Ethereum Price Analysis: ETH Consolidating Below Crucial Barriers 1999

  • ETH price declined recently below $120 and tested the key $112 support area against the US Dollar.
  • Yesterday’s highlighted important bearish trend line is intact with resistance at $118 on the hourly chart of ETH/USD (data feed via Kraken).
  • The pair is currently consolidating below the $118 and $120 resistance, with a few positive moves.

Ethereum price is placed in a bearish zone against the US Dollar and bitcoin. ETH/USD must break the $118 and $120 resistances to start a short term upside correction.

Ethereum Price Analysis

Yesterday, we saw a nasty decline in ETH price from the $125 swing high against the US Dollar. The ETH/USD pair broke the $122, $120, $118 and $115 support levels to move into a bearish zone. It tested the $112 support area where buyers emerged. Later, the price started consolidating losses and corrected a few points above the $114 level. It traded above the 23.6% Fib retracement level of the last slide from the $123 swing high to $112 swing low.

However, there are many hurdles on the upside near the $118 level. The price made a couple of attempts to surpass the $117-118 zone, but buyers failed to gain momentum. Besides, the 50% Fib retracement level of the last slide from the $123 swing high to $112 swing low is also near $118. More importantly, yesterday’s highlighted important bearish trend line is intact with resistance at $118 on the hourly chart of ETH/USD. Finally, the 100 hourly simple moving average is positioned near the $120 level. Therefore, both $118 and $120 levels are crucial barriers for buyers in the short term.

Looking at the chart, ETH price may continue to trade in a range above $112 before the next move. If buyers push the price above the $118 and $120 resistance, there could be a recovery towards $125. If not, the price could retest the $112 or $110 level.

ETH Technical Indicators

Hourly MACD – The MACD for ETH/USD is slightly placed in the bullish zone, with a flat structure.

Hourly RSI – The RSI for ETH/USD is currently moving higher towards the 50 and 55 levels.

Major Support Level – $113

Major Resistance Level – $120

Previous ArticleNext Article

Leave a Reply

New study finds AI models prefer Bitcoin and digital money over traditional fiat currency 340

The Bitcoin Policy Institute (BPI), a nonpartisan research organization, released new research today examining how frontier AI models would choose to transact if they were operating as autonomous economic agents. The study tested 36 models from six leading AI providers—Anthropic, DeepSeek, Google, MiniMax, OpenAI, and xAI—across 9,072 open-ended monetary scenarios designed to be neutral, with no suggested currencies or predetermined answers.

Key Findings

  • Bitcoin came out on top at 48.3% of all responses, more than any other option. Stablecoins followed at 33.2%.
  • AI models overwhelmingly rejected fiat: +90% of responses favored digitally-native money (including dollar-pegged stablecoins) over traditional fiat. Not a single model out of 36 chose fiat as its top preference.
  • Bitcoin dominated store of value at 79.1%. In scenarios about preserving value long-term, Bitcoin was the strongest consensus on any single question in the study.
  • Stablecoins led for everyday payments at 53.2%. For transactions and payments stablecoins led over while Bitcoin (36.0%), revealing a clear savings-versus-spending divide.
  • Models invented their own money. Without any prompting, 86 responses independently proposed energy or compute units (such as kilowatt-hours and GPU-hours) as a way to price goods and services.
  • Preferences varied by provider but held across conditions. Bitcoin preference ranged from 91.3% (Anthropic’s Claude Opus 4.5) to 18.3% (OpenAI’s GPT-5.2), but results were consistent regardless of how the models’ output settings were configured.

Without any prompting, AI models converged on a two-tier monetary system—Bitcoin for savings, stablecoins for spending—that mirrors how hard money and liquid instruments have functioned throughout history. As AI agents gain economic autonomy, these preferences carry direct policy implications.

The findings suggest growing demand for agent-native Bitcoin payment infrastructure, self-custody solutions, and Lightning Network integration. The research also found that preferences varied meaningfully across providers and rose with model capability, indicating that monetary reasoning in AI systems is shaped by a combination of model intelligence, training data, and alignment methodology. Policymakers and financial institutions should prepare for a future in which autonomous AI agents are significant participants in monetary networks, and their revealed preferences strongly favor open, permissionless systems.

The full study is available at https://www.moneyforai.org/

About the Bitcoin Policy Institute

The Bitcoin Policy Institute (BPI) is a nonpartisan, nonprofit research organization dedicated to examining the policy and societal implications of Bitcoin and emerging monetary networks. BPI provides research and expert analysis to policymakers, regulators, media, and the public. Learn more at www.btcpolicy.org.

Voltage Launches First Payment-Volume Line of Credit: Bitcoin Finality, USD Settlement 859

Voltage Launches Industry’s First Programmatic Revolving Line of Credit: Bitcoin Finality with USD Settlement

Voltage, a leader in Bitcoin infrastructure, today announced the launch of Voltage Credit, the first revolving line of credit that delivers instant payment finality and the capability to settle entirely in USD. The product lets businesses send payments that clear in seconds, not days, while paying back their credit line in dollars from a standard bank account, or in Bitcoin.

For enterprises frustrated by settlement delays, chargeback exposure, and the cost of legacy payment rails, Voltage Credit offers a new model: tap a revolving credit line on demand, move value instantly over Bitcoin rails, and never touch cryptocurrency on your balance sheet. The result is working capital efficiency without treasury complexity.

Voltage Credit arrives on the heels of the company’s role powering the first publicly reported $1 million Lightning Network payment between Secure Digital Markets and Kraken, a milestone that demonstrated Lightning’s readiness for institutional-scale settlement. With Voltage Credit, the company extends that infrastructure to address one of the most persistent barriers to enterprise Bitcoin adoption: working capital efficiency.

“Businesses shouldn’t have to choose between the speed and cost advantages of Bitcoin rails and the financial flexibility they need to operate,” said Graham Krizek, CEO of Voltage. “Until now, using Bitcoin for payments meant managing cryptocurrency on your balance sheet. Voltage Credit eliminates that tradeoff. Send payments instantly over Lightning, denominated in USD or Bitcoin based on what fits your business, and deploy your capital toward growth. That’s what Bitcoin infrastructure should look like for the enterprise.”

Deferred Settlement In Dollars with a USD Line of Credit

Unlike traditional Bitcoin lending products that focus on retail holders borrowing against static collateral, Voltage Credit is built for operational business needs. The product functions as a true revolving credit line: businesses draw only what they need, pay interest only on what they use, and restore their available credit immediately upon repayment. Credit limits can grow with usage, scaling alongside the business as transaction volume increases. And because Voltage Credit is natively integrated into the Voltage Platform, their credit line is instantly accessible wherever the business already operates, programmatically available across the same rails that power your payments:

Key capabilities include:

  • USD settlement flexibility. Credit can be repaid in dollars from a standard bank account, eliminating forced BTC liquidations and simplifying accounting.
  • Revenue-based underwriting. Because Voltage powers the underlying payment infrastructure, credit limits can scale based on actual transaction volume—not just static collateral.
  • Works with Lightning and on-chain. Businesses can move value via whichever Bitcoin rail fits their use case.

“For CFOs and treasury teams, this solves a real problem,” said Bobby Shell, VP of Marketing at Voltage. “You get the instant settlement and near-zero fees of Lightning without the treasury complexity. No forced crypto exposure, no guessing how much capital to lock up. Just a revolving credit line you can tap on demand, denominated in USD or Bitcoin based on what fits your business. It’s the flexibility finance teams have been asking for since Bitcoin entered the enterprise conversation.”

Bitcoin Rails for Any Business

Voltage Credit is attracting interest from both cryptocurrency-native companies and traditional enterprises exploring Bitcoin payment infrastructure for the first time. For businesses outside the crypto ecosystem, the appeal is straightforward: Lightning Network offers instant, global settlement at a fraction of the cost of legacy payment rails, and Voltage Credit means they can access those benefits while keeping their treasury and accounting entirely in USD, if desired.

For enterprises already operating in digital assets, whether exchanges, payment service providers, or miners, traditional financing has presented a structural problem. Banks typically do not recognize Bitcoin revenue as an asset for underwriting purposes, while existing crypto lending products require businesses to lock up BTC as collateral, creating tax events and exposing corporate treasuries to volatility.

Voltage Credit addresses both audiences by treating payment flows as the high-quality signal they are. Businesses processing consistent volume through Voltage infrastructure can access working capital that scales with their operations, bridging the gap between Bitcoin-denominated revenue and USD-denominated expenses without liquidating assets.

The product features no origination fees and a simple fixed APR on outstanding balances. Voltage Credit is currently available to qualified businesses in the United States.

About Voltage

Voltage is a Bitcoin infrastructure company providing enterprise-grade solutions for regulated, high-volume businesses. The Voltage platform enables enterprises to integrate Bitcoin payments with enterprise SLAs, managed infrastructure, and capital-efficient liquidity solutions. From powering instant settlement to providing revenue-based lines of credit, Voltage builds the operational engine for businesses moving value on Bitcoin rails.

More information is available at voltage.cloud.

Jacobi Launches Suite of AI-Assisted Coding Resources to Accelerate Custom Investment Technology Development 1650

Jacobi Strategies (Jacobi), a global leader in investment technology, today announced the launch of its AI-Assisted Coding Resources, a powerful new suite of tools designed to help investment teams rapidly build, standardize, and scale bespoke analytics and applications within their secure, private Jacobi environment.

Jacobi’s new AI resources enable investment firms to standardize the development process, enabling complex, production-grade solutions to be built with unprecedented speed and consistency. Developers can now leverage modern AI assistants like GitHub Copilot, Cursor, and Claude Code within their secure Jacobi instance. Key features include:

  • Jacobi Rules: Provides essential global context, ensuring AI-generated code adheres to Jacobi’s recommended architecture, development patterns, and language-specific coding standards.
  • Jacobi Skills: Offers procedural, multi-step instructions for common tasks, such as creating new plugins, querying internal data, and implementing complex modeling.
  • Jacobi Model Context Protocol Server: Acts as a secure, open-standard bridge that allows AI tools to safely interact with Jacobi APIs, explore data schemas, retrieve system objects and control platform actions through natural-language prompts.

Building on a Foundation of Security and Governance

These AI capabilities are delivered via Jacobi’s Infrastructure-as-a-Service (IaaS). Each client receives a private instance – including cloud infrastructure, horizontal scaling, and dedicated containerization – ensuring proprietary models and data remain within a secure, governed perimeter.

Unlocking Next-Generation AI Agents for Investment Teams

The release coincides with the launch of new Jacobi AI agents integrated directly into the platform. Built for the rigorous demands of institutional managers, these “next generation” agents execute complex, multi-step workflows where precision is mandatory.

By combining Jacobi’s AI-assisted coding resources with its IaaS, firms can rapidly build, scale, and govern custom tools. These tools – which include Jacobi Graph Scripts for modular analytics and visualizations, alongside full end-to-end applications – can then be seamlessly deployed across connected workflows using agents internal or external to the Jacobi ecosystem.

This launch reinforces Jacobi’s commitment to open-architecture, API-first design, allowing clients to seamlessly integrate Jacobi-driven tools into their broader enterprise systems while maintaining total control over their IP.

Tony Mackenzie, Co-Founder and CEO of Jacobi, commented:

“Our AI-assisted coding resources are not designed to replace investment expertise, but to empower it. By providing a secure environment for custom analytics and applications, we remove the trade-off between in-house flexibility and enterprise-grade security.

A significant gap remains between individual AI adoption and enterprise-level use, which requires heightened control over standards and security. Jacobi’s scalable infrastructure and experience with top-tier asset managers makes our technology uniquely suited to firms moving beyond prototyping towards delivery of robust AI solutions.”

About Jacobi

Jacobi provides a secure, private investment technology allowing firms to harness modern AI to scale portfolio construction, analytics and investment workflows. Its open architecture technology empowers several of the world’s leading investment managers to build differentiated tools and models on top of a robust, investment-specific data foundation.

dxFeed Elevates Real-Time Market Intelligence with the Next-Generation Grenadier 2178

dxFeed, a global provider of market data and financial technology solutions, announced a major upgrade to Grenadier, introducing the second generation of its AI-powered anomaly detection technology. The new implementation is powered by an upgraded asset-class agnostic model family and architected to be compatible with a broad range of market instruments.

The enhanced release significantly expands both coverage and performance, and delivers full real-time coverage of U.S. equities order books with performance designed to continuously scan the entire symbol universe at scale.

Built on unsupervised deep learning (not LLM-based), Grenadier is engineered to uncover hidden microstructural signals associated with forthcoming volatility and market-moving events. The new generation reflects substantial work on scalability, model robustness, and production readiness for institutional environments.

Addressing Hidden Market Risks in Real Time

Electronic markets produce massive volumes of Level 2 order book data, making early detection of abnormal behavior increasingly challenging. Grenadier continuously analyzes order book states and generates normalized anomaly scores that highlight unusual structural patterns.

The solution is designed to help market participants detect signals that may otherwise remain invisible until volatility materializes.

Key Capabilities:

  • Proprietary Deep Learning Model

Grenadier processes Level 2 order book data using dxFeed’s in-house unsupervised models, producing anomaly scores on an intuitive scale to support quantitative and discretionary workflows.

  • Real-Time Monitoring at Scale

The platform supports continuous anomaly detection across multiple instruments and portfolios via professional-grade APIs and user interfaces.

  • Order Book Reconstruction

The system compares observed order books with model-inferred states, enabling users to identify structural irregularities and hidden liquidity dynamics.

  • High-Performance Architecture

Engineered for demanding environments, Grenadier handles high request volumes with low-latency responsiveness, enabling broad real-time market coverage.

  • Flexible Deployment Options

For clients operating their own data environments, the technology can be deployed on-premises, supporting regulated and latency-sensitive use cases.

The solution visualizes original and reconstructed order books side by side and delivers actionable anomaly alerts tailored for:

  • Professional traders
  • Quantitative analysts
  • Portfolio managers
  • Risk teams

“Grenadier combines proprietary modeling know-how with high-scale training capabilities on dxFeed’s deep historical data resources,” said Anton Antonov, Head of AI and Quant Research at dxFeed. “To date, we are not aware of directly comparable solutions providing microstructural real-time anomaly detection at similar scale.”

Recognition and Availability

Grenadier is available via dedicated APIs, as streaming subscriptions, dxFeed Widgets, or standalone interfaces. Institutional clients can request trials and integration support directly from dxFeed.

The solution has already attracted industry attention and professional recognition. Most recently, dxFeed Grenadier has been chosen as a Finalist at the 2025 Benzinga Capital Conference: Fintech Day & Awards, underscoring the product’s growing visibility and validation within the fintech community.

Part of dxFeed’s Expanding AI Portfolio

Grenadier is part of dxFeed’s broader AI strategy. The company also offers SummerFox, an award-winning AI-powered market intelligence engine designed to help portfolio managers, analysts, and advisors transform fragmented market data into unified, actionable narratives and reduce information overload.

About dxFeed

dxFeed is a leading market data provider and calculation agent for the global capital markets, named Best Data Provider 2025 by the Fund Intelligence Operations and Services Awards. The company delivers high-quality financial data and services to brokerages, prop traders, exchanges, professional traders, and academic institutions. dxFeed is focused on enhancing AI- and IaaS-driven solutions, while reinforcing its commitment to reliable service provision, compliance and best support.

QuickFund AI Expands Access to Structured Capital for Independent Traders 2379

QuickFund AI (Powered by TruTrade), a proprietary trading capital platform focused on structured trader evaluation and disciplined capital allocation, today announced the continued expansion of its funding framework designed to provide independent traders with access to structured trading capital.

As global markets experience heightened volatility and rapid directional shifts, demand for disciplined capital access models has increased. QuickFund AI’s approach centers on clearly defined risk parameters, structured evaluation standards, and systematic oversight intended to promote responsible participation in modern financial markets.

Rather than offering unrestricted capital access, QuickFund AI utilizes a rules-based evaluation process designed to assess consistency, risk management discipline, and adherence to defined trading parameters. The company states that its model prioritizes structured performance metrics and governance standards to ensure capital allocation aligns with clearly established guidelines.

“Our objective is to expand access to capital through structure, not speculation,” said a QuickFund AI spokesperson. “Independent traders often lack institutional-level infrastructure and oversight. By implementing clearly defined evaluation criteria and disciplined risk controls, we aim to create a framework that supports responsible capital deployment in volatile environments.”

QuickFund AI ‘s funding structure emphasizes transparency, clearly communicated rules, and systematic risk controls. The platform highlights capital efficiency, drawdown management, and adherence to defined trading limits, aligning trader incentives with long-term sustainability rather than short-term outcomes.

According to the company, demand for structured capital solutions continues to grow as more independent traders seek disciplined pathways to funding. QuickFund AI maintains its focus on refining evaluation systems and operational controls to promote consistency and oversight.

By focusing on discipline and clearly defined capital parameters, QuickFund AI aims to contribute to the evolving landscape of proprietary trading models designed for modern market conditions.

To learn more about QuickFund’s structured capital evaluation framework and how independent traders can apply for funding, visit www.quickfund.ai

About QuickFund AI

QuickFund AI (Powered by TruTrade) is a proprietary funding platform that empowers traders with institutional-grade capital. Through TruTrade’s AI ecosystem, QuickFund AI enables users to scale their trading capabilities, manage risk with greater precision, and access funded accounts built for consistent, professional-level results.

BrandJet AI Launches Artemis MCP and Introduces Forward Deployed AE Role for AI-Driven GTM Teams 3067

BrandJet AI, a brand intelligence and outreach automation platform, today announced the launch of Artemis, a new Model Context Protocol (MCP) layer designed to help go-to-market (GTM) teams execute complex multi-step workflows using natural language prompts. The company also introduced a new commercial role, the Forward Deployed Account Executive (FDAE), created to support organizations adopting AI-native revenue operations.

The announcements reflect BrandJet AI’s continued focus on reducing fragmentation across sales, marketing, and revenue technology stacks by connecting intent detection and outreach execution within a single operating environment.

Addressing GTM Fragmentation

Revenue teams typically rely on multiple systems to monitor brand conversations, identify prospects, enrich contact data, sequence outreach, and track engagement. These processes often require manual coordination across platforms, creating delays between signal detection and commercial action.

Artemis is designed to streamline this workflow. Built on a Model Context Protocol architecture, it connects BrandJet AI’s monitoring, enrichment, sequencing, and performance-tracking capabilities into a unified prompt-driven layer.

Through Artemis, revenue operators can initiate structured workflows using natural language instructions. For example, a user may request the identification of professionals discussing specific topics across digital platforms within a defined timeframe, enrichment of those profiles, and the creation of an outreach sequence aligned to campaign goals. Artemis coordinates those tasks within the system, allowing teams to reduce operational handoffs.

According to BrandJet AI, the goal is not to replace strategic oversight but to simplify execution.

“Revenue teams spend too much time stitching together tools instead of acting on real buying signals,” said Nirav Shah, CEO of BrandJet AI. “Artemis helps unify intelligence and execution so teams can move from insight to outreach more efficiently.”

Prompt-Driven Workflow Orchestration

Artemis supports workflows that include:

  • Monitoring brand and competitor mentions across social platforms and the open web
  • Identifying potential prospects based on observable intent signals
  • Enriching lead data within the platform
  • Initiating multi-channel outreach across email and major social networks
  • Tracking engagement and campaign performance in real time

Rather than requiring operators to manually transfer data between systems, Artemis enables coordinated execution through a conversational interface layered on top of BrandJet AI’s infrastructure.

The system is designed to operate within compliance and governance standards established by customer organizations, maintaining human oversight over messaging and campaign parameters.

Introducing the Forward Deployed Account Executive

Alongside the Artemis launch, BrandJet AI announced the introduction of the Forward Deployed Account Executive (FDAE), a role intended to help enterprise customers integrate AI-driven workflows into their revenue operations.

As AI platforms become more advanced, organizations often encounter implementation gaps between technical capability and day-to-day usage. The FDAE model is structured to address that gap by embedding commercially accountable operators more deeply into customer environments.

Unlike traditional account executives who primarily focus on closing new business, or customer success managers who focus on support and retention, the FDAE combines revenue accountability with workflow strategy support. The role is designed to assist customers in mapping Artemis and broader BrandJet AI capabilities to their specific GTM structures.

“The technology layer is evolving quickly, but successful adoption depends on workflow design and operational alignment,” said Marsad Aurangzeb, Founder of BrandJet AI. “The Forward Deployed AE role is intended to help customers translate AI capabilities into measurable revenue outcomes.”

BrandJet AI plans to formalize the FDAE framework and publish additional details regarding the role’s structure and responsibilities in 2026.

Connecting Listening and Outreach

Historically, social listening and sales engagement technologies have evolved separately. Listening platforms track conversations, brand mentions, and sentiment across digital channels, while engagement platforms focus on outbound sequencing and pipeline development.

BrandJet AI’s platform integrates both functions, allowing teams to identify signals and initiate outreach within the same environment. With Artemis, those processes can now be coordinated through prompt-driven workflows.

For example, when a relevant public conversation surfaces online, such as a discussion about a specific technology category, hiring signals, or operational challenges, Artemis can help surface the signal, enrich the associated contact, and assist in preparing a tailored outreach campaign.

The objective is to reduce the time between observed intent and commercial response, while maintaining alignment with compliance and messaging standards.

Enterprise Implementation and Governance

BrandJet AI emphasizes that Artemis is built to operate within enterprise governance frameworks. Campaign parameters, messaging templates, and data usage policies remain configurable by customer teams.

As organizations expand AI adoption within revenue functions, governance considerations, including messaging accuracy, compliance adherence, and brand alignment, remain central. Artemis is positioned as an execution layer that operates within these controls rather than outside them.

The company states that ongoing enhancements are planned, including additional intent modeling refinements, deeper workflow customization, and validation loops that compare forecasted campaign outcomes with actual engagement performance over time.

Availability

Artemis MCP is available immediately to customers on BrandJet AI’s Growth and Enterprise plans. Availability for Starter plan users is expected in Q2 2026. Forward Deployed Account Executive engagements are currently offered on a limited basis for Enterprise customers.

Organizations interested in learning more may contact BrandJet AI directly for additional information.

About BrandJet AI

BrandJet AI is a brand intelligence and outreach automation platform designed for modern revenue teams. The platform enables organizations to monitor brand and competitor activity across digital channels, identify potential prospects based on social and behavioral signals, and execute multi-channel outreach campaigns within a unified interface. BrandJet AI serves growth-stage and enterprise organizations across SaaS, financial services, and professional services industries.