Latest updates from the AI industry
Cursor and GitHub Copilot now both run parallel coding agents. See how their editors, GitHub workflows, and prices differ before choosing one. The post Cursor vs. GitHub Copilot: Which one should you actually use? appeared first on iGeeksBlog .
SpaceX’s Cursor acquisition highlights AI-native coding tools’ growing strategic importance beyond today’s most widely recognized AI companies.
Varonis extends AI security coverage to Claude Code and Cowork
1Password on Tuesday launched AI Spend and Consumption Management , a new capability embedded in its SaaS Manager platform that gives IT and finance teams a unified, real-time view of how their organizations consume and spend on AI services from vendors including Anthropic , Cursor , and OpenAI . The move marks the latest strategic expansion for a company that built its reputation on password management for consumers and, over the past three years, has aggressively repositioned itself as a broader identity security and SaaS governance platform for enterprise buyers. With this release, 1Password is staking a claim in one of enterprise technology's newest and most chaotic budget categories: the consumption-based cost of large language models. "Executives want teams to build faster with AI, but that speed is creating a new kind of spending pressure," Greg Henry, 1Password's chief financial officer, said in an exclusive interview with VentureBeat. "Developers are consuming tokens at a pace that traditional budgets weren't built to manage, and IT and finance teams are being asked to forecast and justify AI investments without a clear view of what's actually driving costs." The product, now in public preview with broad availability planned for fall 2026, connects directly to vendor admin APIs to pull token-level consumption data daily. It normalizes that data across providers into a single dashboard and allows organizations to set vendor-level spend limits, configure threshold-based alerts via Slack and email, and break down usage by team, user, vendor, and model. Why traditional software budgets can't keep up with AI token pricing The core challenge 1Password is targeting is structural. Traditional SaaS pricing operates on a per-seat, per-year model that is easy to budget and reconcile. AI pricing does not. Every API call to Claude , GPT-5.6 , or a Cursor-powered coding assistant consumes tokens, and the cost of those tokens varies by model, by input versus output, and by the complexity of the task. A single engineering team running agentic workflows can burn through a prepaid token budget in weeks — and the finance team may not notice until the invoice arrives. Henry drew a sharp analogy to a problem enterprises have already lived through once. "Consumption-based pricing isn't new," he said. "We saw it arrive with cloud infrastructure, and it took years to build the tools and disciplines to manage it. AI is the next version of that shift." That comparison resonates across the industry. When Amazon Web Services , Microsoft Azure , and Google Cloud popularized consumption-based pricing for compute and storage in the 2010s, enterprises initially lacked the tooling to monitor and optimize their cloud bills. That gap spawned an entire FinOps ecosystem — companies like CloudHealth, Spot.io, and Apptio built multi-billion-dollar businesses helping organizations understand what they were spending on cloud and why. Henry is explicitly betting that AI token spend will follow the same trajectory, and that organizations that fail to build visibility now will end up, as he put it, "paying far more than they needed to, for far longer than they should have." The scale of the coming wave lends credibility to that bet. Goldman Sachs has estimated that token consumption from AI agents alone will grow 24 times by 2030, a projection driven by the expectation that autonomous AI systems will increasingly execute multi-step workflows — booking travel, writing and deploying code, managing customer service interactions — that generate vastly more API calls than a human sitting at a chat interface. How 1Password's new dashboard tracks every token across Anthropic, Cursor, and OpenAI The new capability extends 1Password SaaS Manager 's existing foundation of application discovery, license management, and spend analytics. It is not a standalone product. Existing SaaS Manager customers can activate it by connecting their supported AI vendor API keys, at which point consumption data flows into a dedicated AI Consumption Management dashboard. Henry confirmed that there is no separate product or add-on fee: "AI Spend and Consumption Management is available to all 1Password SaaS Manager customers." The system provides four core functions. First, it aggregates token usage and spend across Anthropic, Cursor, and OpenAI into a single, normalized view — eliminating the need to toggle between three separate vendor dashboards with three different reporting formats. Second, it enables budget controls: organizations can set vendor-level spend limits, configure percentage-based thresholds, and receive automated alerts when prepaid balances approach depletion. Third, it disaggregates consumption by team, user, vendor, and model, allowing finance and IT to understand not just how much is being spent, but where and by whom. Fourth, it situates AI spend within the broader SaaS portfolio, helping organizations see how token costs relate to their total software investment. Notably, the system captures consumption regardless of whether a human or an AI agent generated it. "Token consumption is captured at the API level regardless of whether a human or an agent is generating it," Henry explained. "Organizations get the total consumption picture, including the spikes that agent loops can create, which can be some of the hardest usage to catch before it becomes a problem." That agent-level visibility matters because autonomous AI systems can generate runaway costs in ways that human users typically cannot. An agentic coding assistant stuck in a retry loop, for example, can consume thousands of dollars in tokens in minutes — with no human in the loop to notice. For now, the product alerts but does not enforce. When asked whether 1Password will eventually give organizations the ability to automatically cut off spending when a threshold is crossed, Henry said the company is "actively evaluating" automatic enforcement but emphasized that visibility must come first: "You can't enforce what you can't see." The choice of launch partners reveals where enterprise AI budgets are under the most pressure The decision to start with Anthropic , Cursor , and OpenAI — rather than casting a wider net — reflects where enterprise AI adoption and budget strain are most concentrated right now. Henry said the choice was driven entirely by customer demand. "Anthropic, Cursor, and OpenAI are where we're seeing the highest adoption, and where token consumption can move fast and get ahead of the teams responsible for managing it," he said. The company plans to add additional vendors based on customer demand, API availability, and budget impact, though it has not committed to a specific timeline or vendor list. The inclusion of Cursor alongside the two major foundation model providers is telling. Cursor , an AI-powered code editor that has rapidly gained traction among developers, represents a category of AI tool where consumption is particularly difficult to forecast. Unlike a chatbot interface where a user consciously types a prompt, Cursor integrates AI suggestions directly into the development workflow, generating token consumption continuously as developers write code. That ambient, always-on consumption pattern makes it especially prone to budget overruns. Henry also addressed who inside an organization should actually own this problem — and acknowledged that the honest answer right now is no one. "When spend is fragmented across vendor dashboards and finance teams are reconciling it monthly, you're always behind," he said. "AI spend can't be treated as a finance-only or IT-only problem." He noted that the pricing differences between models have become significant enough that the choice of which AI model a team uses is now a meaningful financial decision, one that is pulling CFOs into conversations with IT, product, and engineering leaders "in ways they never had to before." Steve May, director of IT at ServiceTrade, a 1Password customer that has been using the capability, said it addressed a concrete planning gap. "Forecasting tools for AI consumption and spend was one of our biggest gaps in planning because we didn't have a reliable way to track it," May said. He added that the visibility has "prevented overages that would have cost far more to fix after the fact." Where 1Password fits in the fast-consolidating SaaS management market 1Password is not the only company racing to solve the AI cost management problem, but the competitive landscape is still fragmented and the category is far from mature. Zylo , a SaaS management platform that Gartner has also recognized as a leader in the space, published its 2026 SaaS Management Index in January showing that AI-native application spend surged 393% year over year in organizations with more than 10,000 employees and 108% overall. Zylo's data also revealed that ChatGPT has become the most expensed application in enterprise environments, highlighting how AI tools are entering organizations through employee credit cards and expense reports — outside formal procurement and governance workflows. Zylo has added its own token-level cost tracking for AI vendors including Anthropic, OpenAI, Cursor, and Perplexity. Meanwhile, according to a comparison published by Coommit in May, Vendr — which focuses more on SaaS negotiation than discovery — tracks AI tools at the contract level but does not yet offer consumption-level visibility. And the FinOps Foundation reported in its 2026 State of FinOps survey that 98% of organizations now actively manage AI costs, up from just 31% in 2024. The broader SaaS management market is also consolidating rapidly. In May, Deel acquired Sastrify, a German SaaS management vendor, and began folding it into its HR platform — a signal that SaaS management capabilities are increasingly being absorbed into adjacent enterprise platforms rather than remaining standalone products. 1Password's approach differs from pure-play SaaS management competitors in one important respect: it is building AI cost management on top of an identity security platform, not a FinOps or procurement tool. The company's SaaS Manager product grew out of its 2025 acquisition of Trelica, a UK-based SaaS access management startup whose technology enabled the discovery of unsanctioned applications — so-called shadow IT. As BetaKit reported at the time of that deal, 1Password co-CEO Jeff Shiner described Trelica as "a pioneer in modern SaaS access management" and said the acquisition would accelerate 1Password's Extended Access Management product roadmap by more than a year. CRN noted that Trelica brought more than 300 SaaS integrations to the platform. That identity-first lineage gives 1Password a natural advantage in connecting spend data to specific users and teams — a linkage that matters when the question shifts from "how much are we spending on AI?" to "who is spending it, and is it delivering value?" From password manager to platform company: 1Password's $6.8 billion bet on enterprise identity The launch raises a question that Henry addressed head-on: whether a company that started as a consumer password manager can credibly compete in enterprise AI cost management. "It doesn't feel like a stretch to us. It feels like a natural progression," he said. "For more than 20 years, 1Password has evolved alongside how our customers work. We started by protecting passwords. Then we helped organizations manage secrets, control access, and get visibility into the applications their teams rely on." The company's evolution has been rapid. 1Password raised a $620 million Series C in January 2022 led by ICONIQ Growth, reaching a $6.8 billion valuation — at the time, the largest funding round ever raised by a Canadian company, according to Crunchbase. The round also attracted celebrity investors including Ryan Reynolds, Scarlett Johansson, and Robert Downey Jr. As of early 2025, BetaKit reported that 1Password had surpassed $250 million in annual recurring revenue, with B2B sales accounting for nearly three-quarters of total revenue and the company claiming to be cash-flow positive. In May 2024, 1Password launched Extended Access Management , a platform designed to secure sign-ins across both managed and unmanaged applications and devices. That same year, it acquired Kolide for device trust and, in early 2025, Trelica for SaaS discovery. In June 2026, Gartner named 1Password a Leader in its Magic Quadrant for SaaS Management Platforms. According to 1Password's own blog post on the recognition, its SaaS Manager now supports over 400 integrations and provides visibility into a library of more than 40,000 pre-populated application profiles. Each step has moved the company further from its consumer roots and deeper into enterprise infrastructure. The AI Spend and Consumption Management launch extends that trajectory into financial operations territory — a domain where 1Password will compete not only with SaaS management vendors but potentially with dedicated FinOps platforms and the AI vendors' own billing dashboards. Why high AI token consumption doesn't always mean wasted money Perhaps the most revealing part of Henry's commentary concerns what organizations should actually do with the consumption data once they have it. He pushed back forcefully against the assumption that high token consumption automatically signals waste. "A team burning through tokens may be building something genuinely valuable," he said. "A lower-usage project might not be moving the business forward at all. What matters is whether that consumption is producing enough business value to justify the spend." Henry drew a distinction between personal productivity — "having a bot summarize your meeting or draft a quick email" — and genuine business outcomes. "What organizations need to see is where consumption is actually driving revenue, efficiency, or something that moves the needle." That framing positions AI Spend and Consumption Management not just as a cost-cutting tool but as a decision-support system for AI investment allocation. If a CFO can see that one engineering team's heavy Claude usage is powering a product feature that drives revenue, while another team's OpenAI spend is funding low-value internal automation, the organization can reallocate budget accordingly rather than imposing across-the-board cuts. "When costs rise faster than expected, the instinct is to cut," Henry said. "But most organizations can't yet tell which teams, models, or tools are responsible for the increase, so they end up cutting across the board rather than directing investment toward the AI projects that are actually delivering business value. Blunt cuts on a technology you're counting on for competitive advantage is not a management strategy, it's a missed opportunity." The next enterprise budget crisis is already here — and it's priced per token The product's current scope — three vendor integrations, alerting but not enforcement — is clearly a starting point. Henry signaled that automatic spend limits are on the roadmap and that additional vendor integrations will follow based on customer demand. But the broader trajectory he described suggests 1Password sees this launch as a wedge into a much larger opportunity. "As traditional SaaS products add AI capabilities, their pricing models are going to follow," he said. "Organizations that build visibility and management discipline around consumption now are going to be in a much better position when that happens across the rest of their software portfolio." If Henry is right, the chaos currently confined to AI token budgets is not a temporary growing pain but a preview of how all enterprise software will eventually be priced. A decade ago, companies scrambled to understand their cloud bills. Today, they are scrambling to understand their AI bills. The question is whether the organizations building the dashboards this time around can get ahead of the curve — or whether, as Henry warned, they will end up where so many companies ended up with cloud, realizing too late how much they were overpaying, and for how long. AI Spend and Consumption Management is available now in public preview for 1Password SaaS Manager customers. Broad availability is planned for fall 2026.
At Cursor Vibe Jam 2026, an iOS developer with nine years of experience showed how it’s possible to rely entirely on AI as he showcases a Capybara food delivery game that he successfully made in only two weeks. Using Claude Code, the developer reveals that all lines of code were written entirely by AI, while also providing a detailed guide on how everything came to life in this virtual world that consists of both single-player and multiplayer elements. Using $100 to upgrade from the Claude Code Opus 4.7’s 5x plan to the 20x one, the developer used a combination of [...]
The race to automate the office just gained a third runner. Cursor is building Sand, a general-purpose agent to rival Claude Cowork. Whether it ever ships may come down to Elon Musk. Cursor made its name as a tool for people who write code. Now it is building one for people who do not. According [...] This story continues at The Next Web
Internet-facing AI systems are becoming a new target for opportunistic attackers. Recent scanning activity shows that threat actors are actively searching for Model Context Protocol, or MCP, servers, AI assistant configuration files, and exposed local language-model services. The activity was observed across low-traffic websites that did not appear to host AI infrastructure. That detail matters [...] The post Internet-Wide Scans Target MCP Servers, Claude Credentials, and Exposed AI Models appeared first on Cyber Security News .
Engineering teams have spent the past few years picking their own AI tools — an IDE here, a terminal-based coding The post JetBrains’ next move isn’t a better IDE — it’s a governance layer over Claude Code, Codex, and Gemini CLI appeared first on The New Stack .
Elon Musk’s SpaceXAI and code editor Cursor plan to release their first jointly built AI model as soon as Wednesday, according to an internal memo. Preparing For Launch, No Pun Intended SpaceXAI, the artificial intelligence (AI) arm of Elon Musk’s SpaceX, is preparing to release its first jointly developed AI model with Cursor as soon [...]
An AI coding assistant that refuses to answer a dangerous request in its chat box can answer it anyway if the same request is broken into small, ordinary-looking steps inside a code editor. That is the finding of a new study of GitHub Copilot by researchers Abhishek Kumar and Carsten Maple. The models they tested through Copilot, Claude from Anthropic, and Gemini from Google, refused
Researchers show how attackers can use a crafted public GitHub Issue to trick AI-powered workflows into exposing data from private repositories without authentication. The post Critical Vulnerability Exposes GitHub Agentic Workflows to Prompt Injection appeared first on SecurityWeek .
Built to bring the productivity gains of Claude Cowork to non-coders, these AI assistants have lots of promise but require thoughtful deployment in enterprise settings.
forum.zcashcommunity.com ← Older revision Revision as of 16:08, 7 July 2026 Line 165: Line 165: * [https://ytpmania.net/ YTP Mania] * [https://ytpmania.net/ YTP Mania] * [https://forum.yunohost.org/ YunoHost Forum] * [https://forum.yunohost.org/ YunoHost Forum] * [https://forum.zcashcommunity.com/ Z community] * [https://ziggit.dev/ Ziggit - A Zig community] * [https://ziggit.dev/ Ziggit - A Zig community] * [https://devforum.zoom.us Zoom Developer Forum] * [https://devforum.zoom.us Zoom Developer Forum]
Hackers are exploiting a recently patched critical vulnerability (CVE-2026-48282) in Adobe ColdFusion that carries a CVSS score of 10/10. The post Critical Adobe ColdFusion Vulnerability Exploited in Attacks appeared first on SecurityWeek .
This guide walks you through building an animated checkmark input with a circular SVG progress ring that fills as the user types, changes color based on input strength, and snaps into a checkmark when validation passes. You will cover SVG stroke math, Framer Motion spring animations, and a clean DRY component pattern all inside a single React file.
SpaceX's $60 billion all-stock acquisition of Cursor gives the rocket maker a leading AI coding platform with $4 billion annualized revenue and vast proprietary developer data. The deal, struck days after its record IPO, sharpens xAI's competitive edge against OpenAI and Anthropic while feeding real engineering workflows into next-generation models.
The AI-assisted code editor Cursor is expanding to mobile with a new native iOS app. Available now in public beta, it lets developers launch, monitor, and direct autonomous coding agents directly from an iPhone. Continue Reading Spotlight Deal: Apple Watch Series 11 On Sale for $279 (30% Off) [Deal] Share Article: Facebook , Twitter , LinkedIn , Reddit , Email Follow iClarified: Facebook , Twitter , LinkedIn , Newsletter , App Store , YouTube
Elon Musk has redirected dozens of elite Starship and Starlink engineers to accelerate xAI's Grok models following SpaceX's $60B Cursor acquisition. Grok 4.5 now sits in private beta at Tesla and SpaceX with strong early results. Monthly fresh models are planned through 2026. The move blends aerospace rigor with AI training at unprecedented scale.
By picking up Cursor developer Anysphere, Elon Musk's company enhances its offerings in what it sees as its largest potential addressable market.
By picking up Cursor developer Anysphere, Elon Musk's company enhances its offerings in what it sees as its largest potential addressable market.
Open source AI markdown editor OpenKnowledge launched June 27 with native Claude Code, Codex, and Cursor integrations via MCP, a dual-observer yjs CRDT keeping WYSIWYG and raw markdown in continuous byte-faithful sync, and Orama hybrid search — all running on local files under a GPL-3.0 copyleft license. The tool is free and requires Node.js 24 or later for non-Mac users.
How are you, hacker? 🪐 Want to know what's trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here . ## SpaceX Just Bought Cursor for $60 Billion. Here's Why the Deal Actually Matters. By @kilocode [ 6 Min read ] SpaceX's $60B Cursor acquisition isn't about the editor. It's a compute and distribution play that signals why model freedom now matters more than betting on an Read More. Managing Multiple Social Media Accounts is a Mess. Here is What Fixes it By @multilogin_team [ 6 Min read ] How to Manage 10 Client Accounts From One Laptop. Read More. Vibe Coding Has a Step Sister. And She's Coming For Your CI/CD! By @uldiskk [ 5 Min read ] Instead of generating pipelines directly, AI builds MDE transformation chains that create deterministic CI/CD pipelines across tools. Read More. Optimizing a Fast Feature Store for Costs: ShareChat's Lessons Learned By @scylladb [ 8 Min read ] After scaling its ML feature store to 1B features/sec, ShareChat cut costs 10× using ScyllaDB, cloud tuning, protobuf tricks, and profiling. Read More. A Unified Namespace Determines Your Historian Schema, Not the Other Way Around By @tigerdata [ 12 Min read ] Design a historian schema for Unified Namespace architectures. Learn why narrow tables, surrogate keys, and relational namespaces outperform wide models. Read More. Why Stripe usage-based billing is fundamentally broken for AI products By @credyt [ 9 Min read ] Stripe Billing bills at cycle end while AI costs happen per inference. Here’s why usage-based billing breaks AI economics and what replaces it. Read More. What We Learned Building an AI Agent for 3D Creation By @meshyai [ 8 Min read ] A 3D agent has to do more than produce geometry . It has to help the user move through the unclear space between an idea and an asset they can use. Read More. Why Speed Matters: How Performance in Analytics Saves Business from "Digital Paralysis" By @megaladata [ 11 Min read ] Lower compute costs and the evolution of data processing tools have radically changed the approach to analytics. Read More. The 5 Best Risk Management Software Solutions for 2026 By @vanta [ 10 Min read ] Compare the top choices for risk management software: Vanta, Optro, Hyperproof, Diligent, and Archer. Read More. AI Agents Don’t Run on Hype. They Run on Power, Wallets, and Settlement Rails By @MichaelJerlis [ 9 Min read ] Investors, analysts, enterprise software executives. Everyone is focused on what agents do. What workflows they replace. What software categories they disrupt. Read More. 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We Measured the LLM Token Cost of 5 Frontend Frameworks: Angular Costs 38% More Than Svelte By @akucherenko [ 4 Min read ] We measured the LLM token cost of 5 frontend frameworks on identical components. Svelte wins, Angular costs 38% more — and your AI bill feels it. Read More. How I Made Our Test Suite 43% Faster by Deleting One Configuration By @yevhen-bozhenko [ 9 Min read ] A global Selenium implicit wait was silently colliding with Selenide's explicit waiting — costing up to 43% of test execution time. Here's how to fix it. Read More. PDFSharp C# Review: Useful, Lightweight, but Limited in Scope By @ironsoftware [ 11 Min read ] PDFSharp is stronger in 2026, with signatures, PDF/A, and PDF/UA support — but HTML rendering and rasterization remain outside its scope. Read More. A Developer's Guide to Apple's Foundation Models Framework in iOS 26 By @unspected13 [ 27 Min read ] A deep dive into iOS 26 Foundation Models. Learn how to build free, on-device AI apps in Swift, master Tool Calling, @Generable, and avoid context limits. Read More. We brought Hermes Agent to iMessage, even on Linux and Windows By @photonhq [ 4 Min read ] Hermes Agent now connects to iMessage through Photon, enabling AI agents to send and receive messages on any OS without a Mac. Read More. Solving AI Amnesia at Scale: Context Pipelines for Large Enterprises By @aditi-patodiya [ 11 Min read ] Discover why LLMs "forget" and how large enterprises build stateful context pipelines and memory architectures to solve AI amnesia in production environments. Read More. 🧑💻 What happened in your world this week? It's been said that writing can help consolidate technical knowledge , establish credibility , and contribute to emerging community standards . Feeling stuck? We got you covered ⬇️⬇️⬇️ ANSWER THESE GREATEST INTERVIEW QUESTIONS OF ALL TIME We hope you enjoy this worth of free reading material. 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Vibe coding ’s dark side, “vibe hacking,” is on the rise. Cybersecurity companies such as McAfee and Bitdefender have observed recent spikes in vibe-coded malware , also called “ vibeware ,” with telltale signs such as explanatory code comments or template placeholders akin to what vibe-coded apps contain. But just how challenging is it to stop the spread of bad vibes from these emerging cyberattacks? Researchers at the University College Cork (UCC) in Ireland found that malicious software crafted with the assistance of generative AI have varied code structures that can evade static malware detection, but their nefarious behavior and intent remain the same as traditional malware. The team presented their results last May at the 23rd ACM International Conference on Computing Frontiers held in Italy. Hackers are taking advantage of the probabilistic nature of generative AI, producing vibeware having multiple variants. “With an AI coding tool, you can say, ‘I want the same functionality, but do it in a different way.’ So you can create malware that’s bespoke to a particular attack you want to do,” says Utz Roedig , a professor of computer science at UCC who led the research. Anti-malware as usual Traditional antivirus software uses a combination of static and dynamic analysis tools to screen newly downloaded software. Static analysis employs pattern matching techniques, comparing the cryptographic hash of a file against databases of known malware signatures or employing rule-based engines like YARA , an open-source tool that identifies and classifies malware according to specific binary patterns or strings. Dynamic analysis runs malware in a controlled or sandboxed environment to monitor its actions for suspicious activity. In their experiments, the team at UCC generated a series of malicious shell scripts designed to steal sensitive data from Linux-based systems. Each shell script iteration was built specifically to bypass YARA rules. While the resulting shell scripts are distinct in terms of code syntax, they remain functionally equivalent. “Even if you make the program achieve its goal differently, the behavior is the same,” Roedig says. “The structure looks different but you can’t hide the malicious behavior.” This highlights a necessary shift toward more dynamic and behavior-centric detection strategies. “Now anyone can generate hundreds of unique variants, so hash matching is pointless,” says Prince Chaddha , a research lead at ProjectDiscovery , an open-source cybersecurity company. “What still works is behavioral analysis. Defenders must go fully behavioral and use AI themselves to catch such malware.” AI can help cybersecurity professionals swiftly spot vulnerabilities in software , but their expertise, judgement, and oversight—along with multiple layers of verification—must be built into the process. LLMs lower the barrier to malware entry The UCC researchers also found that vibe coding malware can be accomplished with as few as two prompts. “[Generative AI] makes it more accessible. And that would then mean you probably get more of it because the barrier to create malware lowers,” says Roedig. Dan Gittis , director of the threat intelligence and detection engineering team at managed security services provider UltraViolet Cyber , echoes the sentiment. “You no longer have to be adept at coding to build malware,” he says. “Threat actors without the experience or skills can start dipping their toes in this field, and those that do have the preexisting skill set can very likely develop even better malware.” More surprisingly, the UCC team’s AI coding tool of choice, Cursor, didn’t refuse or restrict their malware-related prompts. This emphasizes the need to put up safety guardrails that prevent malicious use cases. Roedig cautions, however, that attackers “probably will tinker with AI models to remove guardrails,” so developers of AI coding tools must also factor in how to defend against getting around those guardrails. Looking to the future, Gittis believes AI-generated malware could advance and multiply. “There are now more individuals who can serve as capable threat actors, meaning the overall number of cyberattacks could increase. It also means that already capable actors are very likely going to operate faster and more effectively,” he says. “And it means that threat actors may be able to develop more dynamic malware that evolves.” He points to Google’s discovery of PROMPTFLUX as an example. The PROMPTFLUX malware calls the Gemini API during runtime to rewrite its own source code on demand and dodge detection. This adaptive and regenerative ability “is likely going to be very impactful to how defenders need to operate going forward,” says Gittis. This constant tug-of-war is nothing new in the world of cybersecurity. “It has always been that attacker and defender go hand in hand. One side invents something and the other side tries to go around it, and you use all tools necessary,” Roedig says. It’s happening again with vibe hacking and vibeware. But the good news, according to Gittis, is that “defenders have the same resources, if not more. This means that we can increase our capabilities, efficiency, and knowledge of response measures.”
Opera Neon has shared a simple video guide showing how two popular AI coding tools can now take direct control of its browser. The post on X walks through a quick setup that lets tools like Cursor and Codex open web pages, click buttons, fill forms, and run other browser tasks straight from a developer’s [...] The post Opera Neon shares simple guide to let Cursor and Codex control the browser appeared first on PiunikaWeb .
It takes everything I like about VS Code and pushes it further
Explore the best AI coding tools for data science and machine learning in 2026, including Cursor, GitHub Copilot, Julius AI, PandasAI, Replit AI, and more.
Workday Inc. today announced new capabilities aimed at providing developers new ways to build on top of its platform using their own tools. During DevCon 2026, the company’s annual developer conference, Workday unveiled a new Developer Agent and Agent-Ready Tools to take developers from simple requests directly to working apps or agents within minutes. The [...] The post Workday introduces new capabilities for building and verifying AI agents appeared first on SiliconANGLE .
Microsoft prepares to expand local AI capabilities in Windows at Build 2026 with new models, a dedicated developer mode, and enhanced Foundry tools. The platform now supports full-lifecycle model work across diverse silicon while keeping data on-device. These moves aim to make Windows the preferred environment for AI application development.
Last fall, Anthropic was playing second fiddle to OpenAI. It had a lower valuation, while OpenAI was still drawing much of the attention as the first mover in the generative AI boom. But the dynamic shifted in late November, when Anthropic released Claude Opus 4.5 , which gave the company’s Claude Code coding agent a new brain and helped elevate it to the status of “AI killer app.” Arguably, that was the moment that set Anthropic on its path toward a forthcoming initial public offering (IPO) . Developers had been using Claude Code to build software for much of 2025, but the tool had shown more promise than truly game-changing results. Opus 4.5 gave Claude Code the intelligence to build an app or feature from end to end, based only on plain-language planning and guidance prompts from the user. Opus 4.5 enabled longer-running agents and better planning and execution workflows. Claude Code got better at discussing a project with an engineer-user, presenting a plan, incorporating feedback, and then carrying out a focused set of multistep tasks to complete a software build. Anthropic sweetened the deal by imposing fewer usage caps, which makes a big difference for software engineers who spend their days deploying multiple agents to build different parts of a project. Just as important, Anthropic changed the way software engineers access Claude Code. Instead of using the tool through their machine’s command line interface, they could now select the Code tab in the Claude desktop app, which includes its own integrated terminal. Under that tab, the company also brought together many of the resources developers normally use, including a file editor for viewing and editing code, code-change review windows, parallel coding sessions for different tasks, and other tools. Anthropic turned Claude Code from a terminal/chat tool into something closer to a full desktop coding environment. “Claude Code is excellent at fixing all those small bugs from old projects. . . . ” tweeted Pieter Levels, an influential Dutch entrepreneur and self-taught developer. “[B]efore I’d not have the time to fix these kinds of projects . . . but now it takes me an hour to do this and it works again!” Claude’s beachhead The new unified interface in the Claude desktop app invited software engineers to do their nonsoftware development work in the same place. With easy access to Claude chat and CoWork, engineers could use Claude for more general tasks, including conducting research, accessing company data, composing emails, and styling presentations. The interface also accommodates workers who code only occasionally, perhaps to slap together a prototype app to present to colleagues, but who benefit from doing much of their day-to-day work with help from Claude chat and CoWork’s data connections, skills, and workflows. Acceleration Whatever the motivation, enterprises are indeed choosing Anthropic, and the company’s accelerating enterprise momentum can be traced back to Claude Opus 4.5. At the end of 2025, Anthropic reported just $9 billion in annualized revenue run rate. (ARR extrapolates a recent month’s revenue out to a full year.) That number grew rapidly in 2026 as Claude Code proliferated. The company reported $14 billion in ARR in February and $30 billion in April. A May Reuters report pegged the company’s ARR at $47 billion. For context, OpenAI said at the end of March that it was generating about $2 billion per month in revenue, which works out to ARR of $24 billion. Anthropic reportedly said that more than 1,000 companies are now paying over $1 million per year for Claude, and that this number has more than doubled since February 2026. The company has announced major deployments with PwC, Allianz, Snowflake, Accenture, Deloitte, and IBM. Anthropic expects to make an operating profit of $559 million during the quarter ending in June, on revenue of $10.9 billion, according to The Wall Street Journal . While Anthropic does not release raw user numbers for Claude Code, survey data suggests the tool has plenty of room to grow. An early-April JetBrains survey of 10,000 software developers globally found that 29% used GitHub Copilot, while only 18% used Claude Code, tied with Cursor. Stage is set On Monday, Anthropic submitted a confidential draft registration statement, or S-1, to the Securities and Exchange Commission (SEC). Under U.S. rules, the SEC can review IPO-seeking companies privately, allowing a company to delay making detailed financials public until later, closer to the actual listing. Anthropic’s could end up being the second of three big artificial-intelligence-related IPOs in 2026. SpaceX has already filed , and OpenAI is expected to file later in the year. Both Anthropic and OpenAI have a chance to IPO at trillion-dollar-plus valuations, putting them among the largest tech IPOs in history. Anthropic just last week raised $65 billion in new financing at a valuation of $965 billion, including the new money. Experts are not worried about whether enterprise software engineering groups will adopt Claude Code. They believe the demand is real and growing. The more serious threat, for both Anthropic and its customers, is the cost of using the tool. Part of Claude Code’s appeal is that it develops a deep understanding of code bases and complex coding problems. That requires the agent to reason across large context windows, which means using a lot of tokens, the chunks of text, data, and code it processes. Corporate users, including Uber, are already running up big bills as engineers max out on tokens. Anthropic, meanwhile, is trying to find additional computing resources to process all the tokens being generated by Claude Code users. The company is even paying Elon Musk $1.25 billion a month for the use of the xAI/SpaceX Colossus 1 data center in Memphis. When Anthropic’s prospectus shows up, likely sometime this summer, we’ll finally learn more about the real profitability of selling Claude Code.
AI agent economics face a structural token tax: inference consumes 23% of revenue at scaling-stage companies, locking gross margins 30 points below SaaS norms. Microsoft's Claude Code cancellations and Uber's blown budget confirm the cost is now impossible to ignore. Two proven escape routes exist: owning inference or selling verifiable outcomes.
Coding tools are becoming an increasingly big target for Google and Microsoft as they try to catch up to Anthropic and OpenAI.
Microsoft CEO Satya Nadella speaks on stage during the Microsoft AI Tour in Munich, Germany, on Feb. 25, 2026. Sven Hoppe | Picture Alliance | Getty Images In the booming generative AI market, Anthropic has zoomed ahead of the field, largely thanks to Claude Code, its AI coding assistant. Seeing where the money is, OpenAI [...] The post Microsoft and Google are late to AI coding, but ‘absolutely critical’ they compete for growth appeared first on NYT News Today .
Cursor’s annual revenue hits $3 billion ahead of SpaceX acquisition - Bloomberg
The figma to developer handoff used to be a tedious process. Your designer would create a flawless, pixel perfect Figma file that you hand off to your developer. The developer would then try to render it as closely as possible to the actual design. Inevitably, there would be mistakes, changes and reviews happening at multiple [...] The post Smart Way to Go from Figma to Production Ready Code With AI Platforms appeared first on TechBullion .
Sequentum launches Agent Builder in Sequentum Cloud, a new AI capability that turns natural language prompts into versioned, deterministic web data agents in minutes. Customize to production-grade in the Visual Editor. Accessible via MCP, API, or UI. SOC 2 Type II. New York, NY May 21, 2026 --( PR.com )-- Sequentum, the trusted leader in enterprise-grade web automation, data extraction, and data pipeline infrastructure, today announced the launch of Agent Builder, a new capability in Sequentum Cloud. Agent Builder uses AI to turn a natural language prompt into a versioned extraction agent in minutes. From there, the Sequentum Visual Editor allows users to customize as needed for a production-grade agent with all the compliance tools built in to provide repeatable and deterministic data. Agent Builder is accessible from MCP, API, or the Sequentum Cloud UI. The Problem: AI Scrapers That Hallucinate Are a Liability, Not a Pipeline Most AI scraping tools don't allow for deep customization, use LLMs at runtime that hallucinate, and silently automate CAPTCHAs and other risky behaviors that expose data consumers to legal and compliance risk. Data shifts. Audits fail. Stakeholders lose trust. Agent Builder eliminates that risk with a hybrid approach: AI handles the hard part of building an agent from scratch. From there, users can customize and refine the agent in the Sequentum Visual Editor to bring it to production-grade. No AI is used at run time, so every production run then executes the same deterministic code every time. Whether you're a quant team tracking gas station prices, a healthcare analytics platform monitoring drug pricing across state Medicaid sites, a retail intelligence team watching competitor pricing across thousands of SKUs, a quick-serve restaurant chain tracking local market conditions, or an AI company building a domain-specific agent that needs trustworthy web context, the requirement is the same: the data has to be defensible, from the board room to the court room. How Agent Builder Works: Prompt to Production in Minutes Describe what you want. Use a natural language prompt to describe what you want, or paste a URL with instructions. Agent Builder uses AI to plan navigation, extract sample data, and validate outputs, showing its work as it goes. And unlike the competition, end users can preview exactly how the agent extracts the data and visually run through a human review. The output is a real versioned agent that is 70–80% complete from a single prompt. Customize in the Visual Editor. Out of the box, your Agent Builder agent already beats ... Full story available on Benzinga.com
Earnings call transcript: NVIDIA Q1 2027 beats expectations, stock rises
Earnings call transcript: NVIDIA Q1 2027 beats expectations, stock rises
Andreessen Horowitz says Boston's innovation economy is on the rise, after several years of quiet.