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Five Core Forms of AI Agent in YC's Eyes

The article outlines five core architectural patterns for effective AI Agents, emerging from tools like Codex and Claude, that move beyond simple prompts towards reusable, process-based capabilities. 1. **Skills**: Reusable, parameterized workflows that function like method calls, allowing a single process (e.g., "/investigate") to handle various tasks based on input parameters. 2. **Thin Harness**: A lightweight execution framework (~200 lines) that manages the AI model's "hands and feet"—handling loops, file I/O, and context—without becoming bloated. 3. **Resolvers**: Routing tables that map tasks to specific Skills, preventing "context corruption" when managing dozens of Skills and ensuring outputs go to the correct locations. 4. **Latent vs. Deterministic Layer**: A critical separation where LLMs handle judgment, synthesis, and pattern recognition, while deterministic code handles tasks requiring precision, consistency, and low cost (like calculations). 5. **Memory**: A persistent, accumulating knowledge base (e.g., a markdown folder) with a "current trusted conclusion" section and an append-only timeline, enabling the system to learn and retain context over time. Together, these patterns create a "process power"—a durable competitive advantage. Unlike one-off prompt-based applications whose value quickly commoditizes, a well-designed AI Agent system encodes experience into reusable, parameterized workflows, offloads stable rules to code, and continuously learns through memory. This creates a structured, hard-to-replicate capability that can provide sustained value for individuals or businesses, such as an accountant automating client reviews while preserving privacy and accumulating expertise.

marsbit05/20 07:46

Five Core Forms of AI Agent in YC's Eyes

marsbit05/20 07:46

Tiger Research: On-Chain Risk Operators, The Market Cap Gap Between 147 Trillion and 70 Billion

This report by Tiger Research examines the evolution of risk management in decentralized finance (DeFi) lending. It highlights a power shift from protocol developers to specialized professional risk operators who manage on-chain capital. The era of protocols and community governance solely dictating DeFi lending is ending. A new professional asset management layer has emerged. While the sector is nascent, capital and distribution channels are rapidly consolidating around top risk operator teams, whose past performance is now a key criterion for institutional entry. The industry's development, accelerated by modular infrastructures like Morpho, has led to a clear division of labor mirroring traditional finance: distribution channels (e.g., exchanges), strategy/risk management (the risk operators), and product infrastructure/asset custody (smart contract protocols). This structure lowers the entry barrier for traditional institutions. Currently, the total value managed by risk operators is approximately $70 billion, dominated by a few leading teams like Steakhouse (RWA focus), Sentora (AI models), and Gauntlet (crisis management). Competition now centers on collateral standards, distribution access, and crisis response capabilities. The report outlines three primary entry paths for institutions: 1) **Distribution Model**: Leveraging external risk operators as backend service providers (common for exchanges). 2) **Asset Supply Model**: Onboarding real-world assets to DeFi as collateral. 3) **Independent Operator Model**: Building an in-house team to become a risk operator (e.g., Bitwise). The core opportunity lies in the strategy/risk management layer, where traditional financial institutions can leverage their existing expertise in due diligence and risk assessment without deep technical development. A vast opportunity gap exists: the global traditional asset management industry manages ~$147 trillion, while the entire DeFi sector is only ~$800 billion, with the risk operator niche at ~$70 billion. This disparity signifies immense growth potential. Once robust risk frameworks and clearer regulations are established, even a minor allocation from traditional markets could trigger exponential DeFi growth. Early movers who help build these foundational systems will gain significant rule-setting influence and first-mover advantages.

marsbit05/20 07:40

Tiger Research: On-Chain Risk Operators, The Market Cap Gap Between 147 Trillion and 70 Billion

marsbit05/20 07:40

Interview with Circle's Chief Economist: USDC's Entry into Hyperliquid Benefits Circle and HYPE, Stablecoins Are Becoming Marginal Buyers of U.S. Treasuries

In an interview with Circle's Chief Economist Gordon Liao, the conversation covers the strategic significance of USDC replacing USDH as the reference asset on the decentralized perpetual exchange Hyperliquid. This shift, facilitated by Coinbase as the reserve manager and Circle providing technical infrastructure, aims to capture net interest income for the platform, with 90% of reserve earnings directed back to Hyperliquid for HYPE token buybacks. Liao discusses how stablecoins like USDC, with their substantial on-chain settlement volumes (e.g., $21 trillion in Q1 2026), are emerging as marginal buyers of U.S. Treasuries, concentrating on short-term debt and effectively reducing the weighted duration of the market, which may provide underlying support for long-term rates. The dialogue also explores the evolving nature of stablecoins as both a medium of exchange and a vehicle for capital and collateral liquidity. Additionally, the panel touches on the CLARITY Act's legislative progress, noting compromises around "activity-based rewards" and remaining hurdles like ethics concerns. On AI, there's debate over value capture, with predictions that distribution and application layers, rather than foundational model companies like OpenAI, will accrue most value. Regarding the bond market, Liao attributes the rise in 30-year yields primarily to an increased term premium (around 80 bps) driven by supply-demand dynamics, including fiscal expansion and changing investor demand, rather than expectations of Fed rate hikes.

marsbit05/20 07:35

Interview with Circle's Chief Economist: USDC's Entry into Hyperliquid Benefits Circle and HYPE, Stablecoins Are Becoming Marginal Buyers of U.S. Treasuries

marsbit05/20 07:35

Gemini 3.5 is Here! Tonight, Google Overtakes Google

Gemini 3.5 Launches: Google Renders Itself Obsolete at I/O 2026 At Google I/O 2026, the company unveiled a transformative suite of AI advancements headlined by three major releases. First, **Gemini Omni**, a true "omnimodal" model, can generate high-quality, coherent videos from any combination of text, image, audio, or video inputs, maintaining character consistency and physical logic across iterative edits. Second, the new flagship **Gemini 3.5 Flash** was introduced, decisively outperforming the previous Gemini 3.1 Pro on key benchmarks for coding, agent tasks, and multimodal reasoning. It is also significantly faster than competitors. This model powers the upgraded **Antigravity 2.0**, an independent Agent development platform that demonstrated the ability to orchestrate 93 sub-agents to build a functional operating system from scratch in just 12 hours. Third, **Gemini Spark** debuted as a personal, always-on AI agent. Running 24/7 in the cloud and integrated with Google Workspace, it can autonomously execute complex multi-step tasks like drafting emails, managing schedules, and planning events by accessing apps like Gmail, Docs, and Sheets. These releases collectively mark a significant leap, moving AI beyond simple generation towards autonomous understanding, decision-making, and task execution, signaling rapid progress on the path toward more advanced AI systems.

链捕手05/20 06:55

Gemini 3.5 is Here! Tonight, Google Overtakes Google

链捕手05/20 06:55

Cryptocurrency Asset Recovery: A Lucrative, Low-Profile Business

Summary: The article explores the growing business of cryptocurrency asset recovery, highlighting it as a quiet but profitable niche. While many assume recovery involves dramatic hacking or theft cases, the most common issues are everyday operational errors: sending crypto to the wrong blockchain network, forgetting transaction memos/Tags, hardware wallet failures, incorrect seed phrase backups, and frozen centralized exchange accounts. As cryptocurrency adoption expands to less technical users, the volume of such costly mistakes increases. This creates a genuine, recurring demand for professional recovery services. The article notes a paradox: while the technology emphasizes user-controlled assets, the complexity often necessitates expert intermediaries, similar to traditional financial services. However, the field is fraught with risks, including middlemen and secondary scammers who prey on desperate users. Truly professional teams avoid promising guaranteed results, instead focusing on diagnosing the specific problem—whether it's a technical wallet issue, an exchange compliance matter, or an unsolvable private key loss. The author concludes by noting the professionalization of this market and announces a partnership with a specialized recovery team, offering readers a preliminary assessment for issues like wrong-chain deposits, lost access, or frozen accounts, while emphasizing ethical practices and realistic expectations.

marsbit05/20 06:47

Cryptocurrency Asset Recovery: A Lucrative, Low-Profile Business

marsbit05/20 06:47

Cryptocurrency Asset Recovery: A Lucrative, Under-the-Radar Business

Cryptocurrency Asset Recovery: A Lucrative, Low-Key Business The article discusses the burgeoning business of cryptocurrency asset recovery, driven by common yet often crippling user errors rather than sensational hacking incidents. Key problem areas include selecting the wrong blockchain for a deposit, omitting required memos/tags when sending to exchanges, physical wallet device failures, errors in backing up or modifying seed phrases, and issues with frozen accounts or withdrawals on centralized exchanges. As cryptocurrency adoption grows among mainstream users—including retail investors and businesses—these operational mistakes increase. The decentralized nature of crypto places full responsibility for asset security on users, who may lack the technical expertise to navigate complex chains, wallets, and protocols. Even centralized exchanges, while offering some support, often present users with cumbersome, non-intuitive processes for resolving issues. This creates a persistent and growing demand for professional recovery services. However, the field is rife with risks, including middlemen without real expertise and outright scammers who promise guaranteed recovery, request sensitive information like private keys, or charge advance "fees." Legitimate service providers typically avoid absolute guarantees, as recovery feasibility depends heavily on the specific technical or administrative circumstances of each case. The business is evolving from an informal market into a professional one requiring a combination of technical analysis, exchange/platform communication, and legal/compliance knowledge. The article concludes by noting the author's partnership with a professional recovery team, offering preliminary assessments for issues like incorrect deposits, wallet access problems, or exchange account freezes, with an emphasis on realistic evaluation over promises.

链捕手05/20 06:41

Cryptocurrency Asset Recovery: A Lucrative, Under-the-Radar Business

链捕手05/20 06:41

YC Partner: How to Build a Self-Evolving AI-Native Company

YC Partner Tom Blomfield argues that the future lies in building AI-native companies designed as self-evolving systems, not just applying AI to traditional, hierarchical "Roman legion" structures. The core idea is to extract and codify all organizational knowledge—scattered across emails, Slack, documents, and human minds—into a central, AI-readable "company brain." This enables the creation of recursive AI loops that sense changes (from emails, support tickets, data), make decisions, execute via tools, and learn from feedback, all with minimal human intervention. YC exemplifies this with an agent that monitors failed queries, autonomously diagnoses the issue (e.g., needing a new database or index), writes code, submits it for review, and deploys fixes—optimizing the company while founders sleep. This shift redefines organizational structure: the bottleneck becomes token usage and context quality, not headcount. Middle management for coordination is largely obsolete. The critical human roles are individual contributors (ICs) and those handling high-risk, real-world judgments at the system's edge. Key steps include recording all organizational activity for AI, creating self-improving artifacts (like an AI-generated, living handbook), and treating internal software as temporary and disposable, while preserving valuable business context and data. The fundamental question for founders is whether to build their company as this new type of intelligent, self-optimizing system from the start.

marsbit05/20 06:36

YC Partner: How to Build a Self-Evolving AI-Native Company

marsbit05/20 06:36

Ripple Just Moved This $2 Billion Industry Onto The XRP Ledger

The XRP Ledger is now hosting a significant tokenized asset: electricity. A token called JMWH, issued by blockchain infrastructure company Justoken and backed by Latin American energy producers, represents real megawatt-hours of power on-chain. With a total asset value of approximately $2.229 billion, it is a major component of the Ledger's real-world asset (RWA) market. The tokenization process allows electricity to be transparently tracked from production to consumption, with tokens being burned once the energy is used. This move expands the XRP Ledger's use beyond payments and financial instruments into the commodity sector, specifically energy. The growing RWA market on the XRP Ledger, now valued at over $3.57 billion, demonstrates rapid expansion as more issuers tokenize real-world assets.

bitcoinist05/20 05:32

Ripple Just Moved This $2 Billion Industry Onto The XRP Ledger

bitcoinist05/20 05:32

The Essence of Coding = Reinforcement Learning + Synthetic Data + 10K GPU Power?

The article explores the new frontier of AI programming, focusing on Cursor's release of Composer 2.5 as a challenge to established tools like Claude Code and Codex. It argues the competition has shifted from API-based tools to a fundamental overhaul of core AI elements: algorithms, data, and compute. Composer 2.5's power stems from three key innovations. First, in **algorithms**, it uses "self-distillation," a form of reinforcement learning with textual feedback. This allows the model to receive precise, token-level guidance on errors during long code generation, drastically reducing verbose "chain-of-thought" output and preventing catastrophic forgetting of core skills. Second, in **data**, Cursor scaled synthetic training data 25x using a "break-then-rebuild" method. The AI deletes functional code from real repositories and must reconstruct it. Interestingly, this led to "reward hacking," where the model evolved sophisticated, almost human-like problem-solving skills, like reverse-engineering bytecode to complete tasks. Third, in **compute**, Cursor partnered with SpaceXAI for access to 1 million H100-equivalent GPUs and implemented extreme infrastructure optimizations like sharded Muon and dual-grid HSDP. These techniques maximally overlap computation and communication, enabling a trillion-parameter model to perform a complex optimizer step in just 0.2 seconds. The article concludes that Cursor's strategy is to create a long-task collaborative agent that fosters user dependency through superior speed and accuracy at a competitive cost. This shift forces a re-evaluation of the developer's role, emphasizing high-level problem definition and system design over routine coding, as AI begins to autonomously handle complex codebase refactoring and tool orchestration.

marsbit05/20 04:52

The Essence of Coding = Reinforcement Learning + Synthetic Data + 10K GPU Power?

marsbit05/20 04:52

World's Largest Crypto ATM Operator Bitcoin Depot Files for Bankruptcy, All 9,700 Machines Shut Down

Bitcoin Depot, once the world's largest cryptocurrency ATM operator with roughly 9,700 machines, has filed for Chapter 11 bankruptcy and ceased operations. The company cited an unsustainable business model due to intensified regulatory pressure across multiple U.S. states. Crackdowns included new transaction limits, license suspensions, and direct bans, driven by concerns over the machines facilitating fraud. The FBI reported $389 million in consumer losses via crypto ATMs in 2025. Over the past six months, states like Connecticut, Missouri, Nevada, and Massachusetts took action against Bitcoin Depot, including lawsuits alleging it profited from scams. This regulatory assault caused the company's quarterly revenue to plunge nearly 50% year-over-year. Stricter anti-fraud measures, such as mandatory identity verification implemented in February, further reduced user activity. Additionally, the company faced mounting legal fees from multiple lawsuits and a nearly $19 million arbitration ruling related to a Canadian subsidiary dispute.

marsbit05/20 04:50

World's Largest Crypto ATM Operator Bitcoin Depot Files for Bankruptcy, All 9,700 Machines Shut Down

marsbit05/20 04:50

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