AI/ML News & Innovations Hub

Training own recommendation model for diploma thesis

For part of my thesis project, I need to create a mechanism that will generate recommendations for the user based on data stored in my database. For example, the system has a list of specific tools and their descriptions. I need to create a chat that, when asked how to assemble a cabinet, will provide recommendations and a list of tools. But the list of tools must be from the database I have, not just any list, but specifically those tools provided by my system. As I understand it, I need to train some kind of model, but I don't know what I need for this or even the technologies that are needed for this. Where can I start? Can you please recommend what I should study? What ready-made examples on a similar topic can I look at? And in general, how to build the architecture of this part of the project? Thank you all very much in advance.

Etihad Rail launches today - here's what the passenger journey from Fujairah to Abu Dhabi looks like

Meta blocks human rights accounts from reaching audiences in Saudi Arabia and the UAE

The post Meta blocks human rights accounts from reaching audiences in Saudi Arabia and the UAE appeared first on Access Now .

Intelligence is collective, not artificial — Prof. Michael I. Jordan (UC Berkeley / Inria)

Michael I. Jordan, described by Science magazine as the most influential computer scientist alive, has never thought of himself as an AI researcher. In this conversation he explains why that distinction matters. SPONSOR: --- Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open. Apply now: https://cyber.fund --- Jordan trained as a statistician and cognitive scientist, and his career has been spent building machine learning systems that work in the real world: supply chains, commerce, healthcare, and large economic systems. When the field rebranded itself as AI and then AGI, he did not follow. Instead he argues that the framing is wrong. AI is better understood as a collective economic system than as a race to build a disembodied superintelligence. We talk about why AGI is mostly a PR term, what machine learning achieved before the LLM hype cycle, and why the assistant-on-your-shoulder vision may be less compelling than it sounds. Jordan explains why explanations need to be actionable, not merely mechanistic; why AlphaFold's missing error bars matter; how prediction-powered inference changes the picture; and why drug discovery is an incentive-design problem rather than a pure pattern-matching problem. ERRATA: Science magazine ranked him the most influential computer scientist, not Nature --- TIMESTAMPS: 00:00:00 Cold open: A demoralizing message to young builders 00:02:04 CyberFund sponsor read 00…

LWiAI Podcast #245 - TML-Interaction, Claude For Legal, Sam Altman on Stand

OpenAI launches new voice intelligence features in its API, Thinking Machines drops a new, highly responsive model designed for humanlike interactions in real time, and more!

Kyutai and ELLIS Tübingen launch KE:SAI

A Premier Franco-German Partnership for Open Science in Physical AI

Criminalizing truth: Gulf governments must end the crackdown on information

The post Criminalizing truth: Gulf governments must end the crackdown on information appeared first on Access Now .

How researchers built a record-setting quantum circuit

New demonstration of a 52-qubit quantum Fourier transform shows how ecosystem innovation improves algorithm performance on quantum hardware.

Build your next story with Gemini Omni.

Scaling reinforcement learning at Applied Compute

How Applied Compute trains custom agents with Reinforcement Learning for enterprises like DoorDash, Cognition, and Mercor on Modal.

Announcing etcd 3.7.0-beta.0

SIG-Etcd announces the availability of the first beta release of etcd v3.7.0 . This new version of the popular distributed database and key Kubernetes component includes the long-requested RangeStream feature, as well as a refactoring and cleanup of multiple legacy components and interfaces. v3.7 will deliver improved security, better operational reliability, and an improved experience for working with large resultsets. First, however, the project needs users to test the beta. You can find v3.7.0-beta.0 here: Source code Binaries Official container images Please try it out and report issues in the etcd repo . This beta also determines the EOL of version 3.4. RangeStream In etcd v3.6 and earlier, it is challenging to work with requests that return large resultsets. The client or requesting application is forced to wait for the full result set, leading to unpredictable latency and memory usage. The RangeStream RPC lets calling applications accept result sets in chunks, reducing latency and making buffering memory usage more predictable. Much of the work on RangeStream was done by a relatively new contributor to etcd, Jeffrey Ying , a software engineer at Google. New contributors can have a substantial impact on etcd development. "I've always been fascinated by database internals, and building RangeStream was a great opportunity to solve a bottleneck we were hitting in production with Kubernetes. It was the perfect opportunity to collaborate across projects and improve the ecos…

ClearML Joins the Dell AI Ecosystem Program and Launches AI Factory Blueprints, Making It Easier for Enterprises to Operationalize AI

ClearML is deepening its partnership with Dell Technologies by joining the Dell AI Ecosystem Program, announced at Dell Technologies World 2026. As part of this collaboration, ClearML is launching two pre-validated deployment blueprints — for Kubernetes and OpenShift — available in the Dell Automation Platform catalog, giving enterprises a fast path from bare metal to […]

Frontier Risk Report (February to March 2026)

Assessment Window: Feb 16, 2026 – Mar 16, 2026 Download PDF Redaction summary statement: Except where explicitly noted in the report, there was no additional redacted information that was important to our conclusions from any of the participating companies. Executive summary and guide to the report Starting in February 2026, METR conducted a pilot exercise to assess misalignment risks from AI agents used inside frontier AI developers, with participation from Anthropic, Google, Meta, and OpenAI. We make three main contributions in this report, each detailed in a separate section. First, we motivate and outline the process we followed for this exercise. 1 Each participant provided: Access to their most capable internal model(s) at the time of assessment, including raw chains of thought. A wide range of non-public information about the capabilities of the shared model(s), how AI was used and monitored internally, and trends in the pace of progress. METR then prepared private reports for each participant, participants approved what non-public information could be disclosed, and METR wrote this public report. This exercise is entity-based rather than model-specific, and is designed to be repeated periodically rather than tied to public releases. Second, we present six key facts that inform our assessment, drawing on evaluations we conducted on the models that participants shared, 2 evaluations we conducted on public models, information shared by participants, 3 findings from a re…

Informe de riesgos de la IA de frontera (febrero–marzo de 2026)

Periodo de evaluación: 16 de febrero de 2026 - 16 de marzo de 2026 Descargar PDF (inglés) Declaración resumida sobre omisiones: Salvo donde se indique explícitamente en el informe, no hubo información omitida adicional de ninguna empresa participante que fuera importante para nuestras conclusiones. Resumen ejecutivo y guía del informe En febrero de 2026, METR inició un ejercicio piloto para evaluar los riesgos de desalineación de los agentes de IA usados dentro de empresas desarrolladoras de IA de frontera, con la participación de Anthropic, Google, Meta y OpenAI. En este informe hacemos tres contribuciones principales, cada una detallada en una sección separada. Primero, motivamos y describimos el proceso que seguimos para este ejercicio. 1 Cada participante proporcionó: Acceso a su(s) modelo(s) interno(s) más capaz/capaces en el momento de la evaluación, incluidas cadenas de pensamiento sin procesar. Una amplia variedad de información no pública sobre las capacidades del/de los modelo(s) compartido(s), cómo se usaba y monitoreaba la IA internamente, y las tendencias en el ritmo de progreso. Luego, METR preparó informes privados para cada participante, los participantes aprobaron qué información no pública podía divulgarse, y METR escribió este informe público. Este ejercicio está basado en entidades en lugar de ser específico para un modelo, y está diseñado para repetirse periódicamente en lugar de estar ligado a lanzamientos públicos. Segundo, presentamos seis hechos clav…

前沿 AI 风险报告（2026 年 2–3 月）

评估时段： 2026 年 2 月 16 日至 3 月 16 日 下载 PDF（英文） 关于删节： 除报告中明确标注之处外，参评公司没有删去任何会影响我们结论的重要信息。 报告摘要与导读 2026 年 2 月，METR 开展了一项试点研究，对前沿 AI 公司在实验室内部使用 AI 智能体时可能出现的不对齐风险进行了评估。Anthropic、Google、Meta 和 OpenAI 参与了这项试点。报告正文分为三部分： 第一部分中 ，我们 阐述了这项试点研究的设计动机和具体流程 1 。参与研究的各家公司向 METR 提供了： 评估期间使用其最强内部模型的权限，包括原始思维链。 大量非公开的信息，包括所共享模型的能力、公司内部使用和监测 AI 的方式，以及模型能力进展速度的趋势。 随后，METR 为每家参与公司撰写了一份内部报告。在与各公司确认内部报告中可公开的信息后，METR 撰写了这份报告。本次试点研究以 公司实体 为评估对象，而非局限于具体模型。评估将周期性进行，不与 AI 模型公开发布的时间挂钩。 第二部分中 ，我们梳理了支撑评估结论的 六项关键事实 。这些事实基于多类证据，包括：METR 对所共享的公司内部模型和公开模型的评估 2 、参与评估的公司所提供的信息 3 、近期开展的一次 嵌入式红队测试 的发现 4 、AI 模型的系统卡，以及其他公开资料。我们从三个角度组织这些事实：“手段” (means)，即智能体能否实施有害行动；“动机” (motive)，即智能体是否可能尝试这些行动；“机会” (opportunity)，即在现有防护下，这些尝试能否成功。 最后 ，我们评估了这些公司在 2026 年 2 月至 3 月这段时间里内部使用的 AI 智能体，检测其是否已经具备发起“ 失控部署 ”所需的手段、动机和机会。这里的失控部署是指一组智能体在不同强度的安全防护和检测情况下，未经授权地持续自主运行且不被人类所察觉。总体来看，我们认为评估时的内部 AI 智能体可能已经具备发起小规模失控部署的手段、动机和机会，但它们目前还缺少让这类部署高度稳健的手段。 All sources All models Incidents With risk regions Public materials METR evaluations Shared by companies ? Hypothetical Download incidents chart (PNG) Download incidents chart with risk regions (PNG) Download incidents.json × 在单独的页面上查看事件图表 鉴于模型能力提升很快，我们预计未来几个月内，失控部署可能会变得更难发现和关闭。我们暂定于 2026 年下半年开展一次类似的试点研究。 感谢 Anthropic、Google、Meta 和 OpenAI 参与本次研究。与以往的外部评估合作相比，这一流程让 METR 能更直接地接触到了内部信息，又保持了高度的编辑独立性 5 。我们由衷感谢各公司的工作人员：他们投入了大量精力，与我们共同打磨这项既无现成模板、又涉及诸多变动因素的试点流程。我们认为针对开发者内部使用 AI 所带来的风险的第三方定期评估应推广为全行业的通行做法。 Pilot process To date, third-party evaluations of frontier AI have largely focused on evaluating individu…

Four Experts on the Questions We Should Be Asking About AI Right Now

The post Four Experts on the Questions We Should Be Asking About AI Right Now appeared first on Partnership on AI .

Generating novel scientific hypotheses with Co-Scientist

In an era of information overload, the search for transformative scientific ideas has become a significant bottleneck for progress. Every great scientific breakthrough begins with a single, transformative idea. The spark of discovery relies on a researcher's ability to connect disparate facts and formulate the right hypothesis to test. We believe AI can help dramatically accelerate the pace of breakthroughs by serving as a dedicated partner in the generation and refinement of breakthrough scientific hypotheses. That’s why we’ve developed Co-Scientist, a Gemini-based multi-agent AI system that iteratively generates, debates, and evolves novel hypotheses for complex scientific problems. Read the Nature paper: https://www.nature.com/articles/s41586-026-10644-y and learn more at labs.google/science #googleio #ai #science ____ Subscribe to our channel https://www.youtube.com/@googledeepmind Find us on X https://x.com/GoogleDeepMind Follow us on Instagram https://instagram.com/googledeepmind Add us on Linkedin https://www.linkedin.com/company/deepmind/

Using AI to outsmart drug-resistant bacteria

Globally recognized as a silent pandemic, antimicrobial resistance continues to rise as bacteria outpace the development of new antibiotics. When patients stop responding to standard treatments, routine infections can quickly become life-threatening. At the University of Cambridge, Ben Luisi and his team are combining structural biology with advanced AI tools like AlphaFold, Gemini, and Co-Scientist to decode these hidden defense mechanisms. By compressing a process that once took years into just minutes, they are uncovering the critical insights needed to outsmart bacterial evolution. Learn more about science at Google DeepMind: https://deepmind.google/science/ #googleio #ai #science ___ Subscribe to our channel https://www.youtube.com/@googledeepmind Find us on X https://x.com/GoogleDeepMind Follow us on Instagram https://instagram.com/googledeepmind Add us on Linkedin https://www.linkedin.com/company/deepmind/

Understanding cancer at a genetic level with AI

In Uganda, the incidence of early-onset breast cancer is growing at an alarming rate. Dr. Daudi Jjingo and his team at Makerere University are working to identify genetic targets for potential vaccine development. By utilizing tools like AlphaFold, AlphaGenome, and Antigravity, they can conduct this research using only a laptop and a server, enabling seamless collaboration with local hospitals and institutions. By analyzing a protein highly expressed among breast cancer patients, the team successfully evaluated 15,000 potential binding sites, narrowing the scope to just 15 viable targets for laboratory validation. While a vaccine remains a future milestone, their work represents a critical step forward for global oncology and public health. Learn more about science at Google DeepMind: https://deepmind.google/science/ #googleio #ai #science ___ Subscribe to our channel https://www.youtube.com/@googledeepmind Find us on X https://x.com/GoogleDeepMind Follow us on Instagram https://instagram.com/googledeepmind Add us on Linkedin https://www.linkedin.com/company/deepmind/

Predicting a historic storm earlier with WeatherNext

Tropical storms and hurricanes are notoriously volatile, changing structure and intensity in a matter of hours. This unpredictability makes them some of the most challenging weather systems to forecast—putting lives and livelihoods at risk. WeatherNext, our global weather forecasting AI model, successfully predicted the intensity and track of Hurricane Melissa in October 2025. By providing high-confidence signals and advanced notices days before the Category 5 storm made landfall in Jamaica, WeatherNext enabled meteorologists and local authorities to issue life-saving evacuation warnings and protect vulnerable communities. Read more about the role of AI in meteorology and how we're collaborating with institutions like the National Hurricane Center to build a more weather-resilient world: https://deepmind.google/blog/how-weathernext-helped-the-national-hurricane-center-better-predict-hurricane-melissas-historic-landfall-in-jamaica #googleio #ai #science ___ Subscribe to our channel https://www.youtube.com/@googledeepmind Find us on X https://x.com/GoogleDeepMind Follow us on Instagram https://instagram.com/googledeepmind Add us on Linkedin https://www.linkedin.com/company/deepmind/

This is Gemini Omni ♾️ #GoogleIO

Introducing the 2026 MLCommons Rising Stars

Fostering a global community of emerging leaders at the intersection of ML and systems research The post Introducing the 2026 MLCommons Rising Stars appeared first on MLCommons .

A strategic blueprint for safe health AI implementation: Your 2026 roadmap

The Health AI Implementation Toolkit is a practical five-stage framework developed by Vector Institute to help health system leaders, AI solution vendors, and clinical teams deploy AI safely and responsibly […] The post A strategic blueprint for safe health AI implementation: Your 2026 roadmap appeared first on Vector Institute for Artificial Intelligence .

Expanding our AI and Healthcare Portfolio

The healthcare industry is ground zero for AI companies and the rollout of their products: Microsoft tells us that AI is better than doctors at diagnosing complex medical conditions. Nvidia claims that its chatbot, a partnership with the startup Hippocratic AI, can outperform nurses on detecting over the counter drug toxicities. AI firms suggest that […] The post Expanding our AI and Healthcare Portfolio appeared first on AI Now Institute .

Can I get my agents on the phone?

I haven’t used OpenClaw in weeks

Announcing Claude Managed Agents on Cloudflare

Cloudflare has integrated with Anthropic's Claude Managed Agents to provide a fast, isolated execution environment for autonomous code delivery. This means builders can scale agent workflows globally while strictly controlling access to private backends and easily customizing their agent’s tools and runtimes.

OlmoEarth v1.1: A more efficient family of models

OlmoEarth v1.1 is a more efficient family of remote-sensing models that cuts compute costs by up to 3x while maintaining similar performance, making large-scale satellite mapping faster and cheaper to run.

How GoPerfect Built an Agentic Recruiting Workforce with Qdrant Cloud

GoPerfect mission is to use an AI recruiting workforce that replaces the manual, low-leverage parts of recruiting. Instead, an agent decomposes recruiter intent and runs the work end to end to find top talent. Their agentic platform handles sourcing, scanning, reviewing, outreach, admin work as well as candidate conversations for recruiters, hiring managers, agencies, and CEOs who hire at volume. Recruiting is a needle-in-a-haystack problem with two complications: the haystack is massive (200M+ profiles enriched with 1B+ data points drawn from professional networks, code repositories, company data, and AI-derived signals), and the definition of the “needle” is more nuanced than any keyword filter can express. A product manager is not a product marketer, even though the two sit close together in any reasonable embedding space.

Introducing Claude Managed Agents with Modal Sandboxes

AI Weekly Issue #493: Meta hired $145B in capex and fired 8,000 people

Six days after we called $725B a bet on what no one wanted, the receipts started landing. Meta committed $145B to AI infrastructure the same week it began firing 8,000 people. Standard Chartered described its own cuts as replacing "lower-value human capital." Pope Leo XIV announced he'd co-launch his first AI encyclical with Anthropic's Christopher Olah at the Vatican on May 25.

EpiCache: Episodic KV Cache Management for Long-Term Conversation on Resource-Constrained Environments

Modern large language models (LLMs) extend context lengths to millions of tokens, enabling coherent, personalized responses grounded in long conversational history. However, the Key-Value (KV) cache grows linearly with the extended dialogue history, causing the model’s memory footprint to quickly exceed device limits. While recent KV cache compression methods attempt to reduce memory usage, most apply cache eviction after processing the entire context, incurring unbounded peak memory usage. Additionally, query-dependent eviction narrows the cache semantics to a single query, leading to failure…

At aged care provider Regis, AI takes on paperwork so staff can focus on residents

The post At aged care provider Regis, AI takes on paperwork so staff can focus on residents appeared first on Source .

Does Google Have a Claude Code Counterpunch?

Do-everything, ‘super agents’ are dominating the AI story today. Will we see one from Google this week?

Import AI 457: AI stuxnet; cursed Muon optimizer; and positive alignment

Welcome to Import AI, a newsletter about AI research.

A Conversation with Carnegie Ethics Fellow Alex Urwin

This conversation features Alex Urwin, head of strategic partnerships & projects at the UK prime minister's office.

Better Experiments with LLM Evals — A funnel, not a fork

TL;DR LLM evals, automated judges that assess relevance, coherence, and quality at scale, are a powerful new... The post Better Experiments with LLM Evals — A funnel, not a fork appeared first on Spotify Engineering .

📈 Data to start your week: The cost of tokenmaxxing

Bye, bye fixed costs. Hello token anxiety.

SA AI Association & Western Cape Government Launch AI Cluster Collaboration

CAPE TOWN, SOUTH AFRICA 18th May 2026 – The South African AI Association & Western Cape Government launch new provincial AI Cluster collaboration. The Western Cape AI Cluster (WCAIC) will research, support and grow the artificial intelligence opportunity in the province as well as engaging with relevant stakeholders on key themes such as AI Policy, […]

Microsoft: it’s time to come clean about your ties to the Israeli military

Through a new joint letter, we're calling on Microsoft to publish the findings of its review into the Israeli military’s use of the company services. The post Microsoft: it’s time to come clean about your ties to the Israeli military appeared first on Access Now .

Joint letter to Microsoft regarding Israeli military use of Azure cloud and AI services

A follow up to our open letter regarding Microsoft’s formal review of recent allegations about Israel’s usage of Azure cloud for the surveillance and targeting of Palestinians. The post Joint letter to Microsoft regarding Israeli military use of Azure cloud and AI services appeared first on Access Now .

Project Glasswing: what Mythos showed us

In recent weeks, we pointed Mythos and other security-focused LLMs at live code across critical parts of our infrastructure. We share what we observed, the models’ strengths and weaknesses, and what the work around them needs to look like before any of it can scale.

🔮 Exponential View #574: Inside Anthropic’s rocket ship; AI pluralism; love commoditized, context-maxxing & Voltaire++

This is the most relevant newsletter I receive in my inbox every week.

Latest open artifacts (#21): Open model bonanza! Gemma 4, DeepSeek V4, Kimi K2.6, MiMo 2.5, GLM-5.1 & others. On CAISI's V4 assessment.

An eventful month with one flagship release after another

Recent Developments in LLM Architectures: KV Sharing, mHC, and Compressed Attention

From Gemma 4 to DeepSeek V4, How New Open-Weight LLMs Are Reducing Long-Context Costs

LLM Cost Tracking Solution: How to Monitor and Control AI Spend in Agentic Systems

The first sign of trouble isn’t always performance. Sometimes it’s the invoice. Your team ships a new agent that routes requests, calls tools, runs retrieval, and orchestrates multiple LLM calls to deliver high-quality answers. It looks like a win until the first full-month bill hits, and your LLM spend has quietly tripled. Finance wants answers, […] The post LLM Cost Tracking Solution: How to Monitor and Control AI Spend in Agentic Systems appeared first on Comet .

Advancing Collaborative Research: Introducing URP 3.0

Advancing Collaborative Research: Introducing URP 3.0 robyn.cherinka… Fri, 05/15/2026 - 14:48 By Kate Tsui, Program Director, University Research Partnerships University Research Program 3.0 Kick-Off Event at TRI HQ At Toyota Research Institute (TRI), we believe meaningful innovation happens when industry and academia work side by side. Today, we are introducing the next five-year phase of our University Research Program, called URP 3.0 , which expands our collaboration with leading North American universities and welcomes new partners into the community. Starting in 2026, URP 3.0 supports 69 research projects across 31 universities, bringing together 88 TRI researchers and 104 faculty members . This is the biggest cohort since the program’s inception, with 11 new institutions participating for the first time and bringing fresh perspectives and expertise. The program also continues to build on long-standing relationships with foundational research partners, such as MIT, Stanford, and the University of Michigan. TRI funds collaborative university research projects that are intentionally structured for deep collaboration. Each project is co-led by a university researcher and a TRI co-investigator working as peers to ensure that fundamental research and real-world application evolve together. The portfolio also includes 10 Young Faculty Research projects, continuing TRI’s investment in the next generation of researchers. Although these projects span very different domains, many…

Kubernetes v1.36: New Metric for Route Sync in the Cloud Controller Manager

This article was originally published with the wrong date. It was later republished, dated the 15th of May 2026. Kubernetes v1.36 introduces a new alpha counter metric route_controller_route_sync_total to the Cloud Controller Manager (CCM) route controller implementation at k8s.io/cloud-provider . This metric increments each time routes are synced with the cloud provider. A/B testing watch-based route reconciliation This metric was added to help operators validate the CloudControllerManagerWatchBasedRoutesReconciliation feature gate introduced in Kubernetes v1.35 . That feature gate switches the route controller from a fixed-interval loop to a watch-based approach that only reconciles when nodes actually change. This reduces unnecessary API calls to the infrastructure provider, lowering pressure on rate-limited APIs and allowing operators to make more efficient use of their available quota. To A/B test this, compare route_controller_route_sync_total with the feature gate disabled (default) versus enabled. In clusters where node changes are infrequent, you should see a significant drop in the sync rate with the feature gate turned on. Example: expected behavior With the feature gate disabled (the default fixed-interval loop), the counter increments steadily regardless of whether any node changes occurred: # After 10 minutes with no node changes route_controller_route_sync_total 60 # After 20 minutes, still no node changes route_controller_route_sync_total 120 With the feature…

Further Notes on Our Recent Research on AI Delegation and Long-Horizon Reliability

Our recent paper, “LLMs Corrupt Your Documents When You Delegate”, has generated discussion about the reliability of AI systems in delegated workflows. We appreciate the interest in this work and want to clarify several important points about what the paper does—and does not—claim. The research aims to develop robust evaluation methods for long-horizon delegated and […] The post Further Notes on Our Recent Research on AI Delegation and Long-Horizon Reliability appeared first on Microsoft Research .

Kubernetes v1.36: Mixed Version Proxy Graduates to Beta

Back in Kubernetes 1.28, we introduced the Mixed Version Proxy (MVP) as an Alpha feature (under the feature gate UnknownVersionInteroperabilityProxy ) in a previous blog post . The goal was simple but critical: make cluster upgrades safer by ensuring that requests for resources not yet known to an older API server are correctly routed to a newer peer API server, instead of returning an incorrect 404 Not Found . We are excited to announce that the Mixed Version Proxy is moving to Beta in Kubernetes 1.36 and will be enabled by default! The feature has evolved significantly since its initial release, addressing key gaps and modernizing its architecture. Here is a look at how the feature has evolved and what you need to know to leverage it in your clusters. What problem are we solving? In a highly available control plane undergoing an upgrade, you often have API servers running different versions. These servers might serve different sets of APIs (Groups, Versions, Resources). Without MVP, if a client request lands on an API server that does not serve the requested resource (e.g., a new API version introduced in the upgrade), that server returns a 404 Not Found . This is technically incorrect because the resource is available in the cluster, just not on that specific server. This can lead to serious side effects, such as mistaken garbage collection or blocked namespace deletions. MVP solves this by proxying the request to a peer API server that can serve it. sequenceDiagram parti…

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