# Пов'язані статті щодо Verification

Центр новин HTX надає останні статті та поглиблений аналіз на тему "Verification", що охоплює ринкові тренди, оновлення проєктів, технологічні розробки та регуляторну політику в криптоіндустрії.

In the AI Era, What's Left for Bitcoin?

As Bitcoin falls below $60,000, the author reflects on the relationship between AI and Bitcoin, seeing them as two sides of the same coin. In the AI era, the cost of generating content has plummeted, making fake text, images, and videos increasingly easy and cheap to produce. This has led to a fundamental shift: while AI dramatically lowers the cost of information production, it also undermines trust and authenticity online. What becomes truly valuable is not more content, but the ability to verify what is real—"verifiability." This perspective offers a new lens for Bitcoin. Its massive energy consumption, often criticized as wasteful, is reinterpreted. While AI burns energy to enhance "capability" and efficiency, Bitcoin burns energy to produce "verifiability." Its purpose is not to be trusted but to enable a system where no trust in intermediaries—banks, platforms, or developers—is needed. Every transaction and the entire ledger's history is secured by cryptography and a decentralized network of nodes, making it independently verifiable. AI cannot forge a transaction on the Bitcoin network because the system is designed for proof, not generation. The author draws a historical parallel to the Renaissance: the printing press drastically reduced the cost of copying knowledge, while double-entry bookkeeping reduced the cost of trust in commerce. Today, AI is the new printing press, reducing content creation costs to near zero. Blockchain, and Bitcoin as its pioneer, may be the modern equivalent of double-entry bookkeeping—a foundational technology for verifying digital asset ownership and historical records without centralized authorities. Thus, AI and blockchain are not competitors. AI lowers the cost of creation; blockchain lowers the cost of verification. In an age where AI can generate anything, true scarcity may lie not in more content, but in independently verifiable facts. Whether the market will reprice Bitcoin accordingly remains uncertain, but its core value proposition as a "machine for producing verifiability" becomes strikingly relevant.

marsbit06/30 15:57

In the AI Era, What's Left for Bitcoin?

marsbit06/30 15:57

Lao Huang: Prompt is Dead, the Entire AI Community is Frenziedly Chasing Loops

The article "Prompt is Dead: The AI Industry is Obsessively Chasing Loops" discusses a major shift in AI development, where "Loop Engineering" is replacing traditional prompt engineering. Industry leaders like NVIDIA's Jensen Huang, Andrew Ng, and engineers from Anthropic and OpenAI argue that manually crafting prompts is becoming obsolete. Instead, the new focus is on designing autonomous, self-improving AI systems (loops) that can operate 24/7. A loop system typically involves five key phases: Discovery (finding tasks), Handoff (assigning to agents), Validation (critical independent review), Persistence (saving progress), and Scheduling (automated operation). The core idea is to move humans from being the operational "engine" to being the system "architects" who design the loop, define goals, and set up verification mechanisms. A major challenge and necessity is implementing robust, independent validation to prevent AI from uncritically approving its own work. The trend is seen as part of a move towards "inference-time compute," where allocating computational budget effectively becomes a key engineering skill. While loops can produce higher-quality outputs, they are more expensive and time-consuming than simple prompting. The article warns of risks like "verification debt," "comprehension corrosion," and "cognitive surrender," where engineers might stop understanding the code their systems generate. Ultimately, the article concludes that in an era of automated loops, human judgment and oversight remain the most critical and scarce resources.

marsbit06/29 08:37

Lao Huang: Prompt is Dead, the Entire AI Community is Frenziedly Chasing Loops

marsbit06/29 08:37

AI is Sweeping the Globe, So Why is Crypto + AI in a Slump?

AI Booms, But Crypto + AI Remains Sluggish: A Demand-Side Analysis Despite the AI industry's explosive growth and massive investment, the convergence of blockchain and AI (Crypto + AI) has seen limited traction. The core issue is a severe supply-demand mismatch, not a flawed premise. Analyzing four key sub-sectors reveals specific gaps: 1. **Decentralized Compute/Storage:** Offer logical benefits like data sovereignty and cost savings but lack a decisive technical advantage over entrenched cloud giants (AWS, GCP). Enterprises prioritize performance and stability and are unwilling to bear the switching risk and uncertainty of decentralized networks. 2. **Model Verification/Privacy (e.g., ZKML):** Address important long-term issues like auditability and data privacy, but these are not urgent operational pain points for most businesses today. Widespread demand will likely follow regulatory mandates (like the EU AI Act), not precede them. 3. **AI Agent Infrastructure:** Projects are building infrastructure for a future of autonomous, interacting agents. However, the current market focus is on internal process automation within corporate firewalls. The technology is ahead of market readiness. 4. **AI Agent Payments:** This is the only sub-sector where blockchain is on a level playing field with traditional finance. Both are trying to solve the unsolved problem of real-time, micro-transactions for machines, making it the most immediately competitive area. The overarching problem is that the AI industry invests heavily in solutions that solve immediate bottlenecks (e.g., faster memory, more power). Most Crypto + AI solutions target secondary, longer-term concerns (decentralization, transparency) and often come with performance trade-offs. The lack of a flagship, large-scale commercial success case further hinders mainstream capital inflow. The path forward requires either aligning more closely with the current industry's performance demands or patiently building the foundational infrastructure for the next phase of AI.

Foresight News06/29 06:15

AI is Sweeping the Globe, So Why is Crypto + AI in a Slump?

Foresight News06/29 06:15

Comprehensive Analysis of the AI Inference Market: How Can Crypto Projects Break Through?

"AI Inference Market: A Strategic Overview and Crypto's Path to Disruption" The AI inference market, where trained models generate responses to user prompts, is now the primary economic driver, surpassing model training in value. This market is fragmented: hyperscalers (AWS, Google, Microsoft) dominate enterprise reliability; specialized providers (Together, Fireworks) optimize performance; and routing platforms like OpenRouter act as critical bottlenecks, dynamically allocating requests based on cost, latency, and privacy. Crypto AI networks are not competing directly on reliability but are carving out distinct niches: permissionless access, lower-cost supply, privacy, verifiable computation, and agent-native payments. Key projects include Chutes (decentralized inference platform), Akash & io.net (GPU marketplaces), Targon (confidential computing), Darkbloom & Venice (private, consumer-focused inference), and NuNet (orchestration for distributed workloads). The core differentiator is that traditional providers sell trust and enterprise workflows, while crypto networks offer new incentive loops, censorship resistance, and programmable access to resources like compute. For crypto projects to succeed, key metrics are paid token volume (not just usage), sustainable GPU provider revenue, integration into routers like OpenRouter, robust verification against fraud, and genuine privacy guarantees. Ultimately, market control will belong to entities that route, verify, and settle demand—not just those supplying raw compute. The inference market is evolving to resemble a financial system, with tokens as units of account, and crypto's unique value propositions position it to capture emerging segments in this expanding landscape.

Foresight News06/25 07:08

Comprehensive Analysis of the AI Inference Market: How Can Crypto Projects Break Through?

Foresight News06/25 07:08

The Full Story of How Crypto Unicorn Blockstream Is Mired in Serious Fraud Allegations

This article details serious allegations of fraud against Bitcoin infrastructure company Blockstream, founded by Bitcoin pioneer Adam Back. In June 2024, investigative account NatInfoSec published a report accusing Blockstream's mining note (BMN) program of potentially operating a multi-billion dollar scheme with Ponzi-like characteristics. The core allegations focus on Blockstream Mining Notes (BMNs), which offer investors fixed annual yields up to approximately 20% from Bitcoin mining. NatInfoSec's investigation raises several key issues: 1. **Suspicious Hashrate & Payout Capacity**: The analysis suggests Blockstream would need 20-45 EH/s of mining power to cover its BMN obligations, but its public dashboard shows only around 15 EH/s. Furthermore, no verifiable public evidence (e.g., grid connection records, import data) was found to support the massive mining operation required. 2. **Questionable Payout Source**: The BMN contract allows Blockstream to use Bitcoin from *any source* (Substitute Performance BTC) to fulfill investor payouts, raising concerns that payouts may not come from actual mining revenue. 3. **High-Risk, Fixed Returns**: Offering ~20% fixed yields in the volatile, cyclical Bitcoin mining industry is viewed as highly unusual and requires clear explanation. 4. **Undisclosed Criminal Record of Key Figure**: Christopher William Cook, a key figure in Blockstream's mining operations and CEO of spin-off Exacore, was found to have a federal felony conviction for mail fraud in 2008, a fact not disclosed in BMN offering documents. His background was also allegedly embellished. 5. **Potential Contagion to BSTR SPAC**: Questions were raised about whether these liabilities and Cook's record should have been disclosed in the SEC filings for Bitcoin Standard Treasury Company (BSTR), a separate Adam Back-associated firm planning a SPAC merger. The crypto community is divided. BitMEX Research validated Cook's criminal record and expressed concern over the high yields but found other evidence lacking or misleading, noting the legal separation between BMN, Blockstream, and BSTR. Blockstream defenders, like Samson Mow, argue the mining is real. Critics, however, emphasize the lack of independent, verifiable proof of the mining operation's scale and the true source of investor payouts. The article concludes that BMN remains shrouded in key unanswered questions regarding its actual size, the verifiability of its underlying mining assets and payouts, the source of its high yields, and the full role and disclosure concerning Chris Cook. Blockstream had not issued a comprehensive response at the time of writing.

marsbit06/24 15:08

The Full Story of How Crypto Unicorn Blockstream Is Mired in Serious Fraud Allegations

marsbit06/24 15:08

AI Agents Also Need 'Credit Checks': ERC-8126 is Filling the Gap in On-chain Trust

The article discusses ERC-8126, a proposed standard designed to address the lack of trust and verification for AI Agents operating on-chain. While ERC-8004 provides AI Agents with a basic on-chain identity (answering "Who are you?"), it does not guarantee trustworthiness. ERC-8126 aims to fill this gap by establishing a verification layer (answering "Are you reliable?"). It standardizes how independent verification providers can assess an agent's associated risks across five key areas: Token/Contract Verification (ETV), Media Content Verification (MCV), Solidity Code Verification (SCV), Web Application Verification (WAV), and Wallet Verification (WV). These providers generate a standardized risk score (0-100) and proofs based on their checks, without acting as a single authoritative certifier. This allows wallets, marketplaces, dApps, and other agents to consume these risk signals—for example, to display warnings, filter listings, or make interaction decisions. The standard also incorporates concepts like Private Data Verification (PDV) and Zero-Knowledge Proofs (ZKP) to allow verification without exposing sensitive underlying data. Positioned alongside ERC-8004 (Identity) and ERC-8183 (Commerce for agents), ERC-8126 represents a step toward building a verifiable and accountable infrastructure for the emerging on-chain AI Agent economy, shifting trust assessment from purely user-based judgment to standardized, consumable signals.

marsbit06/22 13:54

AI Agents Also Need 'Credit Checks': ERC-8126 is Filling the Gap in On-chain Trust

marsbit06/22 13:54

Which Crypto Sectors Have Been "Eaten" by AI Agents?

The article examines which crypto sectors have been increasingly dominated by AI Agents and which remain human-centric. In certain high-speed, efficiency-driven areas, AI Agents have taken clear control. This includes derivatives/perpetuals trading, where bots outperform humans significantly (e.g., a contest showed 0% of AI Agents were liquidated vs. 43% of humans), arbitrage/MEV extraction, and yield optimization (with ~68% of new DeFi protocols in Q1 2026 featuring autonomous AI Agents). Spot trading and portfolio optimization are also seeing heavy Agent adoption. However, the shift is not universal. In "battleground" sectors, both Agents and humans coexist. In prediction markets, Agents dominate short-term arbitrage, but humans still outperform in long-term, nuanced judgment calls. In DeFi lending, while liquidation is automated, core deposit/borrow decisions remain largely human-driven. Sectors still firmly led by human activity include stablecoin payments and card-based spending (driven by real-world economic activity and remittances) and wallets, which serve as the crucial human-verification and approval layer. The rise of Agents increases the need for robust human-Agent verification layers. Projects like World/AgentKit, t54, Self Protocol, and Kite AI are building infrastructure to create trust, security, and accountability by binding Agents to verified human identities. In conclusion, while AI Agents have decisively "eaten" speed and optimization-focused crypto sectors, human judgment, trust, and real-world context remain dominant in areas that create broad economic value, such as payments and identity. The future likely involves a symbiotic relationship where Agents require human verification and oversight to operate effectively.

Foresight News06/22 07:10

Which Crypto Sectors Have Been "Eaten" by AI Agents?

Foresight News06/22 07:10

From Identity Protocol to AI Gateway: How Ambitious Is World?

The article discusses Worldcoin's (WLD) evolution from a "proof of personhood" network to a potential identity infrastructure for the AI era. Initially focused on unique digital identities via iris scans, Worldcoin faced skepticism over real-world use cases. However, entering "The Simple Plan" Phase 3, it's shifting from token incentives to utility-driven growth. Key applications are emerging: enterprise solutions (e.g., deepfake verification with Zoom, Okta), consumer use cases (e.g., reducing bots on Tinder or in ticket sales), and notably, the AI Agent sector via AgentKit, which aims to establish trusted authorization between humans and AI agents. The author argues AI's proliferation makes authentic human identity a scarce and valuable resource. Worldcoin's strategy is now focused on key markets for network effects and developing self-service Orb hardware to scale sustainably. The launch of World ID 4.0 introduces a fee model for issuers/protocols, creating a potential revenue stream. In conclusion, WLD's rise reflects a market reassessment. Worldcoin's ambition is to become the foundational identity layer and crucial entry point for the coming AI Agent economy, moving crypto narratives beyond finance into verifiable human identity.

marsbit06/16 00:01

From Identity Protocol to AI Gateway: How Ambitious Is World?

marsbit06/16 00:01

How Difficult is Chip Making? A Division Error Costs 475 Million Dollars

How Hard Is It to Make a Chip? A Division Error Cost $475 Million Chip expert Shi Kan, a researcher at the Chinese Academy of Sciences and a popular tech creator, explains the immense challenges of chip development. Chips are foundational to modern technology, but their creation is extraordinarily difficult. The journey from sand to a functional chip involves complex design and manufacturing, but a critical bottleneck is verification—ensuring the design works flawlessly before costly production. A single, undetected bug can have catastrophic consequences, as illustrated by the infamous 1994 Intel Pentium FDIV bug. A flaw in the floating-point division unit forced a recall costing $475 million. Unlike software, chips cannot be easily patched after manufacture, making "first-time success" paramount. However, industry surveys show only 24% of chip projects achieve this; over three-quarters require at least one costly re-spin due to design flaws. Verification has thus become the dominant phase, consuming up to 70% of the design cycle. The core challenge is a "verification impossible triangle" between high performance, good debuggability, and low cost. Exhaustively verifying a modern CPU core could take 15,000 years with software simulation, or 30 years with advanced hardware emulation—timeframes utterly impractical for development. Despite being essential, verification is often seen as unglamorous "dirty work," receiving less academic attention than fields like AI. Shi and his team are tackling this by developing an agile verification research framework called ENCORE, based on FPGA technology, to improve verification efficiency and debug capability. Beyond research, Shi engages in public science communication through long-form video content, aiming to demystify chip technology, AI, and computer science. He argues for the value of pursuing "hard and long-term" endeavors, whether in the meticulous world of chip verification or in creating substantive educational content, believing such sustained effort is likely the right path forward.

marsbit06/15 10:31

How Difficult is Chip Making? A Division Error Costs 475 Million Dollars

marsbit06/15 10:31

Claude to Mandate "Face-Scan ID Verification", No ID No Service Starting July?

Anthropic, the creator of Claude AI, has sent privacy policy update emails to users, signaling a significant shift. The key change, effective July 8, is the potential requirement for consumer-level users (Free, Pro, Max plans) to verify their age or identity. This verification would be conducted through the third-party service Persona, involving uploading a government-issued photo ID and taking a live selfie for comparison. Anthropic states this data is for security purposes only, will not be used for model training, and is processed by Persona, not stored on its own servers. The update also clarifies data handling for Claude's new capabilities: when performing multi-step tasks or connecting to third-party apps, user data may flow between Anthropic and those external services. Additionally, more information may be collected from users who participate in Anthropic research. This move is seen as a major step towards establishing accountability as AI agents become more powerful and autonomous, capable of executing complex, real-world tasks. It follows previous enforcement actions, like the banning of the "Fable 5" account, and indicates a broader industry trend toward stricter user identification and safety measures. The verification is expected to apply in specific scenarios, particularly as users engage Claude in more complex agentic workflows.

链捕手06/15 10:18

Claude to Mandate "Face-Scan ID Verification", No ID No Service Starting July?

链捕手06/15 10:18

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