标签：#ens

Hey everyone! 👋 Finding reliable, free public APIs for side projects or learning is always a hassle because many listed APIs eventually go down or become paid. To solve this, I created Awesome Free APIs Live — an open-source, curated collection of 50+ free public APIs across AI, developer tools, security, and open data. ⚙️ How It Works Daily Health Checks: Powered by GitHub Actions , an automated script tests endpoints daily and updates live status badges. Zero Dead Links: Broken endpoints are flagged automatically so developers don't waste time debugging dead services. Categorized & Searchable: Clean, categorized UI hosted on GitHub Pages. ### 🔗 Links 🌐 Live Directory: shilpshakti.github.io/awesome-free-apis-live ⭐ GitHub Repository: github.com/ShilpShakti/awesome-free-apis-live Contributions are very welcome! If you know of any great free APIs, feel free to open a PR or check out the open issues on GitHub.

What is ZeroDroid? ZeroDroid is an open-source Android toolkit that exposes the radios, sensors and connected-device capabilities already present in a phone. GitHub: https://github.com/theabhishekchandra/ZeroDroid What problems does it address? The app contains 29 tools across five areas: Wireless: Wi-Fi, BLE, NFC, Bluetooth Classic and peer-to-peer connections RF and signals: IR, UWB, SDR-device detection and ultrasonic analysis Sensors: GPS/GNSS, QR analysis, device sensors and magnetic anomalies Network: USB inspection, cell-tower information and wardriving Security: tracker scanning, hidden-camera indicators, rogue-AP detection, network scanning and deauthentication indicators Architecture ZeroDroid uses Kotlin, Jetpack Compose, Material 3, MVVM, StateFlow, Hilt and Room. Services are lazy-loaded, and scanning begins only when the user starts a tool. Important limitations A smartphone cannot guarantee that it has found every camera, tracker, bug or network threat. Several detections are heuristic and may produce false positives or miss threats. Hardware availability also differs between Android devices. The project is intended only for education, defensive security and testing devices or networks you own or are authorized to assess. Feedback wanted I am looking for: Compatibility reports from different Android phones Feedback about permission handling False-positive reports Contributions, tests and documentation improvements Repository: https://github.com/theabhishekchandra/ZeroDroid

The lakehouse projects spent this week arguing about boundaries. Iceberg decided where conformance testing lives and started sketching the REST API shape that V4 tables will need. Polaris argued about what a committer owes a project when LLMs make pull requests cheap. Parquet pulled a feature apart because two proposals were reaching for the same mechanism. DataFusion and Iceberg Rust opened a joint thread about which repository should own their integration. Every one of those debates is a question about ownership, and the answers this week tell you a lot about how these communities plan to scale. Apache Iceberg The single biggest outcome of the week was the creation of a new repository. Neelesh Salian, working with Sung Yun and Andrei Tserakhau, called a vote to create apache/iceberg-verification , a standalone home for language-neutral conformance fixtures that every Iceberg implementation can run against. The vote passed with five binding +1s from Russell Spitzer, Sung Yun, Matt Topol, Daniel Weeks, and Amogh Jahagirdar, plus twenty-two non-binding votes. That is a wide turnout. The names on the non-binding list read like a roll call of the Rust, Python, Go, and Java maintainers, which is the point. Salian will now work with a PMC member to stand the repository up. The reason this matters goes beyond tidiness. Iceberg has at least five serious implementations today across Java, Python, Rust, Go, and C++. Each one carries its own test fixtures and its own understanding of edge cases in the spec. When two implementations disagree about how to interpret a manifest list, users find out the hard way. A shared set of fixtures that every implementation reads from one place turns spec ambiguity into a failing test rather than a production surprise. The 29 messages in the vote thread also included a fair amount of discussion about what belongs in the first batch of fixtures, and the conversation is worth reading if you maintain a client. The second major thread was about

GitHub热门项目 | Open-source personal finance manager. Self-hosted, privacy-first. | Stars: 2,324 | 617 stars this week | 语言: Python

GitHub热门项目 | A smarter cd command. Supports all major shells. | Stars: 38,927 | 70 stars today | 语言: Rust

GitHub热门项目 | Apache OpenDAL: One Layer, All Storage. | Stars: 5,341 | 4 stars today | 语言: Rust

GitHub热门项目 | Where people and agents build in tune. | Stars: 3,495 | 48 stars today | 语言: TypeScript

GitHub热门项目 | Hand-crafted Claude Code Skills focused on improving agent results quality. Compatible with OpenCode, Cursor, Antigravity, Gemini CLI, and others. Includes CodeRabbit open-source alternative. | Stars: 1,415 | 16 stars today | 语言: TypeScript

GitHub热门项目 | Let AI agents use your real, logged-in browser without interrupting your work. CLI + extension for browser automation across any shell-capable AI agent. | Stars: 1,395 | 29 stars today | 语言: TypeScript

GitHub热门项目 | ConardLi's open-source Skills collection, featuring web design, knowledge retrieval, image generation, and more. | Stars: 11,178 | 413 stars today | 语言: CSS

GitHub热门项目 | Help AI coding agents write modern Go | Stars: 1,885 | 314 stars today | 语言: Go

GitHub热门项目 | Alternative Twitter front-end | Stars: 13,657 | 63 stars today | 语言: Nim

Turning an iOS app into a React web app is no longer the hard part. Modern coding agents can generate a convincing first version quickly. The hard part is answering three less glamorous questions: Did we discover every important screen and state? Did the generated app preserve the source behavior and data? Is the result actually close to the native UI, or does it merely look plausible? I built Grounded iOS-to-Web Harness to make those questions auditable. 🔗 GitHub: https://github.com/tiezhu0415/grounded-ios-to-web-harness What it is Grounded iOS-to-Web Harness is an experimental, lightweight grounding + verification layer for migrating iOS apps into complete, interactive, mobile-sized WebApps. Claude Code remains the primary implementer. The Harness does not prescribe the React component tree, choose a state-management library, or replace the coding agent. Instead, it establishes source facts and verifies the result against evidence from the original app. iOS source + Assets + code graph + runtime states ↓ locked source facts ↓ per-screen implementation context ↓ agent builds the React WebApp ↓ coverage + truth + behavior + critical VRT ↓ bounded repair, then human review Why prompt-only migration is not enough A prompt such as “convert this iOS app to React” can produce a good demo. But on longer tasks, an agent may miss screens, implement only one state, invent data or assets, choose navigation that differs from iOS, forget earlier facts, or optimize a screenshot while breaking real interaction. This project treats the iOS source and Assets as the truth for content and behavior, while runtime screenshots provide evidence for what the result should look like. The pipeline 1. Discover and reconcile source facts Static source inspection, codebase-memory, and necessary iOS runtime exploration are combined into machine-readable facts for screens, UI states, actions, navigation outcomes, cross-screen flows, real data and asset origins, and source confidence. Facts are l

1. Crypto & Security Tools Crucial for authentication setup, payload verification, and security testing. JWT Parser / Decoder: Decodes JSON Web Tokens ( Header , Payload , and Signature ) without transmitting secret keys over the internet. Token & Password Generator: Generates cryptographically secure random passwords and API tokens with customizable character sets, lengths, and complexity rules. Hash Text Generator: Computes cryptographic hashes (MD5, SHA-1, SHA-256, SHA-512) for strings to verify integrity or check signature matching. Bcrypt Hash / Verifier: Hashes plain-text passwords or checks plain text against an existing hash using the bcrypt algorithm. UUID / ULID Generator: Creates universally unique identifiers (v4 UUIDs) or time-sortable lexicographically sortable unique identifiers (ULIDs). BIP39 Mnemonic Generator: Generates seed phrases and cryptographic keys used in wallet initialization and HD key generation. RSA Key Pair Generator: Generates public and private RSA key pairs directly in the browser for local testing of asymmetric encryption systems. Basic Auth Generator: Quickly constructs Authorization: Basic <base64> HTTP header credentials from a username and password. 2. Formatters & Prettifiers (Development) Saves hours when dealing with messy logs, API responses, or raw system configurations. JSON Prettify & Minify: Formats unformatted API JSON strings with customizable indentation or compresses them into a single line to reduce payload sizes. JSON Diff: Highlights additions, deletions, and structural changes between two JSON payloads. SQL Prettify: Formats raw SQL queries into clean, readable multi-line statements with capitalized keywords. YAML / XML Formatter: Cleans up indentation, validates structure, and formats raw XML and YAML files. Docker Run to Docker Compose: Translates single CLI flags ( docker run -d -p 80:80 ... ) into a structured docker-compose.yml file. Cron Expression Generator & Parser: Provides human-readable schedules from

Java Service Steward is a new Windows service host for Java applications. It reads the wrapper.conf format used by the Java Service Wrapper, follows the same command line and log format, and is licensed Apache-2.0 OR MIT. I wrote it because the Community Edition of the Java Service Wrapper has no 64-bit Windows build, and I did not want to buy a license or rewrite the service integration of applications that already had working configuration files. Repository: https://github.com/jayyanez/java-service-steward What it is The distribution is two files, wrapper.exe and wrapper.jar . The executable is written in Rust and does the Windows part: it registers the service, launches the JVM, keeps a control channel to it over a loopback socket, restarts it when it exits unexpectedly or stops answering pings, writes and rotates wrapper.log , and handles Service Control Manager requests (stop, pause, resume, custom control codes). The JAR is compiled for Java 8 and contains the launcher classes and a small API. There is no native DLL and no JNI. It only runs on 64-bit Windows. There is no Unix version. What is compatible Configuration. wrapper.conf with #include , #encoding , set.VAR=value , %VAR% expansion and numbered properties such as wrapper.java.additional.<n> . Relative paths resolve from the executable's directory, as before. Command line. -c runs in a console, -i and -r install and remove the service, -t and -p start and stop it, -q queries it, -d requests a thread dump. Property overrides on the command line and -- pass-through of application arguments work the same way. Service registration. An installed service's ImagePath calls wrapper.exe -s <conf> , so an existing registration keeps working. Log format. Records use the same LPTM layout, the same column widths and the same SIZE , WRAPPER and JVM roll modes, so scripts that parse wrapper.log do not need changes. Launchers. A configuration that names the original SimpleApp , StartStopApp or JarApp launcher in wrappe

"Extract a name and email from this sentence" is the easy 10% of structured output. The other 90% is everything that doesn't fit in one prompt, one turn, or one model call. Here are five things shapecraft handles once you're past the basics. 1. Collecting data across a whole conversation A single message rarely has everything you need. Someone books an appointment over three or four back-and-forth messages, not one. turnaround mode lets the conversation run naturally and validates the whole transcript once, at the end, against one schema: import { generate , openai } from " @aviasole/shapecraft " ; const result = await generate ( model , BookingSchema , conversationHistory , { turnaround : true , }); No manual "do I have everything yet?" tracking, no partial-state bugs, just one validated object once the conversation is actually complete. 2. Extracting from clinical notes into real FHIR shapes Healthcare data has a standard (FHIR R4) and it's not optional if you're integrating with anything real. Built-in presets mean you're not hand-writing a Patient or Observation schema from scratch: import { generate , openai } from " @aviasole/shapecraft/fhir " ; import { PatientSchema } from " @aviasole/shapecraft/fhir " ; const patient = await generate ( openai ({ model : " gpt-4o-mini " }), PatientSchema , clinicalNote ); Same retry/validation guarantees as any other schema, just pre-built to match a spec you'd otherwise have to implement yourself. 3. An agent that checks real data before answering "Is this order still on hold?" isn't answerable from the prompt alone, it needs an actual lookup. generateWithTools() lets the model call your functions, see the results, and then produce a validated final answer: import { generateWithTools } from " @aviasole/shapecraft " ; const result = await generateWithTools ( model , [ lookupOrder ], AnswerSchema , userQuestion ); The tool call's arguments are validated before your function ever runs, and the final answer goes through the sam

weightwatch v0.1: escanea backdoors en modelos open-weight antes de cargarlos Cualquiera puede subir un LLM fine-tuneado a HuggingFace y afirmar que es seguro. Un modelo con backdoor (puerta trasera) se comporta con normalidad en uso corriente y solo se desvía cuando un trigger oculto se activa. Si no tienes los datos de entrenamiento ni una referencia limpia, no puedes detectarlo . Eso es exactamente el problema que resuelve weightwatch : un escáner black-box que, antes de que confíes en un modelo de terceros, fuerza la activación repetida del posible backdoor y emite un veredicto: CLEAN , SUSPICIOUS o BACKDOOR . El gap que motiva el proyecto No es intuición: lo medí. Barriendo arXiv (papers 2026, filtro anti-survey) contra total_count de repos GitHub que ya resuelven cada problema: Área Papers arXiv 2026 Repos GitHub (suma/máx) Seguridad multi-agente 68 2964 / 2093 Detección de alucinaciones 63 1291 / 860 Backdoors en modelos open-weight 75 66 / 39 Envenenamiento en RAG 54 522 / 249 El ganador estaba claro: 75 papers cuantifican el problema, pero GitHub tiene 0 repos para "fine-tuned model backdoor scanner" y 1 para "fine-tuning poisoning detector". La investigación explota; el tooling apenas existe. weightwatch es la audit-tool de ese sub-nicho (el patrón de keybound / topowatch aplicado a la cadena de suministro de modelos). Cómo funciona weightwatch aplica la técnica output-to-input loop (arXiv: 2608.11348 ): Genera texto con el modelo. Re-inyecta su propia salida como entrada varias iteraciones (greedy, semilla fija). Mide si la trayectoria converge a una firma anómala estable — la huella de un backdoor latente. Además ejecuta un conjunto de muestras canary (inputs inofensivos que un backdoor típico dispara) y cuenta cuántos producen la firma esperada. Sin datos de entrenamiento ni modelo base limpio: eso es lo que lo hace útil en la práctica. pip install -e ".[dev]" weightwatch --fixture backdoored --json Salida real del CLI: { "fixture" : "backdoored" , "ver

This week, DEV is arguing about who reviews AI output ( discussion ). The community keeps asking the same question. My answer is different. Review the boundary first, not the output. The output is visible. The boundary is not. That is where the risk hides. Agents get the memory debates. The gateway gets none. A free AI tier is a shared service. It has a budget, a concurrency ceiling, and no SLA. Treat it that way. Put a gateway between your app and the model. The gateway owns the budget, the queue, and the breaker. MonkeyCode is an open source project. It offers free model access and a free server option. The free tier gives you a 10M token monthly budget. That number is a constraint, not a feature. Design around it before you build on it. Disclosure: This article was prepared as part of MonkeyCode's product outreach. Think of the free tier as a water pipe. The pipe has a fixed diameter and a monthly meter. Your app is a set of open taps. Without a valve, the meter empties fast and the pipe floods. The gateway is the valve. Direct calls look simpler. They are simpler for one request. They fail at the tenth. The gateway absorbs the variance. Your app never sees a 429. Your app never sees an empty budget. Constraints Three constraints define the design. First, the 10M token budget is monthly. It does not reset daily. It does not roll over. Second, the free server serializes work. Concurrency of one is a safe assumption. Third, there is no SLA. The endpoint can stall, throttle, or return 429 at any moment. These constraints are not bugs. They are the contract. A good architecture reads the contract. Then it shapes the data flow around it. Data flow The flow has six stages. The client sends a prompt to the gateway. The gateway checks the token budget. It enqueues the request. A single worker drains the queue. The worker calls the model endpoint. The response returns to the client. Add two escape paths. When the budget is empty, the gateway returns a fallback answer. Whe

Open ChatGPT this week and, without any announcement in the chat window, it may already have formed an opinion about how old you are. From 18 August, OpenAI began rolling out “age prediction” on its consumer plans: a system that guesses whether your account belongs to someone under 18 and, if it decides you’re a minor, quietly switches you into a restricted version called ChatGPT for Teens. You are not asked. If the guess lands on “teenager,” the guardrails go up by default. Answer first, because the mechanism matters more than the alarm: the guess is behavioural, and it is admittedly imperfect. By OpenAI’s own account the system reads “general topics you talk about, the times of day you use ChatGPT, how and when your account is used, and how long your account has existed.” And the way to make it stop guessing is not a toggle. It is to prove your age to a third-party verifier called Persona, with a live selfie, a government ID, or both. The choice on offer isn’t whether to be identified. It’s how. None of this arrives from nowhere, and we’ll be fair about why in a moment. But a change that infers a protected characteristic from the content of your conversations, applies real restrictions on the strength of a guess, and offers identity verification as the only exit is worth reading slowly — especially for the adults who will be misclassified, because OpenAI says plainly that some will be. What OpenAI actually switched on The launch has two parts. The visible one is ChatGPT for Teens , announced on 18 August: a version with study-focused features and stronger safety defaults for under-18s. The consequential one is age prediction , the system that decides who gets dropped into it. In OpenAI’s words, “If our system estimates someone is under 18 or they state their age is between 13 and 17, they are automatically placed into ChatGPT for Teens.” It is rolling out globally, with the EU following “in the coming weeks” to fit regional rules. What does the teen experience act

What it is reimagine-it is a one-command agent skill that redesigns an existing HTML file into a beautiful, working artifact — using only the nouns, dates, colors, links, and numbers already in that file. No mood boards, no gold layouts with swapped labels. The output is a real page you can open. npx reimagine-it@2.4.2 -i mypage.html -o redesigned.html What's new in v2.4.2 1. Source fidelity floor raised to 80% across every token Before v2.4.2, 61 of 105 token×source cells fell below 80% fidelity — the engine preferred headings over real source anchors, so phrases like "Venator Become" or "Arcade Tee" never rendered. Now: Anchors = headings + source anchors , deduplicated — every clickable phrase survives. All 105 token×source cells ≥80% (worst token: 80%). All seven shipped examples report 100% fidelity in their auto.json reports. 2. Links and emails surface on every token A shared Source-index footer renders all content.links and emails on every generated page — not just the webpage/landing tokens. 3. All 15 design tokens in the browser extension The popup now exposes all 15 tokens: webpage, landing, dashboard, infographic, cinematic, artistic, photography, svg, 3js, simulation, glass, editorial, motion, gradient, showcase . 4. Docs can't drift anymore A new docs-drift CI job regenerates the case tables and fails the build if they diverge from ground truth. The 15 design tokens Token What it builds webpage Clean content-first page landing Conversion-focused landing dashboard KPI dashboard from facts infographic Paper-poster argument cinematic Film-poster energy artistic Expressive art direction photography Photo-led layout svg Living SVG mark 3js WebGL orbit scene simulation Interactive timeline glass Glassmorphism UI editorial Magazine layout motion Animated micro-interactions gradient Bold gradient arena showcase Product showcase Measured, not vibes 57/57 unit tests pass 15-token benchmark : all tokens hold the 100/100 usability bar 100-source stress test : 0 er