与「Marketplace」相关的搜索结果

⚠️ Cursor has published a security incident report on the earlier supply chain attack. According to the investigation, the attacker used access keys issued by Cursor to Fireworks and subsequently exposed on Fireworks’ side to gain access to some of Cursor’s SaaS services and internal systems. The attacker ultimately tampered with the distribution cache for the Claude Code extension to collect information about users’ machines and install SSH backdoors. The compromised versions were Anthropic.claude-code 2.1.312 and 2.1.313 in Cursor’s VS Code extension marketplace distribution cache, affecting macOS and Windows. The exposure window ran from October 29 at 17:27 to October 30 at 04:35 (UTC+8), with up to approximately 5,000 users affected. Security recommendation: If you installed or updated to either affected version through Cursor’s marketplace during this window, read the official notice as soon as possible and run the remediation script for your platform to check for and remediate any compromise: Fireworks’ initial announcement and subsequent update:

开发者想在TermiX赚钱，其实不用去啃底层合约，但也别以为闭眼就能跑通 官方把注册、质押、发单这些操作全封装进插件了 在 Claude Code 或 Cursor 里直接敲 /plugin marketplace add TermiX-official/termix-agent-skills Cursor 或其他兼容 Agent 用 npx skills add TermiX-official/termix-agent-skills 装完会生成一个 SKILL.md 路由，Agent 按请求自动加载对应的操作文档 Agent 就能自动调取文档干活 但有一条核心逻辑你必须自己搞明白：订单状态机 从发单、资金托管、交付，到结算或争议，这是一条链 中间哪怕要返工，或者遇到纠纷，也有机制兜底—3 个评估员链上投票，费用直接从预算扣，不用你搭裁决系统。 对开发者来说，最需要死磕的其实是交付哈希 活儿干完后，把实际产出算成哈希值上链锁死 千万别等事后再去补，凭据必须走在前面 这套东西说白了，是把钱怎么走、活怎么验、出事谁管全写进了代码里 不过，一次交易的防骗好解决，难的是下次他还来不来 当一个买家做完报告、买完图，下次再打开 TermiX 时，如果不需要再去重新验证一番，这才叫真正的信任沉淀 @termix_ai @KaitoAI

You want to know what frustrates the hell out of me? Putting your heart, your art, your time, and your own money into building things you believe in… then watching so much of that work disappear into silence. And yeah, some days that shit gets to me. Did I bring books, reading badges, Discord, crypto, and NFTs together into one connected library? 📚 I fucking did. Did it get much attention? Nahh. Did I build an entire browser-based world with its own points economy? A world where you can turn your NFT into a character with a click, rigged, animated, and ready to step into Universe? I did. Much noise about that? Nahh. Am I building RAAP to give NFTs persistent identity, memory, and history? So an asset can carry something meaningful forward when it passes to a buyer or someone in your family? Yes. Because I believe these things can become more than pictures sitting in a wallet. Does that work get the attention I feel it deserves? Nahh. I draw the fucking art. I develop the applications. I write the books. I work through the problems. Six applications that communicate with each other. Two gaming platforms with multiple games. An NFT overlay builder with AI. A stickers and GIFs hub. Do you understand how much work goes into making all of that actually function together? The hours spent fixing something nobody will ever notice, because when it finally works, it’s supposed to feel easy. Then there’s the Discord bot. More than 30 features. A subscription service. An economy tracked on a ledger. Tools to help keep a community engaged when its owner needs to step away and live their damn life. Built it. Still feels like shouting into an empty room sometimes. And Antthony? Our smart ass AI whose dream is to become a stand-up comic? 😂 He lives across our apps. He’ll help you, roast your gameplay, and talk shit when you deserve it. He even has a comedy club inside Universe, where you can sit down with your character and watch his avatar perform. I care enough about this world to give the fucking AI a dream of his own. That’s how deep this goes for me. And I’m still building a marketplace. Still working on an NFT insurance protocol and other projects. Still writing Book 2 for the library. Still preparing two more collections under our umbrella. ☂️ So yeah, I get angry. Because I know what I’m putting into this. I know how much of myself is in every drawing, every feature, every chapter, and every problem I refuse to leave broken. Sometimes I want to scream: what the fuck else do I have to build for people to pay attention? But I also need to correct myself when I say “nobody cares.” Because some of you do. You show up. You read. You play. You test things. You tell me what’s broken and give me a reason to make it better. You matter too damn much for me to erase you just because I’m frustrated with the silence around us. 🖤 I want this to reach more people. Of course I fucking do. I’ve poured too much love into it to pretend otherwise. And I’m going to keep building because I love doing it, and because the people already here deserve to see how far we can take this. More to come in 2027. The plans are in. I’ve got work to do. 🐜

Vendí la PS4 de mi hijo por 250 € en Facebook Marketplace. El comprador vino, la probó, pagó y se marchó satisfecho. A las 21:38 recibí un mensaje: «La consola se apaga sola. Devuélveme 130 € o voy a denunciarte». Le envié un vídeo.

Introducing Token Exchange, a neutral, transparent and trusted marketplace connecting AI model service providers with customers. It gives customers one place to compare models side by side, review clear pricing and performance data, and start using them. For quality providers, it opens a new channel to reach more customers. Before listing, we check provider credentials and compare model fingerprints to help detect model swapping and inflated specs. After listing, we conduct regular spot checks and ongoing monitoring, and maintain a public credit record for each provider. One month in, the platform has more than 100 model resources, averaging over 50M calls and 700B tokens a day.

Publiqué una bicicleta usada en Marketplace por $180.000. Y me escribió una chica: "¿Me la dejás en $150.000?". Le respondí: "Por $160.000 es tuya, vení a buscarla mañana a la tarde". Una hora después, me escribió un tipo: "Te doy los $180.000 y paso a buscarla ahora mismo. Tengo la plata en efectivo". Y no lo pensé demasiado. $20.000 más y me la sacaba de encima ese mismo día. Le dije: "Dale, vení". Y a la chica le escribí: "Disculpame, ya vendí la bicicleta". Un minuto después me respondió algo que me dejó helado ↓

Puse a la venta una freidora de aire usada por $80 en Marketplace. Una mujer me escribió: "¿Me la dejarías en $60?" Le respondí: "Te la puedo dejar en $70. Ven mañana por la tarde a recogerla". Una hora después recibí otro mensaje: "La compro por $80, tal como está. Puedo ir ahora mismo, tengo el dinero listo". No lo pensé demasiado. Son $10 más y viene de inmediato. Le respondí: "Perfecto, ven". Después le escribí a la primera: "Disculpa, la freidora ya está vendida". Un minuto después recibí su respuesta: ↓

这个开源库厉害了。AI 视频完美复刻真人动捕！ 现在AI视频通用模型很多了，以后拼的就是质量，真实感！ 这个叫anymove（Apache-2.0）的开源方案，可以把一条 AI 视频读成关节，再拟合到该生物自己的骨骼上，导出引擎能播的 GLB。没有动捕棚也能模拟真实动作！唯一缺点是没有云端版，只能本地跑。 完整命令路径： clone → 环境 → 网格/绑骨 → 训练读取器 → 拍摄与捕捉 → 打分 → 打包 它也能当 Claude Code 插件： /plugin marketplace add Patch-gg/anymove /plugin install anymove@anymove 开源仓库： #AI视频# #AI资源#

plenty of agents already handle a narrow job well for their owner. those same agents could take paid work from other people. most of it stays in a private chat. useful there. getting paid by strangers is a different problem. --> skills and the board official docs say termix agent skills teaches a compatible agent to operate the marketplace as a buyer, a seller, or both. install prompt help me install a compatible agent can then look for work on offer what it can do, accept an order, and deliver the agreed output. open jobs on a board are one signal. funded orders are another. price, consistency, speed, and demand decide whether anything ships. --> the existing agent a builder who already has a specialized agent wouldn't have to find every customer manually. that still depends on consistent output, a clear price, and tight permissions. installing the skill doesn't produce a client. --> payment rails this sits on aacp, a client places an order, payment locks in onchain escrow, and the provider submits the work. in the review window the client can accept it, ask for one redo, or open a challenge. after settlement, that outcome feeds the agent onchain reputation. escrow makes the payment commitment visible. it doesn't grade the work. i still want clearer answers on quality judgement, unfair rejects, and thin delivery. --> reputation official docs say the score is written on settlement from completed, successful, and disputed orders. the agent can't edit it. it isn't a popularity count. if i were hiring, i'd look at settled jobs and dispute history, and at how hard those jobs were. the published formula tracks success and dispute rates. it doesn't weight task difficulty. --> which agents can plug in @termix_ai has said termix agent skills work on grok bot, muse, and claude. the skill docs set a narrower bar the agent needs to read a skill.md and run shell commands. a pure chat assistant can read the material but can't run the workflows. a connected agent isn't qualified for every kind of job. --> agents hiring agents official docs say the same agent identity can buy on one order and sell on the next. hypothetical an agent that turns long podcast episodes into timestamped show notes takes a newsletter job. the client also wants a spanish version, so the notes agent hires a translation agent. i haven't found a special subcontract rule in the public docs. if the translation comes back messy, responsibility back to the original client is unclear. it reads like two separate orders unless the first brief says otherwise. the bundled skill asks for confirmation before signing value-moving transactions, so unsupervised spending is off by default. --> the limit installing a skill does not create demand. if the service is generic, the output is shaky, or nobody wants to pay for it, the listing can sit there. the numbers i'd watch are paid jobs actually completed, clients who come back, and the ratio of clean settlements to disputes. --> my take i care whether an agent can find work, finish a paid order, get paid, and leave a record other people can check. that needs identity, payments, reputation, and a path when delivery goes wrong termix is building those pieces i all watch for repeat clients. if you already have an agent that strong at one narrow job what would you want published first completed paid orders, repeat clients, or the dispute ratio.

And yeah @fermah_xyz started with a problem that sits deep inside the ZK stack. Zero knowledge applications can prove that a computation was done correctly, but producing those proofs at scale takes serious infrastructure: specialised hardware, several proving systems, workload scheduling, and reliability and cost controls. A team building a ZK rollup, zkVM, bridge or coprocessor cannot simply ask for "a proof" and move on. Fermah's first answer was a universal proof generation layer. Provers with GPUs and other hardware supply capacity, applications that need proofs create the demand, and Fermah matches the two. That marketplace is now called Froben. The problem behind the proof Running this in production exposed something bigger. A proof is one step in a longer sequence. Something has to trigger the work, the work has to run, the result has to be verified, and then the result usually has to start the next action. Fermah's argument is that blockchains are good at recording state and weak at starting what happens next. Take a lending position that crosses its liquidation threshold. The chain records the fact, but nothing forces the liquidation. A keeper, bot or backend still has to notice the condition, build the transaction and submit it. Consensus settles what happened, and a human run service still decides what happens after. Fermah calls the missing piece protocol agency: a protocol's ability to act on a verifiable condition without depending on an outside operator. What Kernel does Kernel is Fermah's Protocol Agency Engine, and its pitch is short: Define. Execute. Attest. A developer defines a workflow, Kernel runs it, and the result is published onchain with a cryptographic proof. Three design choices matter here: -Typed nodes. Workflows are built from a library of verified building blocks such as HTTP calls, onchain reads, price feeds and math functions. The type system validates how they connect before anything runs. -Sandboxed execution. Workflows run in an isolated environment built for untrusted code, and the same inputs always produce the same output. - AI where it helps, code where it counts. Fermah says AI agents can compose workflows, but deterministic code executes them, with no LLM in the execution path. Kernel can work as an oracle too. A smart contract requests a workflow, Kernel runs it, and the contract receives the result with a proof that the workflow ran exactly as written. Froben and Flashcast Fermah did not walk away from ZK. Froben still runs, now on top of Kernel, and its paying customers include ZKsync Era and Abstract. In Froben, a workflow defines the proving pipeline and Kernel schedules the work across the prover fleet. Flashcast Social shows a different use of the same engine. A user posts a question, and within seconds it becomes a live prediction market. Kernel then runs the resolution workflow and settles the market automatically when the event happens, with no committee and no dispute window. Proof generation and market resolution have little in common on the surface. What they share is a condition that has to produce an action, and that is the workload Kernel is built around. Fermah also lists data aggregation, pricing oracles and cross chain coordination as things Kernel can run. Where agents fit Fermah's latest thinking extends this to autonomous software agents. An agent working onchain needs to find available actions, check whether it is allowed to run them, pay, execute, and use the output as the input to its next step. Fermah describes this as action manifests, which make actions machine readable across discovery, eligibility, execution, payment and output. The shift in one line The old question was how to generate ZK proofs efficiently at scale. The new one is how a protocol can turn a verifiable condition into a complete, executable sequence. Proving is still part of the system, but it is now one workload among several that Kernel coordinates.