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YOUR AI ASSISTANT CAN NOW FIND ANYONE'S EMAIL AND PHONE NUMBER wiza plugged 800M+ contacts into grok, claude, chatgpt and any mcp client the best b2b contact data for sales and marketing - now one prompt away you type who you need, it comes back with names, work emails and phone numbers here's how to put wiza mcp into a real workflow: > find new leads "find vps of sales at us software companies with 51-200 employees" your icp turns into a list of real people > map the account "get company info for [company], then find its sales leaders" you know the whole buying committee before the first call > enrich before you reach out "find the work email and phone number for [name] at [company]" no more jumping between linkedin and 5 tabs > source candidates "find senior backend engineers in seattle with available work emails" a recruiting pipeline without a sourcing agency > build a marketing list "save these prospects as conference follow-up and find their work emails" wiza saves the list and finds the emails in the background > chain it search companies, find the decision makers, enrich them, save the list one conversation instead of an afternoon of manual research setup: 1. add to your ai assistant 2. sign in with your wiza account 3. ask for what you need building your own tools? the same data is in the wiza api start here ↓

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.

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this is how instinct works under the hood - agent loop outside of the sandbox (running on the backend server) - executing commands and controls file system on a sandbox - backs up git memory folder to S3 bucket available skills: - agent-mail - authenticator-codes - browser-management - browser-use - bug-report - company-identity - data-privacy - deep-research - document-signing - files - flight-booking - food-ordering - gamepigeon - google-docs - i2i-management - instinct-faq - instinct-guides - mac-computer-use - messaging - onboarding - place-recommendations - presentations - proactive-value-creation - restaurant-booking - restaurant-recommendations - rideshare - scheduling - shopping - speaking - wake-subscriptions

据我的职场观察，日本人普遍对软件、数据和系统本身缺乏客观的认识，很难认可软件应有的价值。 我倒不认为日本公司是舍不得为软件花钱，很可能是大多数企业根本都不知道自己要买什么服务。 因此从结果上看，个人感觉日本企业普遍没有SaaS文化，尤其是在传统制造业里，这点和美国非常不同。 而且我当初作为外国人入职时明显感到，同样作为是工程师，只要不是IT专业，很多人对现代软件开发的基本概念都不懂。 身边的日本同事可能非常了解机械、电气、材料、生产工艺或者设备设计，但可能甚至连python是什么都不知道，更谈不上数据结构化。 这就导致大部分人可能甚至听都没听说过class、instance、library、API、database、backend，更加不知道软件系统能够把多少重复性的工程工作自动化。 结果就是公司可能并不是不愿意花钱开发软件，而是自己连软件需求都没有能力准确描述。 比如一个电气工程师如果不知道python可以批量处理Excel，不知道数据库可以管理设备参数，不知道API可以让不同系统交换数据，不知道后台程序可以自动检查设计条件、追踪revision，甚至不知道很多规则性的判断本身就可以抽象成程序，那在面对低效率重复工作时，这个电气工程师想到的只可能是继续增加Excel、增加人工确认步骤，或者要求别人做一个看起来像软件的表单。（所以大手企业里有很多xlsm的macro，这就又增加了维护成本） 做工程的不懂软件，无法准确提出需求，于是把开发外包给SIer或者外部软件公司。 外面的软件工程师虽然懂程序，又不懂发电厂、电力系统、机械设计这些专业知识以及实际的workflow。 公司员工说不清楚自己真正需要什么，外边的软件公司也不知道为什么这个工程流程必须要这样设计。最后做出来的系统四不像，根本没有符合实际的workflow，这就导致软件不好用。 这又反过来强化传统行业对软件无用的刻板印象，于是企业更加不愿意为软件和内部开发能力投资。这是个恶性循环。 日本肯定有很优秀的软件公司和工程师，但在我看来，在日本既懂工程，又能把工程数据抽象成数据结构、算法、数据库和软件系统的人太少了。 这也是我到日本之后感受很强烈的地方。 而且我的本科并不是计算机专业，而是机械工程。 我不知道其他的大学怎么样，但至少在华科，即便是机械工程，python也是作为必修课计入学分的。 当年我也不懂为什么要学这个，我以为只是为了让我们学习Numpy这些数学工具当研究室工具人。 但我现在知道了，其实从这一点上来看，中国的本科教育可能是非常成功的。python课程的设计目的可能只是至少让工程师理解计算机可以做什么，能够自己写一些程序解决工程问题。 我认为这种基础教育非常重要。所以我之前有质疑过日本的本科基础教育。。 工程师并不需要会开发大型软件，但至少应该知道什么事情可以自动化、什么问题适合用数据库解决、什么工作可以写脚本处理、什么时候应该调用API，以及一个软件系统大致是怎样组成的。 只有具备这些基本认知，才有可能提出好的软件需求。否则就跟现在日本一样，天天喊DX，结果就只是买几个极其难用的系统、增加几个网页界面、把Excel上传到云端，然后继续用和二十年前几乎相同的方法。 这并不是真正的软件化，纯粹浪费时间罢了。 日本更需要解决的问题可能是把python等纳入本科基础教育。（但我没在日本读本科，不是很懂日本本科教育到底学不学这些）