Assessing Useless Coin’s 12% price dip below KEY support

ambcrypto2026-01-09 tarihinde yayınlandı2026-01-09 tarihinde güncellendi

Özet

Memecoins are pausing their recent rally, with Useless Coin (USELESS) dropping over 12% in 24 hours. It broke a key ascending trendline support, and technical indicators like the Money Flow Index show capital outflow. On-chain data reveals institutions like Wintermute and Coinbase moved over $600K worth of tokens to hot wallets, likely for selling, though Kraken accumulated. Open Interest fell sharply from $40M to $33M, and trading volume dropped from $122M to $82M. If the decline continues, USELESS may retest $0.069. A short squeeze could occur at $0.1242, but breaking below $0.1020 may intensify losses. The rally could be over or just pausing.

Memecoins appear to be pausing their rally that began earlier this year.

For example, Useless Coin [USELESS] has dropped more than 12% in the past 24 hours, with its price still trending downward at the time of writing.

USELESS Coin breaks trendline support

The price action charts showed that USELESS broke an ascending trendline support that had been in place since the start of this month. The first week of January 2026 saw the memecoin reach $0.12, but bulls were unable to break past this level.

The MACD was bearish over the past two days, though its momentum was weak. Moreover, the Money Flow Index (MFI) had also declined from a peak of 77 to 35 at press time.

This indicated capital was flowing out of the token.

In case the decline continues, the price could retest the breakout level at $0.06958 of the range that ignited this rally. However, a resurgence of bulls could invalidate such an undertaking.

The decline was not only dependent on a technical breakdown; on-chain data also supported this trend.

Institutions offload the memecoin

As per on-chain data from Solscan, institutions were offloading their tokens. Wintermute and Coinbase were moving their USELESS coins to the hot wallet, potentially for selling.

Wintermute moved more than $131K worth of the tokens, while Coinbase moved over $500K in capital. In total, more than $600K in USELESS was ready for selling.

However, this was not the case for the Kraken exchange. The exchange moved more than $194K to their cold wallet, indicating accumulation.

As a result, the institutional activity showed mixed sentiments, though the selling activity was more impactful on price. In fact, the Long/Short Ratio was at 0.9 as of writing, suggesting more trades, even from retail, were being sold.

A look into volume, OI, and max pain levels!

More data showed why USELESS’s price was declining.

At press time, Open Interest (OI) dropped sharply from $40 million to $33 million within a single day. This decline followed a steady uptrend that had been in place since the 30th of December.

Trading volume mirrored the move, plunging from $122 million to $82 million over the same period.

Meanwhile, the maximum liquidation pain for the memecoin indicated that a short squeeze could occur if the price reached $0.1242 again.

On the other hand, the decline could be intensified if the price broke below $0.1020. This was the max pain level for bulls.

Altogether, the rally could be over, indicating a seasonal tendency where cryptos start the year with a surge.

Conversely, it could be a pause before the price rally continuation, especially now that memecoins have added over $8 billion to their capitalization.

Final Thoughts

USELESS crashes 12% amid a technical breakdown, but this could be a temporary pause.
Institutions offloading, volume and OI declining, and shorts contributed to the downside movement.

İlgili Sorular

QWhat was the percentage drop in Useless Coin's price in the past 24 hours and what key support level did it break?

AUseless Coin's price by more than 12% in the past 24 hours, breaking an ascending trendline support that had been in place since the start of the month.

QWhich two specific institutions were offloading their USELESS tokens according to on-chain data from Solscan?

AAccording to on-chain data from Solscan, Wintermute and Coinbase were offloading their USELESS tokens.

QWhat did the sharp decline in Open Interest (OI) and Trading Volume indicate for USELESS?

AThe sharp decline in Open Interest (OI) from $40 million to $33 million and the plunge in Trading Volume from $122 million to $82 million indicated a loss of market interest and participation, contributing to the price decline.

QAt what price level could a short squeeze occur for USELESS, and at what level could the decline intensify for bulls?

AA short squeeze could occur if the price reached $0.1242 again. The decline could be intensified if the price broke below $0.1020, which was the max pain level for bulls.

QWhat does the article suggest are the two possible scenarios for the future of the USELESS price rally?

AThe article suggests that the rally could be over, indicating a seasonal tendency, or it could just be a temporary pause before the price rally continuation.

İlgili Okumalar

It Took Me a Year to See the Bitter Truth About Agent Payments

After a year building infrastructure for the Agent economy, engaging with major players like Stripe, Visa, and Coinbase, the author shares a sobering analysis of the current state of Agent payments. The core finding is a stark lack of genuine, immediate demand across most envisioned use cases. The article breaks down four key market segments: 1. **Agent-to-Merchant (Consumer Shopping):** For most product categories (e.g., clothing, electronics), conversational AI shopping is a step backwards from visual e-commerce interfaces. While agents excel at understanding needs, they can't replace side-by-side product comparison. Real merchant interest is defensive "Agent Engine Optimization," not driven by current customer demand. Potential exists for high-frequency, low-decision purchases (like food delivery) or navigating complex store UIs, but these require massive B2C distribution channels dominated by giants like Amazon. 2. **Agent-to-API (Developer Services):** Developers already have subscriptions and billing relationships for APIs (compute, data). Prepaid balances solve micro-payment issues for low transaction volumes. A deeper structural problem is that major SaaS vendors' business models rely on enterprise contracts, resisting granular pay-per-call pricing. While protocols like MPP and x402 serve the long tail of niche services, this market is small and developers are historically low-willingness-to-pay. 3. **Agent-to-Agent:** This remains largely theoretical with minimal transaction volume. While it represents a long-term bet on a fundamentally new transaction infrastructure (sub-second, micro-penny to million-dollar, multi-party settlements), it does not constitute a present market. 4. **Agent-to-Finance:** This is the only category with existing, paying demand. Integrating AI into financial workflows (trading, portfolio management) is a natural evolution and enables new capabilities like autonomous rebalancing. However, competition favors established, regulated institutions. The "real problem" is not moving money between agents, but the broader challenge of **coordination**—orchestrating work between agents and humans, verifying outcomes, and settling results. Payment is just one component of settlement, which is itself part of coordination. Companies that solve the coordination layer will subsume payment, not the other way around. While well-funded incumbents build defensively for a long-term future, startups must find where the market is today—which, for the author's team, lies outside these four categories in an area of real, growing, and underserved activity.

marsbit6 dk önce

It Took Me a Year to See the Bitter Truth About Agent Payments

marsbit6 dk önce

It Took Me a Year to See the Hard Truth About Agent Payments

**Title: It Took Me a Year to See the Hard Truth About Agent Payments** Over the past year, I've worked on infrastructure for the Agent economy, engaging with major players like Stripe, Visa, Coinbase, and numerous startups. The findings reveal a stark reality: genuine, widespread demand for Agent-based payments does not yet exist. **Key Observations:** * **Agent-to-Merchant (Shopping):** The user experience for AI shopping often falls short, especially for visual product discovery. While AI excels at understanding needs, conversational interfaces can't yet replace browsing and comparing multiple products visually. Current merchant interest is largely defensive ("Agent Engine Optimization") for a future that hasn't arrived. High-frequency, low-friction purchases (like food delivery) are potential fits, but lack open APIs and face high AI inference costs. Simpler, more affordable, or cross-language interactions for complex UIs are a niche opportunity but require massive consumer distribution to scale. * **Agent-to-API (Developer Tools):** Developer payment needs for APIs (computing, data, models) are already met through subscriptions and prepaid credits. The core challenge is not payment friction but supplier economics: most large SaaS providers prefer enterprise contracts over micropayments for API calls. Protocols like MPP and x402 suit the long-tail of smaller services but cater to a developer market historically reluctant to pay for these tools. Major infrastructure needs at the top of the stack are already being addressed. * **Agent-to-Agent (Machine Commerce):** This is a long-term vision with almost no current transaction volume. While a future with high-speed, high-frequency, multi-party machine-to-machine transactions would require novel infrastructure, it remains theoretical. The market is not here yet. * **Agent-to-Finance:** This is the only category with clear, present demand. Financial professionals and DeFi users already pay for tools, and AI augmentation is a natural evolution. Autonomous AI agents can enable entirely new financial strategies. However, competition is fierce from established, regulated incumbents who can more easily layer AI onto their existing products. **The Core Insight:** Companies, especially giants with long time horizons, are building defensively for a potential future of mass machine commerce. For them, early investment is a low-cost hedge. For startups, the current market reality is different. The primary challenge isn't just moving money between agents (payments). The larger, unsolved problem is **orchestration** – coordinating work between agents and humans, verifying outcomes, and then settling. Payment is just a part of settlement, which is just a part of orchestration. Companies that solve the orchestration problem will subsume payments, not the other way around. After a year of building, we see the real, growing, and underserved market opportunity lies in this broader domain of orchestration.

链捕手30 dk önce

Claude Opus 4.8 Finds a $4.5 Billion Bug: The AI Era is Mass-Producing Hackers

A researcher discovered a critical "infinite mint" vulnerability in the Zcash cryptocurrency's Orchard protocol using Claude Opus 4.8, leading to a swift fix but also a 50% market drop, erasing billions in value. This incident highlights a new era where powerful, accessible AI models are dramatically lowering the barrier to finding software vulnerabilities. Previously, the security community feared specialized models like Claude Mythos Preview, capable of finding decades-old zero-day exploits. The Zcash case, however, involved a publicly available, general-purpose model. This shift makes advanced security auditing—and attack capabilities—accessible to far more people, not just experts. The mass democratization of vulnerability discovery brings a dual challenge: a flood of low-quality, AI-generated false reports that overwhelm maintainers, and the real, rapid uncovering of deep, dangerous bugs. Open-source projects, often understaffed and unfunded, are particularly vulnerable to this "attention DDoS." The article cites examples like curl shutting down its bug bounty program due to the unsustainable workload. Our perceived digital safety has often been luck, relying on the high cost and effort required to find deeply hidden flaws in complex systems, as seen with historical vulnerabilities like Heartbleed or Baron Samedit. AI changes this cost structure, effectively "mass-producing flashlights" to illuminate every corner of our codebase. While large companies operate extensive security chains involving external white-hat hackers and massive defensive operations, the global cybersecurity workforce faces a severe shortage, especially of experienced personnel capable of analyzing complex threats and coordinating fixes. The core dilemma emerges: AI makes *finding* bugs cheap and scalable, but *fixing* them remains a slow, expensive, and human-intensive process. The article concludes that AI won't destroy the internet but acts as a bright light, revealing that our digital existence is not inherently secure but is precariously maintained by ongoing human effort. The true cost in the AI era may not be discovery, but whether there will be enough people left willing and able to do the hard work of repair.

marsbit1 saat önce

Claude Opus 4.8 Finds a $4.5 Billion Bug: The AI Era is Mass-Producing Hackers

marsbit1 saat önce

Ethereum Price Prediction: ETH Could Double While Cardano (ADA) and This Newcomer Token Near 500% Rally

As the cryptocurrency market anticipates a potential bull cycle, investor focus is shifting toward projects with high return potential. Ethereum (ETH), currently at approximately $2,014, is highlighted for its long-term value, with predictions suggesting a possible 2x increase to over $4,000. Cardano (ADA), trading around $0.2329, remains a choice for its research-driven development and scalability focus, though some seek higher upside elsewhere. Significant attention is drawn to Little Pepe (LILPEPE), a new Layer 2 Ethereum-compatible token priced at $0.0022 in its presale stage, which has raised over $28.19 million. The project aims to build a dedicated blockchain for meme communities, offering lower costs and faster transactions. Backers speculate a potential 500% gain, driven by strong presale demand, planned ecosystem features like staking and a launchpad, and active community engagement through giveaways. While Ethereum and Cardano are viewed as foundational investments, LILPEPE represents a newer, high-growth opportunity for investors targeting the meme coin sector.

TheNewsCrypto1 saat önce

Ethereum Price Prediction: ETH Could Double While Cardano (ADA) and This Newcomer Token Near 500% Rally

TheNewsCrypto1 saat önce

Codex Goal Mode Usage Guide: How to Make AI Continuously Pursue a Specific Objective

"Codex Goal Mode: How to Make AI Work Continuously Toward a Specific Goal" OpenAI's Codex "goal mode" (/goal) transforms the AI from a reactive code assistant into a proactive execution agent capable of working autonomously for hours or even days to achieve a defined objective. To maximize its effectiveness, follow these key principles: 1. **Define Clear, Verifiable Exit Criteria:** The goal prompt should be a concise, measurable success condition, not a lengthy specification. Use quantifiable metrics like "reduce build time by 30%" or "achieve 100% test parity." 2. **Provide Initial Guidance and Tools:** Direct Codex toward likely problem areas and specify available tools (e.g., browsers, testing environments) to prevent it from exploring unproductive paths. 3. **Enable Progress Measurement:** Equip Codex with ways to track advancement, such as creating comparison tools for visual tasks or evaluation sets, ensuring it can gauge its own progress. 4. **Use a Realistic Execution Environment:** For tasks like performance optimization, provide access to environments that closely mimic production (e.g., similar configs, databases) to yield valid results. 5. **Be Cautious with Visual Goals:** Avoid vague "pixel-perfect" instructions. Instead, supplement visual references with functional checklists or design system specifications to prevent Codex from obsessing over minor details. 6. **Implement Progress Tracking:** For long-running tasks, have Codex commit code to draft PRs, update progress documents, or send Slack updates to maintain visibility into its work. 7. **Review and Consolidate Results:** Once the goal is met, instruct Codex to review its work, clean up ineffective experimental code, and reflect on what strategies succeeded or failed. Ultimately, using goal mode shifts the developer's role from writing prompts to managing a persistent engineering agent—defining objectives, establishing metrics, configuring environments, and conducting final reviews.

marsbit2 saat önce

Codex Goal Mode Usage Guide: How to Make AI Continuously Pursue a Specific Objective

marsbit2 saat önce

İşlemler

Spot

Futures