What happened in crypto today? Market crash, U.S. Equities Streams, and more

ambcryptoPubblicato 2026-01-21Pubblicato ultima volta 2026-01-21

Introduzione

Crypto markets experienced a significant downturn amid global tensions and new policy concerns. Bitcoin fell below $90K, and Ethereum dropped under $3K, with other major altcoins like Solana and XRP also declining. The sell-off was partly triggered by U.S. tariff threats, which raised fears of trade-driven inflation. In product news, Chainlink launched its 24/5 U.S. Equities Streams, providing real-time stock and ETF pricing on over 40 blockchains. This aims to support DeFi applications like perpetuals and prediction markets by offering continuous, verified data outside traditional trading hours. Several protocols, including BitMEX and ApeX, have already integrated the solution. Additionally, Solana Mobile announced an SRK token airdrop for users of its Seeker device.

Crypto had an eventful day, with prices pulling back during global tensions and many announcements being made. Here’s the full rundown.

Chainlink pushed U.S. stocks on-chain!

Chainlink [LINK] has rolled out 24/5 US Equities Streams, an upgrade to its Data Streams product. It brings real-time pricing for US stocks and ETFs onto blockchains, even outside regular market hours!

This will open up access to the roughly $80 trillion US equities market for DeFi applications.

The new streams are live across more than 40 blockchains and are designed to support on-chain products such as equity perps, prediction markets, and other trading tools that need reliable price data at all times.

Until now, most on-chain equity feeds relied on a single price update during standard trading hours. Outside those hours, pricing blind spots increased risk.

Chainlink says its new equities streams solve this by converting market data into continuous, cryptographically verified feeds.

Several protocols, including BitMEX, ApeX, Orderly, and HelloTrade, have already integrated the product.

Tariff threats rattle the markets

Crypto markets turned defensive after tariff threats from the Trump administration added fuel to a wildfire. Bitcoin [BTC] slid below the $90K mark during Tuesday’s session and was trading near $89,100 at press time.

With a steady sequence of lower highs on the intraday chart, brief bounce attempts have failed to reclaim key levels.

Ethereum [ETH] followed a similar path, slipping under $3K and posting close to a 5% daily decline.

Selling pressure was rampant across the market. Solana [SOL] fell more than 2% on the day, while Ripple’s XRP [XRP] and Binance [BNB] both dropped over 2% and 4%, respectively.

The weakness came as US Treasury Secretary Scott Bessent reaffirmed that tariffs are a core policy tool, with the possibility of a 10% levy as early as February to assist the acquisition attempts of Greenland.

Markets took it as confirmation that trade-driven inflation risks are back in focus.

However, he later went on to downplay the bond market reaction following his statements.

Bessent argued that rising yields were driven by bond market annihilation in Japan and that the reaction cannot be isolated to the United States’ moves.

Solana Mobile rolls out SRK airdrop for Seeker users

Domande pertinenti

QWhat new product did Chainlink roll out and what does it provide?

AChainlink rolled out 24/5 US Equities Streams, an upgrade to its Data Streams product. It provides real-time pricing for US stocks and ETFs on blockchains, even outside regular market hours.

QHow did the tariff threats from the Trump administration affect the crypto market?

AThe tariff threats caused crypto markets to turn defensive, with Bitcoin sliding below $90K and Ethereum falling under $3K. Selling pressure was rampant across the market, leading to declines in major cryptocurrencies like SOL, XRP, and BNB.

QWhich protocols have already integrated Chainlink's new equities streams?

ASeveral protocols, including BitMEX, ApeX, Orderly, and HelloTrade, have already integrated Chainlink's new equities streams product.

QWhat was the reason given by US Treasury Secretary Scott Bessent for the rising bond yields?

AScott Bessent argued that rising yields were driven by bond market annihilation in Japan and that the reaction cannot be isolated to the United States' moves, downplaying the impact of the tariff threats.

QWhat is the significance of Chainlink's new equities streams for the DeFi market?

AChainlink's new equities streams open up access to the roughly $80 trillion US equities market for DeFi applications by providing continuous, cryptographically verified price feeds for on-chain products like equity perps and prediction markets.

Letture associate

a16z: AI's 'Amnesia', Can Continuous Learning Cure It?

The article "a16z: AI's 'Amnesia' – Can Continual Learning Cure It?" explores the limitations of current large language models (LLMs), which, like the protagonist in the film *Memento*, are trapped in a perpetual present—unable to form new memories after training. While methods like in-context learning (ICL), retrieval-augmented generation (RAG), and external scaffolding (e.g., chat history, prompts) provide temporary solutions, they fail to enable true internalization of new knowledge. The authors argue that compression—the core of learning during training—is halted at deployment, preventing models from generalizing, discovering novel solutions (e.g., mathematical proofs), or handling adversarial scenarios. The piece introduces *continual learning* as a critical research direction to address this, categorizing approaches into three paths: 1. **Context**: Scaling external memory via longer context windows, multi-agent systems, and smarter retrieval. 2. **Modules**: Using pluggable adapters or external memory layers for specialization without full retraining. 3. **Weights**: Enabling parameter updates through sparse training, test-time training, meta-learning, distillation, and reinforcement learning from feedback. Challenges include catastrophic forgetting, safety risks, and auditability, but overcoming these could unlock models that learn iteratively from experience. The conclusion emphasizes that while context-based methods are effective, true breakthroughs require models to compress new information into weights post-deployment, moving from mere retrieval to genuine learning.

marsbit53 min fa

a16z: AI's 'Amnesia', Can Continuous Learning Cure It?

marsbit53 min fa

Can a Hair Dryer Earn $34,000? Deciphering the Reflexivity Paradox in Prediction Markets

An individual manipulated a weather sensor at Paris Charles de Gaulle Airport with a portable heat source, causing a Polymarket weather market to settle at 22°C and earning $34,000. This incident highlights a fundamental issue in prediction markets: when a market aims to reflect reality, it also incentivizes participants to influence that reality. Prediction markets operate on two layers: platform rules (what outcome counts as a win) and data sources (what actually happened). While most focus on rules, the real vulnerability lies in the data source. If reality is recorded through a specific source, influencing that source directly affects market settlement. The article categorizes markets by their vulnerability: 1. **Single-point physical data sources** (e.g., weather stations): Easily manipulated through physical interference. 2. **Insider information markets** (e.g., MrBeast video details): Insiders like team members use non-public information to trade. Kalshi fined a剪辑师 $20,000 for insider trading. 3. **Actor-manipulated markets** (e.g., Andrew Tate’s tweet counts): The subject of the market can control the outcome. Evidence suggests Tate’sociated accounts coordinated to profit. 4. **Individual-action markets** (e.g., WNBA disruptions): A single person can execute an event to profit from their pre-placed bets. Kalshi and Polymarket handle these issues differently. Kalshi enforces strict KYC, publicly penalizes insider trading, and reports to regulators. Polymarket, with its anonymous wallet-based system, has historically been more permissive, arguing that insider information improves market accuracy. However, it cooperated with authorities in the "Van Dyke case," where a user traded on classified government information. The core paradox is reflexivity: prediction markets are designed to discover truth, but their financial incentives can distort reality. The more valuable a prediction becomes, the more likely participants are to influence the event itself. The market ceases to be a mirror of reality and instead shapes it.

marsbit1 h fa

Can a Hair Dryer Earn $34,000? Deciphering the Reflexivity Paradox in Prediction Markets

marsbit1 h fa

Trading

Spot
Futures
活动图片