Bitcoin volatility is down, but data shows that traders are protecting against moves to the downside.
The volume of bets on Bitcoin going down, compared to those on it going up, is at a mark not seen since 2021.
Nevertheless, this level of “defensiveness” has typically signaled that a bottom is near.
Bitcoin’s volatility has dropped as its price has stabilized around $70,000, but traders are still paying up for downside protection, according to a new report from investment firm VanEck.
Bitcoin’s realized volatility, or volatility based on actual observed price movements, has fallen from 80 to 50 over the past month, VanEck notes. But despite stabilizing prices, traders are still cautious and are paying large premiums to maintain positions that pay when Bitcoin goes down.
“Traders continue to pay significant premiums for downside protection,” the report reads. “Total premiums paid to purchase puts declined 24% month-over-month, but at $685 million over the past 30 days, they remain above 77% of monthly observations since the start of 2025.”
In other words, while the total dollar amount paid to make bets on Bitcoin going down has decreased, it is still significantly above typical monthly levels.
Not only are total premiums at significant marks, but the put/call ratio—which compares the volumes of bets on Bitcoin going down to those of Bitcoin going up—jumped as high as 0.84 and averaged 0.77. VanEck says these marks are the highest since 2021, and indicate “unusually strong demand for downside hedging relative to bullish positioning.”
But relief might be hiding in the data for Bitcoin bulls.
“When options markets have been this fearful in the past, Bitcoin has tended to recover,” the report reads. “The current level of defensiveness, while warranted by recent price action, has historically marked periods closer to market bottoms than tops.”
Furthermore, long-term holders parting ways with their BTC “appears to be slowing” as Bitcoin transfers among holders of at least 1 year and above fell month-over-month.
The top crypto asset has dropped nearly 1% in the last 24 hours, but remains up more than 5% in the last month, recently changing hands at $69,891.
At that level, it still sits nearly 45% off its all-time high mark of $126,080 set last October.
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A North Carolina man pleaded guilty to conspiracy tied to an AI-generated music streaming scheme.
Prosecutors say fake accounts generated billions of artificial plays on streaming music services.
The case involves more than $8 million in royalty payments.
A North Carolina man pleaded guilty Thursday to a federal charge tied to a scheme that used artificial intelligence and automated accounts to collect more than $8 million in music streaming royalties, according to the U.S. Department of Justice.
Michael Smith pleaded guilty to conspiracy to commit wire fraud in the Southern District of New York following a yearslong investigation. He agreed to forfeit the royalty payments and faces up to five years in prison.
“Michael Smith generated thousands of fake songs using artificial intelligence and then streamed those fake songs billions of times,” U.S. Attorney Jay Clayton said in a statement.
Sentencing is scheduled for July 29.
The case comes as AI-generated music tools have become widely available, allowing users to create songs with vocals, lyrics, and instrumentation from simple prompts. Platforms like Suno, Udio, and Google’s Lyria have accelerated production, making it possible to generate large catalogs of tracks at scale. At the same time, the technology has raised questions about copyright, ownership, and how streaming platforms handle AI-generated content.
In January, Rolling Stone reported that Smith had spent years pursuing a music career, including charting songs and working with industry collaborators, before investigators tied him to the scheme to manipulate streaming services.
Streaming services, including Spotify, Apple Music, Amazon Music, and YouTube Music, distribute royalty payments based on play counts, creating an incentive to inflate streams.
“Michael Smith used artificial intelligence and automated bots to create the illusion of popularity—and to collect millions in royalties that belonged to real artists,” said U.S. Attorney Jay Clayton. “Today, he has taken responsibility for that conduct.”https://t.co/BG5uBJr5aR
— US Attorney SDNY (@SDNYnews) March 19, 2026
When he was first charged in September 2024, federal prosecutors said Smith had created thousands of accounts on streaming platforms to artificially play songs he owned, using software to generate roughly 661,440 streams per day and around $1.2 million in annual royalties. He was released on a $500,000 bond the following month.
“To obtain the necessary number of songs for his scheme to succeed, Smith turned to artificial intelligence, which he used to create hundreds of thousands of AI-generated songs for which he could manipulate the streams,” prosecutors said.
Rather than concentrate on a small number of tracks, Smith spread streams across a large catalog. Prosecutors said the approach was intended to avoid detection systems that flag irregular activity. The catalog included both his own recordings and hundreds of thousands of AI-generated tracks, allowing the operation to scale.
“Although the songs and listeners were fake, the millions of dollars Smith stole was real,” Clayton said. “Millions of dollars in royalties that Smith diverted from real, deserving artists and rights holders. Smith’s brazen scheme is over, as he stands convicted of a federal crime for his AI-assisted fraud.”
Attorneys for Smith did not immediately respond to requests for comment by Decrypt.
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Kalshi was temporarily banned in Nevada, halting its sports, politics, and entertainment markets for at least 14 days.
A judge sided with the Nevada Gaming Control Board, signaling the state may win a longer-term injunction.
The case highlights a broader clash between states and the CFTC over who regulates prediction markets.
After months of legal back-and-forth, Nevada successfully banned Kalshi on Friday—forcing the prediction market platform to abstain from offering event contracts related to sports, politics, and entertainment in the state for the next 14 days.
The temporary restraining order, or TRO, will last until April 3, when a hearing will be held regarding Nevada’s motion for a preliminary injunction in its case against Kalshi. A preliminary injunction could potentially extend the temporary ban on Kalshi for several more months, as the case moves through the trial process.
The Nevada state judge who granted the restraining order Friday noted that the Nevada Gaming Control Board is “reasonably likely to prevail on the merits” of its case against the prediction market platform.
Daniel Wallach, an attorney specializing in sports betting, told Decrypt the only other hurdle now in the way of a longer-term Kalshi ban in Nevada is a federal hearing in the Ninth Circuit Court of Appeals set for April 16, which could potentially unwind state court enforcement proceedings.
Kalshi has been pushing aggressively to prevent Nevada from banning the platform—even temporarily—for weeks. That campaign suffered a major blow yesterday, however, when the Ninth Circuit declined to prevent Nevada from issuing an initial, temporary restraining order.
“Kalshi has repeatedly stated that its operations are legal in 50 states, which is clearly not true,” Mike Dreitzer, chair of the Nevada Gaming Control Board, said in a statement. “Prediction markets, to the extent they facilitate unlicensed gambling, are illegal in Nevada, and we have a statutory duty to protect the public.”
“We want people in the state to wager safely at a licensed book,” he continued.
Kalshi, which is now valued at $22 billion following a recent $1 billion funding round, declined to comment when reached by Decrypt.
Nevada is the first state in the country to ban Kalshi. Others are close behind. Kalshi is currently, for instance, appealing a potentially similar temporary ban of the platform in Massachusetts.
In the last year, numerous states have sued Kalshi and other prediction markets, including Polymarket and Crypto.com’s platform, arguing the companies are offering unlicensed sports bets that must be regulated at the state level. The prediction market firms have countered that they are not offering sports bets, but, in fact, sports-related event contracts, which are regulated at the federal level by the CFTC.
The Trump CFTC has avidly supported that interpretation, arguing states should not be able to regulate prediction market platforms. Earlier this week, when Arizona’s attorney general filed criminal charges against Kalshi for allegedly operating an illegal gambling service and allowing unlicensed election wagering, CFTC Chair Mike Selig immediately condemned the move.
“This is a jurisdictional dispute and entirely inappropriate as a criminal prosecution,” Selig said. “The CFTC is watching this closely and evaluating its options.”
Editor’s note: This story was updated to note that Kalshi declined comment.
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NEW DELHI, INDIA / ACCESS Newswire / March 20, 2026 / A system-level approach to building, deploying, and operating AI at scale across the enterprise
Data Science Wizards (DSW) today announced the launch of DSW UnifyAI OS, an Enterprise AI operating system designed to help organizations run artificial intelligence as a governed, production-grade system.
UnifyAI OS enables enterprises to build, integrate, deploy, govern, and operate AI at scale in production while retaining full ownership of all enterprise-built artifacts and its source code, avoiding vendor lock-in, choosing their own infrastructure, and building AI-native ecosystems on their own terms.
As AI moves from experimentation into real-world execution, enterprises are increasingly relying on it to power decisions and workflows. However, most environments remain disjointed built on disconnected tools, siloed pipelines, and commercial models that become restrictive as AI adoption scales.
UnifyAI OS introduces a horizontal operating layer that spans the enterprise, enabling AI to run consistently across systems, teams, and workflows while embedding governance directly into execution.
Unlike conventional approaches, UnifyAI OS ensures that:
All AI artifacts models, agents, workflows, and associated source code remain fully owned by the enterprise
AI runs entirely within enterprise-controlled environments across on-premises, private cloud, and hybrid infrastructure
Organizations retain the freedom to choose infrastructure and hyperscalers
Enterprises can build, integrate, and evolve their own AI-native ecosystems without structural dependency
At its core, the system enables organizations to:
Build AI use cases and applications
Integrate internal systems and external AI tools
Deploy AI/ML and agentic workloads into production
Govern execution through policy enforced at runtime
Monitor, manage, and continuously operate AI systems at scale
“What’s emerging is the need for a horizontal layer that can be leveraged across the enterprise one that brings consistency, governance and continuity to how AI runs,” said Sandeep Khuperkar, Founder and CEO, Data Science Wizards.
“As AI scales, the traditional model of building isolated use cases and paying for each one becomes both operationally limiting and economically inefficient. Enterprises need a system that allows them to build and operate AI freely, with full ownership and without structural constraints.”
“This has not been an overnight journey. Over the past four years, we have evolved from building tools to frameworks to platforms while staying focused on a long-term vision of an AI Operating System. With UnifyAI OS, we are at the beginning of that journey, and we are committed to building it with depth, discipline, and long-term sustainability for enterprise customers.”
UnifyAI OS separates control from execution through a system-level architecture where governance, policy enforcement, and auditability are embedded into every AI-driven action. This allows enterprises to operate AI systems in real time with the ability to monitor, intervene, and evolve continuously.
“The challenge is no longer building models and agents it is ensuring their execution is reliable, predictable, and governed at runtime in production environments.” said Pritesh Tiwari, Founder and Chief Data Scientist, Data Science Wizards.
“With UnifyAI OS, governance is not an external layer; it is part of how AI runs, ensuring systems remain transparent, controllable, and aligned with enterprise expectations.”
The launch reflects a broader shift in enterprise AI from fragmented experimentation to AI as infrastructure where systems must
Contact Details:
Name: Deepti DilipEmail: [email protected]
SOURCE: Data Science Wizards
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Smslocal, a leading Indian communication technology startup, has announced the launch of its next-generation Bulk SMS and Bulk WhatsApp Marketing Platform, designed to empower startups, SMEs, and local businesses with high-performance messaging tools at India’s lowest prices.To make business communication effortless, the company is offering ₹60 free SMS credits on first signup and a one-time WhatsApp activation fee of only ₹500 – helping businesses start marketing instantly with no long-term contracts or hidden fees.
Affordable, Scalable & Transparent Pricing
With Smslocal’s “Pay as You Use” model, businesses can run promotional, transactional, or OTP campaigns without major upfront investments. The flexible pricing helps teams personalize customer communication, improve conversions, and maintain budget control – making it ideal for small business owners, startups, and marketing agencies alike.For WhatsApp marketing, Smslocal eliminates subscription hassles by offering a no monthly fee plan. Companies can send unlimited broadcasts, promotional offers, and customer updates at scale, paying only a one-time activation charge.Advanced Features for Smarter Engagement
Smslocal’s dashboard integrates simplicity with automation, offering features like:
Instant Bulk Sending: Deliver messages to thousands of contacts instantly.
Smart Scheduling: Plan campaigns ahead with automated delivery timing.
AI Auto-Responder: Instantly reply to customer inquiries using smart AI.
Personalized Messaging: Automatically insert customer names and details.
Rich Media Support: Share PDFs, videos, images, and links effortlessly.
Live Analytics & Reports: Track delivery rates, reads, and performance.
DLT-Compliant Messaging: Stay fully compliant with TRAI and DLT norms.
24/7 AI-Powered Support: Get instant help any time of the day.
Helping India’s Businesses Go Digital
Smslocal was built to help India’s small businesses communicate faster, smarter, and cheaper,” said a company spokesperson. “With our combination of low pricing, AI automation, and simplicity, we’re making modern marketing accessible to everyone – from local retailers to national brands.
Smslocal’s communication platform is perfectly suited for:
Offers, discount, and new product promotions
Order updates and delivery alerts for ecommerce
OTPs and transaction confirmations
Event invites and booking reminders
Customer support and feedback messages
Start in MinutesBusinesses can sign up at http://www.smslocal.in to claim ₹60 free credits instantly, activate WhatsApp messaging for ₹500, and start running their first campaign within minutes – no technical skills required.
About SmslocalSmslocal is India’s most affordable bulk messaging provider, offering innovative SMS and WhatsApp communication tools for startups, SMEs, and digital marketers. With transparent pricing, a powerful dashboard, and 24/7 AI support, Smslocal helps over thousands of Indian businesses engage customers better and grow faster.
Media Contact:Sadik PatelWebsite: https://www.smslocal.in/Mail: info@smslocal.inPhone: +91 75065 73138
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The Bitcoin Policy Institute urged the Kentucky Senate to remove Section 33 of HB 380, calling it “technologically impossible” for non-custodial wallets.
The provision was buried as a floor amendment in a kiosk regulation bill that passed the House 85-0 and could clear the Senate within days.
An expert told Decrypt that hardware wallet providers would likely exit the Kentucky market entirely rather than redesign products in ways that undermine self-custody.
A last-minute amendment requiring hardware wallet providers to help reset user credentials, tucked into Kentucky’s sweeping crypto ATM bill, is facing mounting backlash, with experts saying it is a fundamental misunderstanding of how crypto infrastructure works.
Section 33 of House Bill 380, added as a last-minute floor amendment during House debate, would require hardware wallet providers to furnish customers with a mechanism to reset “any password, pin, seed phrase, or other similar information” needed to access a wallet.
“BPI is sending a letter to the Kentucky Senate informing them of the harmfulness of this language,” the group wrote on X.
Hardware wallets are physical devices that store crypto private keys offline and ensure only the user, not even the manufacturer, can access or recover them.
“This is likely far more indicative of a misunderstanding than a deliberate attempt at control,” Joe Ciccolo, Founder and President of BitAML, told Decrypt.
“Policymakers often struggle with the concept of self-custody,” Ciccolo said, noting that “there is no central authority capable of resetting access credentials,” unlike traditional systems where recovery is standard.
BPI described the mandate as “technologically impossible for non-custodial wallets,” noting that requiring a backdoor undermines Bitcoin‘s fundamental security model and pushes users toward centralized custodians that are more vulnerable to hacks and failures.
“Kentucky is suddenly about to ban self-custody. Tell your friends,” Conner Brown, Managing Director at BPI, wrote on X.
“Requiring hardware wallet providers to recover or reset credentials would effectively force them to redesign their products in a way that undermines self-custody—or exit the market altogether,” Ciccolo said.
“Most non-custodial wallet providers would likely choose not to operate in Kentucky rather than compromise their core security model,” he added, warning of “reduced consumer choice” and “diminished privacy protections.”
“The very consumers the bill aims to protect would lose access to one of the safest ways to store digital assets,” he said.
On safer paths forward, Ciccolo noted “social recovery mechanisms or multi-signature setups” can reduce risk “without introducing centralized control,” adding that “the best protection is ensuring users understand both the benefits and responsibilities of self-custody.”
He also backed BPI’s move, saying “education is critical,” and that when proposals stem from a “knowledge gap,” direct engagement with policymakers is “the most effective path forward,” noting it “directly impacts consumers who value financial autonomy and security.”
HB 380 was introduced in the House on January 14, reported favorably out of the Banking and Insurance Committee on March 4, and passed the full chamber 85-0 on March 13.
The underlying bill regulates virtual currency kiosk operators, establishes licensing requirements, and sets transaction limits, disclosures, and refund rules, provisions that carry broad political support and are expected to move the bill quickly through the upper chamber.
The bill arrived in the Senate on Monday and was referred to the Committee on Committees.
Kentucky’s move follows a broader crackdown on crypto kiosks, with Connecticut halting Bitcoin Depot for compliance failures and Minnesota considering a ban on crypto ATMs.
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Microsoft’s MAI-Image-2 is a new state-of-the-art AI image generation model
The model puts Microsoft in as the third-best AI lab on the Image Arena leaderboard thanks to its strong realism and text rendering.
Strict filters, usage caps, and missing features currently limit real-world usefulness, however.
Microsoft has been quietly building its own image generator. Announced Thursday by the company’s AI Superintelligence team, MAI-Image-2 has already landed at #3 on the Arena.ai leaderboard—behind only the models from Google and OpenAI—making Microsoft a legitimate player in a space it had previously outsourced to its partners.
That last part is worth sitting with. Microsoft has been paying OpenAI billions to power Copilot and Bing Image Creator. Building a competing image model in-house is an interesting business move.
MAI-Image-2 is available now in the MAI Playground, with a gradual rollout to Copilot and Bing Image Creator underway. API access is currently limited to select enterprise customers, with broader availability on Microsoft Foundry coming soon.
The team says it built the model by talking directly to photographers, designers, and visual storytellers. Three things came out of those conversations: improved photorealism, more reliable in-image text generation, and stronger capacity for detailed, imaginative scene construction. Whether or not that process translated into a genuinely useful tool is a different question.
Testing MAI-Image-2
The first thing you notice when you open the MAI Playground is how understated it is. The interface is minimal and clean, visually somewhere between Claude and Hume, with none of the maximalist dashboard energy you get from Midjourney or the chatbot experience you get from Gemini.
The images themselves are genuinely pretty strong. Photorealism is a real strength here—the model has a solid grasp of natural light, surface texture, and spatial relationships. It doesn’t quite hit the level of Google’s Nano Banana Pro, which still rules the leaderboard for a reason, but in some realism tests it comes surprisingly close.
Better prompting likely pushes it further; our initial results improved noticeably as we dialed in our descriptions.
Even complex, unrealistic scenes with parameters that defied logic were properly handled by the model, beating other models in details like the body proportions, limb position, depth, and spatial positioning.
For example, this image of a dog riding a bike in the middle of the ocean is arguably the most accurate one we’ve produced in zero-shot tests.
Text generation is a legitimate highlight. MAI-Image-2 handled complex typography with far more consistency than we expected—large blocks of text in images, posters, signage—without the typical garbling you see from most models.
We even pushed it toward multilingual text: It managed to generate some hanzi Chinese characters, though the accuracy wasn’t perfect. Still, the fact that it tried and got partway there is notable.
The model understands artistic style well, shifting between photographic realism, graphic design aesthetics, and illustrated styles without much friction. It reads prompts carefully, including stylistic instructions, and delivers something coherent on the other end. For a broad range of visual tasks, it’s versatile.
Now for the harder truths.
MAI-Image-2 is aggressively filtered—more so than Google Imagen, and more so than OpenAI’s DALL-E. We ran our usual test of a cartoon drawing of a spider chasing a woman, and got a flat refusal. Again, that’s a drawing—of a spider. The content moderation here is tuned to a level that will frustrate anyone doing creative work in gray areas, horror illustration, or anything that reads as remotely tense.
The usage limits are equally restrictive. Each generation triggers a 30-second cooldown. After 15 images, you’re locked out for 24 hours. For casual experimentation, that’s manageable. For any kind of production workflow, it’s a dealbreaker in the native UI.
There’s also only one resolution: 1:1. No landscape, no portrait, no custom ratios. In 2026, that’s a significant limitation—particularly for social media content, which is precisely where Microsoft presumably wants this embedded in Copilot.
And speaking of Copilot: MAI-Image-2 isn’t there yet. The rollout is happening, but as of today, the product you’d actually want it in doesn’t have it.
One more missing piece: This is purely a text-to-image tool. No image-to-image, no inpainting, no outpainting, no reference image support. For users expecting anything close to Firefly or Midjourney’s editing capabilities, this will feel half-finished.
]]>
Our take
MAI-Image-2 performs better than its leaderboard ranking suggests. In our hands-on tests, it beat GPT-Image on image quality and text rendering, which is interesting given that GPT-Image sits above it on Arena.ai’s leaderboard. Benchmark positions don’t always tell the full story.
The strategic logic behind building this is clear. Microsoft has been licensing OpenAI’s image models for Copilot while simultaneously funding OpenAI’s biggest competitor, Anthropic. Having a capable in-house model reduces dependency, cuts costs at scale, and gives Microsoft something to iterate on without asking for permission.
From that angle, MAI-Image-2 doesn’t need to beat Nano Banana. It just needs to be good enough—and it is.
The problem is the product constraints. The generation caps, the strict content policy, the 1:1-only output, the missing editing features, etc; these are the kinds of limitations that put a ceiling on real-world utility. A model this capable deserves infrastructure that matches it.
MAI-Image-2 is a strong technical foundation hamstrung by conservative product decisions. Once Microsoft loosens the restrictions, this becomes a serious contender. Right now, it’s a promising preview of what Microsoft’s image stack could actually become.
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Trading begins on MEXC
Playnance’s G Coin has entered open-market trading, with the G Coin/USDT pair going live on MEXC after the project’s Token Generation Event on March 18.
MEXC’s official announcement said deposits were open and withdrawals would begin on March 19, while the exchange’s live G Coin page shows the pair as active. For Playnance, that shifts G Coin from an ecosystem-native utility token into a publicly traded asset with continuous price discovery.
The listing matters because Playnance is not presenting G Coin as a blank-slate token. In its white paper and product documentation, the company describes G Coin as a utility asset tied to gameplay, missions, rewards, loyalty features, and broader participation across its platforms, not as a governance or profit-sharing token.
That gives the MEXC debut more substance than a typical launch, because market access is arriving after utility has already been built into the ecosystem.
Staking becomes the first post-listing signal
The clearest early signal is staking participation. Playnance’s site highlighted a launch phase in which more than 250 million G Coin were locked within hours, and later launch coverage tied to the MEXC debut said staking had already moved above 1 billion G Coin shortly after going live. Playnance’s staking page shows four lockup periods, 6, 9, 12, and 18 months, with reward allocations weighted toward longer commitments.
That matters because lockups can do more than generate a headline number. They can reduce immediately available supply, encourage longer-term alignment, and give the market a measurable signal of confidence just as trading opens.
Playnance’s public G Coin Tracker adds another reference point. Indexed snippets from the tracker show a fixed 77 billion token supply and more than 3.15 billion G Coin in locked supply or treasury categories, alongside live fields for price and holder data.
Utility and token design now face the market
Playnance says its broader ecosystem runs on PlayBlock, a Layer-3 infrastructure built for gaming, trading, betting, and prediction markets, with gasless execution and sub-second finality. Within that framework, G Coin is meant to power gameplay interactions and fees, rewards and incentives, partner revenue distribution, and treasury flows.
That gives the token a clearer operational role than many newly listed assets that reach exchanges before their use cases are live.
Supply design is also central to the pitch. Playnance’s docs and white paper say G Coin has a fixed maximum supply of 77 billion tokens. Tokens lost through gameplay are locked for 12 months before returning to circulation, while unsold tokens at TGE are subject to a 12-month cliff followed by 24-month linear vesting. The white paper also says the token already provided access to an operational ecosystem before admission to trading.
The next test is simple, whether the exchange listing, staking participation, and underlying product activity reinforce each other over time. MEXC gives G Coin liquidity and visibility. Staking gives the market an early demand signal. The tracker gives users a public dashboard. What matters now is whether volume, user growth, and on-chain usage keep moving in the same direction after launch-day attention fades.
4K HDR GigE PoE Camera | Sony IMX678 STARVIS™ 2 Color Sensor | Up to 120+ dB HDR Imaging | LED Flicker Mitigation | Low-Light Performance | ONVIF-Compliant Camera | NDAA Compliant
FORT WORTH, TX / ACCESS Newswire / March 19, 2026 / Vadzo Imaging today announced the launch of the Innova-678CRS, a high-performance 4K HDR GigE PoE camera designed to deliver consistent imaging in challenging lighting environments. Built around the Sony IMX678 STARVIS™ 2 sensor (1/1.8″ CMOS, 8.4 MP), the camera combines high sensitivity, low noise performance, and advanced HDR capabilities to capture reliable image data across scenes with extreme contrast.
The Innova-678CRS supports Clear HDR and Digital Overlap HDR (DOL-HDR) modes, enabling up to 120+ dB dynamic range while preserving detail in both highlights and shadows. With GigE connectivity and Power over Ethernet (PoE), the camera enables scalable, network-based deployment across distributed machine vision and smart infrastructure systems. Support for ONVIF Profiles S, T, G and M ensures seamless integration into existing video and automation ecosystems.
“Capturing consistent image data in challenging lighting conditions remains a key limitation in many vision systems,” said Alwin Vincent, Vadzo Imaging. “With the Innova-678CRS, we are addressing this at the system level, combining HDR imaging, low-light sensitivity, and GigE connectivity to enable reliable, deployment-ready vision performance in real-world environments.”
Key Capabilities of the Innova-678CRS Sony Starvis IMX678 4K HDR GigE PoE Camera
4K High-Resolution Video: The Innova-678CRS delivers 8.4 MP resolution (3856 × 2180) at 30 fps, enabling detailed inspection and measurement in machine vision workflows. The high pixel density supports accurate detection of fine surface defects, dimensional variations, and subtle inconsistencies, improving reliability in quality control and AI-based vision systems.
Advanced HDR Imaging (Up to 120+ dB): The camera supports Clear HDR and Digital Overlap HDR (DOL-HDR) modes, combining multiple exposures to retain detail across extreme lighting conditions. This ensures consistent image quality in scenes with glare, shadows, and backlighting, improving detection accuracy and reducing false positives in real-world deployments.
STARVIS™ 2 Sensor Architecture for Low-Light Performance: Built on the Sony IMX678 STARVIS™ 2 sensor, the camera utilizes a back-illuminated (BSI) pixel design to deliver high sensitivity, low noise, and improved signal-to-noise ratio. This enables reliable image capture in low-light and nighttime conditions, reducing the need for additional illumination and simplifying system design.
Stable Imaging Under LED Lighting Conditions: The Innova-678CRS incorporates LED flicker mitigation, minimizing intensity fluctuations caused by modern lighting sources. This ensures stable frame-to-frame consistency, which is critical for inspection, tracking, and analytics systems operating under artificial lighting.
GigE Camera with Power over Ethernet (PoE) for Simplified Deployment: The camera features a GigE interface with integrated PoE, enabling both data and power transmission over a single cable. This reduces wiring complexity and supports long-distance, network-based deployment, making it suitable for distributed industrial and monitoring systems.
Scalable Multi-Camera Integration: With GigE connectivity, the Innova-678CRS supports multi-camera system architectures, enabling synchronized imaging and centralized processing. This is particularly valuable for large-area inspection, traffic monitoring, and coordinated vision systems, where multiple viewpoints improve coverage and accuracy.
Seamless Integration with ONVIF-Compliant Systems: Support for ONVIF Profiles S, T, G and M enables interoperability with video management systems (VMS) and networked platforms. This ensures standardized control, easier system integration, and flexibility in multi-vendor environments.
Vadzo NXT Software Platform and VISPA NXT SDK
The Innova-678CRS IMX678 4K HDR GigE POE camera is supported by the Vadzo NXT software platform and VISPA NXT SDK, providing developers and system integrators with a unified environment for camera configuration, control, and deployment.
The platform enables end-to-end camera management, including network discovery, IP configuration, live image streaming, and failsafe local firmware update. Advanced controls for exposure, gain, HDR modes, and other imaging parameters allow precise tuning for application-specific requirements.
The VISPA NXT SDK provides APIs for C, C++, C#, and Python, enabling seamless integration into custom applications and vision pipelines. The platform is accessible through modern web browsers including Google Chrome, Mozilla Firefox, and Microsoft Edge, and supports deployment across Windows, Linux, Android, and embedded edge-AI systems.
Together, Vadzo NXT and VISPA NXT reduce integration complexity, accelerate development cycles, and enable efficient deployment of scalable vision systems.
Target Applications
The Innova-678CRS is designed for vision systems that require high-resolution imaging, reliable performance under variable lighting, and scalable deployment. Typical applications include:
Industrial Inspection and Quality Control: High-resolution imaging for detecting micro-defects, surface variations, and dimensional inconsistencies on production lines, enabling accurate inspection without impacting throughput.
Robotics and Machine Perception: Real-time vision for navigation, pick-and-place operations, and environmental awareness in industrial robots, autonomous guided vehicles (AGVs), and automation systems.
Traffic and Smart Monitoring Systems: Reliable detection and tracking of vehicles and pedestrians in complex lighting conditions, including shadows, glare, and LED-based street lighting environments.
Multi-Camera and Distributed Vision Systems: Synchronized multi-camera deployments for wide-area monitoring, surround inspection, and coordinated data capture across factory floors, warehouses, and large facilities.
Low-Light and High-Contrast Imaging Environments: Consistent image acquisition in nighttime, dimly lit, or high dynamic range scenes, where maintaining detail in both highlights and shadows is critical.
Security, Fleet, and Event Monitoring: High-resolution video capture for incident analysis, operational monitoring, and event verification across industrial and commercial environments.
Aerial and Mobile Vision Systems: Imaging for UAVs and mobile platforms, supporting inspection, mapping, and surveillance tasks in environments with rapidly changing lighting conditions.
Industry Impact
Modern vision systems must operate reliably across dynamic lighting conditions while scaling across distributed deployments. The Innova-678CRS addresses these challenges by combining advanced HDR imaging, strong low-light sensitivity, and GigE-based connectivity, enabling more consistent image acquisition and improved detection accuracy.
By simplifying deployment through network-based architectures and Power over Ethernet, the camera reduces system complexity while supporting scalable, multi-camera installations across industrial and real-world environments.
Availability
The Innova-678CRS 4K HDR GigE Vision Camera is available for OEM evaluation and production deployment. Engineering teams can access technical documentation, procure evaluation units, and receive design-in and integration support directly from Vadzo Imaging.
About Vadzo Imaging
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The plan covers Russell 1000 stocks and some index ETFs at launch, with the same rights, symbols, and priority.
Tokenized trades would still flow through DTC and revert to traditional settlement if needed.
During the review, commenters questioned its mechanics, market risks, and how much say issuers should have.
Nasdaq’s proposal to trade some stocks in tokenized form received formal approval from the U.S. Securities and Exchange Commission on Wednesday, though the structure would still keep trading and settlement within traditional market rails.
Covering some securities already listed on the national securities exchange, the changes would begin with Russell 1000 stocks and certain index ETFs, with tokenized shares required to match their traditional counterparts in terms of rights, symbols, and trading priority.
Tokenization is the process of turning a traditional asset, such as a stock or ETF, into a digital asset on a blockchain, tied to the original security that carries the same rights.
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Participating brokers can mark an order for tokenized settlement when they enter it, and Nasdaq would pass that instruction to the Depository Trust Company after the trade is executed, the SEC said.
If DTC cannot carry out the request because the broker or security is not eligible, or because the blockchain or wallet is not compatible, the trade will settle in traditional, non-tokenized form.
Decrypt has reached out to Nasdaq for comment on when tokenized trading could begin and what still needs to be in place before launch.
Same rails, new wrapper
Nasdaq filed its proposal in September last year, comparing tokenization to earlier market innovations like decimalization and electronification.
At the time, the exchange argued that existing regulatory structures “mandated by Congress” already apply to tokenized securities regardless of their blockchain properties.
The SEC acknowledged in its approval letter that during the review process, several commenters raised questions about how Nasdaq’s tokenization model would work.
SIFMA, the main trade group for the U.S. securities industry, and Cboe Global Markets, one of the largest U.S. exchange operators, focused on the lack of clarity around DTC’s role.
The Digital Chamber, a blockchain policy and advocacy group, argued the SEC should avoid favoring specific firms or technologies and give issuers more say.
Better Markets, a nonprofit focused on financial reform, opposed the proposal due to potential price gaps, surveillance concerns, and legal uncertainty.
By late November, major exchanges and market groups urged the SEC to avoid broad exemptions on tokenized securities, warning that looser relief could create uneven rules and new risks around tokenized stocks.
The SEC’s nod for Nasdaq comes as regulators and exchanges take a more structured and coordinated approach to tokenization, though the limits set for Nasdaq suggest that, at least for now, these can only be done through the existing system rather than a separate on-chain venue.
Earlier this year, SEC staff said that tokenized assets are securities first, technology second, affirming its position that placing a security on a blockchain does not alter its legal classification under federal law.
Opening the door
The approval matters because “it starts to make listed equities more programmable, not just more digital,” Steven Wu, chief operating officer at tokenization engine Clearpool, told Decrypt.
“The SEC is opening the door for these assets to move beyond trading and into broader financial use cases,” Wu said. “The real signal is where this is heading. Market structure has already moved from T+3 to T+1, but the endgame is T+0 and continuous, 24/7 markets.”
The changes introduce “flexibility at the infrastructure layer without disrupting how markets function today,” he noted, pointing to a longer-term shift toward faster settlement and eventually markets that can operate closer to real time.
“Tokenized equities point toward a model where price discovery is no longer constrained by traditional market hours,” he said.
For institutional players, the SEC’s approval “creates more flexibility at the asset level, Samar Sen, head of international markets at institutional digital asset firm Talos, told Decrypt.
“Institutions will be looking closely at how tokenized securities plug into post-trade infrastructure, especially where settlement still runs through central clearing and settlement systems, and whether liquidity develops consistently across both formats,” he added.
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