Rhythm X Zhihu Hong Kong Event Skills Recruitment, Sign Up Now for a Chance to Showcase On-Site

marsbitPubblicato 2026-04-03Pubblicato ultima volta 2026-04-03

Introduzione

Six months ago, "how to write good prompts" was the hottest topic in group chats. Now, that question is clearly outdated. It has been replaced by Skills. The shift was largely triggered by the emergence of OpenClaw, which brought the concept of AI agents into the mainstream. Unlike a smart search engine that answers questions in isolated interactions, an agent can plan, remember, and complete entire tasks autonomously, creating the novel feeling that it is genuinely working for you. This has led to the rise of Skills—specialized capabilities that equip agents to handle specific domains efficiently. Without Skills, an agent is like a smart but untrained newcomer; with them, it can execute complex, precision-sensitive workflows without constant guidance. Popular Skills currently spreading within communities focus on areas like workflow automation, domain-specific rule injection (e.g., for law, finance, or medicine), personalization, and even financial operations such as identifying arbitrage opportunities on Polymarket or executing quantitative trading strategies. This shifts the门槛 from requiring programming and financial expertise to simply installing a Skill. The underlying change is that people are starting to view agents as long-term collaborators, not just disposable tools. Now, with vibe coding, turning an idea into a functional Skill no longer requires a technical team, code, or infrastructure—it can be done over a weekend. The gap between a good idea and a working p...

Six months ago, "how to write good prompts" was the hottest topic in group chats. Now this question is clearly outdated. What has replaced prompts is Skills.

The most obvious tipping point for this shift was, of course, the emergence of OpenClaw.

Even if you could call it plagiarism, and it wasn't the original creator of the agent concept, it truly brought the concept of an agent into the mainstream view, closer to the AI you've seen in movies: possessing personality, able to remember things, capable of planning, and truly able to complete tasks for you, rather than just answering your questions.

In the past, when people used AI, they were essentially using a very smart search engine—you ask, it answers, and the next round starts fresh. The agent lengthens this thread. It actively pushes tasks forward, finds ways around obstacles, and after completing one step, it moves on to the next. The first time you see it actually handle a complete task, you get a strange feeling: this thing is really working for me.

Then people started thinking: how to make it more capable.

This is the real reason Skills have become popular. It's not because Skills themselves are particularly novel, but because the agent made people seriously consider this question for the first time. What Skills do is equip the agent with specialized capabilities.

Why are Skills so important now?

An agent without Skills is like a smart but completely uneducated newcomer. If you ask it to do financial analysis, it can think, but it's slow, prone to errors, and requires you to guide it step-by-step through many stages. Skills are equivalent to it having pre-learned the complete workflow of that field—it can get started immediately without you having to repeatedly correct it.

The most widely shared Skills in the community currently focus on a few areas: workflow automation, stringing together operations that originally required jumping between multiple tools into a chain the agent can run through on its own; injection of rules for professional fields, ensuring the agent doesn't improvise freely when performing tasks requiring high precision, like in law, medicine, or finance; personalized adaptation, tuning the agent to your most efficient way of working, remembering your preferences, language style, and judgment criteria; and, of course, a category of Skills related to money, such as trading.

Arbitrage opportunities on Polymarket are something the average person can't decipher from the order book, nor do they have the time to watch the trends and calculate price differences. But an agent equipped with specialized Skills can: monitor in real-time, identify discrepancies, judge whether to enter the market, running the entire suite without you needing any background knowledge in predicting markets.

The same goes for quantitative trading. In the past, this was the domain of investment banks and hedge funds, requiring writing strategy code, connecting APIs, and watching backtesting data. Now, people have packaged the entire process into Skills; an agent can install them and start executing strategies on exchanges. The barrier to entry has shifted from "knowing how to program and understanding finance" to "knowing how to install Skills".

This change isn't about making people lazier; it's about pushing the boundaries of capability outward.

Behind these needs lies a common logic: people are starting to seriously treat agents as long-term collaborators, not just tools to be closed after use.

So, what novel ideas do you have that you want to turn into a skill for your agent?

Before, you had an idea, you spotted a market gap, but couldn't implement it. You didn't know how to code, didn't have time to learn, hiring外包 was expensive and slow, and eventually that idea just rotted in your notes. Now it's different. Using vibe coding, you can directly shape your idea into a Skill—no need to make a webpage, no need to make an app, no server required, no maintenance team needed.

The underlying logic of this is: agents will be a necessity for everyone. The Skills you make don't need to find their own users; they naturally run on the agent that everyone is already using. The market is there, the channel is there, you just need to build the thing that nobody else has made yet.

Before, there was a technical team standing between "I have a good idea" and "I have a working product". Now that distance has been compressed to a weekend.

Domande pertinenti

QWhat has replaced 'how to write good prompts' as the hottest topic in the community, according to the article?

ASkills have replaced 'how to write good prompts' as the hottest topic.

QWhat concept did OpenClaw bring into the mainstream, making AI more like the kind seen in movies?

AOpenClaw brought the concept of 'agent' into the mainstream, making AI more like a personalized, memory-capable, planning entity that can complete tasks.

QWhat is the primary function of Skills for an AI agent?

AThe primary function of Skills is to equip an AI agent with specialized capabilities, allowing it to perform specific tasks efficiently without constant guidance.

QWhat are some of the key areas where widely shared Skills are concentrated?

AWidely shared Skills are concentrated in workflow automation, injection of professional domain rules (like law, medicine, finance), personalization, and financial transactions.

QHow does the article describe the change in the barrier to creating a product from an idea?

AThe barrier has been compressed from needing a technical team to potentially 'a weekend' using vibe coding to create a Skill, eliminating the need for building a webpage, app, server, or maintenance team.

Letture associate

The "Impossible Triad" Is Fundamentally a Pseudo-Problem

The article argues that blockchain's fundamental limitation is not the scalability trilemma (decentralization, scalability, security), which has been largely solved, but the lack of **privacy** and, until recently, clear **legitimacy**. Blockchain is described as a slow, expensive, globally shared computer whose core value is censorship resistance and verifiability. While ideal for native digital assets like money (e.g., stablecoins), its default transparency acts as a **tax**, exposing all transactions and enabling MEV extraction, which deters serious institutional capital. Simultaneously, its permissionless nature created regulatory ambiguity. The piece contends that **privacy** is the missing critical feature. It rejects the false choice between total transparency and complete anonymity. Modern cryptography (like zero-knowledge proofs) enables **compliant privacy**: users can prove facts (solvency, KYC status, compliance) without revealing the underlying sensitive data (specific holdings, identities). This preserves auditability for regulators and eliminates the leak of financial information. With recent regulatory progress (e.g., the GENIUS Act) addressing legitimacy, adding default, provably compliant privacy becomes a pure upgrade. It transforms blockchain from a costly, public ledger into a confidential settlement layer, finally bridging the gap to mainstream institutional and individual adoption of on-chain finance.

链捕手6 h fa

The "Impossible Triad" Is Fundamentally a Pseudo-Problem

链捕手6 h fa

Optical Chips: Collective Capacity Expansion

The global optical chip industry is experiencing a massive wave of expansion driven by surging AI data center demand. Major players across the US, Japan, Europe, and China are aggressively investing to ramp up production capacity. In the US, Coherent is expanding its 6-inch Indium Phosphide (InP) semiconductor fab in Texas, supported by CHIPS Act funding and a $2 billion strategic investment from NVIDIA. Lumentum is building a new factory for InP optical devices, and Nokia is scaling its advanced photonic chip packaging and testing capabilities. NVIDIA's investments aim to secure future supply of critical lasers and optical interconnect products for AI infrastructure. Japan's JX Advanced Metals, a leading InP substrate supplier, plans a multi-billion yen investment to increase its capacity 7-10 times, strengthening its grip on the crucial upstream materials market. In Europe, IQE and Tower Semiconductor settled a patent dispute and signed a multi-year InP epitaxial wafer supply agreement, highlighting that next-generation silicon photonics platforms will integrate high-performance InP components. STMicroelectronics and Sivers Semiconductors are also expanding silicon photonics production and partnerships. China is rapidly building out its domestic supply chain. Dongshan Precision's subsidiary, Source Photonics, announced a $12 billion project to expand optical chip and module production. Companies like Sanan Optoelectronics and Yunnan Germanium are scaling up InP chip manufacturing and substrate production, moving towards vertical integration from materials to modules. While debate continues around the exact future architecture—whether CPO (Co-Packaged Optics), NPO, or pluggables will dominate—analysts like Morgan Stanley argue the underlying driver is unchangeable: the explosive growth in bandwidth demand. This will inevitably increase the volume of optical engines, lasers, and related content per GPU, regardless of the final technical path. The competition for "more light" in the AI era has intensified into a global, full-chain capacity race.

marsbit8 h fa

Optical Chips: Collective Capacity Expansion

marsbit8 h fa

Stablecoins Finally Find Real Yield: An In-Depth Look at On-Chain Reinsurance Re | A Conversation with Re Founder Karan Saroya

Stablecoin Real Yield Found: A Deep Dive into On-Chain Reinsurance with Re's Karan Saroya As stablecoin supply exceeds $170 billion, the search for sustainable, non-speculative yield intensifies. Re, an on-chain reinsurance platform, provides an answer: connecting stablecoin capital to the trillion-dollar traditional reinsurance market. Re operates as a regulated reinsurer, accepting stablecoin deposits as collateral to back US insurance companies. These insurers pay premiums, generating yield that flows back to on-chain depositors. Currently supporting 35 insurers and underwriting $500 million, Re projects scaling to over $1 billion soon. Key insights from a Bankless podcast with founder Karan Saroya and investor Avichal of Electric Capital: 1. **Uncorrelated, Real-World Yield:** Re offers stablecoin holders access to reinsurance returns (targeting 12-14%+), an asset class entirely separate from crypto or equity markets. 2. **Operational Efficiency via Smart Contracts:** Re replaces traditional, labor-intensive capital fundraising with smart contracts, allowing a ~12-person team to compete with industry giants. 3. **Regulatory Leverage:** For every $1 of collateral, regulations allow backing $5-7 in written premiums. This leverage amplifies returns from the underlying risk-free rate. 4. **DeFi Integration:** Depositors receive receipt tokens, which can be used in protocols like Morpho for "looping," potentially pushing yields to 18-20%+. 5. **The "DeFi Mullet" Model:** A compliant front-end (regulated reinsurer) paired with a decentralized back-end (smart contracts, DeFi capital markets). 6. **RE Governance Token:** Modeled on Lloyd's of London, the token governs the central capital pool's allocation, counterparty acceptance, and parameters. 7. **Real Economic Impact:** Capital funds real-world productivity (factories, clinics, businesses) via insurance, moving beyond crypto's internal loops. The discussion highlights a pivotal moment: DeFi's supply-side infrastructure is now met by real demand for productive yield, potentially kickstarting a flywheel where vast on-chain stablecoin capital seeks these real-world returns.

链捕手10 h fa

Stablecoins Finally Find Real Yield: An In-Depth Look at On-Chain Reinsurance Re | A Conversation with Re Founder Karan Saroya

链捕手10 h fa

1996 or 1999? Walsh's First Test is 'How to View AI'

"1996 or 1999? Wall's First Big Test Is 'How to View AI'" Federal Reserve Chairman Wall's initial challenge is not whether to raise or cut rates, but a more fundamental judgment: what kind of boom is the current AI boom? This will determine the Fed's policy path and define his legacy. Economics is split between two opposing views, according to reporter Nick Timiraos. One sees imminent productivity gains that will increase supply and cool inflation, allowing the Fed to hold steady. The other argues that while productivity benefits are distant, demand shocks are here now, and waiting for data confirmation risks missing the intervention window, forcing sharper rate hikes later. Wall has signaled a leaning toward the first view, echoing 1996-era Alan Greenspan, who embraced strong, productivity-driven growth without fear of inflation. However, Wall faces a different macro environment than Greenspan did, with tariff pressures, expanding fiscal deficits, and diminishing globalization benefits, which could force more significant inflation pressures even if AI benefits materialize. Wall's logic, expressed before taking office, is that AI-driven productivity gains won't show in official data for years. If the Fed waits for confirmation, it might mistakenly tighten policy and choke off the very growth that could suppress inflation. This argues for using forward-looking narratives over lagging data. Chicago Fed President Austan Goolsbee presents a key counter-argument. He distinguishes between expected and unexpected productivity booms. A widely anticipated boom, like the current AI wave, can cause people to spend future wealth gains in advance, overheating the economy before productivity actually rises, thus requiring preemptive rate hikes. He cites rising costs for AI data centers as evidence of such overheating. Fed Governor Christopher Waller offers a rebuttal to Goolsbee, noting the "expected spending" mechanism only works if people can borrow against future income, which many households cannot do due to borrowing constraints. Wall also faces a paradox related to his desire to reduce the Fed's use of "forward guidance" (pre-announcing policy moves). This practice was established in 1999 when Greenspan began signaling hikes to avoid market shocks. If the economy follows a less optimistic path, Wall may be forced to choose between using the guidance he wants to abolish or risking market volatility by staying silent. The ultimate question defining Wall's first major test remains: Is this 1996 or 1999?

marsbit11 h fa

1996 or 1999? Walsh's First Test is 'How to View AI'

marsbit11 h fa

Trading

Spot
Futures
活动图片