An investment firm is offering a way to invest in the broad market without owning Elon Musk’s companies. New York-based Subversive ETFs has filed with the SEC for two “Ex-Elon” funds, Bloomberg reports.

One fund tracks the Nasdaq-100 and the other the S&P 500. Both exclude any company “founded, controlled or led by” Musk.

The products are the Nasdaq-100 Ex-Elon Enterprises ETF and the S&P 500 Ex-Elon Enterprises ETF, ticker symbols QQNE and SPNE. Filings point to a launch around 21 September.

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For now, the exclusions mean no Tesla and no SpaceX. Other Musk ventures, like Neuralink and the Boring Company, are not publicly traded, and the screen could extend to OpenAI, which Musk co-founded, if it ever lists.

The funds are actively managed and aim to hold at least 80% of their assets in their index minus the excluded names. Active management typically carries higher fees than a plain index tracker.

Why a fund to avoid one man

The direct trigger was SpaceX joining the Nasdaq-100. A rule change fast-tracking mega-cap listings meant the newly public rocket firm entered major indexes quickly, pulling it into the funds that track them.

That forces passive investors to hold SpaceX whether they want to or not. Analysts have flagged that hundreds of billions in index-tracking assets were set to buy the stock automatically, a dynamic critics call a wealth transfer toward existing shareholders.

Not everyone wants that exposure. Some object to SpaceX’s governance, where Musk keeps dominant voting control, while others question its valuation, and a Danish pension fund has already blacklisted it.

Conviction has a cost

Screening out a company is not free, and that cuts both ways. SpaceX slipped nearly 7% on its first day in the Nasdaq-100, which would have spared Ex-Elon holders the drop.

But excluding a stock also means forgoing its gains, and Musk’s firms have created enormous wealth for early backers. An investor betting against them is making an active call, not a neutral one.

The funds also sit in a small but growing niche of values- and politics-driven investing. Whether many savers actually route money through a single-person exclusion screen is the open question these tickers will answer.

For now, the pitch is simple, and narrow. If you want the index without one particular founder, there is finally a box to tick.

Looking for the most recent Strands answer? Click here for our daily Strands hints, as well as our daily answers and hints for The New York Times Mini Crossword, Wordle, Connections and Connections: Sports Edition puzzles.

Today’s NYT Strands puzzle was really kind of tough. I did not figure out the theme for a while. Fans of Welcome Back, Kotter, will remember this particular item as being a running joke on the show. If you need hints and answers, read on.

I go into depth about the rules for Strands in this story. 

If you’re looking for today’s Wordle, Connections and Mini Crossword answers, you can visit CNET’s NYT puzzle hints page.

Read more: NYT Connections Turns 1: These Are the 5 Toughest Puzzles So Far

Hint for today’s Strands puzzle

Today’s Strands theme is: Fishy fare

If that doesn’t help you, here’s a clue: Recipe ingredients for an old-fashioned dish

Clue words to unlock in-game hints

Your goal is to find hidden words that fit the puzzle’s theme. If you’re stuck, find any words you can. Every time you find three words of four letters or more, Strands will reveal one of the theme words. These are the words I used to get those hints but any words of four or more letters that you find will work:

• SPAT, PEER, PELT, POSE, SLIM, SLIME, LIME, LIMES, APES, AUNT, SLOP

Answers for today’s Strands puzzle

These are the answers that tie into the theme. The goal of the puzzle is to find them all, including the spangram, a theme word that reaches from one side of the puzzle to the other. When you have all of them (I originally thought there were always eight but learned that the number can vary), every letter on the board will be used. Here are the nonspangram answers:

• SALT, MILK, PEAS, SOUP, TUNA, CHEESE, PEPPER, NOODLES

Today’s Strands spangram

Today’s Strands spangram is CASSEROLE. To find it, start with the C that is five letters down from the far-left row, and wind across and back.

Casio released the Loopy in Japan back in 1995 as a home console aimed at young girls. It featured a built-in thermal printer that could turn any game screen into a physical sticker. Only about eleven games ever appeared for the system before Casio ended production a few years later. The hardware sat largely forgotten outside a small group of collectors until a new flash cartridge and one determined developer changed the picture.

The Loopy is powered by a Hitachi SH-1 CPU that operates at around 16 megahertz and uses big-endian byte ordering throughout. It has 512 kilobytes of main RAM and 128 kilobytes of video memory, with graphics handled by a unique video display engine that operates in bitmap mode. Audio was initially based on four channels of 12-bit PCM, but the Loopy port adopts a different approach, separating music and effects over separate devices. The port was programmed by ThroatyMumbo, who also released the complete source code on GitHub under the name LoopyDOOM. Its roots can be traced back to GBADoom, an open-source version already modified for the Game Boy Advance, which in turn was derived from PrBoom and all the way back to the original id Software code; this new effort just added the Loopy-specific pieces.

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The biggest issue was that there was no straightforward way to install new software on a real Loopy, as retail carts were read-only, and no official developer tools were available. All of that changed when a hobbyist developed the Floopy Drive, a flash cartridge that allows you to write custom code directly to the cart via USB when it is outside the console. With that little tool, the port could move from emulation testing to running on real hardware.

Graphics are handled using an 8-bit per pixel bitmap layer with double buffering, with one frame drawing in the background and the previous one remaining on the screen. That keeps everything running smoothly even though the processor is handling every pixel on its own, with no fancy 3D chip to aid. On actual hardware, the game runs at between eight to fifteen frames per second. Even in quiet portions, movement feels quick, but things slow down a little when you’re ascending stairs or fighting a swarm of enemies.

Working on music was challenging because the original tracks were in this strange MUS format that the build tools had to translate into note tables for the Loopy’s uPD937 processor. Because the chip uses its own sound library rather than General MIDI, the developer had to listen to each file in the first six levels, select the closest accessible tones, and occasionally run some variations via an AI computer before making final judgments by ear. The result is played with the console’s native audio and includes every map available in the game.

Sound effects take a different approach because the upd937 synthesizer lacks a PCM channel, therefore all that is delivered over a serial port is a series of short command frames to the RP2040 chip, which happens to be located on the Floopy Drive cartridge. A little extra gear helps get the correct digital audio from that chip out, into an external DAC, and back into the console. So, after updating the cartridge’s firmware, you can expect to hear gunfire, item being picked up, and monsters.

It appears that one of the options menu items simply sends the current screen directly to the thermal printer built into the console, allowing you to take a sticker of your progress (or your favorite moment) with you when you walk away, as intended by the console’s designers. Of course, the one negative to this function is that it is dependent on the printer not jamming on the paper path, so keep your fingers crossed.

There is an emulator called LoopyMSE that will run the same binary for testing, but it will never be completely realistic because it cannot duplicate all hardware limits, such as sprite-per-scanline restrictions. When it comes down to it, the real hardware determines whether or not everything works smoothly. The port currently only includes the first six maps from the shareware episode, complete with music and sound, which is a good start. As previously stated, all of the code is available on GitHub, so anyone may add new levels, enhance speed, or experiment with some of the hardware’s additional features.

AI and ML

Standalone experiment killed after less than 12 months as model maker redirects agentic ambitions towards workplace productivity

OpenAI has decided its AI browser experiment has run its course, pulling the plug on ChatGPT Atlas less than a year after launch and moving its browser-based agent features into ChatGPT and Codex.

The company said Atlas will stop working on August 9 as it rolls out the newly unveiled ChatGPT Work platform.

Atlas arrived last October with no shortage of ambition. Rather than trying to out-Chrome Chrome, OpenAI wanted to bolt ChatGPT directly onto the web, promising a browser that could read pages, rewrite them, answer questions, and eventually start doing the clicking itself.

However, within days of its debut, security researchers demonstrated prompt injection attacks that could manipulate the browser’s AI assistant into following malicious instructions embedded in web pages. A few days later, researchers uncovered another flaw that allowed malformed URLs to cause Atlas to expose information about previously visited sites. Neither flaw proved fatal, but they quickly exposed the gap between an AI browser on launch day and one ready for the open web.

OpenAI hasn’t fully abandoned the idea of AI-powered browsing, but appears to have decided it doesn’t need a standalone browser to deliver it.

ChatGPT Work looks less like a browser and more like OpenAI’s attempt to become the operating system for office work. The desktop application combines ChatGPT, Codex, and Atlas into a single package that can connect to files and business applications, browse the web, generate documents, spreadsheets, presentations, and websites, and stick with long-running projects for hours at a time instead of answering one prompt after another.

Powering it all is GPT-5.6, OpenAI’s latest model series, which the company says is better at reasoning through multi-step tasks and producing work that follows users’ templates and reference material.

The pitch is different this time around too. Atlas was all about rethinking the browser, but ChatGPT Work is about getting office workers to spend more time with OpenAI. To help with that, OpenAI has bundled plugins into a single directory, allowing ChatGPT to pull context from tools including Slack, Microsoft Teams, Google Drive, SharePoint, email, calendars, CRMs, and project trackers, either automatically or when users explicitly call them into a prompt. 

For developers, the biggest change is that Codex is no longer treated as a separate product. Codex is losing its standalone app and moving into ChatGPT Work, picking up inline diff editing, pull request reviews, and multi-repository support.

ChatGPT Work is available to all plans on desktop, rolling out to Plus, Pro, Business, Enterprise, and Edu on web and mobile over the next few days.

AI browsers have attracted no shortage of hype over the past year, but convincing people to swap Chrome for an AI-first alternative was always going to be a taller order than bolting another chatbot onto the web.  ®

Looking for the most recent Connections answers? Click here for today’s Connections hints, as well as our daily answers and hints for The New York Times Mini Crossword, Wordle, Connections: Sports Edition and Strands puzzles.

Today’s NYT Connections puzzle was a true mix. The blue category words jumped right out at me and the purple category is really creative when you realize what it is. Read on for clues and today’s Connections answers.

The Times has a Connections Bot, like the one for Wordle. Go there after you play to receive a numeric score and to have the program analyze your answers. Players who are registered with the Times Games section can now nerd out by following their progress, including the number of puzzles completed, win rate, number of times they nabbed a perfect score and their win streak.

Read more: Hints, Tips and Strategies to Help You Win at NYT Connections Every Time

Hints for today’s Connections groups

Here are four hints for the groupings in today’s Connections puzzle, ranked from the easiest yellow group to the tough (and sometimes bizarre) purple group.

Yellow group hint: The big top.

Green group hint: Like a placid lake.

Blue group hint: To infinity, and beyond!

Purple group hint: Repeated letters in special spots.

Answers for today’s Connections groups

Yellow group: Circus equipment.

Green group: Undisturbed, as water.

Blue group: Toy Story characters.

Purple group: Double letters appearing in that letter’s alphabetical position.

Read more: Wordle Cheat Sheet: Here Are the Most Popular Letters Used in English Words

What are today’s Connections answers?

The yellow words in today’s Connections

The theme is circus equipment. The four answers are cannon, stilts, trapeze and unicycle.

The green words in today’s Connections

The theme is undisturbed, as water. The four answers are calm, flat, glassy and still.

The blue words in today’s Connections

The theme is Toy Story characters. The four answers are Bo Peep, Jessie, Slinky and Woody.

The purple words in today’s Connections

The theme is double letters appearing in that letter’s alphabetical position. The four answers are aardvark, bocce, ebbing and twiddle.

U.S. federal cybersecurity agency CISA said it did not have a prepared response plan for how it should handle a cybersecurity incident in May, after an investigative reporter notified the agency that a contractor had publicly exposed sensitive keys and credentials for accessing U.S. government systems.

CISA, the Homeland Security unit tasked with defending federal networks and helping to safeguard critical infrastructure, revealed Friday in a postmortem report that its staff “had to spend time building [a playbook] during the early stages of the incident.” The agency said it is important to prepare playbooks for “all anticipated needs” to ensure that organizations are ready to respond in the event of a security incident rather than scrambling to improvise one in real time.

The agency did not say how long the missing playbook delayed CISA’s response, and a spokesperson did not immediately respond to TechCrunch’s request for comment. 

Independent cybersecurity journalist Brian Krebs reported in May that a security researcher with cyber firm GitGuardian alerted him to reams of exposed passwords stored in a publicly accessible GitHub repository, which an employee of a CISA contractor had uploaded.

According to Krebs, the researcher tried to alert the contractor but didn’t hear back. Only after Krebs contacted CISA did the agency take the repository offline and revoke and replace all of the exposed credentials to prevent any potential future abuse.

CISA said that no customer or mission data was exposed in the incident and thanked the researcher and reporter for their help. The agency said that its channels for allowing security researchers to notify CISA of potential incidents “were not well defined,” and that it has made changes to make it easier and faster for researchers to contact the agency.

CISA has been without a permanent director since the start of President Donald Trump’s second term in January 2025. The agency has also been affected by cuts, furloughs, and layoffs affecting about a third of its workforce since Trump took office. 

The Dutch National Police (Politie) says it has found “strong indications” that Dutch hackers have been involved in a February breach at the telecommunications provider Odido.

“This includes a telephone conversation that was made with Odido customer service shortly before the hack. In this conversation, a Dutch-speaking man posed as Odido’s IT employee. The company was then misled through phishing, after which the data theft took place,” the police said in a Thursday press release.

“This type of investigation is often complex and takes time, but cybercriminals are also vulnerable and leave traces. Traces have been secured at several times during the investigation into the hack at Odido, which the research team continued to work on,” added Stan Duijf, the head of operations at the National Investigation and Interventions Unit.

Odido is one of the largest Dutch telecommunications companies, offering mobile, broadband, and television services to millions of customers across the Netherlands.

When it disclosed the breach on February 12, the company said the attackers accessed its customer contact system on February 7 and downloaded the personal data of many of its users. It also told local media that the resulting data breach affected 6.2 million customers and that the threat actors reached out to say they had stolen millions of user records.

According to the telecom firm, the exposed information varies per customer, and it may include a combination of full name, address and city of residence, mobile number, customer number, email address, IBAN (bank account number), date of birth, and some identification details (passport or driver’s license number and validity).

However, it added that no call details, location, data, billing data, scans of identity documents, or Mijn Odido passwords were exposed during the incident.

While Odido has yet to attribute the incident, the ShinyHunters extortion gang claimed responsibility for the breach on its dark web leak site, releasing an 88GB archive containing over 15 million records, including data the company had already disclosed as exposed in the attack.

​ShinyHunters has been behind widespread vishing campaigns targeting Okta, Microsoft, and Google single sign-on (SSO) accounts, impersonating IT support staff to trick targets’ employees into entering credentials and multi-factor authentication (MFA) codes on phishing sites.

After breaching corporate SSO accounts, the threat actors steal data from connected SaaS applications, including Microsoft 365, Google Workspace, Salesforce, SAP, Slack, Zendesk, Dropbox, Adobe, Atlassian, and others.

The cybercrime group has been linked to a growing number of breaches involving companies such as Google, Cisco, PornHub, the online dating giant Match Group, the European Commission, Rockstar Games, and the McGraw-Hill edtech giant.

They were also behind security breaches at over a dozen Snowflake customers, various other third-party integration providers, and, more recently, a new series of breaches that hit over 100 organizations (including the University of Nottingham) following data-theft attacks exploiting an Oracle PeopleSoft zero-day flaw.

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An enterprise AI agent answers with total confidence, but the number is wrong. Nobody catches it until someone traces it back to a stale metric definition or a document the retrieval system never pulled. The model did not fail. The context it was given did.

In the past six months, 57% of enterprises traced a confident but wrong AI agent answer to missing or inconsistent business context, and 31% said it happened more than once, according to a VB Pulse June 2026 survey of 101 qualified enterprises with more than 100 employees.

The reason is not hard to find. Retrieval over documents is the default way agents get business context for 38% of enterprises, nearly double the next closest approach. The way most enterprises choose a retrieval system compounds the problem. Ease of ingestion and operational simplicity lead the selection criteria, with retrieval accuracy running behind both. The accuracy problem only shows up after the system is already live.

There is a known fix for this, a governed context layer every agent reads from instead of guessing. Vendors are racing to roll out context platforms while most enterprises are still figuring out what it is.

75% don’t have an agentic context layer yet

The context layer is meant to be a shared model of what business data actually means, built once and referenced consistently instead of re-derived by every agent that touches it. 

The VentureBeat research shows the enterprise response to that idea is broad but unfinished. Twenty-five percent of respondents run one in production. Thirty-four percent are building one right now. The remaining 41% have not started.

Among companies already building or running a governed context layer, 78% report a confident-wrong failure — an AI agent that answered with total certainty and was still wrong. Among companies with no plans to build a layer, only 20% report the same thing. Companies that already got burned are far more likely to be building the fix. Companies that haven’t been burned yet see no urgency.

What governed context looks like when someone actually builds one

Every major data and AI platform vendor is now building some version of this layer, and they are not converging on the same architecture. 

• DataHub is treating catalog metadata and years of analyst query behavior as a knowledge source, then keeping it current as a living system rather than a static wiki. 

• Microsoft’s Fabric IQ is building a business ontology that any agent, not just Microsoft’s own, can query over MCP. 

• Couchbase is pushing agent memory and context retrieval down to the edge, arguing the operational database is a more natural home for it than a search or analytics layer bolted on after the fact. 

• Pinecone’s Nexus is compiling structural logic into the metadata layer ahead of runtime, betting that agents need pre-built structure more than they need faster search.

• Snowflake runs a two-layer system, Horizon Context for customer-managed definitions and Cortex Sense for context the platform infers on its own. 

• Oracle’s Unified Memory Core takes the opposite approach, folding vector, graph and relational data into one transactional engine so there is no sync layer left to go stale. 

• Google’s Knowledge Catalog mines query logs and usage patterns to curate semantic context automatically.

• AWS’s Context service makes the same bet, a knowledge graph that gets smarter from how agents actually use it rather than from manual re-curation.

Analysts converge on one diagnosis

The vendor approaches differ. What analysts and practitioners have told VentureBeat about the underlying problem, across a run of interviews this year, does not.

When DataHub’s context layer push landed this spring, Constellation Research VP and principal analyst Michael Ni framed the stakes in blunt terms. “Whoever controls runtime context controls the AI decision layer for enterprise data,” Ni said. He was equally direct about how far any single product actually gets a buyer. “Vector memory isn’t business meaning, business meaning isn’t governance and governance isn’t execution,” Ni said.

In the same interview, BARC analyst Kevin Petrie pointed to a narrower but concrete gap. Most context platforms concentrate on structured tables, he said, which give agents trusted facts but miss the harder, messier context locked in documents and unstructured content, exactly the material a business actually runs on day to day.

Stephanie Walter, practice leader for AI Stack at HyperFRAME Research, made a related point earlier this year when VentureBeat asked her about enterprise context fragmentation. 

“The market is converging on the same conclusion,” Walter said. “Agents don’t just need more tokens or better models. They need governed, current, low-latency context.” She made a similar case in an earlier review of Pinecone’s Nexus launch, careful not to overstate how new any of this is. Nexus, she said, “shifts knowledge work from runtime chaos to pre-compiled structure. But it’s an evolution of RAG architecture, not a complete reinvention.” 

Gartner’s Arun Chandrasekaran, reviewing the same launch, offered the more forward-looking read. Agentic AI, he said, is moving from pure information retrieval toward a reasoning architecture, one where long context works as short-term memory and a vector database functions as deep storage underneath it.

The fragmentation problem shows up hardest at the practitioner level, where separate tools for retrieval, memory and access control were never built to agree with each other. Steven Dickens, CEO and principal analyst at HyperFRAME Research, put it bluntly after Oracle’s AI database push landed this spring. “Data teams are exhausted by fragmentation fatigue,” Dickens said. “Managing a separate vector store, graph database and relational system just to power one agent is a DevOps nightmare.” 

Matt Kimball at Moor Insights and Strategy, in that same story, put the production reality more simply. Getting an agent working is not the hard part, he said. The struggle is running it in production, where the goal becomes removing the distance between data and execution rather than adding another layer on top of it.

What this means for enterprises

Here’s what this adds up to for enterprises building on this layer.

Retrieval alone will not close the context gap. RAG is the default source for context in most enterprises today, and it is also the layer most closely associated with the confident-wrong-answer failure. Adding more documents or a bigger index does not fix a definition that is inconsistent across systems.

The semantic context layer is where the budget is actually moving, even where it hasn’t shipped. Fifty-eight percent of enterprises are already engaged — building or in production — but only 25% have actually gotten a layer live. That gap shows where enterprises have decided to spend, not where they’ve arrived.

No single vendor owns the architecture yet, and that is likely to stay true for a while. Enterprises evaluating this layer should expect to integrate rather than pick a single winner, at least for the next several quarters.

The buying decision is happening this year, and it is concentrated among the companies already burned by it. Fifty-seven percent of enterprises plan to switch or add a retrieval or context platform within the next twelve months. That intent is not spread evenly. Enterprises that reported a repeat confident-wrong failure plan to switch or add a provider at roughly 81%, against 32% among enterprises that never hit the problem. The companies shopping for new context tooling right now are largely the ones whose agents already got it wrong.

The agents are already running. The context underneath most of them is still being built, and the vendor selling the fix is being chosen this year.

This data will be part of a broader conversation at VB Transform 2026 on July 14 and 15 in Menlo Park: the context gap enterprises are racing to close, and which of the emerging approaches — governed semantic layers, hybrid retrieval, provider-native bundles — actually holds up in production.