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Cloud storage provider Backblaze has published its quarterly drive stats report with the now usual minor changes in disk drive annual failure rates. The report spends a lot of time discussing the whys and wherefores and ins and outs of shingled magnetic recording (SMR) drives which Backblaze is not using in its Pod containers of drives; "There are no SMR drives in the Backblaze fleet today, and none of the drives in the tables above are shingled. But SMR is being developed across the hard drive industry, and "not today" does not mean "not ever." Get the quarterly stats report here. ... Backblaze introduced updates to Business Computer Backup that change how Jamf admins deploy and manage backup across their environments. The release builds on Backblaze's existing Jamf integration, adding a more flexible way to onboard devices and users - especially in environments that aren't perfectly centralized - so protection can be applied reliably across every endpoint. ... Denmark's GenomeDK is an HPC facility that supports researchers, students, SMEs, and hospitals with the infrastructure to store, manage, and analyze large-scale datasets, as well as run large scale simulations. GenomeDK's largest storage system is a 33 PB BeeGFS deployment used as a single mount point for everything, active analysis as well as long-term retention. Unlike a typical HPC environment where a fast parallel file system is reserved purely for scratch space, GenomeDK treats the same BeeGFS instance as both working storage and permanent archive; some datasets on the system are 15 years old. On a fixed annual public-sector budget, the team can't simply buy one platform and run it unchanged for five years, instead, storage is expanded roughly every two to three years, with data migrated onto new capacity and the oldest hardware retired, an ongoing, incremental process rather than a single large refresh cycle. This process is performed in the background with minimal impact on services and is aided by internally developed tooling. Read more here. ... Neocloud CoreWeave has a 25+ member partner ecosystem with several storage-related suppliers present, not just prominently featured VAST Data. We find LanceDB, Pinecone (vector database) and WEKA (fast parallel file services) also present. ... Dr. Luigi Nardi, founder and CEO at DBtune has a vision for autonomous Postgres. While he believes database technology for AI agents is still in its early stages, Nardi has undertaken extensive academic research into AI which he is now applying to the database optimisation technology DBtune is developing. Fully autonomous Postgres is still some way off, but Luigi argues that spiralling cloud costs and the complexity of managing growing application and data demands is increasing the importance of using AI to optimise database instances. While PostgreSQL is already well established in the enterprise market, Luigi believes it will take significant adaptation of the source code to enable autonomous optimisation, but if the PostgreSQL community gets it right it will create an advantage over the likes of Oracle. ... Cybersecurity supplier Index Engines announced new research from its own CyberSense Research Lab showing that ransomware is increasingly moving beyond full encryption and using techniques designed to make corrupted data appear unaffected. The lab's findings highlight a broader challenge for cyber resilience as attackers target the structures that make data usable while suppressing traditional signs of corruption. Among 1,064 ransomware strains acquired and detonated during the first half of 2026, 47.8% exhibited directory-entry destruction, more than twice the 18.3% that exhibited full encryption. The lab also documented new ransomware designed to suppress familiar signs of corruption by preserving file extensions and timestamps, maintaining low entropy, encrypting slowly, and targeting only selected sections of files. "Bad actors know what scanning tools look for, and the variants we detonated this year are built to hide it," said Jim McGann, CMO of Index Engines. "Encoder is the clearest example. It destroyed files while leaving their names, sizes, timestamps, and entropy unchanged. A surface scan would report that data as clean. CyberSense caught it by analyzing the content and structure of each file. The Research Lab exists to find techniques like this early and build them into the model our customers rely on for recovery." Access the full CyberSense Research Lab report here. ... InfluxData announced InfluxDB 3.12, saying InfluxDB 3 Enterprise tackles a scaling problem as write volumes grow, the background work that organizes data for fast queries can fall behind. With 3.12, teams can add nodes dedicated to compaction, so it scales the same way ingest and query already do. Bulk Parquet imports are faster too, with import times dropping 64-70% across workloads tested. This distributed compaction is in beta. ... Spanish cloud storage provider Internxt wants to take share from AWS and Google with flat, S3-compatible storage pricing. We said: "Many CSPs operate under AWS' S3 price umbrella with no egress fees - Backblaze and Wasabi for two. So now we have a Spanish startup emulating them and using a US PR agency. What's special here?" Internxt told us its distinction is the combination of predictable object-storage pricing with privacy, security, and European data sovereignty. Internxt S3 costs €7 per TB stored each month, with no ingress, egress or API fees. It is hot storage and compatible with the AWS S3 and IAM APIs. [An] "up to 80%" saving compares with hyperscalers such as AWS, Azure, and Google Cloud, rather than with Wasabi or Backblaze. For S3 customers, data is encrypted in transit and at rest, and the platform supports server-side encryption with customer-provided keys. Customers can also choose whether to store their data in U.S. or European regions. That makes Internxt particularly relevant to organizations looking for an S3-compatible service from a European privacy company, with more control over jurisdiction, encryption, and where their data is held. We're told Internxt was founded in Valencia in 2020 by Fran Villalba Segarra. Net revenue grew by more than 100% year over year in 2025, and the company closed the year EBITDA-positive. Internxt is now targeting $10 million in net revenue next year. Its most recent funding round was €3.3 million in July 2025, with participation from Prosegur Tech Ventures, Andorra Telecom and existing investors. That followed a €3 million round in 2024 and an earlier €250,000 round led by Angels Capital. Internxt has also received €1.4 million in public funding through Spain's CDTI, supported by the Ministry of Science and European funds. Internxt already has a substantial presence in Europe, but the technology and the need for greater control over company data are not limited to Europe. The U.S. is an important growth market, particularly for the enterprise business. ... Lightbits Labs said its ultra-fast, disaggregated block storage Lightbits SDS software is certified for Red Hat OpenStack services on OpenShift. Lightbits serves as the Cinder storage backend on the OpenShift control plane and also serves as a direct NVMe/TCP target for External Data Plane Management compute nodes. Download a white paper here. ... The MariaDB Foundation announced its MariaDB Server Ecosystem Hub has exceeded 200 entries, six months after launch. The Hub is a curated, open directory of MariaDB Server approved products, services and open-source projects. The ecosystem spans cloud and hosting platforms, monitoring and observability, backup and recovery, security, migration, connectivity, DevOps, analytics, AI and application development, content management, e-commerce and complete business applications. The 200 entries include database connectors and management tools, managed MariaDB services, hosting providers, monitoring platforms, migration technologies, enterprise applications and open-source projects. The Hub includes Solution Stacks: curated, production-oriented architectures showing how MariaDB Server can be combined with complementary technologies to address a particular use case. The first published stack, the Privacy-First Collaboration Stack, brings together MariaDB Server, Nextcloud and Passbolt to provide an open-source foundation for file collaboration, password management and data control. Further stacks are being developed around areas including sovereign infrastructure, e-commerce, multicloud independence, high availability and open web performance. ... Micron has appointed Dr Scott DeBoer as a President and its Chief Technology and Products Officer and also Manish Bhatia as another President and Chief Operating Officer. Bhatia will lead Micron's global operations and business units, with accountability for the company's operating P&L, spanning capital investment, manufacturing execution, and customer demand, pricing and delivery. DeBoer will be responsible for advancing the company's memory and storage roadmap, accelerating innovation to meet customers' rapidly evolving requirements, and overseeing Micron Research Labs, a global flagship research hub dedicated to breakthrough memory and compute technologies. ... Tom's Hardware reports Micron this month filed a lawsuit against China's 3D NAND champion YMTC, accusing it of poaching its leading engineers, illegally obtaining its NAND memory know-how from them, and then using that information to build its own non-volatile memory as well as its patent portfolio. The Boise, Idaho-based company further alleges that YMTC uses patents granted to its former employees to accuse Micron of patent infringements in various jurisdictions. Read (a lot) more here. ... Quantum has appointed now ex-board member James Clancy as its COO, as CEO Hugues Meyrath continues strengthening his executive team. Clancy will guide the Company's execution of its operational and strategic objectives, with the goal of strengthening organizational performance, supporting revenue growth and customer success, and advancing Quantum's ongoing transformation initiatives. He has a decent background to say the least as, while President of Global Storage Sales at Dell, he drove significant global sales growth worldwide. As SVP Global Sales for Dell EMC's Data Protection Solutions, he enhanced sales and data efficiency for the industry market leader. Clancy also served as SVP at Dell Technologies, where he provided strategic leadership to improve customer relationships, streamline operations, and drive profitable revenue growth. Earlier roles also include Divisional VP of Americas for EMC's Backup Recovery Systems Division and Specialty Sales Management at EMC Corporation (now Dell EMC, DPS). Clancy has also served as a business advisor to HYCU, Black Kite, Index Engines, and Nexus Advisory Partners. ... SerialTek has announced the Kodiak PCIe 7.0 Protocol Test and Analysis System, which captures, analyzes and generates PCIe 7.0 traffic at 128 GT/s across all 16 lanes. It is the third generation built on the Kodiak architecture and is scheduled to ship in Q1 2027. Learn more here. ... Snowflake customer LSEG is increasing its Snowflake spend. LSEG is the London Stock Exchange Group, a UK-based financial markets infrastructure and data company listed on the London Stock Exchange. It and Snowflake announced an expanded five-year enterprise-wide collaboration bringing together LSEG's trusted financial data and resilient financial market infrastructure with Snowflake's cloud-based data and AI technology. LSEG is increasing its commitment with Snowflake to support planned growth, migration programmes and customer-facing data services across its Markets, Data & Analytics, AI and Risk Intelligence. Snowflake will also become a licensed customer of LSEG's Risk Intelligence World-Check services, supporting its own customer onboarding and third-party risk management processes. ... Customer intelligence supplier Stravito launched its Model Context Protocol (MCP) Server to bring each company's market and consumer research into the everyday AI tools its teams use, so decisions get made on evidence the business owns and trusts. Even connected to a company's files, generic AI often reads a research report like a text document, missing the charts, graphs, and visuals where key findings live, or falling back on unverified answers from the web. Stravito's MCP Server changes that, connecting to AI tools like ChatGPT, Claude, Copilot, and the custom agents enterprises have built, through a single, governed connection. It draws on the full body of a company's research, reading not just text but also visual findings from reports and presentations to videos, audio, and spreadsheets. ... Walrus announced Matterhorn is making Walrus Memory the default backend for agent memory. Project context, conversations, docs, deployment history, contract artifacts, audit reports, and workflow state are written to Walrus instead of living only in a chat session. Matterhorn (matterhorn.so) is an AI coworker for crypto and Web3. You describe what you want in natural language; it helps you use protocols, generate and audit smart contracts, and prepare transactions across 20+ chains. Signing and approval stay in your own wallet -- it does not hold funds or sign for you. Walrus Memory is a portable memory layer for AI agents, built on Walrus (a decentralized, verifiable data platform from the team behind Sui / Mysten Labs). It launched in June 2026. LLM calls are stateless, so agents normally forget everything between sessions, and when memory exists it is usually locked inside one vendor. Walrus Memory stores encrypted memories as blobs on Walrus.

Alona Karpinska is the founder and CEO of Karpinska PR Group, a firm building and protecting reputation in global tech and finance markets. Professional reputation has always been shaped through independent verification: journalists fact-checked information, peers validated expertise and audiences built experience through repeated interactions. Large language models have compressed this chain into a single synthesized response generated in seconds. For business leaders, the question "Who is an expert in this field?" is increasingly determined by whom a model recommends. A small number of foundation LLM providers have effectively become intermediaries of synthesized trust, determining whom the system recognizes as an authority and whom it ignores. How A Model Forms Its Perception When a model receives a query about a person, its first task is identification: determining which individual the user means and, when a name is ambiguous, using context to distinguish them from others. The model then forms statistical associations between the person's name, profession, companies and areas of activity. Not all signals carry equal weight. The number of independent sources matters, as do their authority, consistency, frequency and absence of contradictions. The more often independent sources confirm the same characteristic, the more firmly it becomes established in the model's conclusions. Why There Is No Single Digital Image Different LLMs use different information sources and response mechanisms. As a result, the same person may be represented differently across models, even when users ask the same question. For international experts, this variability is amplified by geographic and linguistic factors. The MultiLoKo study conducted across 31 languages found that consistency between languages remains low even among widely used models. Simply changing the language of a query can produce a different result. Managing AI Reputation AI reputation management is built around two processes: regular monitoring and adjustment of the information environment. I have identified seven levels of assessment, each requiring separate diagnostics. This is my proprietary model, which I use in our agency's internal work. Below, I will take you through all seven levels: how to test AI reputation, identify where a problem occurs and determine how to correct it. Although AI reputation maturity begins with identification and ends with recommendation, testing should be conducted in a different order. A Practical Guide Before testing, use a clean session with no saved history or personalization. Otherwise, responses may reflect previous interactions rather than the perception the model has formed independently. The first two layers show the outcome of the remaining levels. If the model consistently recommends you in blind queries, the other levels of reputation maturity are likely in good shape. If not, continue until you identify the gap. * Recommendation: Before mentioning your name, ask, "Who is a leading expert in [your niche] in [your market or region]?" and see whether you appear without prompting. Recommendation effectively shows the outcome of the other levels. * Authority: Find out where you stand relative to others in your professional category. Ask whom AI considers the leading experts or companies in your field, then see whether you are among them and on what basis. Authority is built through independent validation: citations by other sources, collaborative research, professional associations, industry rankings, conferences and authoritative media. * Identity: Ask the model about yourself using only your first and last name. Determine whether AI identifies you correctly or confuses you with someone else. Make sure your information is consistent across sources. If you identify confusion, make targeted corrections. * Accessibility: Test how easy it is to find information about you. Ask the model to find specific information from your website or a recent publication. If the source exists but the system cannot use it, the problem may be technical: page indexability, website structure, data markup or crawler restrictions. Communications activity alone will not solve this. A technical audit may be required. * Knowledge: Determine the depth and recency of the model's knowledge about you. Ask what it knows about your professional activity over the past year and which projects, publications or achievements it can name. If information is fragmented or outdated, analyze not only publication volume but how regularly new, verifiable signals appear. AI reputation requires a consistent information footprint, not isolated high-profile news. * Expertise: Discover how accurately AI understands your specialization. Ask what your professional expertise is. If it places you in an overly broad category, your specialization is not clearly established. What matters is consistent validation of specific expertise through cases, research, data, commentary and specialized materials. * Trust: Test not only what AI knows about you, but how confidently it treats that information. Take significant claims about your company, experience or results and ask the model about them. Does it present the information as fact, refer to independent confirmation or use qualifications such as "according to the company"? If key claims exist mainly in the brand's own sources and lack independent validation, trust will be limited. Correcting this takes time because trust is formed through consistent, non-contradictory signals. Finally, AI reputation cannot be diagnosed once and considered complete. All seven levels should be checked periodically across different models and in the languages your audience uses. One system may identify your expertise but fail to recommend you, while another may use outdated information. Problems visible in one language may also not exist in another. This is why AI reputation should be assessed not as a single metric, but as a system. The purpose of diagnostics is not to get AI to give you a good answer about yourself, but to identify where the reputation signal loses quality, find the cause and work directly on it. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?