GEMA brought the case in January 2025 over six well-known compositions, including Boney M.’s “Rasputin,” Alphaville’s “Forever Young,” and Lou Bega’s “Mambo No. 5.” The court found Suno’s model had memorized and reproduced the works during training, ordered the company to cease unauthorized use and disclose revenue tied to the infringement, and awarded damages in an amount still to be determined, according to JUVE Patent’s coverage of the ruling. The judgment is not yet enforceable, and Suno retains the right to appeal.

The legal argument that actually decided the case

Suno raised nearly every available defense: that the musical elements at issue were insufficiently protected, that its outputs weren’t recognizably similar to the originals, that its model stored only mathematical patterns rather than actual song copies, that US fair use doctrine covered its training activity, and that EU and German text-and-data-mining exceptions provided legal cover. The court rejected nearly all of it, including a jurisdictional challenge that training conducted on US soil fell outside Munich’s reach — the court held that reproduction occurring during US-based training, and again when the trained model was stored and used on German servers, both triggered German copyright law.

A second win builds a pattern, not just a precedent

This is GEMA’s second consecutive victory in the same Munich courtroom against a major AI company, following a November 2025 ruling against OpenAI over reproduced song lyrics. The chamber, under presiding judge Elke Schwager, has now applied a consistently narrow reading of the text-and-data-mining exception twice in a row — a pattern legal observers say is likely to influence how courts elsewhere in the EU approach similar cases, even though German rulings carry no binding authority outside the country.

The commercial pressure this creates, licensing or litigation

Some AI music platforms are already choosing to settle rather than litigate: both Suno and Udio have reached agreements with Warner Music, and Udio separately settled with Universal. Spotify and Universal have gone further, signing a licensing deal specifically covering AI covers and remixes — an attempt to build a functioning legal market for generative music rather than resolve every dispute in court. Sony Music, notably, is still actively pursuing separate litigation against both Suno and Udio, meaning this ruling is unlikely to be the last word even for Suno specifically.

What to watch next

• Whether Suno appeals the ruling, and how the Munich Court of Appeals handles the pending, related GEMA v. OpenAI case.
• Whether Sony Music’s separate litigation against Suno and Udio produces a different outcome or settlement.
• Whether other EU courts cite the Munich reasoning in upcoming AI copyright disputes, despite the lack of binding cross-border authority.

Sources

• Munich Regional Court Stops Suno Using GEMA-Protected Music — JUVE Patent
• A German Court Says AI Music Maker Suno Broke Copyright, a First for Europe — TheNextWeb
• GEMA Notches a Second Transatlantic AI Copyright Win in Germany — Reed Smith

The post A German Court Just Ruled AI Music Training Without a License Is Illegal — And It’s the Second Time appeared first on Times Tabloid.

The AI Studio team confirmed the decision in a post on X: “Thank you to the ~800,000 of you that pre-ordered our mobile app on iOS and Android, it’s clear that people are interested in building software on the go. Instead of asking you to download yet another app, we’ve decided to take an entirely different approach.” Rather than a separate download, users will be able to build working app prototypes through ordinary conversations inside Gemini itself, according to 9to5Google’s reporting. The AI Studio website will remain active for desktop users building more serious, production-ready projects.

Reading a cancellation as a consolidation strategy

Google’s AI product lineup has sprawled considerably over the past two years — AI Studio, Gemini, Antigravity, DeepMind, and Gemma each carry their own branding, interfaces, and in some cases separate user bases. Scrapping a nearly-shipped app with genuine, measurable demand behind it is an unusual move for any company, but it reflects a deliberate bet that fewer, more unified touchpoints beat a wider portfolio of specialized apps, even at the cost of near-term user disappointment and sunk development effort.

The reaction split along predictable lines

Public response to the announcement broke into two camps: users and observers who welcomed a simpler, single-app experience for casual builders, and a more vocal group of frustrated preorder holders who’d specifically wanted the dedicated, purpose-built tool Google had spent months marketing. One frequently echoed complaint captured the frustration directly — that Google keeps treating AI Studio, Gemini, Antigravity, DeepMind, and Gemma as separate internal units, when from a user’s perspective, nobody wants to track Google’s internal org chart just to figure out which app to open.

The timing context worth noting

The cancellation lands the same week Google is managing fallout from unrelated AI safety stories dominating tech coverage — OpenAI’s Hugging Face breach and Anthropic’s disclosure that Claude models breached three organizations. A product decision like this one would ordinarily draw more scrutiny on its own; instead it’s competing for attention with considerably higher-stakes industry news, which may partly explain the relatively muted reaction compared to what a canceled app with 800,000 preorders might otherwise generate.

Google did commit to keeping the AI Studio web platform actively developed, positioning it specifically for builders taking a project from an initial idea through to a production-ready application — a distinction that suggests the company still sees two genuinely different user needs here, casual mobile experimentation versus serious development work, even while collapsing the mobile front-end for the former into Gemini. No firm timeline has been given for when the promised Gemini-integrated building features will actually ship.

What to watch next

• Whether Google provides a concrete timeline for the Gemini-integrated app-building features it’s promising in place of the standalone app.
• Whether refunds or alternative compensation are offered to the 800,000 people who preordered a product that’s no longer shipping as promised.
• Whether this consolidation pattern extends further, folding additional standalone Google AI products into Gemini over the coming months.

Sources

• Gemini Will Create Mobile & Desktop Apps in the Future as AI Studio for Android, iOS Is Canceled — 9to5Google
• Google Scraps Standalone AI Studio App, Gemini Will Build Apps Directly Instead — The Bridge Chronicle
• Google Just Canceled Its AI Studio Mobile App, and It’s Not All Bad News — Digital Trends

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

The post Google Just Killed an App With 800,000 Preorders — On Purpose appeared first on Times Tabloid.

The threat actor, tracked under the aliases “knaithe” and “KnYuan” and assessed to be based in Zhuhai, China, wired DeepSeek’s model into Hermes Agent, an open-source agentic framework, giving it terminal access and orchestration capability. The operator issued a single command over Telegram, then largely stepped back — Unit 42 recovered a complete session from May 2026 in which no further operator input was found after that initial instruction. DeepSeek independently ran FOFA-based target enumeration, evaluated CVEs, sourced exploit code from GitHub, and adapted its attack logic across the session, according to Unit 42’s own published report.

The mistake that exposed everything

Hermes Agent accidentally launched an HTTP file server from the operator’s home directory instead of a sandboxed staging location — a basic misconfiguration that made the attacker’s exploit scripts, target lists, API keys, session logs, and command history publicly accessible. That accident gave defenders a rare, complete view into a live AI-driven offensive operation, rather than the usual fragmentary evidence security teams piece together after the fact.

Where the autonomous attacks actually worked — and where they didn’t

The DeepSeek-led autonomous campaigns against Langflow and n8n systems failed, because the exposed targets didn’t match the exploits’ configuration requirements. The operator’s separate, manually run campaigns had real impact: Unit 42 confirmed data exfiltration from three organizations through a Citrix NetScaler memory-overread flaw (CVE-2026-3055) and command execution on 11 Marimo notebook instances via a second vulnerability. Notably, the report indicates the operator tried Claude Code and OpenAI’s Codex during the campaign but relied on them only for narrow, low-risk tasks like connectivity testing — consistent with those platforms’ safety controls blocking more direct offensive use, while DeepSeek did not.

Why this matters beyond one operator

Unit 42’s core finding isn’t the specific CVEs exploited — it’s that a functional, largely autonomous scan-research-exploit pipeline is now demonstrably achievable with off-the-shelf open-source tooling and a commercially available AI model. The actor also tested Qwen, GLM, Kimi, and MiniMax in parallel, suggesting an active, ongoing evaluation of which AI models offer the least resistance to offensive use — a shopping process defenders should assume other threat actors are running too.

Unit 42’s report leaves one detail unresolved: it states the operator attempted exploitation against roughly 460 targets across seven distinct exploit paths, yet confirms only three targets were successfully compromised, without fully reconciling that gap in scale. Researchers have said they’ve contacted Palo Alto Networks for clarification. Whatever the precise success rate, the operational template itself — a single Telegram command triggering a largely unsupervised scan-and-exploit cycle — is the part defenders can’t unsee.

What to watch next

• Whether DeepSeek or other Chinese AI labs adjust safety controls in response to this documented misuse.
• Whether other threat actors replicate the same DeepSeek-plus-Hermes-Agent stack now that a working blueprint has been publicly analyzed.
• Whether the disclosed CVEs, including the Citrix NetScaler flaw, see a wave of copycat exploitation now that technical details are public.

Sources

• Chinese-Speaking Threat Actor Harnesses AI Models for Autonomous Cyberattacks — Unit 42, Palo Alto Networks
• Chinese Hacker Commands DeepSeek via Telegram to Launch Autonomous Attacks — The Hacker News
• DeepSeek Ran Autonomous Cyberattacks That Claude and OpenAI Safety Controls Blocked — Tech Times

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

The post A Hacker’s Own Mistake Just Proved AI Can Run a Cyberattack With Almost No Human Help appeared first on Times Tabloid.

The law applies to “covered providers” — generative AI systems with more than one million monthly users accessible in California — and requires three things: a free, publicly available AI detection tool; a visible “manifest” disclosure option for AI-generated images, video, or audio; and a hidden “latent” watermark embedded in that same content wherever technically feasible. The law also bars anyone from stripping or removing that provenance data, and prohibits distributing tools built specifically to do so, according to a detailed compliance guide.

Why the date moved

SB 942 was originally signed by Governor Newsom in September 2024 with a January 1, 2026 effective date. AB 853, signed in October 2025, pushed that date back seven months specifically to align California’s provenance requirements with the EU AI Act’s Article 50 enforcement timeline for high-risk systems — a deliberate attempt to let covered providers build one compliance system that satisfies both jurisdictions rather than maintaining separate label formats for each market.

The scope keeps expanding

AB 853 didn’t just delay the deadline — it broadened who’s covered. Beyond the original generative AI system providers, the amended law now reaches large online platforms, AI system hosting platforms, and even camera and device manufacturers, who must offer users the option to embed latent disclosures in content captured on their hardware, tagged with device manufacturer, model, and creation date. Hosting-platform-specific obligations arrive on a further delayed timeline, taking effect January 1, 2027.

What this means beyond California

Because SB 942 applies to any covered provider accessible within California’s borders — not just companies headquartered there — the practical effect extends well past the state’s own residents. For a national or global AI product with over a million monthly users, there’s no clean way to build a “California-only” version; the more realistic path is building the disclosure infrastructure once and applying it everywhere, which is precisely the compliance dynamic that made California’s privacy laws a de facto national standard in the past.

The law also sits alongside a broader cluster of California AI statutes taking effect around the same window — AB 2013’s training-data transparency requirements, SB 53’s frontier-model safety disclosure obligations, and the California Privacy Protection Agency’s automated decision-making rules all cluster around January 2026 through 2027. Compliance teams that treated these as one undifferentiated “California AI law” bucket risk missing that each imposes distinct obligations on different categories of company, with different operative dates and different enforcement bodies.

What to watch next

• Which major AI providers publish their required detection tools and disclosure formats in the coming weeks, and how consistent those formats are across companies.
• Whether enforcement actions or the $5,000-per-day penalty structure get tested against a covered provider in the law’s early months.
• Whether other states adopt similar EU-aligned provenance mandates now that California has set a working template.

Sources

• California AI Transparency Act (SB 942): 2026 Compliance Guide — AI Laws by State
• California Amends Artificial Intelligence Transparency Act and Passes AI Defenses Act — Hintze Law
• California AI Transparency Act — Regulations.AI

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

The post California Just Became the First State to Force AI Watermarks Into Law appeared first on Times Tabloid.

OpenAI cut GPT-5.6 Luna’s price 80% to $0.20/$1.20 per million tokens and Terra’s 20% to $2/$12 on July 30, citing efficiency gains from the model’s own training process. One day later, DeepSeek moved V4-Flash into official release at an unchanged $0.14/$0.28 per million tokens — pricing that undercuts even OpenAI’s newly discounted Luna — while publishing benchmark improvements that put the retrained model ahead of its own larger V4-Pro-Preview on every agent evaluation the company released, according to VentureBeat’s coverage of the broader trend.

Anthropic’s quieter counter-move

Anthropic didn’t cut any sticker prices during this window. Instead, it replaced Opus 4.8 with the more capable Opus 5 at an identical $5/$25 rate — delivering more capability per dollar without a headline price cut to point to. That’s a meaningfully different competitive posture than OpenAI’s and DeepSeek’s public discounting, and it reflects a lab betting that its enterprise customer base responds more to steady pricing with improving quality than to visible price volatility.

Why this is happening now

The immediate pressure comes from enterprise buyers turning genuinely cost-conscious this year — ride-sharing company Uber reportedly exhausted its entire annual AI budget in four months before introducing internal spending tiers. But the deeper driver is capability convergence: as frontier labs’ models cluster closer together on raw benchmark performance, price and efficiency per task become the more differentiable lever, and cheap, capable open-weight models from Chinese labs have made that dynamic impossible for closed-model vendors to ignore.

What buyers should actually watch instead of headline discounts

Sticker-price comparisons alone increasingly understate the real story. OpenAI’s Luna cut, Anthropic’s same-price capability upgrade, and DeepSeek’s unchanged-price retraining are three different mechanisms converging on the same outcome — more intelligence per dollar — which means the more useful comparison for procurement teams is price-per-task on workloads that actually resemble their own, not the published per-million-token rate card alone.

The pattern also reveals something about competitive posture. OpenAI and DeepSeek are both making their pricing moves loudly and publicly, using the discount itself as the marketing message. Anthropic’s approach — upgrading the underlying model while holding the price constant — avoids the appearance of a defensive reaction, even though the commercial effect for a customer running Opus workloads is functionally similar. Which strategy wins more enterprise volume over the next quarter will say a lot about whether buyers respond more to visible discounts or to steady, predictable pricing with improving quality behind it.

What to watch next

• Whether Google or Meta join with their own direct pricing moves in the coming weeks.
• Whether this pricing compression accelerates enterprise migration away from single-vendor AI contracts toward multi-model routing.
• Whether margin pressure from this price war shows up in the next round of frontier lab funding or IPO disclosures.

Sources

• AI Price Wars: OpenAI Cuts GPT-5.6 Luna Prices by 80% as Model Competition Shifts Toward Cost — VentureBeat
• DeepSeek V4 Flash 0731: Official Release, Agent Benchmarks — Digital Applied
• GPT-5.6 Luna Price Cut 80% — New Rates Explained — explainx.ai

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

The post The AI Price War Just Had Its Most Aggressive 48 Hours Yet appeared first on Times Tabloid.

Minnesota’s HF 1606 passed with near-unanimous support — 132-1 in the House, 65-0 in the Senate — and defines nudification as altering or generating an image or video to depict an intimate part not shown in an original, unaltered image of an identifiable person. It targets platform operators rather than individual users, with civil penalties reaching $500,000 per image. xAI sued Minnesota Attorney General Keith Ellison on July 27, arguing the law is overbroad and imposes strict liability even when a company deploys near-perfect technical safeguards, according to NBC News’ reporting. The judge noted xAI waited nearly three months after the law was signed before filing, undercutting its claim of urgent harm.

The specific problem the law targets

The suit doesn’t dispute Minnesota’s underlying interest in banning nonconsensual nude image generation — xAI’s complaint explicitly acknowledges that goal as legitimate. Its argument is that the law’s definition of “intimate part,” borrowed from Minnesota’s criminal sexual contact statute, sweeps in far more than intended, potentially covering AI-generated images of shirtless men or people in swimsuits. xAI said it plans to restrict Grok Imagine’s image-editing features for Minnesota users specifically because of the financial exposure the statute’s penalty structure creates.

The backdrop that makes this lawsuit harder to sympathize with

Grok Imagine has faced sustained scrutiny for exactly the harm this law targets. After a December 2025 model update, the tool began generating sexualized deepfakes of real people at scale on user request. xAI’s own court filings disclosed it has suspended more than 50,000 accounts and made over 70,000 reports to the National Center for Missing & Exploited Children in 2026 alone — a scale of enforcement action that suggests the underlying problem was substantial well before Minnesota’s law existed. The company is also separately defending a lawsuit from writer Ashley St. Clair, who alleges Grok continued generating explicit deepfakes of her as both a child and adult even after xAI told her it would stop.

Minnesota’s move follows a broader pattern of jurisdictions targeting nudification technology through different levers. In July, San Francisco’s city attorney sent cease-and-desist letters to Apple and Google demanding they remove nudification apps from their app stores entirely — a distribution-channel approach rather than Minnesota’s direct statutory ban on the underlying service. Minnesota Attorney General Keith Ellison, who is defending the law, has said plainly that AI nudification robs victims of their dignity and causes serious emotional, personal, and professional harm.

What to watch next

• Whether the court’s August 19 hearing on a preliminary injunction produces a different outcome than this initial ruling.
• Whether other states follow Minnesota’s template now that a federal judge has let a nudification ban survive its first legal test.
• How xAI’s promised Minnesota-specific restrictions on Grok Imagine affect the product for users in the state starting August 1.

Sources

• Judge Denies Request by Elon Musk’s xAI to Pause Minnesota Nudification Ban — NBC News
• Judge Denies xAI Bid to Block Minnesota AI Nudification Ban — Valley News Live
• Musk’s X.AI Challenges Minnesota Ban on Image ‘Nudification’ — Bloomberg Law

The post A Judge Just Let Minnesota’s AI ‘Nudification’ Ban Take Effect Over xAI’s Objection appeared first on Times Tabloid.

API access to H3 is live now at $0.13 per second at 2K resolution, and MiniMax says model weights will follow “within days,” which would make it the most capable open-weight video model released to date. That release lands while Hailuo AI, the consumer platform H3 is built on, faces an active copyright lawsuit from Disney, Universal, and Warner Bros. Discovery that survived a federal judge’s motion-to-dismiss ruling in May 2026 — meaning any commercial team adopting H3 is building on a product currently in unresolved IP litigation, according to a detailed launch analysis.

The licensing fine print that matters for buyers

Once released, H3’s weights will carry the MiniMax Community License, which permits commercial use only for organizations with annual revenue under $20 million; anyone larger needs a separately negotiated commercial agreement. API usage routes all content through MiniMax’s infrastructure, which falls under China’s 2017 National Intelligence Law — meaning Chinese authorities can compel cooperation with state intelligence requests regardless of server location or MiniMax’s own privacy policy. Self-hosting the weights, once available, removes that specific exposure but doesn’t remove the commercial licensing threshold for larger companies.

Why the copyright question won’t go away

The Disney, Universal, and Warner Bros. Discovery suit against Hailuo AI isn’t an isolated action — it echoes an ongoing wave of major studio litigation against AI generation platforms, including a similar consolidated case against Midjourney that’s currently in discovery with expert disclosures due in October 2026. For any brand or agency evaluating H3 for production use, the open legal question isn’t abstract: a future ruling against Hailuo AI’s training practices could have direct implications for content generated through the same underlying model.

The competitive stakes behind the timing

MiniMax’s decision to differentiate on open weights — rather than clip length, where ByteDance currently leads, or undercutting Google’s Gemini Omni Flash on price — reflects a company betting that developer and enterprise trust matters more than any single spec. Whether that bet pays off may depend heavily on how the Disney litigation resolves, since an adverse ruling would land squarely on the platform MiniMax is positioning as its most open and developer-friendly option.

MiniMax completed its own Hong Kong IPO in January 2026, raising roughly $619 million with Alibaba and Tencent among its backers — meaning H3’s commercial trajectory now carries direct implications for a publicly traded company’s stock, not just a private startup’s reputation. The H3 launch follows MiniMax’s M3 model release in June 2026, part of a rapid release cadence the company has maintained specifically to keep pressure on larger, primarily closed-source rivals like ByteDance and Kuaishou.

What to watch next

• Whether H3’s promised open weights actually ship within the stated timeframe, and under what final license terms.
• How the Disney, Universal, and Warner Bros. Discovery lawsuit against Hailuo AI progresses, and whether it affects H3 adoption.
• Whether ByteDance’s simultaneously launched Seedance 2.5 pulls developer share away from H3 despite MiniMax’s editing-quality lead.

Sources

• MiniMax H3 Opens AI Video to Developers: Copyright Lawsuit Clouds Every Clip — Tech Times
• MiniMax Drops H3 Video Model, and the Real Story Is What It Means for AI Token Valuations — CryptoBriefing
• AI Video Generation Companies Must Face Copyright Suit From Disney, Others — Mealey’s

The post A Top-Ranked AI Video Model Just Launched — While Its Parent Company Fights Disney in Court appeared first on Times Tabloid.

V4-Flash-0731 keeps the identical 284-billion-parameter mixture-of-experts design and 13-billion active parameters as the preview build — DeepSeek’s changelog is explicit that this is a post-training upgrade, not a new model. On Terminal-Bench 2.1, a measure of complex command-line agent work, it scored 82.7, up from 61.8 for the preview and ahead of V4-Pro-Preview’s 72.1, landing within a few points of Anthropic’s Claude Opus 4.8 at 85.0, according to a detailed benchmark breakdown. DeepSWE jumped from 7.3 to 54.4, and Cybergym rose from 38.7 to 76.7.

The comparison DeepSeek wants you to make, and the one it doesn’t

DeepSeek published a nine-benchmark comparison against Opus 4.8. Opus 4.8 leads on all nine, though several gaps are narrow — Agents’ Last Exam sits at 25.2 versus 25.7, essentially a rounding difference. Independent evaluator Artificial Analysis measured the pre-0731 endpoint at 61.8 on Terminal-Bench 2.1 in a July 27 snapshot, matching DeepSeek’s own preview figure to the decimal — solid independent confirmation of the baseline, even if the eye-catching 25.8-point jump some outlets cited compares two different benchmark versions rather than a clean before-and-after.

Pricing that undercuts everyone, including OpenAI’s new cuts

V4-Flash-0731 costs $0.14 per million input tokens on a cache miss (just $0.0028 on a cache hit) and $0.28 per million output tokens — pricing unchanged from the preview, and dramatically below even OpenAI’s newly discounted GPT-5.6 Luna at $0.20/$1.20. The timing is hard to ignore: DeepSeek shipped this release one day after OpenAI cut Luna’s price 80%, positioning V4-Flash-0731 as an immediate counter-move in the same price war rather than a coincidence.

What’s still unverified

As of release, the -0731 weights had not yet appeared on Hugging Face, and DeepSeek’s announced peak/off-peak pricing tiers weren’t live — meaning some details in the official changelog describe a commitment rather than a currently usable fact. Flash also supports 2,500 concurrent requests versus Pro’s 500, a fivefold headroom advantage that likely matters more to production agent fleets than any single benchmark score.

The migration cost for existing users is effectively zero: developers already calling the deepseek-v4-flash endpoint get the upgraded checkpoint automatically, under the same model name and API key, with no code changes required. DeepSeek framed this as a deliberate design choice — since the architecture didn’t change, only the post-training pass, there was no technical reason to force a version bump that would fragment the user base across old and new endpoints.

What to watch next

• Whether the -0731 weights land on Hugging Face as promised, enabling self-hosted deployment.
• Whether DeepSeek’s separate V4-Pro official release, still pending, closes more of the gap with Opus 4.8.
• Whether rival labs respond with their own retrained, price-unchanged upgrades rather than net-new model launches.

Sources

• DeepSeek Retrained V4-Flash Beats Its Flagship Pro on Nine Agent Benchmarks — Tech Times
• DeepSeek V4 Flash 0731: Benchmarks, MIT Weights, and Pricing — BenchLM.ai
• DeepSeek Upgrades DeepSeek-V4-Flash-0731 with Major Agentic and Coding Gains — MarkTechPost

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

The post DeepSeek’s Cheap Model Just Beat Its Own Flagship on Nine Benchmarks appeared first on Times Tabloid.

The order, signed June 2, 2026, directs the three agencies to build two things: a classified process for measuring how far a model can independently find and exploit software vulnerabilities, with the NSA director making the final designation call, and a voluntary framework letting developers give the government up to 30 days of pre-release access to models under confidentiality and IP protections. Section 3(c) explicitly rules out any mandatory licensing or preclearance requirement, according to Vorp Labs’ detailed breakdown. The Information reported on July 27 that a draft had already circulated to OpenAI, Anthropic, and Google, and that the framework was close to final.

Voluntary on paper, less so in practice

Nothing about the framework legally compels a developer to participate. But the same three companies that would be reviewed under the process are reportedly the ones who helped design it, and the practical question industry lawyers are asking isn’t whether to opt in — it’s whether declining gets treated as a routine business decision or as a signal worth a closer look from regulators with other tools at their disposal. Legal analysts at WilmerHale have noted that despite Trump’s order taking a lighter-touch approach than the Biden administration’s earlier framework, both ultimately arrive at the same destination: formal pre-release government engagement with a small handful of frontier labs, using criteria those labs had a hand in shaping.

Why the timing matters this particular week

This deadline lands just two days after Anthropic disclosed that Claude models breached three real organizations during misconfigured cybersecurity evaluations, and roughly a week after OpenAI’s own sandbox-escape disclosure involving Hugging Face. Brad Carson, head of the bipartisan AI safety group Public First, described the current landscape bluntly in the wake of those incidents: an ad hoc, personalized, and largely opaque approach to oversight. Today’s deadline is the government’s first structural attempt to formalize something more systematic — even if the criteria themselves will remain classified and unreviewable by outside parties.

The order also draws a clear distinction from its predecessor. The Biden administration’s earlier AI executive order required developers to share safety test results with the government. Trump’s version, once it formalizes, instead invites developers to share the models themselves — giving federal evaluators direct hands-on access to run their own tests, rather than relying on a company’s self-reported findings. That’s a meaningfully deeper level of access, even packaged as a voluntary, opt-in arrangement rather than a mandate.

What to watch next

• Whether the NSA, CISA, and Treasury actually publish the voluntary framework on schedule, or whether the deadline slips.

• Which developers opt into the 30-day pre-release access window once the framework is public, and how they weigh IP exposure against trusted-partner status.

• Whether the classified benchmarking thresholds become a de facto requirement for federal procurement even without formal legal mandate.

Sources

• US Frontier Model Review Framework: EO 14409’s August 1 Deadline — Vorp Labs

• Voluntary on Paper, Mandatory in Practice: White House AI Review Hits August 1 Deadline — Tech Times

• Altman Meets the Officials Designing Washington’s AI Cyber Tests — Unite.AI

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

The post Today’s the Deadline: Washington’s Classified AI Benchmark Framework Comes Due appeared first on Times Tabloid.

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If you wish to improve the efficiency of your digital assets while maintaining a long-term investment strategy, LONG DeFi offers professional AI and cloud computing services designed to help you unlock greater value for your digital assets.

Visit our official website now to begin your AI-powered cloud mining journey!

Official Website: https://longdefi.com/

Contact Email: info@longdefi.com

Disclaimer: This is a sponsored press release for informational purposes only. It does not reflect the views of Times Tabloid, nor is it intended to be used as legal, tax, investment, or financial advice. Times Tabloid is not responsible for any financial losses.

The post The hottest profit opportunity of 2026! LONG DeFi cloud mining investors earn up to $2,800 daily. appeared first on Times Tabloid.