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AI 资讯

Just shipped @modyra/core: a tiny state layer for complex frontends

I've just published a small npm package called @modyra/core that came out of a very specific pain: handling "slightly complex" app state in frontend projects without ending up with a mess of context, custom hooks and scattered stores. The focus of the package is: Minimal API, all in TypeScript, no hidden magic No heavy runtime: just tools to model the core of your app as a set of state modules Designed to plug into React/Angular/vanilla JS without forcing you to rewrite everything The idea is to treat your app's domain as a set of "core units" with clear responsibilities: each unit exposes state, actions and rules, and the rest of the app simply consumes them. It came out of a few projects where Redux/Zustand/signals etc. were fine, but started to feel either too verbose or not very close to the actual business domain. If you feel like taking a look and tearing it apart, feedback (including harsh ones) is very welcome: naming, API design, examples - everything is still fresh enough to change. And if you think the approach is worth exploring, a star on GitHub would really help me keep pushing the project forward.

2026-07-20 原文 →
开发者

Why wordpress.org Won't Let You Install Composer Packages From a Plugin

Cross-posted from the Loopress docs Loopress is a toolset to make WordPress reproducible and reviewable via Git . Versioned snippets, Composer without SSH, and more coming... Check Loopress WordPress doesn't have a package manager. If you want a PHP library in your project, be it Guzzle for HTTP calls or a PDF generation library in a snippet, you're either vendoring the code by hand or running Composer somewhere the WordPress admin can't see. We built a feature to fix that: a Composer UI inside the WordPress admin. Search Packagist, install a package, audit it for known vulnerabilities, all without SSH access ( full walkthrough here ). Before shipping it, we asked the wordpress.org plugin review team whether it would be acceptable in the official directory. The answer was no. The rule Here's the relevant line from Guideline 8 of the wordpress.org plugin directory: "Plugins may not send executable code via third-party systems." Installing a PHP package from Packagist is, by definition, downloading executable code from a third party and writing it to disk. It doesn't matter that our plugin never calls the installed code's autoloader itself, that the user has to load it deliberately from their own snippet. The review team was clear: the indirection changes nothing. The mere presence of that capability is enough to trigger the rule, whether or not it's ever used. There's no folder you can hide it in that makes it acceptable, they weren't shy about saying that outright. If you want PHP dependencies in a plugin distributed on wordpress.org, the only accepted path is to vendor them at submission time: ship the code, with a compatible license and readable source, not fetch it dynamically. For us, that meant Composer package management could never live in the same plugin that ships on wordpress.org. Full stop, no clever workaround changes that. What we tried first, and undid Our first move was the obvious one: split into two plugins. A "Core" plugin with snippet sync, distri

2026-07-20 原文 →
AI 资讯

ADA: an open-source AI data analyst that shows its math

I watched my sister paste a CSV into on of our LLM overlords and ask a question then reword it and get a different number. That's the problem with letting an LLM both read your question and trust it's math. ADA splits the two jobs. Drop in a CSV or Excel file, get a dashboard, anomaly detection, a forecast, and plain-English answers, all calculated locally using pandas (locally in the sense of never leaving the server and going to the AI apis.). The LLM layer that you can use (it's optional) but that only sees column names and never your rows, and the whole thing works with zero API key if need be (not the LLM part of course). MIT licensed, solo-built, open to contributors (in fact would need help to make it move out of LLMs and have a few open issues). I wish to make it as LLM free as possible as I want to make it deterministic. Live demo: automated-data-analyst.streamlit.app Repo: https://github.com/saineshnakra/automated-data-analyst

2026-07-20 原文 →
AI 资讯

FROST 深度：为什么 AI Agent 需要「家族谱系」？

FROST 深度：为什么 AI Agent 需要「家族谱系」？ 前言：一个古老的管理学问题 你有没有想过，为什么人类社会的组织方式大多是「层级制」？ 从部落、到王国、到现代公司，我们习惯了一种结构： 有人决策，有人执行，有人监督 。 但当我们构建 AI Agent 系统时，为什么大多数框架都把 Agent 做成「孤岛」？每个 Agent 有自己的记忆、自己的工具、自己的行为逻辑——像一个没有家族传承的独立个体。 FROST 的核心理念是：Agent 应该有谱系、记忆和荣誉感。 1. 什么是「家族治理」？ FROST 引入了生物学的隐喻： Agent 家族 。 细胞会死，但谱系会存续。 Agent 会消亡，但宪法会传承。 资产会永存。 在这个框架里，有三个关键角色： 祖辈（Elder） ：定义不可违背的规则，是系统的「宪法」 父辈（Parent） ：负责领域协调，可以递归委托子任务 孙辈（Child） ：执行具体任务，用完即散 这不是简单的角色扮演，而是一套 结构化的治理协议 ： 协议一：层级 Store 继承 祖先 Store 对后代是只读的。后代只能继承和扩展，不能篡改祖先的记忆。 # 祖辈定义的宪法 ancestor_store = Store () ancestor_store . save ( " constitution " , { " rule_1 " : " always_validate_before_act " , " rule_2 " : " never_expose_raw_context " }) # 子孙只能继承，不能修改宪法 child_store = ChildStore ( ancestor_store ) 协议二：SOP 宪法校验 每个 SOP（标准操作流程）在执行前，必须经过祖辈审核。 # 子孙编写的 SOP child_sop = [ " fetch_user_data " , " process_payment " , " send_confirmation " ] # 执行前必须经过宪法校验 if not elder . validate_sop ( child_sop ): raise PermissionError ( " SOP violates constitution " ) 协议三：编排层级限制 禁止越级 spawn。父辈只能调度子辈，不能跨代指挥。 class Elder : max_spawn_generation = 0 # 祖辈不能 spawn class Parent : max_spawn_generation = 1 # 只能 spawn 子辈 class Child : max_spawn_generation = 2 # 只能 spawn 孙辈 协议四：选择性持久化 只有经过父辈审核的产出，才能进入家族记忆库。 # 子孙的临时产出 temp_result = child . execute ( task ) # 父辈决定是否收割 if parent . approve ( temp_result ): parent . merge_from ( child ) # 吸收有价值的结果 2. 为什么 Agent 需要「记忆传承」？ 当前大多数 Agent 框架的痛点： 每个对话都是全新的开始 。 你问 ChatGPT 一道数学题，它不会记得你上周问过类似的题目。你用 AutoGPT 做项目，它不会继承你之前调试的经验。 FROST 的解决方案是 分层的记忆系统 ： 层级 生命周期 用途 瞬时记忆 单次任务 工作区，只读不存 世代记忆 代际传递 子辈可继承祖先数据 宪法记忆 永久 不可修改，家族共享 经验记忆 按需收割 父辈选择性地吸收有价值产出 class HierarchicalStore ( Store ): """ 层级记忆系统 """ def __init__ ( self , ancestor_store = None ): self . ancestor = ancestor_store # 只读祖先记忆 self . local = {} # 本地可写记忆 def save ( self , key , value ): if key in self . ancestor : raise ValueError ( " Cannot override ancestor memory " ) self . local [ key ] = value def load ( self , key ): if key in self . local : return self . local [ key ] if key in self . ancesto

2026-07-20 原文 →
AI 资讯

I Found a Silent Bug in Formbricks That Crashes Live Surveys at Runtime

This is a submission for DEV's Summer Bug Smash: Clear the Lineup powered by Sentry . Project Overview Formbricks is an open-source survey and experience management platform built with Next.js, TypeScript, React, and Tailwind CSS. It lets teams create and deploy surveys across websites, apps, and email. Developers can self-host it or use the cloud version. With over 12,000 GitHub stars and hundreds of contributors, it is one of the most actively maintained open source alternatives to Qualtrics. Bug Fix or Performance Improvement Formbricks supports custom regex validation rules on survey questions. A survey creator can set a pattern that user responses must match before they are accepted. The problem was in how that pattern was stored. The validation schema for regex pattern rules only checked that the input was a non-empty string: // Before the fix export const ZValidationRuleParamsPattern = z . object ({ pattern : z . string (). min ( 1 ), flags : z . string (). optional (), }); z.string().min(1) means "give me any string with at least one character." It does not verify that the string is actually a valid regular expression. So a survey creator could type [invalid as their pattern, the schema would accept it, it would be saved to the database, and then when a real user submitted a response, the system would try to run new RegExp("[invalid") , JavaScript would throw a SyntaxError , and the survey would crash silently at runtime. The bug never surfaced during setup. It only appeared when a real user was trying to submit a real response. Code Pull Request: github.com/Tobore005/formbricks/pull/1 Here is the fix: const isValidRegexPattern = ( pattern : string ): boolean => { try { new RegExp ( pattern ); return true ; } catch { return false ; } }; const isValidRegexFlags = ( flags : string | undefined ): boolean => { if ( flags === undefined ) return true ; try { new RegExp ( "" , flags ); return true ; } catch { return false ; } }; export const ZValidationRuleParamsPa

2026-07-20 原文 →
AI 资讯

Are AI Agent Engrams Open Source or Proprietary?

Are AI Agent Engrams Open Source or Proprietary? The short answer: both, and the split matters. The major agent-memory engines — Mem0, Letta, Cognee, Graphiti, LangMem, and PLUR — are all Apache-2.0 or MIT licensed on GitHub. But "open source" and "open format" are not the same thing. A project can ship under Apache-2.0 while storing your memories in opaque vector blobs you cannot read, edit, or export. The real question is not whether the software is open — it usually is — but whether your memories are. This distinction separates the field into three tiers: fully open (software + format + data you own), open-core (software is open, but the hosted memory is not portable), and fully proprietary (memory baked into a model provider's infrastructure, no export at all). Why this question exists AI agents face a brutal constraint: they forget. Every new session starts blank. Every context window overflows. The fix is persistent memory — but persistent where, and in whose format? The term "engram" comes from neuroscience. Richard Semon coined it in 1904 for the physical trace a memory leaves in biological tissue (Semon, 1904; cited in Wikipedia, "Engram (neuropsychology)"). Applied to AI agents, an engram is one discrete thing an agent has learned — a correction, a preference, a procedure — stored so it survives across sessions. The question "open source or proprietary?" asks two things at once: Is the engine that stores and retrieves engrams open source? Is the format those engrams are stored in open — readable, editable, portable? Conflating the two is how vendors end up with open-source repos and locked-in data. Tier 1: Fully open — software, format, and data These projects ship under permissive licenses (Apache-2.0 or MIT) AND store memory in a format you can inspect, edit, and export. Project License Stars (Jul 2026) Memory format Data ownership PLUR Apache-2.0 ~215 Human-readable YAML engrams Yours — plain files PLUR stores each engram as a plain-text YAML entry — an

2026-07-20 原文 →
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