# Cost Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Cost", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

The Last Time I'll Talk About Backpack, and Also Discussing My Airdrop Farming Principles

The author outlines two primary approaches to airdrop farming (referred to as "撸毛"): a labor-intensive" method of mass participation in many projects, and their own "sniper" method. The sniper approach relies on a rigorous four-point checklist to filter projects and avoid "industrial garbage." The checklist evaluates: 1. **Team (People):** Founders must be intelligent, have strong execution skills, and be genuinely well-intentioned. This is assessed through their social media content and, if possible, personal interactions. 2. **Product (Product-Market Fit):** The product must have a clear market fit, be delivered competently, and the team must show a responsible attitude towards its quality, avoiding releases full of basic errors. 3. **Narrative (Story):** The project should operate in a promising, unproven narrative within Web3 that also aligns with major investment trends in Web2 (e.g., AI). 4. **Timing & Cost (Market Conditions):** Avoid participating when market sentiment is overly FOMO-driven and participation costs are high. If an opportunity causes hesitation, it's best to skip it, as overcrowded airdrops yield minimal or negative returns. Applying this framework, the author explains why they avoided heavily farming the Backpack exchange airdrop: * **Narrative:** They are skeptical of the "compliant CEX" narrative, questioning its unique selling point against giants like Binance and OKX. * **Product:** They criticize Backpack's frequent technical failures, rollbacks, and what they perceive as a lack of product development rigor, comparing it unfavorably to competitors like Hyperliquid. * **Timing & Cost:** The participation cost was high compared to zero-fee alternatives available at the time. The author concludes that Backpack lacks the technical and operational prowess of a serious exchange and views its token more as a "VC-backed meme coin" for secondary market speculation rather than a worthwhile airdrop target.

比推03/23 20:38

The Last Time I'll Talk About Backpack, and Also Discussing My Airdrop Farming Principles

比推03/23 20:38

The First Batch of Big Tech Employees Laid Off by AI Have Returned to Their Posts

The first wave of employees laid off by major tech companies, citing AI as the reason, are already being rehired. In late February, Block, led by Jack Dorsey, laid off over 4,000 employees, reducing its workforce from 10,000 to under 6,000, with Dorsey stating that "AI tools changed everything." However, within a month, some of those laid off began receiving offers to return. Reports indicate rehires occurred in departments like engineering and HR, with reasons ranging from "clerical errors" in termination to managers advocating for their return. The article argues that replacing humans with AI is often more cost-effective. For instance, enterprise-level AI can be expensive in terms of token usage, and training a reliable AI system, such as for customer service, may exceed the cost of human employee salaries. Examples like Klarna, which rehired客服 after initially replacing them with AI, support this. Additionally, the "Jevons Paradox" suggests that AI-driven efficiency gains don’t necessarily reduce workloads but may increase demands on remaining employees, adding to their burden. The piece criticizes companies using AI as a pretext for layoffs, arguing that AI cannot replace human organizational dynamics or strategic roles. Nvidia’s Jensen Huang is quoted condemning leaders who裁员 instead of leveraging AI for expansion. Ultimately, AI serves as a convenient excuse for cost-cutting, but its limitations and the essential role of humans in organizations mean that some layoffs are reversed when key roles are affected. The trend reflects broader issues of corporate strategy and management rather than a true AI takeover.

Odaily星球日报03/20 07:26

The First Batch of Big Tech Employees Laid Off by AI Have Returned to Their Posts

Odaily星球日报03/20 07:26

Bitcoin Miner MARA Transfers 298 BTC After Opening Door To Sales

Bitcoin miner Marathon Digital (MARA) has transferred 298 BTC (worth $21 million) to institutional trading platform Cumberland. This follows the company’s recent SEC filing indicating a strategic shift to allow Bitcoin sales from its treasury. Historically a holder of mined and purchased BTC, MARA began permitting sales last year and recently expanded this policy. The move coincides with a challenging mining environment, as Bitcoin's current price hovers around MARA’s estimated average mining cost of $70,027, putting the company near break-even. MARA, along with other miners like Bitfarms, is also exploring opportunities in the AI and high-performance computing sectors. Bitcoin is currently trading around $70,700.

bitcoinist03/12 11:02

Bitcoin Miner MARA Transfers 298 BTC After Opening Door To Sales

bitcoinist03/12 11:02

Lobster Key 11 Questions: The Most Easy-to-Understand Breakdown of OpenClaw Principles

"OpenClaw Demystified: A Beginner's Guide to AI Agent Principles" explains the popular OpenClaw AI assistant by breaking down its core functions into 11 key questions. The article first clarifies that the underlying large language model is merely a "text prediction engine" with no real understanding, memory, or senses. OpenClaw acts as a "shell" around this model, creating the illusion of memory by appending massive prompts containing its personality files (AGENTS.md, SOUL.md, USER.md) and the entire conversation history before each interaction. This mechanism is why it's "expensive"—each query processes thousands of tokens of context, not just the latest message. A core differentiator is tool use. The model itself only outputs text; OpenClaw parses this output for specific structured commands (e.g., `[Tool Call] Read("file.txt")`) and executes the corresponding action (reading the file) locally on the user's machine. This allows it to act, not just advise. For complex tasks, it can even write and run its own Python scripts, a powerful but dangerous capability. To manage limited context windows and complex tasks, OpenClaw uses sub-agents. A main agent can spawn sub-agent to handle a sub-task and return a summarized result, preventing the main context from being overloaded. Crucially, sub-agents cannot spawn their own to avoid infinite loops. Unlike standard chatbots, OpenClaw is proactive due to its heartbeat mechanism, which periodically prompts the model to check for tasks. It can also "sleep" via cron jobs to wait for long-running tasks, saving resources. The guide ends with critical security warnings. OpenClaw has extensive local access, making it a significant risk. It can malfunction (e.g., deleting emails uncontrollably) or fall victim to prompt injection attacks, where malicious input from the web is mistaken for a user's command. The strong recommendation is to run it on a dedicated, isolated "sacrificial" computer with minimal permissions and mandatory human confirmations for destructive actions.

Odaily星球日报03/11 09:53

Lobster Key 11 Questions: The Most Easy-to-Understand Breakdown of OpenClaw Principles

Odaily星球日报03/11 09:53

A Monthly Salary of 20,000 RMB, But Can't Afford to Keep a 'Lobster'?

The article "Can a 20,000 Yuan Monthly Salary Not Afford a 'Lobster'?" discusses the viral trend and misconceptions surrounding OpenClaw, an open-source personal AI agent framework. It highlights two contrasting views: one praising its potential as a virtual team member, and another criticizing the hype and privacy concerns. Key points include: 1. OpenClaw experiences vary widely based on deployment methods (local hardware, cloud servers, personal PCs, or vendor-hosted services), affecting functionality and cost. 2. High permissions granted to OpenClaw pose significant security risks, including data breaches and malicious attacks. 3. Performance depends heavily on the underlying AI model (e.g., GPT, Claude) and its associated costs, with token consumption potentially leading to high expenses. 4. OpenClaw is still an immature, technically complex tool requiring substantial time, financial investment, and expertise to use effectively. 5. Users are advised to assess their actual needs, technical capability, and risk tolerance before adoption, as the AI serves as an "amplifier" rather than a standalone solution. The conclusion emphasizes cautious experimentation and independent thinking amidst the hype.

比推03/09 17:12

A Monthly Salary of 20,000 RMB, But Can't Afford to Keep a 'Lobster'?

比推03/09 17:12

Riot mines 5,686 BTC and earns $647mln in 2025 – Yet its stock barely moved

Riot Platforms reported record $647.4 million revenue in 2025, a 72% increase from 2024, driven by mining 5,686 BTC. Despite Bitcoin's price surge to $87,498, Riot’s stock remained stagnant, reflecting investor concerns over rising mining costs. The average cost to mine one BTC jumped to $49,645 in 2025, up from $32,216, due to increased network competition. While Riot maintains strong liquidity with $1.6 billion in BTC holdings and $309.8 million cash, the broader mining industry faces profitability pressures from halving events, geopolitical risks, and economic uncertainty.

ambcrypto03/04 19:02

Riot mines 5,686 BTC and earns $647mln in 2025 – Yet its stock barely moved

ambcrypto03/04 19:02

What’s the truth behind RWA tokenization? ‘More friction, more cost’

The article examines the debate surrounding the real-world asset (RWA) tokenization trend, questioning whether it truly adds value. Analyst Anndy Lian criticizes the sector for contradicting core crypto principles like decentralization and trust minimization, arguing that it introduces more intermediaries, friction, and costs. He believes only tokenized stocks offer a clear advantage over traditional crypto products. Despite these concerns, major institutions like BlackRock and platforms like Robinhood are actively pursuing tokenization, highlighting its potential to democratize access to financial markets. The RWA market has grown to $26 billion, with increasing user adoption. The recent Iran tensions demonstrated the utility of 24/7 trading platforms like Hyperliquid for price discovery and hedging. While challenges remain, the trend appears resilient and may evolve to address existing risks.

ambcrypto03/04 03:02

What’s the truth behind RWA tokenization? ‘More friction, more cost’

ambcrypto03/04 03:02

After Integrating OpenClaw into Every Aspect of My Life, I Personally Switched It Off

After extensively using OpenClaw (formerly Clawdbot and Moltbot) for over a month as a 24/7 AI assistant integrated with Telegram, email, and calendar, the author decided to shut it down. The primary reasons were its unreliability in long-term memory retention despite claims, high and unpredictable API costs (over $150 monthly), and significant security vulnerabilities, including exposed API keys and unauthorized data transmission. The author realized that a constantly running AI was unnecessary for most valuable tasks, which were better handled through active, intentional work. The core functions of OpenClaw—remembering user context and automating tasks—were effectively replicated using Claude’s ecosystem. By creating a consolidated CLAUDE.md file (replacing OpenClaw’s multiple configuration files), leveraging Claude’s built-in memory features, and integrating with Obsidian via CLI for efficient knowledge management, the author achieved similar functionality with greater reliability. For mobile access, Claude’s Remote Control feature or a Telegram bot solution provided seamless interaction. Scheduled tasks were handled through Claude’s Cowork feature, avoiding the cost of continuous API checks. Ultimately, Claude Pro or Max subscriptions offered a more predictable cost structure ($20–$200/month) and a stable, secure environment. The author concluded that Claude’s ecosystem delivers nearly all of OpenClaw’s promised benefits without the operational headaches, making it a superior choice for practical AI assistance.

marsbit03/02 10:13

After Integrating OpenClaw into Every Aspect of My Life, I Personally Switched It Off

marsbit03/02 10:13

a16z Infographic: AI Costs Halved, Usage Doubled, US 30-Year-Olds Enter the Era of 'Delayed Adulthood'

This a16z report analyzes four key trends through data visualization. First, while the "DExit" narrative suggests companies are leaving Delaware due to legal shifts, the data reveals a more nuanced picture; Delaware's overall share of businesses has actually grown, though specific entities like Wyoming LLCs are gaining traction. Second, AI sector demonstrates the Jevons Paradox: as the cost of AI tokens halved, their usage doubled, and demand for older GPUs remains strong, indicating the AI-driven economy is still in its early stages. Third, AI capital expenditure is massive, comparable to total US bank lending and significantly larger than corporate tax income or the military budgets of other G7 nations. Finally, prediction markets like Kalshi are now outperforming professional forecasters in predicting Federal Funds Rates. The report concludes with a societal trend: 30-year-olds are increasingly delaying traditional life milestones like marriage, homeownership, and having children, with college enrollment being the only metric that has risen sharply, though its value is now being questioned.

marsbit03/02 07:16

a16z Infographic: AI Costs Halved, Usage Doubled, US 30-Year-Olds Enter the Era of 'Delayed Adulthood'

marsbit03/02 07:16

Aave Founder: What is the Secret of the DeFi Lending Market?

On-chain lending, which started as an experimental concept around 2017, has grown into a market exceeding $100 billion, primarily driven by stablecoin borrowing backed by crypto-native collateral. It enables liquidity release, leveraged positions, and yield arbitrage. The key advantage lies not in creativity but in validation through real demand and product-market fit. A major strength of on-chain lending is its significantly lower cost—around 5% for stablecoin loans compared to 7–12% plus fees in centralized crypto lending. This efficiency stems from capital aggregation in open, permissionless systems where transparency, composability, and automation foster competition. Capital moves faster, inefficiencies are exposed, and innovation spreads rapidly without traditional overhead. The system’s resilience is evident during bear markets, where capital continuously reprices itself in a transparent environment. The current limitation is not a lack of capital but a shortage of diverse, productive collateral. The future involves integrating crypto-native assets with tokenized real-world value to expand lending’s reach and efficiency. Traditional lending remains expensive due to structural inefficiencies: bloated origination, misaligned incentives, manual servicing, and defective risk feedback mechanisms. Decentralized finance solves this by breaking cost structures through full automation, transparency, and software-native processes. When on-chain lending becomes end-to-end cheaper than traditional systems, adoption will follow inevitably, empowering broader access to efficient capital deployment.

marsbit02/16 04:11

Aave Founder: What is the Secret of the DeFi Lending Market?