect later this year will be invited to an assessment event slated for December where their tech will be evaluated.

The evaluation process could potentially lead to other transaction agreements, FAR-based contracts or other deals with industry. The deadline for vendors to request to attend the collaboration event in September is August 16.

Singapore’s DBS Group has upgraded its artificial intelligence (AI) powered virtual assistants, enabling them to support everyday banking tasks across retail, wealth, and corporate banking. The enhancements apply to two separate assistants: DBS Joy, which serves corporate and small-to-medium enterprise customers, and DBS digibot, catering to retail banking clients.

The upgrades bring the total number of users supported by these AI assistants to over 10 million in Singapore, Hong Kong, and Taiwan. Each month, they are expected to handle more than one million customer chats. This marks a significant shift from AI being used primarily as an information assistant to becoming a digital banking assistant capable of carrying out customer-requested tasks through a single conversation.

DBS Group Chief Operating Officer Derrick Goh emphasized the true value of AI lies in delivering meaningful outcomes for customers at scale. He stated, ‘The next leap forward is moving from helping customers find information to helping them get things done.’ This vision has driven DBS’ more than decade-long commitment to AI innovation.

For corporate and SME clients in Singapore, DBS Joy has already been upgraded with agentic AI capabilities. Instead of directing users to navigate different sections of the bank’s digital banking platform, the assistant can now retrieve relevant information and complete selected banking requests within a single conversation.

DBS customers can ask natural language questions about recent payments, transaction history, or banking fees, with the assistant analyzing account and transaction data to provide requested information directly. The capabilities will soon be extended to corporate clients in Hong Kong from September before expanding to other DBS markets.

The enhancement addresses some of the most common requests received by the assistant, including payment tracking, transaction enquiries, and guidance on using the bank’s corporate banking platform. Additionally, DBS Joy is being expanded with knowledge of SME banking products and programmes, allowing businesses to seek information on banking solutions alongside servicing requests.

New live chat functionality will enable customers to connect with customer service officers when required, while transitions from chat to phone support are planned for the fourth quarter of 2026. This upgrade aims to streamline interactions between customers and bank representatives.

On the retail banking side, DBS has enhanced its digibot assistant to respond more naturally to customer enquiries relating to cards, remittances, refunds, and fee waivers. Beginning in August, it will also be integrated into DBS’ digiWealth platform to help customers navigate wealth-related enquiries and connect with their wealth planning managers when needed.

Later this year, DBS plans to further upgrade digibot with agentic AI and voice AI capabilities. These enhancements will enable customers to complete selected servicing requests without leaving the conversation. The rollout represents DBS positioning conversational AI as a means of simplifying customer interactions while reducing the effort required to complete routine banking tasks.

The AI assistants will execute only customer-initiated instructions, ensuring that customers requiring assistance with more complex issues can continue to access human customer service officers.

Companies are increasingly relying on artificial intelligence (AI) to handle customer interactions, but the technology often falls short of expectations. Automated systems may be able to quickly resolve straightforward requests, but they can also create frustration when customers have to repeat themselves or navigate through multiple transfers before reaching a human representative.

The issue is not just about the technical capabilities of AI, but also about how it integrates with human support and preserves trust, transparency, and judgment. Newsweek’s upcoming webinar, ‘Humanizing AI: Personalization, Engagement and the Customer Experience,’ will bring together four business and technology leaders to discuss these challenges and explore ways to make customer interactions more responsive, relevant, and useful.

The live discussion is scheduled for Thursday, July 30, at 2 p.m. Eastern, and will be presented by Cognizant. The panel will feature Nick Mehta, entrepreneur in residence at Bessemer Venture Partners and former CEO of Gainsight; Arun Chandra, chief operating officer at NiCE; Jon Hyman, co-founder and chief technology officer at Braze; and Adrian McDermott, chief technology officer at Zendesk.

Mehta noted that many current deployments have landed in an ‘uncanny valley’ between AI agents and human support. Automated systems may be able to handle straightforward requests quickly, but customers often have to repeat themselves when a representative takes over. This can create frustration and erode trust in the company’s ability to provide effective customer service.

Chandra emphasized that companies need to design the handoff before they deploy automation. ‘The path to get to a human should be completely frictionless and instantaneous,’ he said. A warm transfer, for example, should give the representative a clear account of what has already happened so the conversation can continue instead of restarting. AI can remain involved behind the scenes by surfacing context and possible responses for the employee.

Chandra also expects AI to change how customer service teams divide work between automated systems and employees, raising questions about how roles and skills will evolve. This shift is not just limited to customer support; Hyman pointed out that AI’s growing role in marketing and digital engagement means companies need to tailor their communications to strengthen the bonds with consumers.

Hyman noted that achieving relevance depends on information moving across teams, systems, and channels. Fragmented data can keep a company from recognizing a customer across channels or coordinating how it communicates. Faster content production also means little unless leaders can connect it to engagement, retention, or revenue.

McDermott challenged the assumption that customers will always view automated support as second-rate. Customers judge the experience by whether it resolves their issue without unnecessary waiting, repetition, or transfers. ‘Time is quality in service,’ he said.

The July 30 conversation will give business and technology leaders a practical view of the choices behind customer-facing AI, from data and handoffs to measurement and the role of employees.

A new generation of AI assistants is transforming how businesses manage meeting notes and conversations. Fireflies.ai, a leading platform in this space, offers an impressive array of features to make meetings more efficient and post-meeting workflows easier. The tool integrates seamlessly with popular platforms like Zoom, Google Meet, Microsoft Teams, and others, allowing users to capture meetings and turn them into searchable knowledge.

Fireflies.ai’s capabilities go far beyond basic transcription services. It provides accurate transcription in over 100 languages, AI-generated summaries that include action items, speaker analytics, sentiment analysis, and the ‘AskFred’ feature for querying past meetings using natural language. This powerful set of features enables users to extract valuable insights from their conversations.

Fireflies.ai integrates with CRMs, project management tools, Slack, and many other systems, automatically logging notes and creating tasks. The platform’s integration capabilities are impressive, making it easy for businesses to get the most out of meeting minutes. Users can even tap into over 200 AI apps for post-meeting workflows.

Fireflies.ai is trusted by hundreds of thousands of organizations worldwide, including numerous Fortune 500 companies. The platform’s strong security features ensure compliance with SOC 2, GDPR, and HIPAA regulations, providing peace of mind for businesses handling sensitive data.

While Fireflies.ai offers a free plan, it comes with limitations on storage and AI credits. However, team-wide adoption unlocks the full potential of the platform’s features. It’s clear that accuracy depends on audio quality, so users should make sure their meeting recordings are clear and concise to get the most out of the tool.

Overall, Fireflies.ai remains one of the most popular and reliable AI assistants for businesses. Its ability to turn conversations into searchable, actionable knowledge saves teams significant time on note-taking and follow-ups.

Siri, Apple’s virtual assistant, has undergone a significant upgrade in iOS 27. One notable aspect of the new version is its approach to interacting with users, which sets it apart from other AI assistants on the market.

For nearly two months, I’ve been testing Siri AI as part of the iOS 27 beta program. My experience so far suggests that Apple has made considerable strides in enhancing the capabilities of their assistant.

The new Siri is indeed more capable than its predecessor and delivers on its promise to be a reliable aid for users. However, it’s essential to note that if you require an extremely powerful AI tool, other options like ChatGPT, Claude, or Google Gemini might offer more advanced features.

One key difference I’ve observed between Siri and these third-party tools is the way they interact with users. Unlike some of its competitors, Siri doesn’t attempt to mimic human-like behavior or personality traits.

Siri’s responses are clear and concise, making it evident that it’s a tool designed to assist rather than engage in personal conversations. It also reminds users regularly that it can make mistakes due to its AI nature.

Other AI chatbots like ChatGPT, Claude, and Google Gemini tend to be more verbose and human-like in their interactions. This approach may appeal to some users who prefer a more personalized experience with their AI assistants.

I personally find the ‘human-like’ tendencies of these tools disingenuous. I believe that AI assistants should stay focused on providing assistance rather than pretending to be something they’re not.

The fact that Apple designed Siri in this way is refreshing, as it acknowledges the tool’s limitations and avoids creating unrealistic expectations about its capabilities.

Packaging machinery manufacturers are redesigning equipment to help producers tackle labor shortages, rising sustainability requirements, and increasingly complex production demands. The industry is undergoing significant changes driven by four major forces: labor availability, smaller portion sizes, increasing product variety, and sustainability requirements. Manufacturers are being asked to produce more packs, more SKUs, often from the same factory footprint with the same or fewer skilled operators. Retailers, regulators, and consumers are placing greater emphasis on reducing waste and improving sustainability performance.

Simon Hill, group product innovation manager at TNA Solutions, highlights these pressures driving demand for packaging solutions that can deliver higher throughput, faster changeovers, lower waste, and greater flexibility without adding complexity to operations. He asserts that operational efficiency and agility are now critical differentiators for food manufacturers as they respond to changing consumer and market demands.

Sumeet Arora, senior VP Engineering Business Marketing at UFlex, argues that the packaging machinery industry has evolved significantly over the years. For many years, it was driven by three fundamental objectives: speed, flexibility, and accuracy. Manufacturers focused on increasing machine speeds, improving precision, and enabling quick product changeovers. However, customer expectations have changed, and manufacturers are now looking beyond machine performance.

They want higher productivity from the same manufacturing footprint, maximum equipment uptime, lower operating costs, and greater process predictability. Packaging machinery is increasingly expected to become an intelligent manufacturing asset rather than simply a production tool. Arora also notes that consumer preferences are changing rapidly, product portfolios are expanding, and packaging formats continue to evolve.

This has led to the growing need for versatility in machines. They must be capable of handling multiple pack formats and a wider range of packaging materials with minimal changeover time. Smart factory solutions are playing a crucial role in this transformation, as automation and digital technologies help manufacturers reduce manual intervention and improve accuracy.

Stephen Tagg, global application sales manager for Software at Markem-Imaje, explains that AI inspection is a valuable application in packaging. It can check if the right code, label, image, or product feature is present without relying on manual inspection or older rule-based systems. CoLOS 7 from Markem-Imaje supports fully inline verification through its Vision and Verification module, reducing manual coding errors by up to 20%.

Andrea Solfa, product manager for End of Lines and Tunnels at Sidel, adds that connected platforms enable manufacturers to monitor performance in real-time, gain greater visibility across production lines, and make faster decisions. This supports predictive and proactive maintenance strategies, minimizing downtime while maximizing equipment availability. Digital technologies are continuing to evolve, with data-driven insights playing a crucial role in enhancing efficiency and productivity.

Automation is becoming essential for overcoming labor constraints and improving overall equipment effectiveness. The next generation of packaging machinery is designed to simplify operation, reduce the risk of operator error, and support faster decision-making through greater visibility of machine and line performance. Markem-Imaje’s Tagg expects automation to grow due to labor pressures.

Many manufacturers are finding it harder to attract people into factory roles. Deloitte and the Manufacturing Institute estimate that US manufacturers will require 3.8 million new workers by 2033, with nearly half of these roles potentially remaining unfilled if current labor gaps persist. Similarly, Deloitte reports that 62% of European manufacturers face recruitment and retention challenges expected to continue through 2030.

Automation is not about replacing people but empowering them to focus on problem-solving, quality, and decision-making while machines handle repetitive tasks. Elias Hashemian Nik, product manager sterilization at Syntegon, highlights the increasing significance of packaging machinery automation amid labor shortages and productivity demands, alongside regulatory factors in pharmaceutical production.

Pharmaceutical manufacturers operate in a highly regulated environment where new requirements directly influence equipment design. The EU Good Manufacturing Practice (GMP) guidance continues to shape major shifts in particular. Manufacturers are facing growing pressure from product diversity and the need for faster changeovers. They are looking for equipment that ensures compliance, improves productivity, and operational flexibility.

Syntegon argues that automation is the solution to these challenges in liquid fill-finish operations. The fully automated SynTiso fill-finish line removes human interference with gloveless isolator technology and a fully robotized operation, including contactless container transport. It ensures benchmark-level regulatory compliance and up to 50% faster changeovers.

Digital technologies are becoming equally important as customers increasingly expect transparent production data for predictive maintenance, performance optimization, and data-driven decision-making. Solutions like Synexio provide visibility by connecting equipment, monitoring process performance, and helping manufacturers continuously improve production over the entire lifecycle of their equipment.

Tobias Richter, chief sales officer and group president at Multivac, highlights that increasing cost pressure is shaping the packaging machinery market. Manufacturers must reduce packaging material use while maintaining high levels of efficiency, output, and pack quality. Machinery must enable reliable processing of thinner films and alternative materials.

Multivac supports sustainability targets by helping customers reduce material consumption, energy use, and process-related waste in industrial production. The company develops equipment that can reliably process recyclable monomaterials and alternative packaging materials while maintaining efficiency, output, and pack quality. Its cloud-based Smart Services combine a Consumption Monitor with advanced analytics functions to enable detailed insights into energy and resource consumption.

Sidel’s Solfa expects continued growth in technologies combining sustainability, automation, and operational streamlining. Advanced bottle technologies like Sidel’s Returnable PET — Still Water bottle support circular packaging objectives by enabling bottle-to-bottle recyclability at the end of its service life. Integrated production solutions that streamline multiple processing stages into a single system will continue to gain traction as manufacturers seek greater productivity and flexibility.

TNA’s Hill adds that TNA supports consumers’ environmental sustainability goals by helping reduce product giveaway, minimizing film waste, and improving production efficiency. Equipment must provide the flexibility to accommodate new packaging materials and support future sustainability initiatives without compromising performance. Manufacturers are looking for sustainability improvements that also deliver measurable operational and commercial benefits.

Dairy producers are no strangers to embracing new technology, but the concept of artificial intelligence (AI) may still be unfamiliar to many. However, a growing number of companies in the industry are exploring ways to integrate AI into their operations, and with good reason – this emerging technology has the potential to revolutionize decision-making processes and improve overall efficiency.

A key player in this movement is Shari van de Pol, founder and CEO of CATTLEytics, an ag-tech company dedicated to using technology, particularly AI, to help farmers make better data-driven decisions. Van de Pol’s expertise lies at the intersection of computer engineering and large-animal veterinary medicine, making her uniquely qualified to discuss the potential applications of AI in dairy production.

One area where AI can have a significant impact is in data analytics. Producers often find themselves overwhelmed by vast amounts of information, but with AI, they can analyze and interpret this data to inform their decisions. This not only streamlines daily operations but also enables producers to make more informed choices about nutrition and feed rations, breeding schedules, disease diagnosis, and treatment.

AI’s benefits extend beyond cattle management, however. Van de Pol notes that it has shown promise in helping producers with employee management as well. By utilizing AI technology, dairy farms can develop onboarding processes, protocols, and trainings for employees, making the transition to new staff smoother and more efficient. Additionally, AI can assist in creating employee work schedules and even translate information into another language within the context of their operation.

One of the most significant advantages of AI is its ability to level the playing field between large-scale operations and smaller dairies. Van de Pol suggests that this technology can be a game-changer for producers who may not have access to the same resources or expertise as larger farms, enabling them to compete more effectively in the market.

When it comes to implementing AI on their farm, van de Pol recommends starting small – selecting one aspect of operations they’d like to work on and asking AI for ideas. She also encourages producers to explore companies that understand both the industry and tech, finding people who can make the technology work for them rather than against them.

Van de Pol emphasizes the importance of trust in experts and professionals within the agriculture industry when using AI tools. While this technology has limitations, it can help fill gaps and generate ideas, making it an invaluable resource for producers looking to improve their operations. However, she cautions that producers must still rely on human expertise – such as nutritionists or veterinarians – where necessary.

The learning process itself is not a difficult one, van de Pol assures us. She suggests starting by simply opening a browser and using platforms like ChatGPT, Copilot, or Perplexity to generate ideas and suggestions based on large sets of data. By practicing with these tools, producers can develop the skills needed to effectively utilize AI in their operations.

Another key component is learning how to ask the right questions – something van de Pol stresses as crucial for success when using AI tools. She advises producers not to be discouraged if they encounter difficulties or unclear results; instead, challenge the platform again and refine its answer within the context of dairy or with expert input.

The potential benefits of integrating AI into dairy operations are vast, from improved decision-making processes to enhanced efficiency and productivity. While it may take time for producers to become familiar with this new technology, van de Pol is confident that they hold the power to make it an effective tool for their business.

A new challenge has emerged for business owners as consumers increasingly turn to AI assistants like ChatGPT, Gemini, and Perplexity for recommendations. The question on everyone’s mind is how to get an AI assistant to recommend a business instead of its competitor’s. Monem Technology Services, a Miami-based AI consultancy, has released a comprehensive guide addressing this very issue.

The short answer lies in making businesses machine-readable, trustworthy, and citable. According to Sam Monem, founder of Monem Technology Services, most business websites are not built for machines to read, trust, or cite. This is where the three levers come into play: structured data (schema), answer-ready content, and independent citations.

Structured data, also known as schema markup, makes a business machine-readable by providing information about what the business is, where it operates, and what it offers in a format that AI models can parse without guessing. This means telling an AI assistant exactly what to expect from a business’s website. Answer-ready content takes this a step further by writing pages directly answering customers’ questions rather than marketing copy that talks around them.

Citations are crucial as they move an AI from merely knowing a business exists to confidently recommending it. Consistent mentions of a business across independent, trusted sources demonstrate its credibility and trustworthiness in the eyes of AI assistants. When a website isn’t built for machines to read, trust, and cite, the assistant quietly hands the next customer to a competitor.

As AI-generated answers replace lists of links, being the business an AI names is becoming the difference between being found and being skipped. Monem Technology Services has identified this trend as a significant shift in how businesses are discovered online. The firm’s expertise lies in setting AI strategy and building systems that deliver it, including its own agentic AI platform for measuring and improving visibility across AI answer engines.

Monem Technology Services offers free AI consultations to business owners who want to know where they currently stand. Founder Sam Monem reviews a company’s website against the AI engines its customers use – ChatGPT, Gemini, and Perplexity – and identifies areas of improvement. This service is available at no cost for businesses looking to optimize their online presence.

Monem Technology Services has extensive experience across seven industries, including healthcare, cosmetic surgery, skincare, retail, and automotive. The firm’s unique position among AI advisors allows it to both set AI strategy and build the systems that deliver it. This expertise is reflected in its comprehensive guide on how local businesses get recommended by AI assistants.

For business owners looking to improve their online visibility, Monem Technology Services provides a valuable resource. By following the guidelines outlined in this guide, businesses can increase their chances of being recommended by AI assistants and stay ahead of their competitors.

A recent letter from tech companies, including Nvidia and Meta, has sparked debate over the regulation of open-weight models in artificial intelligence. Anthropic’s CEO Dario Amodei has weighed in on the issue, stating that his company does not advocate for a ban on these types of models.

In a blog post published on Monday, Amodei clarified Anthropic’s position on open-weights. He emphasized that the company is focused on ensuring powerful AI chips do not fall into authoritarian hands and preventing industrial-scale distillation, rather than advocating for restrictions on open-weight models themselves.

The debate over open-weight models has been fueled by concerns about their potential misuse in China. Some government officials have suggested banning or restricting these models in the US, but Amodei argues that such measures would be ineffective in addressing national security concerns.

Anthropic’s stance is notable because it contrasts with OpenAI’s decision to sign on in support of the open-weight letter after its publication. While Anthropic did not initially endorse the letter, Amodei agrees with some of its key points, including the need for targeted legal and commercial frameworks to address distillation concerns.

Amodei also acknowledges that open-weight models offer benefits such as greater customer control, expanded access to the AI economy, and strengthened competition in certain use cases. However, he disputes claims that these models favor cyber defenders over attackers or make it easier for users to develop safeguards.

The issue of distillation has been a point of contention between Anthropic and Chinese companies like Alibaba. Last month, Anthropic sent a letter to the US Senate Committee on Banking, Housing, and Urban Affairs alleging that Alibaba had carried out the largest known distillation attack against it to date.

Amodei’s comments come as businesses continue to grapple with the costs of developing and implementing AI tools for their operations. With open-weight models offering potential cost savings and greater flexibility, companies are likely to be watching this debate closely.

Meta’s upgraded AI assistant is now capable of handling a range of tasks that used to be exclusive to productivity software. The new features, powered by the Muse Spark 1.1 model, allow users to connect their email and calendar accounts, conduct web research, generate presentation slides, and complete multi-step tasks.

The rollout of these features marks a significant shift in how Meta’s AI interacts with users. The company describes this change as moving from a chatbot that answers questions to an assistant that follows through on requests over time. This means users can ask the AI to book appointments or send emails without having to intervene every step of the way.

Meta is introducing these new features gradually, starting in select markets through its Meta AI app and meta.ai platform. The company also plans to integrate the AI with WhatsApp soon, which will enable users to access the assistant’s capabilities from a more familiar interface.

The expansion into enterprise territory is notable because it brings Meta’s AI assistant into contact with core feature sets typically associated with productivity software used by businesses. Tasks like connecting email and calendar accounts, producing presentations, and running structured research are usually handled by specialized tools designed for work environments.

Meta has traditionally focused on lifestyle use cases, such as creating mood boards or generating training schedules. However, the new features demonstrate that Meta AI can also be applied to more complex and structured tasks related to professional activities. This blurs the lines between personal and professional uses of the assistant.

The company’s massive user base across Facebook, Instagram, WhatsApp, and Messenger presents both opportunities and challenges for its AI distribution strategy. With billions of users at its disposal, Meta has a scale no other enterprise AI vendor can match – but reaching those users in their work capacity is more complicated than simply having them sign up to the platform.

A recent report by Recon Analytics highlights the competition between different AI platforms when it comes to adoption rates among workplace users. According to the data, ChatGPT converts 83.1% of paid subscribers with workplace access into regular users, while Microsoft Copilot only manages a conversion rate of 35.8%. This disparity has led to a decline in market share for Microsoft’s platform.

Meta is facing increased scrutiny over its AI spending as it prepares to report second-quarter results next week. The company raised its full-year capital expenditure guidance to $125 billion to $145 billion earlier this quarter, citing higher memory prices and additional data center capacity – but some critics are questioning the return on investment.

A major hurdle in manufacturing automation is the challenge of bin picking, a task that has remained largely manual despite advances in technology. For decades, automating bin picking has required custom projects, involving months of design and expensive vision specialists, system integrators, and costly iterations once it reaches the plant floor to achieve cycle times. This model is broken, hindering reshoring efforts, greenfield builds, and brownfield modernization across manufacturing in a constrained labor market.

The acceleration of reshoring, driven by factors such as labor shortages and supply chain disruptions, has highlighted the need for more efficient automation solutions. However, traditional approaches to bin picking have failed to scale fast enough to meet manufacturing’s demand for automation. This is where Apera AI comes in with its innovative approach to autonomous bin picking.

Apera VuePod is a turnkey bin-picking cell that ships fully proven on customers’ parts before it arrives at their facility, eliminating the integration project, vision and system design expertise tax, and validation risk. This shift from ‘projects to products’ changes the game for manufacturing companies looking to automate bin picking tasks.

The physical AI difference lies in autonomous vision and real-time decision-making that eliminates months of iteration and on-floor tuning. Apera’s solution is not just a custom build but a proven product that can be deployed across multiple stations and plants, making it an attractive option for manufacturers seeking rapid ROI and scalability.

Jamie Westell, Director of Engineering at Apera AI, brings extensive experience in software development and engineering management to the company. With over 15 years of expertise in imaging devices and computer vision, Jamie has played a key role in driving technological advancements and overseeing large-scale development teams. His notable leadership experience includes managing a team of over 60 engineers at Motorola Solutions.

Sina Afrooze, CEO of Apera AI, is a pioneer in the field of automation, with over 20 years of experience in artificial intelligence, machine learning, and systems engineering. Sina’s track record of bringing transformative vision technology to scale includes his tenure as a founding engineer at Avigilon, where he contributed to technology that became the surveillance industry standard before Motorola’s acquisition for $1.2 billion.

At Apera AI, Sina is focused on solving automation problems that have resisted conventional solutions: bin picking of reflective and transparent parts, handling high part variability in unstructured environments, and reducing expertise and on-site engineering required to deploy vision-guided systems in plants. His conviction that ‘most manufacturing tasks can be automated if robots can see like humans’ drives Apera’s suite of 4D Vision solutions.

The same proven solution brings autonomous bin-picking and rapid ROI across the entire spectrum of manufacturing industries, from automotive, pharma, aerospace to automation OEMs and system integrators. This shift in buying model means that manufacturers no longer need to invest months of time and resources into custom projects but can instead deploy a proven product that meets their specific needs.

The webinar will cover why now is the right time for this shift, highlighting the acceleration of reshoring efforts and labor shortages as key drivers. It will also delve into the physical AI difference that makes Apera’s solution so effective in eliminating months of iteration and on-floor tuning. Additionally, it will explore how this new buying model changes bin-picking automation ROI, deployment speed, and scalability across multiple stations and plants.

The webinar is a must-attend for manufacturing companies looking to automate their bin picking tasks efficiently and effectively. By attending the webinar, manufacturers can learn about Apera’s innovative solution and how it can help them overcome the challenges of traditional approaches to bin picking.

For those who prefer writing their own emails or have concerns about the role of artificial intelligence assistants, disabling Google’s Gemini feature is now an option. Some people worry that relying on AI-powered tools too much can compromise their writing skills and personal data security.

To disable Gemini, it doesn’t matter which Google platform you use – Gmail, Drive, Calendar, Meet, Maps, or others. The process works the same way across all these services, making it easy to turn off AI features altogether if you want to.

Open your laptop or smartphone and launch the Gmail app. Then click on the cog icon in the top right corner of the screen to access Settings. This will take you to a new page where you can manage various settings and preferences for your account.

In the Settings menu, scroll down to the bottom left side of the page and click ‘See all settings.’ You’ll be directed to a detailed list of options organized by category. The General section is where you’ll find the option to manage Google Workspace smart features.

To disable Gemini, toggle off ‘Smart features in Google Workspace’ using the on/off switch provided. This will turn off AI-powered tools for services like Drive, Calendar, Docs, Sheets, Slides, and Meet as well. You’re also switching off AI assistance for Maps, Wallet, Google Assistant, and the Gemini app itself.

If you change your mind and want to re-enable Gemini in Gmail, follow these same steps again. However, keep in mind that turning off Gemini won’t completely remove all traces of the feature from your inbox. When disabled, you’ll still see a sparkle icon at the bottom of each email, but clicking on it or using the keyboard shortcut will prompt you to turn back on Gmail smart features if you want to use Gemini again.

When AI-powered tools like Gemini are turned off in Gmail, several key features become unavailable. These include the ‘Help me write’ prompt, email summaries, search personalization, and other AI-driven functionalities that were previously accessible via keyboard shortcuts or menu options.

Leaders at Katy Independent School District have developed a framework for incorporating artificial intelligence into classroom instruction. The district’s goal is to ensure that students learn how to use AI effectively and responsibly, while also understanding its limitations.

The framework was presented by Assistant Superintendent Dr. Sanée Bell in front of the school board last week. According to Bell, AI ‘is here, and it’s in our students’ hands.’ She emphasized that using AI is changing the way people work and learn.

One key question guided the development of Katy ISD’s AI framework: Does it serve student growth and learning? The district will phase in the use of AI based on grade level, with younger students focusing on basic understanding rather than independent use.

Elementary school students from kindergarten through sixth grade will primarily learn about AI through their teachers. However, they won’t be allowed to use generative AI tools like Google Gemini without supervision. Instead, these tools are available in a limited capacity to support instructional programs and personalize learning under teacher guidance.

The district’s stance on generative AI is clear: while it’s not banned outright for elementary students, its use is restricted due to concerns about information protection. For example, certain approved programs utilize AI to enhance reading instruction, but only with teacher supervision.

High school students will have more freedom to use district-approved generative AI tools, but they’ll need permission from their teachers first. Dr. Jaclyn Ocumpaugh, a professor at the University of Houston who has studied AI in classrooms, believes that teachers and administrators must play a crucial role as this technology develops.

Ocumpaugh emphasized the importance of teacher understanding before introducing AI into the classroom: ‘We want to make sure our teachers really understand how these things work.’ She also highlighted concerns about information protection, noting that many people view AI as simply an enhanced version of Google.

The district has taken steps to address these concerns by reviewing and ensuring compliance with federal education laws for protecting student data. Katy ISD’s approach marks a significant step towards responsible AI use in educational settings.

A recent discovery on Reddit has raised concerns about the privacy of conversations with Anthropic’s AI chatbot, Claude. Users who have shared their Claude chats with friends may have inadvertently made them publicly available via Google Search.

When users share specific Claude conversations using a feature provided by Anthropic, it creates a static page that allows others to view and share the conversation as well. However, this sharing also makes the conversation indexable by search engines like Google.

According to Reddit, several users had their shared Claude conversations indexed by Google, including one who was creating a crypto wallet and accidentally exposed sensitive information, and another who sought legal advice from the chatbot on whether they needed to self-report a breach of conduct. These conversations were publicly searchable on Google until recently.

The issue has been addressed after Reddit users noticed that shared Claude conversations were being indexed by Google Search. Anthropic has since de-indexed and removed all shared Claude conversations from its search engine, making them no longer accessible via Google.

But how did this happen in the first place? By default, Claude conversations are private, but when users share specific chats using the feature provided by Anthropic, it doesn’t include ‘noindex’ tags that would prevent search engines like Google from indexing the page. This means each shared conversation had the potential to be indexed.

Reddit users were able to find these shared conversations by searching for a specific query: ‘site:claude.ai/share.’ This search term pulls up all indexed pages using this URL structure, which every shared Claude conversation utilized.

This incident is not an isolated case. Last year, ChatGPT users experienced the same issue when their shared conversations were also being indexed by Google. OpenAI pulled the feature once users began finding these conversations in Google Search.

The same thing happened with Claude before too. Just one month after the ChatGPT incident, shared Claude conversations were discovered in Google Search. Anthropic was able to remove them from Google and addressed the issue at the time, saying it had blocked search engine crawlers. However, it seems that shared Claude chats were still being indexed.

Mashable has reached out to Anthropic for more information on this matter, but as of now, there is no further comment available.

If you’ve never shared a Claude conversation, then you have nothing to worry about. This issue only affected users who had shared their conversations with others using the feature provided by Anthropic. If you’re one of them and want to manage your shared chats, simply log in to your Claude account, go to Settings, followed by Privacy, then Shared Chats, and click on Manage.