DEN HAAG (ANP) - Op één plaats in het noorden van Nederland heeft een NS-trein dinsdagochtend materiaal geraakt dat op het spoor lag. Een woordvoerder van NS zegt dat dit geen direct gevaar heeft opgeleverd voor de mensen in de trein. De exacte locatie wil hij in verband met politieonderzoek niet noemen.

Of het ook hier om een buis gaat, of om ander materiaal, is ook nog niet duidelijk. Spoorbeheerder ProRail meldde dinsdagochtend dat op meerdere plaatsen in het land buizen op het spoor zijn aangetroffen. De politie Oost-Nederland had het over "diverse goederen". Het treinverkeer ligt hierdoor op veel trajecten plat, onder meer rond Zwolle, Deventer en Amersfoort.

Twijfel je weleens aan jezelf na een gesprek met je baas of collega? Grote kans dat je tegen een gaslighter aan bent gelopen. Een nieuwe studie, gebaseerd op onderzoek sinds 1981, brengt drie duidelijke types in kaart die je op kantoor kunt tegenkomen.

Gaslighting – het subtiel manipuleren van iemand zodat diegene gaat twijfelen aan zichzelf, zijn gevoelens of zelfs de werkelijkheid – wordt vaak besproken in de context van relaties. Maar ook op de werkvloer komt het veel voor. En het effect kan verwoestend zijn: twijfel aan je kunnen, een gevoel van hopeloosheid en zelfs het idee dat je gek aan het worden bent.

Drie herkenbare types

De onderzoekers onderscheiden drie typen gaslighters die ook op de werkvloer goed te herkennen zijn.

Allereerst is er de valse mentor. "Deze persoon strooit met complimenten, maakt je 'bijzonder' en trekt je in een web van vleierij." Je voelt je speciaal, maar langzaam raak je het contact met de realiteit kwijt. Beloftes worden niet nagekomen en verantwoordelijkheid wordt vermeden.

Dan is er de valse moralist, misschien wel het meest verraderlijke type. Deze gaslighter lijkt juist onberispelijk: doet veel goeds, gedraagt zich sociaal en loyaal. Maar ondertussen worden problemen geminimaliseerd of genegeerd. "Zo'n aardig persoon kan toch niets verkeerd doen?" Precies dat maakt het zo gevaarlijk.

Tot slot de tough-love leider: de harde, onvoorspelbare baas. Vernederend, kritisch en zogenaamd 'recht door zee'. Deze leider zegt dat zijn of haar aanpak nodig is om mensen te motiveren, maar veroorzaakt in werkelijkheid angst, onzekerheid en een slechte werkcultuur. Terwijl sommige collega's worden gespaard, krijgen anderen alle klappen.

Waarom doen ze dit?

De drijfveren van gaslighters komen vaak voort uit persoonlijke pijn of onverwerkte trauma's. Vooral de harde leiders blijken diep vanbinnen bang om verlaten of afgewezen te worden. Hun controle is een beschermingsmechanisme.

Valse mentoren nemen dan weer zelden verantwoordelijkheid voor de schade die ze veroorzaken; ze zijn vooral met zichzelf bezig.

En gaslighters met plotse woede-uitbarstingen blijken moeite te hebben met concentratie en zelfbeheersing, waardoor ze chaotisch en grillig reageren; wat slachtoffers nog meer in verwarring brengt.

Deze rode vlaggen moet je kennen

Herken je dit gedrag op je werk? Let dan op:

- Overdreven vleierij zonder opvolging

- Plotselinge, onvoorspelbare kritiek

- Een collega of leidinggevende die zich voordoet als redder, maar je laat twijfelen aan je waarneming

- Een 'goede collega' die misstanden bagatelliseert

Wees waakzaam, praat erover met vertrouwenspersonen of HR en normaliseer het gedrag niet. Gaslighting hoort niet thuis in een gezonde werkcultuur.

Bron: Psychology Today

BEIJING (ANP/AFP/RTR) - Een reeks reisbeperkingen aangekondigd door China is vanaf dinsdag van kracht. China zegt hiermee de nationale technologische veiligheid te willen beschermen.

Chinese burgers die terugkeren naar China na betrokkenheid bij illegale activiteiten die de nationale veiligheid bedreigen, kunnen een uitreisverbod opgelegd krijgen. Dit kan zes maanden tot drie jaar duren. Buitenlandse burgers kunnen ook een inreisverbod van een tot vijf jaar opgelegd krijgen als ze onjuiste verklaringen hebben afgelegd bij een visumaanvraag.

Buitenlandse regeringen hebben eerder al meldingen gemaakt van gevallen waarin hun burgers China niet mochten verlaten vanwege een veiligheidsonderzoek.

Tussen China en de VS is een technologische wedloop aan de gang, waarbij beide landen elkaar beschuldigen van het ontvreemden van de capaciteiten van lokaal ontwikkelde kunstmatige intelligentie (AI).

BAD WIMPFEN (ANP/BELGA) - Wielrenner Felix Gall verruilt Decathlon CMA CGM voor de Duitse WorldTour-formatie Lidl-Trek. De 28-jarige Oostenrijker eindigde zondag als derde in het klassement van de Ronde van Spanje, achter de Spanjaard Enric Mas en de Sloveen Primož Roglič. Eerder dit jaar werd hij al tweede in de Ronde van Italië.

"In 2026 heeft Felix laten zien dat hij tot de allerbesten behoort en heeft hij zich gevestigd als een echte podiumkandidaat in de grote rondes. Maar wij geloven dat er nog veel meer in zit", zegt Andy Schleck, directeur van Lidl-Trek.

Willem-Alexander heeft aan de vooravond van Prinsjesdag een dringende oproep gedaan aan het kabinet. Volgens de koning moet een werkweek weer bestaan uit 8 uur in plaats van 4.

‘We willen allemaal de economie van dit land laten groeien’, benadrukt Willem-Alexander in een open brief. ‘Laten we daarom 8 uur als norm stellen. Dit vraagt van iedere Nederlander een extra inspanning, maar het is mogelijk als we gezamenlijk de schouders eronder zetten.’

‘We kunnen het ons simpelweg niet veroorloven om vier uur per week te blijven werken’, vervolgt de koning. ‘Bovendien is het niet eerlijk wanneer sommigen wel 8 uur werken, en anderen na 4 uur zoiets hebben van: eindelijk weekend.’

Willem-Alexander gelooft dat met name jongeren hierin een belangrijke rol kunnen spelen. ‘De jeugd is alleen maar bezig met lintjes doorknippen, jagen in het Kroondomein en skiën in Lech. Maar daar redden we het dus niet mee. De werkweek moet hard omhoog. Soms zullen we zelfs na de lunch in de middag door moeten halen.’

Uit verschillende hoeken klinkt nu al kritiek op de uitspraken van de koning. Zo sneerde zijn neef Bernhard jr. dat er ook mensen zijn die moeten rentenieren. De koning wees hem op zijn beurt wat verder te kijken dan zijn bril groot is.

De koning kijkt ook naar het buitenland. ‘In China zijn er jongeren die wel 14 uur per week werken. Nu zeg ik niet dat we daarheen moeten, we moeten ook aan onze volksgezondheid denken, maar we kunnen wel een voorbeeld nemen aan die bereidheid om een bijdrage te leveren aan de economie.’

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Het uitkeringsstelsel moet op de schop: te complex, te weinig begeleiding. Maar door de bezuinigingsplannen van het kabinet staan partijen vooral tegenover elkaar. Over de noodzakelijke hervorming gaat het nauwelijks.

De vrouwelijke hoofdpersonen in het Afghaanse ‘No Good Men’ en het Amerikaanse ‘Gail Daughtry and the Celebrity Sex Pass’ ontdekken: de prins op het witte paard bestaat niet.

The president’s former press secretary has learned all the right lessons, but how come it took him so long?

The most interesting chapter in Anthony Scaramucci’s All the Wrong Moves is the one where the hedge funder turned podcaster goes to work for Donald Trump. It’s summer 2017, and he’s been given the job of comms director, which he doesn’t know how to do, but he and Trump think it’ll work out because they’re both from New York. Or maybe he took it because he had “Potomac fever”, which afflicts Wall Street financiers who find themselves on the riverfront in Washington DC thinking they know how to run a country just because they’re “full of power and judgmental insight … about to fix everything”.

This appointment lasts only 11 days, not even enough time to be truly tainted by Trump’s first term, though it is enough time for Scaramucci to miss the birth of his son. The account is a mea culpa soup, with the author full of regrets for his hubris, his impulsivity, his “pride, egotism and asinine stupidity”, the fact that, despite the clear-sightedness bestowed by his humble beginnings, he’d come to assume the collective biases of the rich people he hung out with.

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Safety requirements are non-negotiable. They depend on meeting concrete goals, not just adjusting a timeline

It has been a week of high drama in AI, precipitated by the resignation of the AI safety researcher Jacob Coxon from Anthropic. This followed several weeks of increasingly lurid and disturbing revelations about the OpenAI/Hugging Face incident.

My inbox yesterday included a message from Business Insider with the subject line: “AI doomsday debate reaches boiling point”.

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From Stephen Hawking to Jacob Coxon’s viral Anthropic resignation, fears that AI could threaten humanity have shaken the industry without stopping its pursuit

Before an Anthropic researcher resigned and declared human extinction imminent last week, tech leaders and scientists had sounded the alarm about a superintelligent AI ending humanity for over a decade.

The development of artificial intelligence “could spell the end of the human race”, warned professor and astrophysicist Stephen Hawking in 2014 – a little less than a decade before the public got its hands on the generative AI features of the original version of ChatGPT.

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Clara Trischler’s absorbing feature sees residents of the small town of El Alberto hired to play characters in a re-enactment of the migrant experience

In contrast to other migration documentaries, which focus on the new life that awaits those who have crossed the border, Clara Trischler’s absorbing second feature trains its gaze on those who are left behind. The suspenseful opening plunges us into a whirl of familiar images: tense whispers in the dead of night as a ragtag group of people stumble, and even crawl, their way towards an unseen border. All of a sudden a blinding light intrudes, guns are fired in the air: a police car has arrived on the scene.

This terrifying sequence of events is later revealed to be make-believe. In the small Mexican town of El Alberto, whose population has been depleted by a steady flow of migration to the US, role-playing re-enactments function as both a means of deterrent and, oddly enough, a tourist attraction. Advertised as Caminata Nocturna – “night hike” in Spanish – these events recruit local residents to perform as specific characters, including the fearsome “coyotes”, AKA human traffickers. The attendees experience the full scale of a border-crossing operation, from being loaded on vans to intimidation from their coyotes, and police interrogation.

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Imagine you are an AI Agent. You execute inside a $20-40 million-plus Nvidia SuperPOD system’s accelerator hardware, have a skill set and can call up other agents to do your bidding. An insurance company customer, using a PC browser or smart phone, asks a question: “Am I covered for weather-related damage under my current policy?” and it is handed off to an AI agent. What happens next? The agent itself runs on clusters of CPU + GPU/accelerator servers that host the large language model, any Retrieval-Augmented Generation (RAG) components, orchestration logic, and tools that look up the actual policy data. Those same servers (or tightly coupled backend systems) access the policy database/storage to answer the weather insurance coverage question, then return the response to the user’s device. Everything the agent does depends upon data, and it and all the other agents operating at the same time in the SuperPOD system, need to get that data off a storage system. A national or large regional-level insurance company will have petabytes, even exabytes, of data it stores so it can manage its insurance business. There needs to be a central index of this data’s structure, state, location, field names, types and semantics so that AI agents go to one place, a kind of data encyclopedia, to find out what information they need and where it is located. Agents are in a hurry, because GPU servers are hugely expensive and just a minute of idle time can cost $25 or more, a dollar every 2.4 seconds. The agent answering this insurance request needs data and a storage system needs to deliver it as fast as possible so that the GPU servers don’t wait and wait and wait some more for data. This requires the storage system to be able to handle the scale and types of the data involved, for it to be self-describing, and its software to be tightly integrated with Nvidia’s GPU hardware and software server architecture so that there isn’t a wasted millisecond while the GPUs are starved. A modern approach to data processing for AI Par Botes, Everpure’s VP of AI Infrastructure, discussed the difference between traditional enterprise critical data processing and how it’s done in the AI era: “The characteristics of performance, resilience, availability and governance don't fundamentally change, but access patterns are different. When searching for information, metadata can become richer than the data itself because it explains the semantics of data, and how it behaves as it becomes enriched.” Building an AI data architecture around standard dual-controller arrays or siloed direct-attached storage (DAS) quickly hits physical limits. What organizations need is an AI Data Platform, built using the NVIDIA AI Data Platform reference design. There are three distinct bottlenecks involved in this which Everpure’s architecture focuses on solving. First: Throughput starvation at scale: To keep thousands of GPUs saturated, a storage hardware platform needs to scale out past 4 or 5 TB/sec to 10 TB/sec or more of bandwidth. This rules out standard dual-controller arrays, even if equipped with SSDs instead of disk drives, or even cache-centric architectures. At the high-end enterprise level, an Everpure FlashBlade//S, or at the extremely large scale, a FlashBlade//EXA, provides a highly powerful data storage platform built for AI and HPC. With enterprise AI factories expected to scale out to, and perhaps beyond, 10,000 GPUs, there needs to be a solution which can handle this highly performant scale: FlashBlade//EXA scales up unstructured data storage technology to a whole new level for metadata controllers using NVMe fabric-connected flash for the data nodes. It’s Nvidia AI Data Platform-certified, delivering 400 to 450 million IOPS, 220 GB/sec bandwidth and 4.6 billion metadata operations/sec. Second: The KV cache prefill tax: A less obvious efficiency drain occurs inside a GPU’s High-Bandwidth Memory (HBM). Imagine a finance person’s AI model wants to read a business’ SEC 10-K report. It gets chunked, turned into tokens, then vectors, and loaded into the KV cache; the LLM pre-fill stage. When the model completes its run, the tokens are tossed away. But a second finance person could want their model to check the same 10-K but using a different GPU. So, the prefill is done again with, for example, 150,000 tokens recomputed. When this happens, the existing tokens need to be temporarily moved to an extended memory area instead of being evicted and lost, meaning GPU cycles have to be spent to recompute data that’s already been computed. Everpure Key Value Accelerator (KVA) offloads the cached token states directly to shared flash over NVIDIA GPUDirect Storage (GDS) via RDMA. When a subsequent query references that document, the pre-computed KV tensors stream straight into GPU memory, bypassing host CPU overhead and eliminating the compute penalty. Botes said: “It turns out that many, many patterns are far more reusable than people think. So, the more those patterns are reused, the more we can save compute time.” Physical proximity matters, Botes said: “Data speed drops dramatically as it moves further from the CPU or GPU: nanoseconds for local memory, microseconds for disk, and milliseconds for network transfers. Minimizing this distance is critical for performance. If I put that into scale, if memory access is a second, then disc access is an hour. The farther away you move, the more time everything takes and that doesn't work for efficient AI.” Third: Eliminating irrelevant data silos and maximizing model training: Data copying at scale is becoming impossible. An enterprise with data in different silos all over its estate is simply not going to move and copy that data into a single data warehouse or lake. Botes said: “You can't, the gravity is too expensive. [And] the second that data's changed at the source, that data lake is invalid.” Everpure Data Stream is this uniform mechanism, a data re-use platform, that ingests, curates and transforms data, vectorizing it, indexing it and serving it when requested. Data ingestion is automated and sources include the gamut of enterprise data: block, NFS file, S3 object, databases, data warehouses, data lakes, streaming data and enterprise applications, text documents, PDFs, images, and structured tables. It has built-in vector databases with NVidia’s NeMo Retriever enabling GPU-accelerated vector embedding generation, ensuring precise retrieval in RAG pipelines, as well as Nvidia NIM for deploying optimized inference. There are pipeline orchestration, GPU integration, Nvidia NIMs and NVAIE (Nvidia AI Enterprise) APIs plus ones for semantic search and LLM integration. Everpure Data Stream uses Nvidia’s RTX PRO 6000 Blackwell Server Edition GPU and libraries of code to synchronize data delivery across ConnectX-7 NICs with processor cycles: Nvidia Spark Rapids and cuVS. Everpure Data Stream ensures orchestration occurs at the storage level, with FlashBlade acting as a layer where metadata enrichment and relevance reranking transformations are executed in parallel, driving down end-to-end inferencing latency. Everpure Data Stream is a data factory and supply chain delivery system, finely tuned to operate in the Nvidia GPU server and software environment. It supports petabyte-scale RAG data sets, with independent scaling of capacity and performance to accommodate multiple GPU clusters without downtime. Botes said: “Without a RAG mechanism, a model is trained with knowledge from only one moment - by definition it’s always behind. It's at the point in time when it was trained. So what we do with the RAG is, we augment that model with knowledge that is from this very moment. That knowledge can only exist within a storage system. It never exists in a compute system. The more up to date we can make a model, then the more concise and better we can make the answers.” Everpure Data Stream is model-agnostic, Botes said: “Customers pick the models that best suit the data they’re interested in and configure the models.” Optimizing data placement and reuse For our imagined insurance AI agent, proximity between data and GPU is another important element to factor in. Placing data storage right next to the GPU server (as FlashBlade//EXA should be) avoids time-consuming data movement - adding value and providing optimal data placement. With this, there is a memory hierarchy to keep in mind. This kind of repeat token processing and the need for co-located GPU compute and storage is characteristic of AI models and agents operating in the finance analytics, healthcare clinical systems, legal corpora, real-time fraud detection, and cyber-security threat intelligence areas. The requirement for data primacy and data context The whole system downstream of GPUs has to be data-centric with a single logical data catalog or encyclopedia acting as the sole reference platform for AI agents to provide governance, look for and access the data they need. It pre-prepares data for AI use. Exabyte-scale data has enormous gravity and moving it is to be discouraged. But the metadata describing it, petabyte-scale metadata, has gravity too and needs to be stored close to the GPU servers. Metadata performance is key, Botes said: “FlashBlade is an incredibly fast, highly distributed database that can look up key-values and present them as storage protocols extremely quickly.” Botes said: “Hardware innovation is exciting and accelerating rapidly, bringing advancements like faster PCI Express bus speeds, high-bandwidth memory, and improved network interconnects. The only way for the technology to take advantage of the hardware innovation without reinventing infrastructure every single year is to disaggregate,” meaning separate compute from storage, hence the FlashBlade//EXA’s design. “For AI platforms requiring cutting-edge performance, it’s so important to capture these benefits immediately, and at the pace of GPU innovations. How effectively we can manage and deliver data is the most significant impact of this hardware innovation.” He explored this concept further: “Disaggregating compute, network, and storage allows us to scale them independently. This is crucial because innovation happens at different speeds for each component, preventing the need to overhaul the entire infrastructure every time one component advances.” This also allows organizations to upgrade hardware tiers independently through Everpure’s Evergreen subscription model, avoiding destructive forklift overhauls every time a new accelerator architecture arrives. A modern approach means a storage platform for AI agents has the basic IO speed to keep GPUs busy. It also has Everpure Data Stream software to ensure the right data IOs take place, and the data gets to the GPUs in the right way; data logistics as it were. Distributed data spread across an organization’s IT estate is discovered using Everpure Data Intelligence, following Everpure’s acquisition of 1touch. Botes said: “We are trying to make all storage layers self-describing, regardless of who makes them.” Everpure acquired 1touch to deepen data management capabilities and build a “data intelligence system. So we can probe it, describe it, and create indexes to understand it.” And: “We have to do transformation while accessing it.” The self-description add-on, as it were, is happening inside Everpure Data Stream. Isolated islands of truth Botes said: “When we classify, the key thing to think about is, anyone can hook up a system that can read all your data and make it into vector space and search it, kind of what OpenAI does. But the tricky part is different data needs different governance.” “When you classify, you also want to set up separate streams for separate governance rules, because not all data is the same. Every company has many different sources of truth: salesforce.com; an engineering system; the finance system, the HR system. They relate to each other, but they don't need the same governance, or access. There needs to be a uniform mechanism for searching and interacting with preserved governance.” Afterall, he said: “You don't want to have 55 little islands of isolated truths. We've been down that path before and it's really problematic. To date, the application has been the governor of truth. But AI doesn't work that way. We have to create ways to find data by reference, not by location or URL.” He believes that “We have moved to systems that make data self-describing, so that, while we can maintain that source of truth within the application, the data there can also be used by other things.” Because of self-description, he adds: “We're making it queryable through AI and interacting through AI without necessarily having to make a full copy of it. Our capabilities in data intelligence makes it easy to deploy for enterprise data.” Data centric architecture supporting AI workloads For the insurance AI agent mentioned, maximizing the data is the key to returning faster answers, making the most of any AI investment and delivering more ROI to businesses. As enterprise AI factories transition from pilot projects to scaled production, raw GPU count is fading as the primary metric of AI capability. The real benchmark is GPU utilization efficiency—and the battle to eliminate idle cycles is increasingly being fought at the storage layer. The Everpure Data Platform approach to AI has the scale and software intelligence to prevent GPUs waiting for data and avoid them wasting their precious and expensive processing cycles recomputing data they have already wrangled. Sponsored by Everpure

masaki68 has added a photo to the pool:

masaki68 has added a photo to the pool:

masaki68 has added a photo to the pool:

masaki68 has added a photo to the pool:

masaki68 has added a photo to the pool:

Thomas Hawk posted a photo:

date stamped on slide, August 1974, handwritten on slide, “Portobello Road, London, 1974"