Queda acentuada de Moonwell Artemis (WELL)

In 2025, most tokens experienced significant price declines shortly after their Token Generation Event (TGE), but a few—such as ASTER, FOLKS, AVICI, and SENTIS—managed to sustain price increases. These tokens shared several key characteristics that contributed to their relative success: 1. **Token Distribution Over Hype**: Successful projects avoided large internal liquidity at TGE. Examples include AVICI (0% team allocation) and SENTIS (activity-based emissions). 2. **Reasonable Valuation**: Tokens launched at fair valuations, rather than during peak hype, allowed room for market reappreciation. AVICI, for instance, launched with a low FDV despite having a functional product. 3. Demonstrable Utility: Projects like ASTER (Perp DEX volume), FOLKS (lending scale), and AVICI (real-world card spending) showed observable usage rather than just promising future utility. 4. **Controlled Unlock Structures**: Linear and transparent token unlock schedules (e.g., SENTIS’s participation-based emissions) were better received than cliff-style unlocks. 5. **Exchange Listings as Accelerators, Not Foundations**: While major exchange support helped, it wasn’t sufficient alone. Strong fundamentals determined long-term performance. The market in 2025 shifted from rewarding potential to valuing structure: healthy circulation, fair distribution, real adoption, and predictable unlocks. Tokens that prioritized these elements demonstrated resilience post-TGE.

The Block Research's annual prediction report for 2026 presents a mix of bullish and cautious forecasts from its analysts. Key predictions include Bitcoin potentially reaching $140,000 and maintaining over 50% market dominance, while stablecoin market cap is expected to surge, possibly exceeding $500 billion. Notably, several analysts emphasize a shift from token launches to IPOs for crypto companies, with firms like Kraken and Consensys potentially going public. Prediction markets, led by platforms like Polymarket and Kalshi, are anticipated to be among the fastest-growing crypto sectors, with one likely launching its own blockchain. Other highlights include the rise of bank-issued deposit tokens, increased institutional stablecoin adoption for B2B payments, and a continued decline in NFT and memecoin activity. The report also foresees a K-shaped recovery, with quality projects attracting capital while low-quality ones fade.

On January 27, 2026, an attacker executed a well-orchestrated exploit against the PGNLZ token on BNB Smart Chain, resulting in approximately $100k in losses. The attacker initiated a flash loan of 1,059 BTCB from Moolah Protocol, used it as collateral on Venus Protocol to borrow 30 million USDT, and then exchanged 23,337,952 USDT for 982,506 PGNLZ on PancakeSwap. These PGNLZ tokens were intentionally burned (sent to a dead address), drastically reducing the liquidity pool supply. This manipulation caused the price of PGNLZ to surge from approximately $0.10 to over $5,528 per token. The attacker then invoked a fee-on-transfer function, triggering a built-in burn mechanism (_executeBurnFromLP) that further depleted the pool, leaving only a minuscule amount of PGNLZ and artificially inflating the price to an extreme 40 billion times its original value. Finally, the attacker drained the liquidity pool, repaid the flash loan, and netted a significant profit. The root cause was identified as a flawed deflationary economic model with insufficient validation during fee processing and LP burns, allowing price manipulation. The incident underscores the importance of rigorous economic model design and multi-audit practices before contract deployment.

No one truly teaches you how to do research. You're often given a desk, a pre-selected problem, and vague instructions to "create something new." Consequently, many people reverse-engineer the job based on visible outputs—papers, posts, announcements—learning only how to *appear* like a researcher rather than how to *become* one. True research capability is built from stacking small, trainable skills, nearly all of which can be developed through deliberate practice. **Pick Your Own Problem:** Most researchers absorb problems from advisors or trends, lacking the underlying reasoning. Choosing a problem you genuinely care about, as John Schulman advises, leads to original work. Develop "taste" like a muscle: predict experiment outcomes, guess paper results from methods, and track which findings remain important over time. **Upgrade Your Inputs:** Relying on shared reading lists (arXiv hot lists, filtered group chats) leads to unoriginal conclusions. Undervalued old literature often holds crucial insights (e.g., MoE, LSTM, backpropagation). Richard Sutton's "The Bitter Lesson" or Claude Shannon's 1952 talk on creative thinking are more predictive than lengthy modern surveys. Breadth matters as much as depth: draw from neuroscience, mechanism design, hardware knowledge, and honest statistics. Read papers directly, especially appendices and limitations sections. **Write Everything Down:** As Paul Graham noted, writing exposes flaws in seemingly mature ideas. Writing is the cheapest defense against self-deception. Following Feynman's principle, Darwin programmatically wrote down facts contradicting his theory to combat memory bias. Maintain a detailed log of hypotheses, setups, predictions, results, and updated understandings. Reviewing past logs fosters essential humility.

A South Korean developer, remy_notes, was shocked to receive two invoices from Anthropic totaling over $17.8 million within 24 hours, despite being on a free plan with zero API usage and no linked credit card. His bank blocked the attempted charges. This case highlights a broader issue of billing errors in the AI industry. Auditing firm Vaudit, reviewing millions in AI bills, found significant erroneous overcharges, often related to issues like clients being billed for newer, pricier models instead of older ones they used, or being charged for failed requests and retry loops. While Anthropic claims overcharging is not widespread, major providers have refunded a large portion of disputed amounts. The incident adds to growing distrust, following previous billing controversies like the "HERMES.md bug" and a class-action lawsuit over opaque usage calculations for Anthropic's subscription plans. The complexity of AI pricing models creates a "black box" for users, leading to a new competitive frontier: billing transparency. As users turn to audits and lawsuits, clear and fair pricing is becoming a crucial part of the product experience.