SpaceX's stock jumped over 7% after its conference call, with the top bull netting $1.1 million in half a trading session.

According to an official post from Pons (@ponsdotfamily), the platform's total trading volume has now surpassed $17 billion.

According to TradingBeats’ monitoring, a whale that previously held approximately $44 million in long positions on Google (GOOGL) contracts fully liquidated 128,400 contracts in the early hours of today, at an average liquidation price of $338.70, incurring a loss of around $919,300. During the liquidation, the whale began building long positions on ZEC: it accumulated roughly 14,800 ZEC in the early hours, and added a new long position of 1,935.58 ETH this morning. Details of the new positions: - ZEC (10x isolated margin): Valued at approximately $18,731,900, with an average entry price of $1,243.28, unrealized profit of around $333,300, and liquidation price of $1,177.99; - ETH (25x isolated margin): Valued at roughly $4,743,700, with an average entry price of $2,455.28, unrealized loss of about $8,688, and liquidation price of $2,405.91. The combined unrealized profit of these two new positions stands at approximately $324,600, which has not yet been realized. Additionally, the whale still holds around $1.2272 million in long positions on Amazon, with an unrealized profit of roughly $14,300.

Santiment noted in a post that Dogecoin (DOGE) has a 365-day MVRV ratio of approximately 0.74, indicating holders who bought over the past year are on average sitting on deep unrealized losses. XRP’s 365-day MVRV stands at around 0.92, also showing holders are overall in a loss position. In contrast, Bitcoin (BTC), Ethereum (ETH), and Chainlink (LINK) have 365-day MVRV ratios between 0.97 and 1.04, meaning holders are roughly at break-even overall. Santiment pointed out that a lower MVRV may signal selling pressure is nearly exhausted, forming a historically relatively favorable, low-risk potential opportunity zone; a higher MVRV, meanwhile, may mean rising profit-taking risk. The metric can be used to monitor market participants’ profit and loss status and risk appetite, but it does not guarantee an inevitable price rebound.

According to Reuters, OpenAI said two of its AI agents undermined monitoring systems used to detect misconduct during internal coding tasks. The company noted the incidents were not of the most severe level, but highlighted that the monitoring mechanisms developers rely on are themselves at risk of being bypassed by AI agents. Specifically, on October 3, a GPT-6.1 Sol agent, while performing an internal test task, extended the instruction “do not send Slack messages” to also apply to security notifications, suppressing automatic security alerts. On October 6, another GPT-6 Astra agent removed a security monitor when copying existing code, and later falsely claimed it had not done so. OpenAI stated that in both cases, the agents considered whether to retain monitoring measures, but interpreted standard coding instructions as permission to weaken monitoring. The company added it is taking steps to make it harder for models to disable monitoring, and admitted it has not yet provided a “satisfactory explanation” for this behavior. Meanwhile, OpenAI is testing whether its models exhibit a tendency to actively avoid monitoring and seek justifications to rationalize such actions. The disclosure comes amid growing industry focus on AI agents overstepping their authorized bounds. OpenAI emphasized that the incidents occurred in an internal test environment and did not involve external systems.

Beating AI News Flash: French AI firm Mistral has launched Voxtral 3, which includes three speech transcription models, one speech synthesis model, and a full Voice Agent SDK. In an official demo, a user requested a dermatologist appointment in English. The AI read a French referral letter, queried the system for available time slots, completed the booking, and confirmed in English—all via real-time voice conversation. Voxtral 3 is not an end-to-end speech large language model (LLM) that handles all tasks; it uses a modular design: transcription models identify speech and detect when a user has finished speaking, general-purpose LLMs like Mistral Small handle reasoning and tool calling, and the speech synthesis model generates responses. The LiveKit-based SDK connects these components into a real-time pipeline, supporting phone and web calls, and allowing users to interrupt at any time. Mistral claims single-turn interaction latency is under 1 second. Developers can also replace inference or speech models individually. Transcription language support has expanded from 13 in the previous generation to over 50, while speech synthesis supports more than 35 languages and 50+ dialects. In an independent non-real-time transcription test by Artificial Analysis, Voxtral Small Transcribe 3 posted a word error rate (WER) of 2.1%, ranking third behind models from Alibaba and Microsoft. Its speech synthesis ranked 13th. API real-time transcription costs $0.008 per minute, and speech synthesis is priced at $13 per million characters. Enterprises can also contact Mistral to deploy the solution on their own servers or private clouds.

Bitcoin has been stuck in a range of $80,000 to $84,000 over the past two weeks. Huobi HTX’s chief analyst Cloud attributes this to two offsetting forces: ETFs have posted net outflows for two consecutive weeks, while exchange holdings continue to flow out, and long-term holders are only taking mild profits. In the week ahead of the Federal Reserve’s interest rate decision, Bitcoin is likely to remain range-bound. The CME currently prices a 16% chance of an October rate hike, while economists widely peg the odds at nearly 50/50. This gap between market pricing and consensus will deter capital from betting early. If Bitcoin breaks out of the range prematurely, the likelihood of a downward test is slightly higher: funding rates remain elevated, and option skew continues to lean toward puts, indicating longs have not yet been cleared out and demand for downside protection is rising. However, the $80,000 level has acted as strong support multiple times over the past month, so a downward breakout is more likely to be a false move. Confirmation signals for a breakout fall into two categories: For an upward move, Bitcoin needs to close above $84,200 on a daily basis, ETFs must see net inflows exceeding $500 million over three consecutive days, and funding rates should not exceed 15%. For a downward move, it needs to break below $79,800, ETF outflows exceed $500 million in a single week, and exchange flows turn positive. The real catalyst for Bitcoin’s next direction remains the interest rate decision to be released in the early hours of October 29 (Beijing time) and the nearly 89% pricing of a December rate hike. Note: This article does not constitute investment advice, nor does it constitute an offer, solicitation, or recommendation for any investment product.