Shiba Inu breaks past a key resistance – Can SHIB rally to $0.000065?

ambcryptoPublicado a 2026-03-25Actualizado a 2026-03-25

Resumen

Shiba Inu (SHIB) has broken above a key descending trend line resistance after weeks of consolidation, signaling a potential shift in market structure and renewed bullish interest. However, momentum indicators like the RSI suggest a possible short-term correction or retest of the $0.000055 support level. A successful liquidity sweep around that zone could set the stage for a rally toward $0.000065. On-chain data shows significant inflows, with 800 billion SHIB (approx. $4.8 million) entering exchanges in 48 hours, supporting the bullish outlook. While the breakout is positive, confirmation depends on sustained buying pressure and a hold above key support levels.

A shift in structure is beginning to emerge for Shiba Inu [SHIB]. After weeks of consolidation, the token has broken above a descending trend line resistance that had capped price action for nearly two months.

This breakout has started to restore optimism among market participants. Could the breakout present the required spark to keep the bulls going?

Breakout signals early trend reversal

The break above the trend line resistance marks a key technical development. The technical structure shift could usher in renewed interest from SHIB bulls.

However, confirmation requires sustained follow-through. Without it, the price may fall back into consolidation.

Momentum indicators hint at short-term caution. The Relative Strength Index (RSI) has just bounced from an overbought region.

The momentum shift implies that the market may be in for a short correction or retest before the projected bullish run resumes.

A liquidity sweep could precede SHIB’s rally

Current price action points to a likely scenario. SHIB may retest the $0.000055 zone to squeeze the liquidity cluster of around 7.7K, since such zones have attracted price before.

Usually, liquidity sweeps help the market gather orders and also remove weak hands before continuation.

If this retest holds, it could set the stage for the next rally, with $0.000065 emerging as the next key target in case of a successful reversal. A significant liquidity cluster rests near the $0.000065 price level.

Inflows support the market bulls

On-chain activity adds weight to the bullish technicals. SHIB has recorded positive inflows over the past 48 hours.

In fact, the token has recorded a total of 800 billion SHIB, which is approximately $4.8 million based on the trading price at press time. This suggests more capital is entering the exchanges from investors, probably with an intention to take more long positions during the correction.

The timing is on point as most investors are diversifying their portfolios to digital assets and commodities as global tensions hit their peak.

What could be next for SHIB?

The structure is improving, but caution remains. The breakout is a positive signal, especially given the positive liquidity bias and surging inflows into exchanges.

However, SHIB stochastic RSI suggests a possible pause and projects a potential price correction in the short run before the rally resumes.

If buyers defend the retest zone, SHIB could push toward $0.000065. For now, $0.000055 remains the key level to watch.

Final Summary

SHIB breaks trend line resistance, signaling a potential shift toward bullish momentum.
A retest near $0.000055 may precede a liquidity-driven rally toward $0.000065 if inflows and demand hold steady.

Criptos en tendencia

CitreaCTR

wrapped stUSDTWSTUSDT

Velodrome FinanceVELODROME

BrevisBREV

ZRX（0X）ZRX

Preguntas relacionadas

QWhat key technical development did Shiba Inu (SHIB) recently achieve according to the article?

ASHIB broke above a descending trend line resistance that had capped price action for nearly two months, signaling a potential early trend reversal.

QWhat price level is identified as a potential retest zone and a key level to watch for SHIB?

AThe $0.000055 zone is identified as the key retest level and the crucial support to watch.

QWhat is the next key target price for SHIB if the bullish reversal is successful?

AThe next key target price for SHIB is $0.000065 if the reversal holds.

QWhat on-chain metric supports the bullish technical outlook for SHIB?

ASHIB recorded positive inflows of 800 billion SHIB (approximately $4.8 million) over the past 48 hours, indicating capital entering exchanges.

QWhat does the Stochastic RSI suggest for SHIB's price action in the short term?

AThe Stochastic RSI suggests a possible pause and projects a potential short-term price correction before the rally can resume.

Lecturas Relacionadas

Intel CEO Liwu Chen's First Podcast Interview: Our Goal is '10x in 5-10 Years', Betting on Advanced Packaging, Glass Substrate, and Synthetic Diamond

Intel CEO Chen Liwu outlines a bold vision for the company's transformation in a podcast interview, targeting a "10x return in 5-10 years." He emphasizes a strategic shift beyond traditional process node scaling, focusing on advanced packaging (like EMIB), new materials (glass substrates, GaN, SiC, InP, synthetic diamond), and system-level integration. Key to this is Intel's foundry business, where he prioritizes building trust through superior yield, defect density, and cycle times. Chen sees strong CPU demand resurgence driven by agent AI and inference workloads. He details the Terafab collaboration with Elon Musk to address semiconductor infrastructure gaps and stresses the strategic importance of U.S.-based advanced manufacturing. Acknowledging Intel is still in the "crawl" phase of his crawl-walk-run framework, Chen believes the company's full potential—extending from its PC base into edge computing and physical AI—will become apparent by 2030-2032.

marsbitHace 7 min(s)

Intel CEO Liwu Chen's First Podcast Interview: Our Goal is '10x in 5-10 Years', Betting on Advanced Packaging, Glass Substrate, and Synthetic Diamond

marsbitHace 7 min(s)

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Pete Florence, a former senior research scientist at Google DeepMind and a key contributor to the Vision-Language-Action (VLA) model architecture, is deliberately distancing his startup, Generalist AI, from the trendy "world model" label. He argues that the industry should prioritize concrete goals over buzzwords. His goal is to create robots that can perform a vast range of unseen tasks with high speed and success rates, without needing task-specific training data. Recently, his company raised $400 million (¥2.7 billion) at a $2 billion valuation. Notable investors include NVIDIA's NVentures, Bezos Expeditions, NFDG, as well as Xiaomi co-founder Lin Bin, Zoom founder Eric Yuan, and renowned AI scientist Fei-Fei Li. Florence's approach stems from his academic background at MIT under Professor Russ Tedrake, focusing on understanding the physical world. After joining DeepMind, he developed models like Transporter Network and co-created the VLA framework. He left in 2025 to found Generalist AI. The company has launched two models: GEN-0, which demonstrated that scaling laws apply to physical motion, and GEN-1. GEN-1 was trained on over 500,000 hours of physical interaction data collected via a specialized wearable device. It achieves a 99% success rate on precise mechanical tasks like folding boxes and maintains performance three times faster than its predecessor. Florence believes GEN-1 is reaching a commercial utility threshold similar to the GPT-3 inflection point. The substantial funding round, following GEN-1's release, signifies strong investor confidence in Generalist AI's practical, goal-driven path to creating versatile, useful robots, regardless of the "world model" terminology.

marsbitHace 8 min(s)

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

marsbitHace 8 min(s)

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

In three days, Google lost two AI legends. On June 18, Noam Shazeer, co-author of the seminal "Attention is All You Need" paper and Gemini co-lead, left for OpenAI. Just 48 hours later, John Jumper, 2024 Nobel laureate and AlphaFold lead, departed DeepMind for Anthropic. This follows Andrej Karpathy joining Anthropic in May. These moves highlight a structural trend: top AI talent is concentrating at mission-driven, pre-IPO firms like OpenAI and Anthropic, while Google becomes a primary source. The exodus stems from a core mission mismatch. Google's ad-centric model often subordinates AI research to product and revenue goals, creating friction for pioneers like Shazeer, who returned in 2024 only to leave again. In contrast, OpenAI and Anthropic offer singular focus on pushing AI boundaries, whether towards AGI or safety-aligned models, which deeply appeals to top researchers like Jumper. Financial incentives amplify the pull. With both OpenAI and Anthropic nearing IPO, employees stand to gain immensely from equity, an upside Google's mature stock cannot match. Furthermore, the 2023 merger of Google Brain and DeepMind, intended to consolidate strength, has instead created cultural tension and slowed the path from research to product, as evidenced by Gemini's pace. This talent redistribution is reshaping the AI landscape. While Google retains vast data and compute resources, its true crisis is the quiet, continuous loss of the people who define the field's future. The real moat in AI is not infrastructure, but the concentration of brilliant minds—a battle Google is currently losing.

marsbitHace 2 hora(s)

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

marsbitHace 2 hora(s)

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

Beyond the familiar performance charts like MMLU-Pro and MMMU, which major AI models strive to ace, stands a key "examiner": Chinese-Canadian researcher Wenhu Chen. An assistant professor at the University of Waterloo and founder of TIGERLab, Chen addresses the crucial need for more rigorous AI evaluation. As models like GPT-4 began scoring near-perfect results on older benchmarks like MMLU, it became difficult to distinguish their true capabilities. In response, Chen introduced MMLU-Pro in 2024, featuring harder, more reasoning-focused questions with more answer choices, successfully reintroducing meaningful performance gaps. His work extends to multi-modal evaluation with MMMU and its enhanced version, MMMU-Pro. These benchmarks test a model's ability to understand and reason with complex information from images, charts, and text across diverse academic subjects, exposing the significant challenges even top models face in genuine comprehension. Chen's background in complex QA, table reasoning, and his experience at Google DeepMind on projects like Gemini inform his approach. He understands that effective benchmarks must anticipate how models might "cheat" by memorizing data or avoiding visual analysis. His lab also actively researches video understanding and generation models (e.g., UniVideo, Vamba), ensuring his evaluation work is grounded in practical model-building challenges. Now at Meta's Super Intelligence Lab, Chen continues his focus on multi-modal data and evaluation, representing the deep yet often unseen contributions of Chinese talent in shaping the fundamental tools of the AI industry.

marsbitHace 2 hora(s)

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

marsbitHace 2 hora(s)

Alliance Co-founder's Letter to Entrepreneurs: Written at the Moment Cursor Sold for $600 Billion

Alliance Co-founder's Letter to Entrepreneurs: On Cursor's $60 Billion Sale Many aspiring founders see massive exits like Cursor's $60B sale and wonder why they can't achieve the same, often concluding opportunities are exhausted. But great companies aren't built in obvious, crowded spaces. Cursor, like Stripe, Figma, and Shopify before it, started with a non-consensus belief about the future. Before ChatGPT, they believed AI would transform knowledge work. They focused on a genuinely exciting domain, became their own customer, and obsessed over power users. Their journey involved years of "glass-chewing" effort before the market was ready. The pattern is consistent: identify a long-term technological shift, find a missed entry point, and execute for years before the trend becomes obvious. First-generation products (PayPal, Adobe, Amazon) prove a market exists. Second-generation winners (Stripe, Figma, Shopify) rebuild that market around new insights, technology, or changing customer behaviors. Founders must identify their phase in the cycle. Early entrants like Coinbase or Cursor focus on making new technology usable for power users. Later entrants find the "yin" to the established "yang"—the blind spots incumbents miss as they grow distant from individual users. The key is deep market immersion. Use every product in your space. Talk to users. Build an audience. Stop looking for ideas and start *seeing* them everywhere. Then, choose one. The idea must offer a 10x improvement or solve a "hair-on-fire" pain point—something severe enough that users are already crafting workarounds. When building, avoid feature bloat. Ask: why would someone switch? Great startups rarely force new behaviors; they improve familiar workflows with drastically lower friction (e.g., Cursor forked VS Code instead of creating a new editor). Distribution is the underestimated moat. Before product-market fit, achieve distribution-market fit. How do customers discover new tools? Founders like those at Airbnb, Stripe, and Cursor did unscalable, manual work to recruit early users. The final, unteachable ingredient is resilience. Cursor built for years pre-market, faced rejection, and persisted. So did Airbnb, Nvidia, and Rain (which launched post-FTX collapse). The lesson isn't that these founders were smarter, but that they stayed in the game long enough for their insights to compound. Framework: Spot technological cycles. Cultivate unique insight. Obsess over your market. Talk to customers. Find a hair-on-fire problem. Build the simplest wedge. Win your distribution channel. Above all, don't quit when it gets hard. Most people won't do these things consistently. The few who do build the next generation of great companies. Go build.

marsbitHace 2 hora(s)

Alliance Co-founder's Letter to Entrepreneurs: Written at the Moment Cursor Sold for $600 Billion

marsbitHace 2 hora(s)

Trading

Spot

Futuros

Artículos destacados

Cómo comprar SHIB

¡Bienvenido a HTX.com! Hemos hecho que comprar SHIBA INU (SHIB) sea simple y conveniente. Sigue nuestra guía paso a paso para iniciar tu viaje de criptos.Paso 1: crea tu cuenta HTXUtiliza tu correo electrónico o número de teléfono para registrarte y obtener una cuenta gratuita en HTX. Experimenta un proceso de registro sin complicaciones y desbloquea todas las funciones.Obtener mi cuentaPaso 2: ve a Comprar cripto y elige tu método de pagoTarjeta de crédito/débito: usa tu Visa o Mastercard para comprar SHIBA INU (SHIB) al instante.Saldo: utiliza fondos del saldo de tu cuenta HTX para tradear sin problemas.Terceros: hemos agregado métodos de pago populares como Google Pay y Apple Pay para mejorar la comodidad.P2P: tradear directamente con otros usuarios en HTX.Over-the-Counter (OTC): ofrecemos servicios personalizados y tipos de cambio competitivos para los traders.Paso 3: guarda tu SHIBA INU (SHIB)Después de comprar tu SHIBA INU (SHIB), guárdalo en tu cuenta HTX. Alternativamente, puedes enviarlo a otro lugar mediante transferencia blockchain o utilizarlo para tradear otras criptomonedas.Paso 4: tradear SHIBA INU (SHIB)Tradear fácilmente con SHIBA INU (SHIB) en HTX's mercado spot. Simplemente accede a tu cuenta, selecciona tu par de trading, ejecuta tus trades y monitorea en tiempo real. Ofrecemos una experiencia fácil de usar tanto para principiantes como para traders experimentados.

480 Vistas totalesPublicado en 2024.12.11Actualizado en 2026.06.02

Discusiones

Bienvenido a la comunidad de HTX. Aquí puedes mantenerte informado sobre los últimos desarrollos de la plataforma y acceder a análisis profesionales del mercado. A continuación se presentan las opiniones de los usuarios sobre el precio de SHIB (SHIB).