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Anthropic has been on a cloud partnership spree in recent months, and its latest move is reportedly a $10 billion deal with AI cloud startup Volta.
Using continuous imagery from NASA’s PUNCH (Polarimeter to Unify the Corona and Heliosphere) mission, scientists predicted the near-Earth arrival of a solar eruption to within 30 minutes in an initial proof of concept test. The results, presented Tuesday at the Committee on Space Research Scientific Meeting and under review at the journal Space Weather, could […]
At Kilo Code, engineers are reading or writing code themselves only about 1% of the time now, according to co-founder Emilie Schario — the rest is agents. That shift is forcing new questions onto dev teams: which systems are safe to hand over, who cleans up when models goof up, how to support multi-model architectures, and whether skyrocketing token bills mean real progress or just burned IT budget. As far as tech leads from Replit, Kilo Code, and Symbotic are concerned, it’s a natural — and welcome — evolution as agentic AI becomes embedded into more and more enterprise workflows. “Unless something's really broken or debugging, 99% of the time engineers are not reading or writing code anymore,” Emilie Schario, co-founder of Kilo Code, said at VB Transform 2026 . AI good at greenfield, not so great at brownfield For Jared Go, distinguished engineer for AI and cloud at warehouse automation company Symbotic, the current moment is about directing the focus of AI. "These are my criteria," he said. "Let's look at it from the lens of security, elegance, clean, concise code, water tightness." That way, AI does most of the heavy lifting, and human code review isn't as critical. Human involvement becomes necessary further down the line, Go noted, because agents don't make strong product decisions. “Greenfield [building brand new codebases] is so easy for agents. Brownfield [writing, updating, or maintaining existing code] we all know is where the actual challenge lies.” Replit takes a bit of a different tack: While the company has "gone very agentic," they've been more conservative with AI coding, explained Amol Jain, head of product engineering. An agent reviews each pull request (PR) and assigns it a risk score; low-risk PRs are self-merged by their author, while others go to human reviewers who read the code and give feedback. “The idea was human on the loop, not human in the loop,” Jain said. Replit’s internal tool is essentially self-driving for software engineers; devs give a task to agents, which do end to end planning, implementation, and testing. “It's a fleet of agents that run in their own cloud virtual machines (VMs) with access controls behind token proxies so they're secure,” Jain said. He shared one example where an engineer couldn’t repro or solve a “very gnarly bug” deep in its systems. It was sent to an AI manager agent, which told it to go to sleep. The manager agent then spun up a bunch of underlying agents that found the issue; it subsequently spun up a bunch more agents that found the fix. Six hours later, AI had a PR ready for the bug that had puzzled human engineers. Multi-model is the future AI providers are also evolving beyond the lock-in model, as customers increasingly demand multi-model choice. Kilo Code, for its part, supports 500-plus models in its gateway. "Your software that you're using to do agentic engineering should be decoupled from the model that you're using to do it," Schario said. For instance, Schario said companies often use expensive frontier-tier models to architect a project, then switch to a less expensive open-weight model for the rest of the work. It’s also important to respect model provider limitations, such as when they need to work in closed or isolated environments or providers in their specific regions. “It's factoring in what's important to you, what limitations you've set, what data retention policies you've established, what keys you've brought in, what commits you might have … into that routing decision,” Schario said. Replit, similarly, tends to have a better sense of the cost versus capability spectrum than its customers, Jain contended. “We are essentially making the decisions on users' behalf of what model to use when, in what capacity, to minimize cost and maximize capability.” To tokenmaxx or not to tokenmaxx Of course, an important consideration as AI adoption increases is runaway costs, which has led to some enterprises tracking and capping AI use through tokenmaxxing. Concerns come from both sides, Schario said: internally and from customers. From the latter, she's hearing, "I accidentally spent my whole AI budget for the year … so what do I do now?" In response, Schario said Kilo Code points customers to the same workflow: use expensive models for planning, then open-weight models for affordability. Further, sharing skills, strong guidance, and Model Context Protocol (MCP) will empower models. “Realizing where you can really uplevel your team to help them get the most out of the models they're using is going to make a big difference,” Schario said. Internally, meanwhile, Schario noted one particular engineer that has a "heavy foot" and is constantly at the top of the usage board. "I regularly have to nudge, 'What are you doing there?'" she said. It's easy to look at a $600 bill for daily work and react, "Wow, that's so much," but looking at the amount of work completed can sometimes justify the cost. “Cost per pull request is the metric that I'm paying attention to right now,” Schario said. “It feels like the closest proximity for how I can measure value.” Ultimately, AI changes how enterprises are thinking about ROI because spend is not the problem. “The spend with no return on that spend is the problem.” Symbotic, for its part, has set per-month cost tiers for its employees. The company built a tool that gives managers visibility into PRs and usage trends. They can then move users up or down a tier as they see fit, Go explained. “Having a cap and seeing how many people went up in cap this month makes a big difference when you're trying to corral these costs and make things efficient,” Go said. When Cursor — which Symbotic uses heavily — ended a legacy discount that had grandfathered the company into a flat per-request rate even for frontier models, and moved everyone to full pricing, it forced a company-wide reckoning on efficiency, Go said. "People were saying, 'You should try this model … This works better for this C# code, this whatever,'" he said. But the cost problem is increasingly moving out of IT; Replit, for one, broadened agents beyond engineering, and eventually found that a user on the support side had "blown through an insane amount of money," Jain said. When they looked under the hood, they figured out it was because they were running an automation on GPT 5.5 Pro Max. “At least till that point, the ROI was rather clear,” Jain said. “We could see engineering productivity 3X, so no one had questioned it yet.” Visibility that isn’t “anti-productive,” model routing, and sensible defaults are critical, he emphasized. “Most tasks do not need the frontier.”
NVIDIA is participating in the U.S. National Science Foundation’s (NSF) State and Regional Artificial Intelligence Infrastructure Hubs program, an effort launching today to expand access to the advanced computing, data, software and expertise needed for AI-enabled research and education. Consistent with the aims of the Genesis Mission, the program will support state and multistate groups […]
For robotaxis and other autonomous vehicles (AVs), the hardest problems aren’t the everyday scenarios. They’re the rare, complex situations that are difficult to anticipate and train for. Handling these long‑tail events takes more than just object detection and motion prediction. AVs must understand the situation, reason about cause and effect, choose the right action and […]
The UK's biggest solar eclipse since 1999 arrives on 12 August. Here's where to see it safely in the West Country.
Photons traveling through a cloud of atoms can emerge so early that they appear to have spent a negative amount of time inside. Researchers tested whether this was merely a misleading feature of the light pulse by making extremely weak measurements of the atoms. Surprisingly, the atoms confirmed the same negative dwell time. The finding does not break standard physics, but it reveals that one of quantum mechanics’ strangest effects is physically measurable.
Ryan welcomes Anurag Goel, CEO and co-founder of Render, to discuss why most startups shouldn’t start by managing their Kubernetes and cloud infrastructure.
Chinese e-commerce and cloud giant Alibaba's famed Qwen team of AI researchers last night unveiled Qwen3.8-Max , a new flagship 2.4-trillion-parameter mixture-of-experts (MoE) multimodal large language model (LLM) that targets one of the most competitive corners of the frontier AI market: autonomous software engineering and long-horizon enterprise work. If the company's published benchmarks hold up under broader independent testing, Qwen3.8-Max doesn't merely compete with today's leading proprietary models — it surpasses several of them on some key benchmarks in agentic computing. Most notably, Qwen reports that Qwen3.8-Max scores 86.1 on the OSWorld-Verified benchmark measuring how well agents can use a computer operating system and applications on it, ahead of GPT-5.6 Sol Max (83.2) and Fable 5 (85.0). It also posted the highest reported score on PaperBench , the benchmark from OpenAI measuring how well agents can reconstruct scientific research papers from experimental data, and leading or remaining highly competitive across software engineering, research reproduction, multimodal reasoning, and visual web development benchmarks. The release also signals a potentially significant strategic shift for Alibaba: the company says open weights for Qwen3.8-Max will be released next week, alongside Qwen3.8-27B. If that happens under a permissive license, it would represent the first time a Max-class Qwen model becomes available for self-hosted deployment—a move that could substantially reshape enterprise adoption. One important caveat remains, however: Alibaba has not yet disclosed the licensing terms, leaving open the possibility that the release could use a more restrictive custom license, as we saw recently with Chinese rival Moonshot's open Kimi K3 frontier model , rather than a broadly permissive one such as Apache 2.0. A different definition of 'frontier' Over the past year, the competitive landscape for foundation models has become increasingly specialized. OpenAI has largely focused its GPT series on general reasoning, multimodal interaction and enterprise productivity. Anthropic's Claude series has emphasized coding and dependable long-context reasoning. Google continues to push Gemini toward multimodal productivity and web-native workflows. Moonshot AI's Kimi K3 recently entered the conversation by pairing frontier-class performance with an open-weight release. Qwen3.8-Max attempts to combine many of these strengths into a single model aimed squarely at enterprise automation. Rather than emphasizing conversational intelligence, Alibaba is positioning the model as an autonomous coworker capable of executing projects that span days rather than minutes. According to the company, Qwen3.8-Max can autonomously complete software projects lasting more than 10 days, reproduce research papers involving thousands of lines of code, perform iterative chip-design optimization, and continuously revise plans using multimodal feedback loops. Those demonstrations remain company-produced and have not yet been broadly replicated by independent evaluators. Nevertheless, they illustrate a growing industry trend: frontier models are increasingly competing on their ability to finish entire workflows rather than answer individual prompts. Benchmarks increasingly reward autonomous execution The benchmark suite released alongside Qwen3.8-Max reflects this shift. Instead of focusing solely on traditional reasoning exams or coding puzzles, many of the highlighted evaluations measure long-horizon execution. On OSWorld-Verified, which evaluates computer-use agents interacting with desktop environments, Qwen3.8-Max posts 86.1, ahead of GPT-5.6 Sol Max's 83.2, Fable 5's 85.0, and Gemini 3.1 Pro's 76.2. The model also leads: PaperBench: 93.0 TerminalBench 2.1: 86.6 Vision2Web: 69.0 LVBench: 81.8 ERQA: 77.8 Elsewhere, it remains competitive with proprietary leaders while trailing in several categories. On the professional software engineering benchmark SWE-Pro, for example, OpenAI's model posts the highest reported score, while Opus 4.8 continues to lead on certain software engineering evaluations and Agents' Last Exam. Rather than dominating every benchmark, Qwen appears to offer one of the broadest balanced performance profiles currently available. That balance may ultimately matter more for enterprise buyers than isolated benchmark wins. Many organizations increasingly evaluate models based on how reliably they complete heterogeneous workflows—writing code, reading documents, navigating interfaces, generating reports, inspecting images and coordinating multiple subtasks—rather than optimizing for one narrow capability. Where Qwen3.8-Max appears strongest Assuming Alibaba's published results translate into production deployments, several enterprise workloads stand out as particularly well suited for Qwen3.8-Max. 1. Long-running software engineering Alibaba's primary demonstration involves autonomous software development extending beyond ten days. While enterprises should treat these demonstrations as vendor claims until independently reproduced, they align with a growing interest in persistent coding agents that operate continuously rather than interactively. Organizations experimenting with autonomous engineering teams, CI/CD automation, repository maintenance, regression testing or feature implementation may find Qwen particularly attractive if its agentic performance proves consistent outside laboratory settings. 2. Computer-use agents The strongest differentiator may be computer use. OSWorld has rapidly become one of the industry's most closely watched benchmarks because it measures a model's ability to interact with operating systems instead of simply generating text. Models capable of reliably navigating desktop software can automate countless repetitive business processes, including document processing, enterprise software integration, internal operations and legacy workflows where APIs may not exist. Leading OSWorld could therefore translate into real operational advantages if benchmark performance generalizes to production environments. 3. Research automation Qwen's PaperBench leadership suggests strong potential for organizations performing scientific computing, literature review, experiment reproduction and technical analysis. Research institutions, pharmaceutical companies and industrial R&D teams increasingly use LLMs not only for summarization but also for executing reproducible computational workflows. Models capable of maintaining context across extended sessions become increasingly valuable in these environments. 4. Multimodal industrial workflows Unlike earlier multimodal systems that primarily analyze uploaded images, Qwen describes vision as an ongoing feedback mechanism integrated into planning and execution. That architecture could prove particularly useful in manufacturing, logistics, engineering inspection and design review, where visual inputs continuously inform operational decisions rather than serving as isolated prompts. The economics may prove just as important Perhaps the biggest competitive pressure comes not from benchmark scores but from pricing through Qwen's application programming interface (API) on QwenCloud (based in China): Qwen3.8-Max launches at $2/$6 per million input/output tokens, a mid-priced model but undercutting the top U.S. proprietary offerings to which it is benchmarked against by meaningful percentages, less than 1/3 the combined in/out price of Claude Opus 5 and less than 1/4 the price of GPT-5.6 Sol Max. Model Input ($/1M) Output ($/1M) Total ($/1M) Source MiMo-V2.5 Flash $0.10 $0.30 $0.40 Xiaomi deepseek-v4-flash $0.14 $0.28 $0.42 DeepSeek deepseek-v4-pro $0.435 $0.87 $1.305 DeepSeek GPT-5.6 Luna $0.20 $1.20 $1.40 OpenAI MiniMax-M3 $0.30 $1.20 $1.50 MiniMax LongCat-2.0 — limited-time promo $0.30 $1.20 $1.50 LongCat Gemini 3.1 Flash-Lite $0.25 $1.50 $1.75 Google Qwen3.7-Plus $0.40 $1.60 $2.00 Alibaba Cloud MiMo-V2.5 $0.40 $2.00 $2.40 Xiaomi Gemini 3.5 Flash-Lite $0.30 $2.50 $2.80 Google LongCat-2.0 — standard $0.75 $2.95 $3.70 LongCat MiMo-V2.5 Pro (≤256K) $1.00 $3.00 $4.00 Xiaomi GLM-5.2 $1.40 $4.40 $5.80 Z.ai Grok 4.5 $2.00 $6.00 $8.00 xAI MiMo-V2.5 Pro (>256K) $2.00 $6.00 $8.00 Xiaomi Qwen3.8-Max $2.00 $6.00 $8.00 QwenCloud Gemini 3.6 Flash $1.50 $7.50 $9.00 Google Qwen3.7-Max $2.50 $7.50 $10.00 Alibaba Cloud Gemini 3.5 Flash $1.50 $9.00 $10.50 Google Gemini 3.1 Pro Preview (≤200K) $2.00 $12.00 $14.00 Google GPT-5.6 Terra $2.00 $12.00 $14.00 OpenAI GPT-5.4 $2.50 $15.00 $17.50 OpenAI Kimi K3 $3.00 $15.00 $18.00 Moonshot AI Gemini 3.1 Pro Preview (>200K) $4.00 $18.00 $22.00 Google Claude Opus 5 $5.00 $25.00 $30.00 Anthropic GPT-5.5 $5.00 $30.00 $35.00 OpenAI GPT-5.5 Instant (chat-latest) $5.00 $30.00 $35.00 OpenAI Sakana Fugu Ultra (≤272K) $5.00 $30.00 $35.00 Sakana AI GPT-5.6 Sol — Standard mode $5.00 $30.00 $35.00 OpenAI Claude Fable 5 / Claude Mythos 5 $10.00 $50.00 $60.00 Anthropic GPT-5.6 Sol — Fast mode $10.00 $60.00 $70.00 OpenAI Lower inference costs increasingly matter because agentic systems consume dramatically more tokens than conventional chatbots — a reality that likely factored into OpenAI's decision late last week to cut the API prices of its mid- and lower-end GPT-5.6 lineup of models (Terra and Luna) by 20% and 80%, respectively. Indeed, as those running these systems can attest, multi-hour autonomous workflows, iterative planning and continuous self-correction can generate millions of tokens during a single task. For enterprises deploying hundreds or thousands of agents simultaneously, inference costs often become one of the largest operational expenses. Small reductions in per-token pricing therefore compound rapidly. How it compares with American frontier models Despite headline benchmark comparisons, Qwen3.8-Max should not necessarily be viewed as a wholesale replacement for leading American models. Instead, its strengths suggest different deployment strategies. OpenAI's GPT family continues to excel as a broadly capable enterprise reasoning platform with mature tooling, ecosystem integration and extensive commercial deployment. Organizations already invested in Microsoft ecosystems or OpenAI's enterprise offerings may continue to value those operational advantages even if Qwen leads on selected agent benchmarks. Anthropic's Claude Opus remains widely regarded as one of the strongest coding assistants, particularly for careful software engineering and long-context reasoning. Some enterprises may still prefer Claude for human-in-the-loop development where reliability and predictable behavior outweigh raw autonomy. Google Gemini continues to differentiate itself through deep Workspace integration, multimodal capabilities and Google Cloud services, making it attractive for organizations already standardized on Google's enterprise stack. Where Qwen appears most compelling is for enterprises prioritizing autonomous execution, extended planning horizons and favorable inference economics without sacrificing frontier-level performance. The open-weight question remains unanswered The largest unknown surrounding Qwen3.8-Max has little to do with benchmarks. Alibaba says open weights are coming next week. However, neither the announcement nor the provided documentation specifies the license that will govern those weights. That distinction could prove critical. A permissive license such as Apache 2.0 would significantly broaden enterprise adoption by allowing organizations to self-host, fine-tune and integrate the model into proprietary products with relatively few restrictions. A custom license—similar to approaches used by several recent frontier releases—could impose limitations on commercial deployment, redistribution, field of use or model modification. Such restrictions would narrow the appeal for enterprises seeking long-term infrastructure investments, regardless of the model's technical performance. Moonshot AI's recent Kimi K3 release illustrates why this distinction matters. While Kimi K3 made its weights openly available to all, its licensing terms included specific terms including a disclosure and a commercial license requirement for those offering it as a "Model as a Service." Until Alibaba publishes Qwen3.8-Max's license, organizations considering self-hosting should treat the open-weight announcement as promising but incomplete. An increasingly crowded frontier Qwen3.8-Max arrives during one of the fastest-moving periods in the history of foundation models. Within weeks, developers have seen major releases from Moonshot AI, OpenAI, Anthropic and others, each emphasizing different strengths: reasoning, coding, multimodality, autonomous agents or economics. Alibaba's contribution is notable because it combines competitive benchmark performance, aggressive pricing, a million-token context window and a stated commitment to releasing weights for its flagship model. Whether it becomes the preferred platform for enterprise autonomous agents will ultimately depend less on leaderboard positions than on broader independent validation, production reliability and the licensing terms accompanying the forthcoming weight release. Those factors—not benchmark charts alone—will determine whether Qwen3.8-Max becomes a genuine alternative to the leading American proprietary models or simply another impressive entrant in an increasingly crowded frontier AI race.
Enterprise teams building AI agents keep hitting the same wall: a chatbot that can answer a prompt but can't remember what the last five people asked it, and can't tell you whether last month's version actually worked. In a fireside chat with VentureBeat's Sam Witteveen at VB Transform 2026 , Asana's chief product officer, Arnab Bose, unpacked how his team tackled this problem to build a new operating system: Agentic Work Management (AWM). The product treats AI agents as coachable teammates that operate alongside humans rather than as one-to-one assistants. For product builders and developers trying to move beyond basic integrations, Bose provided a look under the hood. He detailed how Asana engineered AWM, offering a blueprint for solving real-world bottlenecks and building agentic systems at scale. The Work Graph: 18 years of company data, repurposed To build an operating system for human-agent teams, Asana needed a ready-made enterprise context graph. They built AWM on top of their 18-year-old architecture: the Work Graph. This graph-based database organizes information through a structure the company calls the Pyramid of Clarity. The smallest unit of work is a task with an assignee and a due date. Tasks belong to projects, projects roll up into portfolios, and portfolios connect to company-wide goals. The graph can help trace for example how a delayed design task impacts a corporate revenue goal. The Work Graph provides a real-time ledger of who does what, by when, and why. AWM leverages this architecture to create a multiplayer teammate. A standard AI copilot is stateless and tied to a single user's prompt. Because AWM plugs into the Work Graph, the AI can view overarching company goals, update project statuses, and share memory with human colleagues. "Because [the agent] is plugged into the Work Graph, it's not just looking at a particular prompt that you're sending it or looking at a particular individual's markdown file system on their local file,” Bose said. “It's working off of that shared ledger for the whole company." AWM is already in production. Bose said Asana has "several customers live and successful on it," including FedEx, which published its own case study on the shift. Building in guardrails for confidential work Shipping AWM to enterprise customers required Asana to solve several technical hurdles. The first was data governance. If an AI teammate acts across a company, it builds a shared memory by learning from workflows and human feedback. Bose highlighted a critical boundary problem: If an executive uses AWM to build workflows for a confidential project, the system must ensure the agent's updated memory does not leak context to an unauthorized employee who interacts with the same agent later. "[I] shouldn't be able to leverage that shared memory when I run the AI teammate if you created that memory using that same teammate on a project that is, let's say, a secret M&A project that I don't have access to," Bose said. Asana engineered a system of access controls to govern what triggers the creation of a memory versus the simple execution of a task. Second, AWM handles dynamic model routing to abstract prompt engineering away from the user. When a user assigns a task to an AI teammate (i.e., drafting a job description for a general manager role), the AI cross-references public job postings, Asana’s internal style guide, and product requirement documents. For a complex task, the system automatically routes the prompt to a heavy frontier model — Bose pointed to Anthropic's Opus and OpenAI's models as examples — while lighter tasks get down-leveled to something faster and cheaper. "We don't want the knowledge worker to have to think through what the best possible prompt, context engineering, and attachments are that they should put into the task," Bose said. "It should feel as if you were assigning the task to a human being." This dynamic routing introduces a third challenge: billing abstraction. Agentic tasks vary in computational complexity, making credit burn rates unpredictable. "We don't want to get into a state where our customers are having to reason about the fact that some of these tasks... are way more complex than others and they'll be burning credits at different rates," Bose said, adding that unpredictable pricing risked customers throttling their own employees by capping how often they could run an AI teammate. To make AWM commercially viable, Asana designed its billing architecture to charge a static cost per task completion. The platform absorbs the complexity of model selection, token counts, and run limits to ensure predictable enterprise pricing. The problem with stateless chatbots AWM targets a specific problem with current enterprise AI deployments: statelessness. Developers can easily connect large language models to enterprise tools like Slack, Google Drive, or Databricks using Model Context Protocol (MCP) integrations. However, basic chat-based agents lack persistence. Bose detailed a scenario where a user asks a chat agent to draft a marketing campaign based on historical performance and competitive research. The agent fetches data from external tools to answer the prompt, but the execution happens in a vacuum. It is a one-off task that benefits a single individual. It fails to create a reusable workflow for the next person building a similar campaign. "The challenge with that is that those calls are stateless, and they are not leveraging a shared company brain that is this graph-based database or a context graph," Bose said. AWM solves this by creating a permanent state. When an AI teammate inside AWM completes a task, the system records the metadata. It registers whether the completion improved the project status and how it moved higher-level company goals. Inside CoreWeave's product launches Cloud provider CoreWeave is an early adopter using AWM to overhaul complex new product launches. "CoreWeave is using both our deterministic AI studio workflow rules as well as multiple AI teammates to do new product launches," Bose shared. In the past, CoreWeave product managers filled out complicated forms detailing infrastructure, parameters, and costs. Human reviewers manually evaluated these forms and broke them out into specific tasks for finance, marketing, and hardware teams. Under the AWM workflow, a product manager writes a standard Google document pointing to their product requirement documents. A deterministic AI workflow reads the document, automatically creates the project structure, and assigns tasks. Specialized agents then take over the execution. One agent then watches overall project status and flags bottlenecks; another, working inside individual tasks, forecasts infrastructure costs and recommends approvals when the numbers align with historical budgets. The system automatically triages the busywork while human beings focus on evaluating the AI's outputs. The frenemy problem The dynamic gets complicated by the fact that the same frontier-model providers powering AWM under the hood — Anthropic, OpenAI — are also shipping their own competing agent products, like Anthropic's Claude in Slack (Tag). Pressed on the overlap, Bose didn't dispute the tension. " I think that's the reality that we all have to live in," he said. His case for AWM's staying power rests on Asana's 18 years of user-experience and workflow data, and prebuilt standard operating procedures for specific industries — expertise he argues raw frontier models don't have. A product like Tag can work well in Slack, he said, but it requires a highly curated channel and its own separate credentials for every downstream app it touches. "There's a big difference between the power of the model plus a lightweight way to demonstrate its value, and something that's pre-built … for true end-to-end use," Bose said.
AWS now allows vibe-coding tool Superblocks to be embedded into the private clouds of AWS customers. It's another step toward decoupling apps from models.
Malware running as an ordinary user on a Windows machine can sign into a victim's passkey-protected accounts without a fingerprint, a PIN, or anything at all appearing on the victim's screen. Unit 42 detailed three attack paths against Chrome's Google Password Manager cloud authenticator, which it calls Pass-ta-key, Silver Pass-ta-key and Golden Pass-ta-key; the strongest targets the master key
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“Kubernetes complexity is pushing teams back to boring servers”
