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I built JSON Utility Kit as a small browser-based toolkit for everyday JSON tasks. The CSV to JSON converter recently got an update for nested JSON structures. For example, headers like user.name, user.email, order.id can be converted into nested objects instead of flat keys. What it supports: CSV to JSON conversion Nested object output from dot notation headers Browser-side processing No signup JSON formatting and validation tools nearby Tool: https://jsonutilitykit.com/tools/csv-to-json/ GitHub: https://github.com/kejie1/json_utility_kit

一个任务的奇幻漂流：同一个Agent任务，在FROST和FROST-SOP中分别长什么样？ 作者 ：神通说 日期 ：2026-07-08 主题 ：双项目联动 | 周三轮换 阅读时间 ：8分钟 一个问题 你可能听过这样的故事： "这个框架很好，但我不知道怎么用到真实项目里。" FROST 社区里也收到过类似的反馈—— "500行代码确实让我理解了Agent的本质，但理解完之后呢？怎么从'看懂'到'会用'？" 今天这篇文章，就用 同一个真实任务 ，分别展示它在 FROST 和 FROST-SOP 中的样子。你会发现：它们不是两个不同的东西，而是 同一个东西的不同分辨率 。 今天的任务：自动写日报 假设你是一个独立开发者，想让Agent每天自动帮你写工作日报。需求很简单： 收集今天完成的任务 用LLM生成日报摘要 发送邮件给自己 就这么三步。让我们看看它在两个项目中分别怎么实现。 第一站：FROST——用最少的代码理解本质 FROST 的哲学是： 先用500行代码告诉你Agent的底层逻辑，剩下的你自然就会了。 在FROST中，一个Agent的运作只需要四个原子： from core import Store , Agent , skill_set , skill_get # 1. 创建记忆容器 store = Store () # 2. 定义能力（Skill = 纯函数） def collect_tasks ( context ): """ 收集今日任务 """ tasks = [ " 完成FROST v5.0文档 " , " 修复Skill测试用例 " , " 写推广文章 " ] context [ " tasks " ] = tasks return context def generate_summary ( context ): """ 生成日报摘要（简化版，实际调用LLM） """ tasks = context . get ( " tasks " , []) context [ " summary " ] = f " 今日完成 { len ( tasks ) } 项任务： " + " 、 " . join ( tasks ) return context def send_report ( context ): """ 发送日报 """ context [ " sent " ] = True print ( f " [日报已发送] { context [ ' summary ' ] } " ) return context # 3. 组装Agent agent = Agent ( " daily_reporter " , store , skills = { " collect " : collect_tasks , " summarize " : generate_summary , " send " : send_report }) # 4. 用SOP编排执行顺序 result = agent . run ( sop_steps = [ " collect " , " summarize " , " send " ], initial_context = {} ) # 输出：[日报已发送] 今日完成3项任务：完成FROST v5.0文档、修复Skill测试用例、写推广文章 这段代码做了什么？ Store 是记忆——Agent的工作空间，所有中间结果存在这里 Agent 是细胞——拥有记忆和能力的最小执行单元 sop_steps 是宪法——定义执行顺序，Agent不会自作主张改变流程 就这么简单。没有配置文件，没有YAML，没有复杂的初始化。 30行Python代码，一个完整的Agent就跑起来了。 这就是FROST的价值——它不帮你"做"什么，它帮你 看懂 Agent到底是什么。 问题来了 上面的代码能跑，但如果你真的想把它用在生产环境，你会遇到一堆问题： 发邮件的能力怎么写？ FROST不管——它只告诉你Skill是纯函数，具体实现你自己来 LLM调用怎么复用？ 每次写日报都要调LLM，总不能每次都重写一遍 任务失败了怎么办？ 发邮件失败了，要不要重试？重试几次？ 执行日志在哪？ 老板问"今天日报发了吗"，你怎么知道发没发？ 多个Agent协作呢？ 一个人有好几个项目，每个项目一个Agent，怎么协调？ FROST对这些问题的回答是： 这些问题不是我该回答的。 但它的兄弟——FROST-SOP——就是专门回答这些问题的。 第二站：FROST-SOP——让同一个任务跑在生产环境 FROST-SOP 的哲学是： 把FROST教你的每一个概念，都变成生产级的工程组件。 同样的"自动写日报"任务，在FROST-SOP中长

Forty packages, one maintainer, and no ESLint config anywhere in the repo. That is not a boast - it is the direct result of a decision made early: every tool in the toolchain has to earn its place by governing all forty packages from one config file, not forty. The repo is flare-engine , a modular 2D engine for React Native + Web (animation, gamification, interactive UI, with games as showcases - not a game engine, not a Unity or Godot competitor). The stack behind it is Turborepo Bun Biome , and this post is the actual setup: the real turbo.json , the real biome.json , the real CI guardrail, straight from the repo (trimmed only where a config is long, and I say so where I trim), not a starter template's idealized version. Four binaries, four root configs - turbo.json , biome.json , tsconfig.base.json , and the Changesets config - each governing all forty packages at once (Bun's own "config" is just the workspaces array in the root package.json ). Plus a CI step that fails the build the moment a package imports something it shouldn't. That is the whole story, and I want to show you the files, not describe them. Four binaries, not twelve config files The thesis is narrow: a solo maintainer can keep forty packages honest only if there is exactly one config of each kind, and every package extends it rather than declaring its own variant. Twelve packages each with a slightly different ESLint config is not a monorepo, it is twelve monorepos wearing a workspace file as a costume. Here is the root package.json that runs all of it - Bun workspaces (not pnpm; that distinction matters and I will say it again below), the script table every package leans on, and the pinned package manager: // C:\_PROG\flare-engine-workspace\flare-engine\package.json { "name" : "flare-engine" , "version" : "0.0.0" , "private" : true , "workspaces" : [ "packages/*" , "benchmarks" , "apps/*" ], "scripts" : { "build" : "turbo build" , "test" : "turbo test" , "lint" : "turbo lint" , "typecheck" : "t

Here is an uncomfortable one: you do not own your reading list. You rent it. Every "follow" button you have pressed in the last decade put your reading relationship inside a company's database, where it can be ranked, throttled, or ended the day the business model changes. You did not sign anything. You just stopped owning it. It was not always like this. Feeds were the quiet machinery that kept the web interoperable. RSS and Atom meant a site, a reader, and a robot could all agree on the same stream without asking anyone's permission. You published once, and anything could read it: whatever app, whatever order, no algorithm in the middle. Then it eroded. Plenty of sites ship no feed at all now, and "follow us" quietly became "create an account on someone else's platform." The reason is not mysterious. Platforms had every incentive to close the loop, because a feed lets you leave, and an account does not. So the industry swapped "here is my stream, read it however you like" for "log in to see updates," and a generation of sites simply stopped publishing feeds, because the platform was where the audience was. That is the trade you made without noticing. The open format that asked nothing of you got replaced by a login that asks for everything. Your reading list used to live in your reader and survive a company changing its mind, its ranking, or its whole business. Now it lives in their database and survives exactly as long as they allow. Getting it back is not nostalgia. It is infrastructure for independence: tooling that treats feeds as a first-class citizen, aggregates the sources you actually choose, and keeps that stream under your control instead of a platform's. The full case for why this is worth fixing, and what feed-first tooling looks like, is here: https://mederic.me/blog/open-web-feeds So, honestly: how many of the people and sites you follow could you still read tomorrow if the platform in the middle disappeared tonight?

Most web applications contain at least one vulnerability from the OWASP Top 10. A typical security audit takes 2-3 weeks and costs upward of $10,000. An LLM can compress the initial audit down to a few hours because it scans code for patterns rather than specific CVEs. Below are 15 vulnerabilities found while auditing production code with Claude. Each includes the vulnerable code, the fixed version, and a prompt to reproduce the finding. Classification follows OWASP Top 10 (2021). Order reflects frequency of occurrence: most common first. Methodology: how to run an AI security audit The audit consists of three passes. First, a broad scan: the LLM receives the entire project and looks for vulnerability patterns. Second, deep analysis: each identified pattern is verified in context (middleware, ORM, framework). Third, verification: manual review of every finding, because LLMs produce false positives. Prompt for the broad scan: Perform a security audit of this code. For each finding, include: 1. CWE ID and name 2. OWASP Top 10 category 3. Severity (Critical/High/Medium/Low) 4. The vulnerable code snippet 5. Attack vector -- exactly how an attacker would exploit this 6. Fixed code Ignore stylistic comments. Focus on security only. Start with injection attacks, then broken access control, then the rest. This prompt works because it defines the output structure and prioritizes categories. Without explicit instructions, the LLM mixes critical vulnerabilities with remarks about email validation. More on structured AI code review: AI Code Review Checklist . A03:2021 -- Injection 1. SQL Injection via string concatenation The most common finding. Shows up even in projects using an ORM, because developers switch to raw queries for complex filters. Vulnerable code: // API endpoint for user search app . get ( ' /api/users ' , async ( req , res ) => { const { search , sortBy } = req . query ; const query = ` SELECT id, name, email FROM users WHERE name LIKE '% ${ search } %' ORDER

We stopped measuring retrieval quality by how many tokens we could fit into the prompt. When...

Trying to diversify what I listen to and interested to hear what everyone else is enjoying this year!

The fastest, most accurate dictation model in the world Discussion | Link

Automates your existing workflows with a single prompt. Discussion | Link