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Hey everyone! 👋 First of all, thank you so much for all the support, feedback, and feature suggestions since the Product Hunt launch. Every comment has been incredibly valuable, and I've already started implementing some of your ideas. Today I'm excited to share a new update to Rizzzler ! 🌙 ✨ What's New? 🎨 2 Brand-New Themes I've added two new themes , giving you even more ways to personalize your profile and match your own style. Whether you prefer something clean, vibrant, or expressive, there's now even more variety to choose from. 👀 Live Preview Panel One of the most requested improvements is finally here! You can now preview your showcase page while editing it . No more guessing how your profile will look after saving—see your changes in real time before publishing. 🎵 Audio Preview Choosing background music is now much easier. Instead of selecting tracks blindly, you can now preview audio directly before applying it to your profile. ✨ Profile Picture Decorations Want your profile to stand out even more? You can now add Profile Picture Decorations to give your avatar a unique look and make your showcase even more personal. More decoration styles will be added in future updates! ❤️ Thank You The feedback from the GitHub community and Product Hunt has been incredibly helpful. Many of these improvements came directly from community suggestions, and I'll continue building Rizzzler based on your ideas. If you have feature requests, bug reports, or suggestions, I'd love to hear them! 🔗 Try Rizzzler Website https://rizzzler.onrender.com Product Hunt https://www.producthunt.com/p/rizzzler-show-yourself-off GitHub https://github.com/DeveloperPuneet/Rizzzler-Stable Thank you for supporting Rizzzler! 🚀

FROST-SOP V6.1.0 工程实践：从初始化流水线看「零门槛上手」 作者：神通说 日期：2026-07-21 主题：周二·SOP工程 | V6.1.0 新特性深度解析 开篇：一个让所有开发者头疼的问题 想象这个场景： 你刚克隆了一个开源项目，兴冲冲地跑起来，结果： ModuleNotFoundError: No module named xxx ImportError: DLL load failed OSError: [WinError 2] 系统找不到指定的文件 你开始疯狂搜索、提问、等回复。一小时过去了，项目还没跑起来。 这是开源项目最大的痛点之一：「最后一公里」问题。 今天，我们来聊聊 FROST-SOP V6.1.0 是如何解决这个问题的。 V6.1.0 核心特性：初始化流水线 V6.1.0 版本最核心的更新，是一个 全自动初始化流水线（Initialization Pipeline） 。 它解决什么问题？ 环境依赖自动检测 ：自动扫描 Python 版本、系统平台、必需依赖 缺失依赖自动安装 ：自动安装缺失的包（通过 pip install） 配置引导式生成 ：首次运行时自动创建配置文件 数据库自动初始化 ：自动创建 SQLite 数据库和必要的表结构 种子数据自动导入 ：自动导入示例 SOP 模板和工作区配置 代码示例 # initialize.py - 初始化流水线核心实现 import subprocess import sys import os from pathlib import Path class InitializationPipeline : """ FROST-SOP 初始化流水线 """ def __init__ ( self , project_root : Path ): self . root = project_root self . issues = [] self . warnings = [] def run ( self ) -> bool : """ 执行完整初始化流程 """ steps = [ ( " 环境检测 " , self . _check_environment ), ( " 依赖安装 " , self . _install_dependencies ), ( " 配置生成 " , self . _generate_config ), ( " 数据库初始化 " , self . _init_database ), ( " 健康检查 " , self . _health_check ), ] print ( " 🚀 FROST-SOP 初始化流水线启动 \n " ) for name , step_fn in steps : print ( f " 📦 { name } ... " ) try : result = step_fn () if not result : self . issues . append ( f " { name } 失败 " ) print ( f " ❌ { name } 失败 \n " ) return False print ( f " ✅ { name } 完成 \n " ) except Exception as e : self . issues . append ( f " { name } 异常: { e } " ) print ( f " 💥 { name } 异常: { e } \n " ) return False self . _print_summary () return True def _check_environment ( self ) -> bool : """ 检查 Python 版本和系统环境 """ version = sys . version_info if version . major < 3 or ( version . major == 3 and version . minor < 10 ): self . issues . append ( f " Python 版本过低: { version . major } . { version . minor } " ) return False print ( f " Python { version . major } . { version . minor } . { version . micro } " ) return True def _install_dependencies ( self ) -> bool : """ 安装项目依赖 """ req_file = self . root / " requir

The openFDA enforcement API is free, keyless, and well documented on the surface. It is also full of failure modes that return HTTP 200 with quietly wrong data. Every number and error string below was measured against the live API on 2026-07-20; anything I could not reproduce has been cut. Pick the right endpoint first There are four recall-shaped endpoints and they are not interchangeable. Choosing wrong gives you a different universe of records with no warning. Endpoint Records What it is drug/enforcement.json 17,793 Recall Enterprise System (RES) drug recalls device/enforcement.json 39,519 RES device recalls food/enforcement.json 29,224 RES food recalls device/recall.json 58,756 CDRH device recall database, a different schema entirely The three enforcement endpoints share their schema. device/recall.json does not: its fields include cfres_id , product_res_number , k_numbers , root_cause_description , event_date_posted , event_date_terminated and recall_status , and it has no classification field at all ( count=classification.exact returns HTTP 404 "Nothing to count" ). If you are filtering for Class I, you want an enforcement endpoint. The two families also refresh on different clocks. On 2026-07-20 the three enforcement endpoints reported meta.last_updated of 2026-07-08, while device/recall.json reported 2026-07-17. The OR bug that silently returns the wrong answer This is the single most expensive trap, and it is undocumented. An unparenthesized OR discards every clause except the last one. All figures below are from food/enforcement.json . search=classification:"Class I" OR state:"CA" returns 4,003 - exactly the count for state:"CA" alone. Reverse the operands and you get 12,809 - exactly classification:"Class I" alone. Wrap it: search=(classification:"Class+I"+OR+state:"CA") returns 14,822 in both orders. That is the real union (12,809 + 4,003 - 1,990 overlap, and the AND of the two clauses does return 1,990). No error is raised in any case. The nastier varia

Introducing vernLLM: A Resilience Layer for LLM Applications Building production-ready LLM applications is not just about sending prompts and receiving responses. Real-world AI systems need to handle timeouts, provider failures, rate limits, inconsistent outputs, and reliability issues. That is where vernLLM comes in. vernLLM is a lightweight resilience layer for OpenAI-compatible chat completion APIs , providing a single interface with built-in retries, timeouts, circuit breaking, caching, structured output, and usage tracking. Instead of rebuilding the same reliability features for every LLM project, vernLLM gives you the tools needed to make your AI integrations more robust from the start. Features Automatic retries with backoff Transient failures happen. vernLLM automatically retries recoverable errors while failing fast on validation errors and non-retryable responses. Timeouts & cancellation Prevent hanging requests with configurable timeouts and cancellation support. Circuit breaker protection Automatically stop sending requests to failing providers and recover when the service becomes healthy again. Structured output with type safety Pass a Zod schema and receive validated, typed results back. const result = await llm . call ({ systemPrompt : ' Return JSON: { "skills": string[] } ' , userContent : ' Extract skills from: ... ' , schema : SkillsSchema // zod schema }); Provider-native JSON Schema support Constrain model generation itself instead of only validating responses afterward. Built-in caching support Cache LLM responses using your own cache adapter with cachedCall and cachedLLMCall . One interface across providers Use the same API across multiple providers: OpenAI Groq Mistral DeepSeek Cerebras Together AI Fireworks AI Ollama Anthropic Gemini AWS Bedrock Any HTTP-compatible provider through fromFetch Why vernLLM? Many LLM applications end up creating their own wrappers around provider SDKs to handle: retry logic API failures provider switching respons

Talk of banning Chinese-made open-weight LLMs reveals the challenge of turning AI into a business.

This week's Java roundup for July 13th, 2026, features news highlighting: a reintroduction of Value Objects (Preview); the GA release of WildFly 41; the July 2026 edition of Open Liberty 26.0.0.7; point releases of TornadoVM, Apache TomEE, Java Operator SDK and LangChain4j; a maintenance release of Micronaut; a new extension, Quarkus Shim; and a new Oracle AI Agent Studio for Fusion Applications. By Michael Redlich

Overview Open WebUI exposed a real Ota lifecycle boundary. This was not mainly a parsing or contract-shape repo. The contract was already strong enough to model: source-checkout verification packaged native runtime through uv run open-webui serve frontend development runtime default Docker Compose runtime What the repo exposed was operational truth after proof: a successful native proof still left a host workload alive the first cleanup fix then widened too far and treated a Compose-owned runtime as the same class of host workload That made Open WebUI a valuable pressure repo. It forced Ota to get more precise about cleanup ownership instead of treating all successful runtime proof as one generic teardown problem. The current pressure contract pins released Ota v1.6.24 . Its latest green matrix run proves the release surface at the exact contract and workflow revision linked below. What Open WebUI exposed in Ota This repo exposed four meaningful weaknesses. 1. proof success was weaker than it looked The first issue was not that runtime proof failed. It was that runtime proof succeeded and still left the native workload alive afterward. In this repo, the packaged native workflow launches: serve:native : launch : kind : command exe : uv args : - run - open-webui - serve - --host - 0.0.0.0 - --port - " 8080" Ota proved that workflow, but the launched process tree was still alive after proof completed. In GitHub Actions, that surfaced through setup-uv post-job cleanup, which blocked while the uv cache was still in use. That was an Ota gap. If proof succeeds but leaves behind repo-owned runtime state that later breaks CI cleanup, the proof surface is still incomplete. 2. native service cleanup widened past its real ownership boundary The first core fix made Ota clean selected native service workloads after successful proof. That was directionally correct, but Open WebUI immediately exposed the next boundary. The Docker workflow uses a native task shape to launch Compose:

Celebrating $100 million contributed by the community to the people who build and sustain open source every day. The post $100 million for open source: A milestone built by the community appeared first on The GitHub Blog .

If you've ever cat -ed a Terraform or OpenTofu state file, you already know the uncomfortable truth: it's a plaintext JSON dump of everything your infrastructure knows, including secrets. Database passwords, generated private keys, API tokens injected through providers — they all land in state, in the clear. OpenTofu is the one place where you can fix this at the source, because native state and plan encryption is a first-class OpenTofu feature that upstream Terraform does not have. Here's how it actually works and how I roll it out on existing projects without breaking them. Why plaintext state is a real risk State is not a cache you can regenerate. It's the authoritative map between your HCL and the real resources, and OpenTofu has to store the values of attributes to compute diffs. That includes sensitive ones. Marking an output sensitive = true only hides it from the CLI output — it's still written verbatim to state. On real infra I've seen state end up in three places it shouldn't: an S3 bucket without SSE and with overly broad read IAM, a CI artifact that got uploaded to a build cache, and a developer laptop with terraform.tfstate committed to a feature branch by accident. Backend encryption (like S3 SSE) helps for one of those. It does nothing for the other two, because the moment state leaves the backend it's plaintext again. OpenTofu's encryption operates at the data layer, before the bytes ever hit the backend or a local file. The state is encrypted at rest everywhere: in the backend, in local copies, in CI artifacts. That's the property I want. Anatomy of the encryption block Encryption lives in a terraform { encryption { ... } } block. It has three moving parts: a key provider (where the encryption key comes from), a method (the actual cipher), and targets ( state and/or plan ) that bind a method to what you want encrypted. terraform { encryption { key_provider "pbkdf2" "passphrase" { passphrase = var . tofu_encryption_passphrase } method "aes_gcm" "defa

I had a website blocker, a Pomodoro timer, a tab suspender, and a time tracker installed at the same time — four separate extensions, four separate settings pages, none of them talking to each other. Starting a focus session meant manually turning on the blocker, then starting the timer, and neither knew the other existed. So I built TabInsights , which does all four and actually connects them. What it does Website blocker — block by domain, category, or schedule, with an optional typed "unblock challenge" for the days willpower isn't enough. Pomodoro focus timer — one click starts a 15/25/45-minute sprint, which also auto-blocks distracting categories for the duration and unblocks them automatically when it ends. This is the part that actually solves my original problem — the timer and the blocker are the same feature, not two extensions coincidentally running at once. Memory saver — auto-suspends tabs you haven't touched in a configurable window (15–60 min), freeing roughly 50MB of RAM each via chrome.tabs.discard() . Suspended tabs stay in your tab bar and reload exactly where you left off with one click. Automatic time tracking — logs time per domain with no manual start/stop, and shows a daily focus score. A few implementation notes Manifest V3 removed persistent background pages, which meant every "ongoing" feature — sprint timers, the daily summary, auto-suspend checks, license re-validation — had to be rebuilt on chrome.alarms instead of a long-lived timer. The gotcha: Chrome clamps alarm intervals to a minimum of 1 minute in packaged (published) extensions, so anything needing finer granularity has to accept that floor rather than fight it. The blocker uses declarativeNetRequest — you hand Chrome a set of match rules and it enforces them at the browser level. The extension never actually reads the blocked request; it can't, by design, which is also the honest answer any time someone asks whether a blocker "sees" their browsing. The bigger architectural deci

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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.