标签：#AI

I used to deliver food on Zepto. 14-15 hours a day. Sun, rain, didn't matter. I saved up, bought a laptop, and started doing video editing for clients. That's when things got messy. I was managing clients on WhatsApp. Tracking who paid me in Google Sheets. Sending invoices as PDF attachments that nobody opened. Every new client meant another chat group, another row in my spreadsheet, another folder I'd forget about. I went looking for one tool that could handle all of this. CRM, invoicing, projects, client communication — in one place. Everything was either $200+/month (when you add up all the separate tools) or missing basic stuff like a client portal. So I started building my own. That was a month ago. What I actually built Arpixa. One dashboard for agencies and freelancers. CRM, invoicing, project boards, AI assistant, file manager, scheduling, analytics, and a client portal where your clients can view projects, pay invoices, and message you. Every agency gets a branded subdomain — youragency.arpixa.io. Your clients see your brand, not mine. I'm not going to dump the whole feature list here. You can check arpixa.io if you're curious. The hard parts nobody warns you about Subdomains are a nightmare. Giving every user their own subdomain sounds simple until you realize auth doesn't work across subdomains by default. I had to build a token handoff system where you log in on one domain and the session gets securely passed to your workspace subdomain. It took longer than I expected going in — auth is the part everyone assumes is solved and nobody explains. Two payment gateways, because one isn't enough. I integrated both Stripe and Razorpay. Stripe for international users, Razorpay for India (UPI is how everyone pays here). The app auto-detects your country and shows the right payment flow. Sounds fancy — mostly it was just a lot of logic and twice the amount of webhook handling. Security rules will humble you. I wrote database-level security rules for every single co

디지털 자산과 인공지능 분야는 핵심 기술은 다르지만, 데이터의 진실성, 규제 체계, 지정학적 함의에 대한 공통된 도전에 직면하며 점차 수렴하고 있다. 최근 일련의 사건들은 탈중앙화와 첨단 연산이 약속하는 미래가 인간의 행동, 경제적 유인, 그리고 국가적 목표라는 현실과 충돌하는 중요한 변곡점을 보여준다. 제재 대상 러시아 스테이블코인의 논란 많은 거래량 주장부터 전 미국 대통령이 약세장 속에서 거둔 전례 없는 암호화폐 수익, 그리고 선두 AI 모델을 둘러싼 당혹스러운 "너프(성능 저하)" 논쟁에 이르기까지, 이 모든 이야기는 혁신과 불투명성이 난무하는 디지털 최전선의 모습을 생생하게 그려낸다. 이 글은 겉으로는 서로 달라 보이는 이러한 현상들을 깊이 파고들어, 그 기저의 메커니즘, 기술적 복잡성, 그리고 글로벌 디지털 경제에 미치는 광범위한 영향을 탐색하고자 한다. 우리는 블록체인 분석이 불법 금융 활동 주장에 어떻게 도전하는지, 정치인들이 신생 산업에 관여하며 제기하는 윤리적 및 규제적 난제는 무엇인지, 그리고 복잡한 AI 시스템을 평가하는 미묘한 기술적 문제들을 살펴볼 것이다. 이러한 분석들을 관통하는 공통적인 실마리는 바로 강력한 검증, 투명한 거버넌스, 그리고 정교한 이해가 필수적이라는 점이다. 정보가 쉽게 조작될 수 있고, 진정한 효용성이 복잡성이나 전략적 오도 뒤에 가려지기 쉬운 생태계를 헤쳐나가기 위해서 말이다. 디지털 자산과 AI가 금융, 거버넌스, 그리고 일상생활을 계속해서 재편하는 가운데, 부풀려진 지표 속에서 진정한 활동을, 시스템적 결함 속에서 실제 역량을 식별하는 능력은 투자자, 정책 입안자, 기술자 모두에게 더없이 중요해지고 있다. 지난 10년간 암호화폐와 인공지능 분야는 폭발적인 성장을 거듭하며 각각 변혁적인 잠재력을 제시하는 동시에 새로운 도전 과제들을 안겨줬다. 예를 들어, 스테이블코인은 본래 암호화폐 시장의 변동성을 완화하기 위해 법정화폐나 다른 자산에 가치를 고정하도록 고안되었으나, 글로벌 디지털 금융 인프라의 핵심 구성 요소로 진화했다. 특히 엄격한 금융 제재를 받는 지역에서 국경 간 결제를 촉진하는 그들의 유용성은 양날의 검이 되어, 합법적인 사용자뿐 아니라 전통적인 금융 통제를 우회하려는 이들까지 끌어들이고 있다. 2022년 이후의 지정학적 환경은 경제 제재에 대한 초점을 더욱 강화했고, 제재 대상 기업들은 디지털 자산이 제공하는 대안적 금융 경로를 모색하게 되었다. 동시에 디지털 자산의 주류 금융 및 정치권으로의 통합은 가속화됐다. 한때 틈새 기술적 호기심에 불과했던 암호화폐는 이제 상당한 경제적 힘으로 자리 잡았고, 기관 투자뿐만 아니라 최근 공개된 바와 같이 유명 인사들에게도 막대한 개인 자산을 안겨주고 있다. 이러한 주류화는 필연적으로 암호화폐를 국가 규제 기관의 감시 아래 놓이게 하며, 업계의 종종 자유지상주의적 정신과 국가의 감독, 과세, 소비자 보호 요구 사이에서 긴장을 유발한다. 특히 규제 환경이 아직 형성되는 단계에서 정치인들이 이 신흥 부문에 관여하는 것은 이해 상충과 공직 내 개인적 금전 이득의 윤리적 경계에 대한 복잡한 질문들을 제기한다. 이러한 발전과 병행하여, 인공지능, 특히 대규모 언어 모델(LLM)은 불과 몇 년 전에는 상상할 수 없었던 능력을 보여주며 빠르게 발전했다. 그러나 종종 "블랙박스"처럼 작동하는 이 모델들의 복잡성은 평가, 제어, 그리고 윤리적 배포를 보장하는 데 상당한 난관을 초래한다. "너프" 또는 성능 저하를 둘러싼 논쟁은 AI 시스템의 진정한 능력을 벤치마킹하고 이해하는 데 내재된 어려움을 강조한다. 특히 안전 분류기와 같은 내부 아키텍처 구성 요소가 관찰되는 동작을 크게 바꿀 수 있기 때문이다. 제재 회피, 암호화폐의 정치경제, AI 모델 평가라는 이 세 가지 독특하지만 서로 연결된 서사는 점점 더 디지털화되고 알고리즘에 의해 움직이는 세상에서 투명성, 책임성, 그리고 정확한 평가를 위한 광범위한 노력을 강조한다. 최근의 뉴스들은 디지털 자산과 AI 생태계에 내재된 기술적 복잡성과 분석적 도전 과제들을 심층적으로 보여준다. 제

How we integrated real-time phrase translation feedback into our AI-powered book translation workflow, and what we learned about latency, context, and prompt engineering. When we launched LectuLibre, our AI-powered book translation platform, users loved the quality of full-chapter translations. But they kept asking for something else: while reading a partially translated book, they'd stumble on an untranslated phrase or an awkward auto-translation and want to quickly get a better version without leaving the page. So we built 即时翻译求助 (Instant Translation Help)—a feature that lets readers highlight any phrase and get a context-aware, human-quality translation within seconds, along with a brief explanation of tricky parts. Here's how we built it, the technical challenges we faced, and the lessons we learned about stitching LLMs into a real-time reading experience. Problem: Real-time, Context-Aware Translation Inside a Book Most web apps offer generic translation via API calls—send a sentence to Google Translate, get a result. But that doesn't work for literary texts. A phrase like "She let the cat out of the bag" needs to be translated idiomatically, and the appropriate rendering depends heavily on the surrounding paragraphs (is the tone formal? sarcastic? part of a metaphor chain?). Our existing translation pipeline processes entire chapters in bulk with carefully crafted prompts, but for instant help, we needed sub-second latency while preserving that same depth of context. Our Approach: Server‑Sent Events and a Smart Prompt Buffer We chose Server-Sent Events (SSE) over WebSockets because the communication is one-directional (server pushes translation tokens) and SSE is simpler to implement with FastAPI. The client (a React app) sends a POST request with: The phrase to translate The book ID and the exact location (chapter/paragraph index) The target language Our backend retrieves the surrounding text from PostgreSQL (we store the original book in chunks), feeds a care

Over the past week, the AI hardware news I've been tracking adds up to more than $610 billion in capital deployed globally — in just seven days. Not valuations. Not market cap. Actual capital expenditure commitments. Korea $550B, Japan $6B, Qualcomm's new accelerator, Kawasaki Heavy Industries' $1B AI infrastructure bond — this round of moves has already surpassed the wildest half-year of the 2000 dot-com bubble in scale. But this time the money isn't flowing into web pages. It's flowing into chips, memory, and power. Watching all of this over the past few days, I've been thinking: for investors and for builders like us making products on top of AI, what does this gamble actually mean? The Real Story Behind AI Training Bottlenecks: From GPU Scarcity → Memory Scarcity → Power Scarcity Honestly, everyone watches AI through the lens of models, but the real bottleneck was never the models — it's been the hardware. From 2023 to 2025, the bottleneck shifted from GPU scarcity to memory scarcity, and is now pushing toward power scarcity. When GPUs were tight, everyone scrambled for H100s and NVIDIA raked it in — but the part that actually throttled the H100 wasn't the GPU core, it was the HBM high-bandwidth memory. On the B200, the HBM3E stacked on top has its capacity locked up entirely by NVIDIA at SK Hynix, while Samsung is chasing hard but its yields can't keep up. That's why South Korea just committed $518B to build 4 memory fabs plus $52B for the central regions, totaling $550B ( TechCrunch ). This isn't just about filling upstream capacity — the key is that Samsung + SK Hynix are trying to flip themselves from being NVIDIA's downstream suppliers into becoming the dominant players in AI hardware. Why did downstream hardware investment kick off so late? Because for the past two years people were still watching and waiting to see if "this AI hype cycle would cool down again." By 2026, GPT-6, Claude 4, and Gemini 3 are all live, inference costs have come down, user numbe

If you've ever wanted an AI that doesn't just chat but actually does things — queries databases, calls APIs, makes decisions, and learns from results — you're in the right place. In this tutorial, I'll show you how to build production-ready AI agents using Solon 4.0's ReActAgent . By the end, you'll have built an agent that can reason through complex problems, use external tools, and adapt its behavior based on real-world feedback. What Makes ReActAgent Different? Traditional LLMs are great at generating text, but they hit a wall when they need to interact with the real world — checking a database, fetching live data, or performing calculations. ReActAgent (Reason + Act) breaks through that wall. It implements a cognitive loop: Thought → Action → Observation → (repeat or finish) The agent thinks about what to do next, acts by calling a tool, observes the result, and decides whether to continue or deliver the final answer. This isn't just theory. Solon's ReActAgent has been used in production for automated customer support, intelligent data analysis, and multi-step workflow automation. 1. Adding the Dependency First, add the solon-ai-agent module to your project: <dependency> <groupId> org.noear </groupId> <artifactId> solon-ai-agent </artifactId> </dependency> Note : If you're using Solon's parent POM, the version is managed automatically. Otherwise, use the latest Solon version. 2. Building a ChatModel (The Agent's Brain) Every agent needs a "brain" — a ChatModel that powers reasoning. Let's build one using the fluent API: import org.noear.solon.ai.chat.ChatModel ; ChatModel chatModel = ChatModel . of ( "https://api.moark.com/v1/chat/completions" ) . apiKey ( "your-api-key-here" ) . model ( "Qwen3-32B" ) . build (); You can also configure it via YAML and inject it: solon.ai.chat : demo : apiUrl : " http://127.0.0.1:11434/api/chat" provider : " ollama" model : " llama3.2" @Inject ( "${solon.ai.chat.demo}" ) ChatConfig chatConfig ; ChatModel chatModel = ChatModel . o

The Problem That Wouldn't Leave Me Alone Pakistan has 220 million people. A functioning legal system. Hundreds of Acts, ordinances, and constitutional provisions that technically protect every citizen. Almost nobody can use them. The median lawyer's consultation fee in Karachi is more than what many families earn in a week. Legal aid is understaffed and geographically concentrated in major cities. And the laws themselves? Written in English — a language most of the population reads functionally at best, and doesn't speak at home at all. So when a landlord illegally locks someone out. When a factory worker gets fired without severance. When a woman wants to know her inheritance rights. When a tenant needs to understand what "Section 16 of the Rent Restriction Ordinance" actually means for their specific situation — they either find a lawyer they can't afford, ask someone who doesn't really know, or quietly give up. This isn't a knowledge problem. It's an access problem. I'm a CS student at Sukkur IBA University in interior Sindh — not Karachi, not Islamabad. The kind of city where you feel the gap between what the law says and what people actually know it says every single day. That gap is where HAQ started. HAQ is an Arabic and Urdu word. It means right — as in, what is rightfully yours. The name felt important. The Core Idea: Ask the Law, Get the Law There's a specific failure mode with AI and legal questions that drove every design decision I made, and it's worth naming clearly. Standard LLMs — any of them — will answer legal questions confidently. They'll cite "Section 144" or "the Transfer of Property Act" with total authority. They are often wrong. Sometimes subtly: the section exists but doesn't say what the model claims. Sometimes obviously: the Act doesn't apply in that province. Always uncitable: the user has no way to verify without finding the source themselves. For an accessibility tool, a confidently wrong answer isn't neutral. It's actively dangerous.

If you've ever tried integrating a large language model (LLM) into a Java application, you've probably written a lot of boilerplate: HTTP clients, JSON parsing, streaming handling, session management. Solon 4.0's ChatModel abstracts all of that away with a clean, builder-oriented API. In this guide, I'll walk through building real, working AI features using ChatModel — from a simple chat call to a streaming chatbot with conversation memory. 1. What Is ChatModel? ChatModel (package org.noear.solon.ai.chat ) is a unified LLM client in Solon's AI ecosystem. Instead of writing raw HTTP calls for different model providers, you use a single API that supports: Synchronous calls — one-shot request, full response Streaming calls — reactive streaming via Project Reactor ( Flux<ChatResponse> ) Tool/Function Calling — let the LLM invoke your Java methods Chat Sessions — automatic conversation memory Multi-modal messages — text, images, audio Dialect adaptation — works with OpenAI, Ollama, Anthropic, Gemini, DashScope, and more The best part? It uses a dialect pattern — you point it at any compatible LLM endpoint, and it adapts automatically. 2. Setting Up Add the dependency to your pom.xml (no parent POM needed — Solon works standalone): <dependency> <groupId> org.noear </groupId> <artifactId> solon-ai </artifactId> <version> ${solon.version} </version> </dependency> This pulls in all built-in dialects (OpenAI, Ollama, Gemini, Anthropic, DashScope). 3. Configuration 3.1 Via YAML (Recommended) solon.ai.chat : demo : apiUrl : " http://127.0.0.1:11434/api/chat" # Full URL, not baseUrl provider : " ollama" # Dialect identifier model : " llama3.2" # Model name headers : x-demo : " demo1" Then create a @Bean to get a ready-to-use ChatModel : import org.noear.solon.ai.chat.ChatConfig ; import org.noear.solon.ai.chat.ChatModel ; import org.noear.solon.annotation.Bean ; import org.noear.solon.annotation.Configuration ; import org.noear.solon.annotation.Inject ; @Configuration public cla

Half a year ago, I wanted to see for myself what can we currently have with local LLMs. I went down the rabbit hole, learned quite a lot in the process, and shared my results in an article . The results were pretty discouraging: even with 32 GB VRAM, the best models I could run were both too slow and too dumb. At the same time, what you could get for free from inference providers was actually decent - and much faster. I remember my conclusion: "Let's wait for the next generation of models, which looks very promising. If we can run something comparable to full-size Qwen3-Coder-480B locally, that would be year of the Linux Desktop age of fully capable local LLMs. And now this day has arrived. Models Half a year later, I'm revisiting this question. And this time, the whole situation has turned upside-down. Almost none of the providers still have free tier, and anything that's still free is barely good enough even for the simplest tasks. And is rate-limited all over. And on the local side, the next Qwen lineup is out. So, that's what I'm going to be looking at. Once again, I have two RX6800's, 16 GB each, and 64 GB RAM. On one hand, this is more VRAM than any "normal person" can have with one GPU - unless you've got something specifically for AI, like an unified-memory Mac or a DGX Spark. On the other hand, RX6800 is "pre-AI" - anything newer will have much better performance thanks to tensor processors. Qwen3.6-27B : This is a dense model, so basically you can't run it at all on anything less than 32 GB VRAM. It's the slowest one, but also the best one if you can run it. Its accuracy is claimed to be on par with Claude 4.5 Opus, and better than Qwen3.5-397B-A17B . This is what I've been waiting for. It runs reasonably fast on my setup, so it's very much usable both in terms of performance and accuracy. Qwen3.6-35B-A3B : This one is MoE, and it's pretty small, so it's the fastest one. It's good for anything that doesn't require too much (i.e. for agentic tasks that don'

We’ve all been there. You start your morning feeling like a Productivity God, sitting straight and typing at 120 WPM. Fast forward four hours, and you've morphed into a literal shrimp, face inches away from the monitor, hunting for a missing semicolon. 🦐 In this era of remote work, real-time posture correction and computer vision for health have become more than just "cool projects"—they are spinal lifesavers. Today, we’re going to build a desktop application using MediaPipe , WebRTC , and Electron that monitors your neck angle and sends a desktop notification the moment you start slouching. By leveraging MediaPipe Pose and TensorFlow.js , we can calculate the Forward Head Posture (FHP) ratio with surgical precision directly in the browser environment. The Architecture 🏗️ Before we dive into the code, let’s look at how the data flows from your webcam to that "Sit up straight!" notification. graph TD A[Webcam Feed] -->|MediaStream| B(WebRTC API) B -->|Video Frames| C[MediaPipe Pose Model] C -->|Landmarks| D{Geometry Engine} D -->|Calculate Ear-Shoulder Angle| E{Threshold Check} E -->|Angle > 30°| F[Electron Main Process] F -->|Trigger| G[System Desktop Notification] E -->|Healthy| H[Continue Monitoring] style G fill:#f96,stroke:#333,stroke-width:2px Prerequisites 🛠️ To follow along, you'll need the following tech stack: MediaPipe Pose : For high-fidelity body tracking. WebRTC : To capture the video stream from your webcam. Electron : To wrap our logic into a desktop app that runs in the background. TensorFlow.js : The backbone for running ML models in JavaScript. Step 1: Setting up the Video Stream (WebRTC) First, we need to grab the camera feed. In a modern browser environment (or Electron's Chromium), we use navigator.mediaDevices.getUserMedia . async function setupCamera () { const videoElement = document . getElementById ( ' input_video ' ); const stream = await navigator . mediaDevices . getUserMedia ({ video : { width : 640 , height : 480 }, audio : false }); v

Second in a series on using AI to review authorization — not to spray reports. Companion reference: AuthZ Smell Catalog . 1. Why AuthZ review is not vulnerability spraying The cheapest thing an AI can do in security is generate suspicion. Point a model at a codebase and it will hand you fifty "possible IDORs" before you finish your coffee. Almost all of them are wrong — guarded three lines up, scoped at the data layer, or protected at a boundary the model never saw. That flood is exactly why several bug bounty programs spent 2026 tightening or pausing: they were drowning in confident, plausible, wrong reports. So this review inverts the usual loop. The AI's job is not to find bugs — it is to over-generate hypotheses cheaply . My job is to kill them. What survives that killing is the only thing worth a human's time, and the record of what died is more useful than the record of what lived. The artifact of an honest review is therefore not a finding. It's a kill table . 2. Target and scope Target: Ory Kratos — an open-source identity and user-management server (login, registration, recovery, verification, sessions, self-service settings). Source-available, Apache-2.0. Why Kratos: it is exactly the shape where authorization goes wrong — multiple identities, a public API and an admin API, and (in Ory's hosted product) multi-tenancy. If a boundary is fragile, this is where it shows. Scope of this write-up: source reading only , on the public repository, single-tenant OSS build. No hosted target was touched. Nothing here is an undisclosed finding — the point is the method and the boundary design , and where relevant, how the design held against the hypotheses I tested. This maps to the reproduction tiers we track: everything below is repo_only , and I say so explicitly rather than implying it reaches a live product. What this review does and does not claim. In this limited, repo-only review, the hypotheses I tested were killed. This is not a claim that Kratos has no vulner

EDRSR — the Unified State Register of Court Decisions — is effectively all of Ukraine's judicial practice in open access. Today Qdrant holds **44M+ vectors : criminal (19M), civil (14.3M), commercial (5.1M), misdemeanors (5.6M). Vectorization of civil cases (CPC, justice_kind=1) — the largest cohort at 33.7M documents — runs on a dedicated EC2 instance (r6a.xlarge, 32 GB RAM, 2 TB gp3). Here's what's under the hood: models, pipeline, cost, rakes, and current status. Why Vectorize Courts When a lawyer searches "is there case law on recovering bank prepayment fees" — they don't want to open 40 decisions and read them through. They want the system to surface the top 5 most relevant ones, pull out key paragraphs, and show how courts reasoned. Full-text search (FTS) over keywords doesn't give that — it returns every document containing the word "fee", and there are thousands. For this semantic task you need vector representations of text. The model turns a paragraph from a decision into a point in a 1024-dimensional space; semantically similar paragraphs sit near each other. A kNN search in Qdrant returns the top K nearest, and an LLM composes the answer from exactly those relevant fragments. The only problem: the register is big. Very big. Scale Our prod database holds full texts of decisions starting from 2006. Breakdown by procedural type: Civil (CPC) — 33.7M documents. The largest category. Consumer, housing, labor, family. Criminal (CrPC) — 12M+ Administrative (CAS) — 14M+ Commercial (CC) — 6M+ Misdemeanors (CUaP) — 6M+ The Qdrant collection edrsr_decisions on a dedicated EC2 currently holds 44M+ vectors (122 segments, on_disk=true): | Proceeding type | justice_kind | Vectors | |—|—|—| | Criminal (CrPC) | 2 | 19,036,347 | | Civil (CPC) | 1 | 14,328,427 | | Misdemeanors (CUaP) | 5 | 5,579,432 | | Commercial (CC) | 3 | 5,098,662 | | Total | | 44,042,868 | Civil cases processed: 14.3M out of 33.7M — that's 42%. After CPC completes there will be roughly 63M+ vectors in

Local LLM Deployment, Agent Handbook, & LLM Cost Reduction: Applied AI Workflows Today's Highlights This week's highlights cover practical guides for running state-of-the-art LLMs locally and building AI agents, alongside an innovative technique to significantly cut LLM API costs for code processing. These resources focus on actionable insights and frameworks for real-world AI application development. Jamesob's guide to running SOTA LLMs locally (Hacker News) Source: https://github.com/jamesob/local-llm This GitHub repository provides a comprehensive, hands-on guide for setting up and running state-of-the-art Large Language Models (LLMs) on local hardware. It meticulously covers the necessary tooling, dependencies, and configuration steps required to get various open-source LLMs operational without relying on cloud APIs. The guide emphasizes practical considerations for local inference, including hardware requirements, model quantization techniques, and performance optimization for different architectures, directly addressing production deployment patterns. It serves as an invaluable resource for developers and researchers looking to experiment with LLMs, develop applications offline, or reduce costs associated with cloud-based inference by leveraging local compute. The guide offers concrete details and actionable steps, making it an essential resource for anyone aiming to implement LLMs in a controlled, private, or cost-effective environment. Comment: This guide is fantastic for anyone wanting to get serious about local LLM development. It covers the nitty-gritty details of setting up your environment and getting models like Llama-3 running efficiently on consumer hardware, which is crucial for privacy and cost savings. 60% Fable cost cut by converting code to images and having the model OCR it (Hacker News) Source: https://github.com/teamchong/pxpipe The pxpipe project introduces an innovative technique to drastically reduce API costs when processing code with Lar

I build and run one of the tools on this list (AGenO — full disclosure below), and I use every other tool here regularly. This is what "free" actually gets you on each one, including the catches. The AI tool landscape has a dirty secret: almost nothing labeled "free" is free. Most tools give you a taste — ten messages, three images, one song — and then the paywall lands. So instead of another list of forty tools nobody has tried, here are nine that give you real value at $0, organized by what you're trying to make, with the actual limits spelled out. Quick comparison Tool Best for What's actually free The catch ChatGPT General chat & writing ~10 msgs/5h on the flagship model Silently switches you to a weaker model after the limit Claude Long documents, nuanced writing 10–25 msgs/5h, varies with demand Limits shrink when servers are busy Gemini Image generation & editing Generous with a Google account Best features drift to the paid tier Perplexity Research with citations Unlimited basic searches Pro searches are capped Suno AI music ~10 songs/day No commercial use on free; failed generations can eat credits Leonardo AI Stylized art & game assets Daily token allowance Confusing token system; images are public on free Character.AI Roleplay & AI characters Unlimited chat Heavy filters; your chats train their models AGenO All of it in one place Images, songs with vocals, chat, characters, stories, coding problems — daily free allowance One-person project — busy hours can mean a short queue Canva Magic tools Quick social graphics 50 text-to-image uses Design-tool add-on, not a real generator Chat and writing ChatGPT is still the default for a reason — the free tier includes the flagship model and it's good at nearly everything. The catch nobody tells you about: after roughly ten messages in five hours, it quietly downgrades you to a mini model without making it obvious. If your answers suddenly get dumber mid-conversation, that's why. Claude writes the most natural prose

Google Trends has no official API, and most wrapper libraries rot within months. But the Trends site itself runs on a keyless JSON API that anyone can call, and it serves the exact numbers you see in the UI. Here is the full recipe, including one gotcha where Google quietly labels your session a scraper. The two step flow Trends works in two steps. First you call explore , which returns a list of widgets, one per chart on the page, each with a signed token: GET https://trends.google.com/trends/api/explore ?hl=en-US&tz=0 &req={"comparisonItem":[{"keyword":"web scraping","geo":"US","time":"today 12-m"}],"category":0,"property":""} Then you call a widget data endpoint with that widget's request and token : GET https://trends.google.com/trends/api/widgetdata/multiline?hl=en-US&tz=0&req=<widget.request>&token=<widget.token> The widget kinds map to endpoints: TIMESERIES uses multiline , GEO_MAP uses comparedgeo , and both RELATED_QUERIES and RELATED_TOPICS use relatedsearches . The cookie trick Call explore cold and you get a 429. The API wants a NID cookie, and here is the counterintuitive part: you get it by requesting the public explore page first, and that page may itself respond 429 while still setting the cookie you need. const res = await fetch ( ' https://trends.google.com/trends/explore?geo=US&q=test ' ); // res.status may be 429. The Set-Cookie header is still there. const cookies = res . headers . getSetCookie (); Grab the cookie from the 429 response, retry explore , and everything works. Strip the anti JSON prefix Every Trends response starts with a junk line like )]}' to break naive JSON.parse calls. Drop everything up to the first newline: const body = await res . text (); const data = JSON . parse ( body . slice ( body . indexOf ( ' \n ' ) + 1 )); The scraper flag Here is the part I have not seen documented. Look inside the widget request object that explore returns to a keyless session: "userConfig" : { "userType" : "USER_TYPE_SCRAPER" } Google knows. And

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.

This week, researchers at the University of Minnesota announced something that genuinely stopped me mid-scroll. A team led by Associate Professors Kate Adamala and Aaron Engelhart built the world's first synthetic cell with a complete life cycle — not modified from an existing organism, not borrowed from biology. Built. From. Scratch. They're calling it SpudCell . It can grow. It feeds. It copies its own genetic material. It divides into new cells. And it does all of this from a starting point of pure chemistry — non-living components assembled with intent. Adamala put it plainly: "We've replicated in chemistry what only used to be possible in biology: the complete set of behaviors of a cell. It proves that the most fundamental functions of life, like growth and replication, do not need a mysterious magical spark." — University of Minnesota That's not hype. That's a scientist who has spent her career working toward this moment, choosing her words carefully. What SpudCell Actually Is SpudCell isn't a copy of a bacterium or a stripped-down version of an existing cell. It's a chemically defined system — meaning researchers know the full ingredient list, every molecule at every concentration. To put the scale in perspective: the human genome runs about 3 billion base pairs. SpudCell's genome is 90 kilobase pairs. Minimal by design. But minimal doesn't mean simple — it means precise . Every component earns its place. — CBS Minnesota The cell mostly resembles a basic bacterium in its behavior, but it carries none of evolution's baggage. No millions of years of accumulated quirks. No legacy code. Just the essential machinery for life's core functions, assembled on purpose. Yuval Elani, a synthetic biology researcher at Imperial College London, framed it this way: "Building a cell from scratch means you are no longer tied to the constraints and evolutionary baggage of natural biology. It opens up the possibility of designing systems and programming them to do things that li

A 62-year-old man in India. Kidney failure, on dialysis three times a week. Diabetes. Hypertension. A stroke six years ago. And one symptom nobody could explain: severe headaches, but only when lying down to sleep. For 25 years, specialists came up empty. Then his nephew uploaded everything into Claude. And the AI asked one question that changed everything: "Does he snore?" The answer was yes. Loudly. For 25 years. That was the clue. The sleep study confirmed severe sleep apnea: 119 breathing stops per night, oxygen dropping to 78%, 47 oxygen desaturations per hour. CPAP treatment started. Headaches gone. ( India Today , NDTV ) What Actually Happened The story was posted on Reddit's r/ClaudeAI community by user u/the_kuka in March 2026. It went viral immediately, covered by India Today, NDTV, Hindustan Times, Economic Times, and Times of India within days. Here's the timeline: 25 years of symptoms. The uncle had loud snoring, daytime exhaustion, and severe positional headaches (only when lying down). Every doctor attributed the fatigue to "dialysis fatigue" or "age." The snoring was something the family joked about. Multiple specialists, zero connections. He saw neurologists. He saw nephrologists. He had brain MRIs and blood work. Each specialist looked at their domain. Nobody stepped back and asked what connected everything. One conversation with Claude. The nephew compiled all medical records, MRI notes, and symptom history, and uploaded them. Over several days, Claude did three things: Identified the positional pattern as the key clue. Headaches triggered by lying down is not random. It points to something that happens during sleep. Pulled research showing 40-57% of dialysis patients have undiagnosed sleep apnea. This is a published statistic, not a guess. Asked about snoring. This is the question no specialist had asked in 25 years. The answer was immediate and obvious in hindsight. ( Substack - Chetan Pujari ) The sleep study confirmed it. Severe obstructive sl

AI agents are only as smart as the context you give them. OKF is a new open specification that packages your organizational knowledge as plain markdown files so any agent can read it without custom integrations or proprietary SDKs. Every team building AI agents hits the same wall. The model is capable. The agent framework is set up. But the agent doesn't know anything about your organization. It doesn't know what your orders table means, what the churn_score metric formula is, or what the on-call runbook says to do when the pipeline breaks. That knowledge exists. It's scattered across Confluence pages, Notion wikis, data catalog entries, Slack threads, and the heads of senior engineers. Getting it into an agent means building a custom integration for every source. Every team solves this from scratch. Published on June 12, 2026, the Open Knowledge Format (OKF) is a vendor-neutral specification that solves this with the simplest possible approach: a directory of markdown files. 🎯 🏗️ What OKF Actually Is An OKF bundle is a directory of markdown files representing concepts: anything you want to capture, including tables, datasets, metrics, playbooks, runbooks, and APIs. Each concept is one file. That's the entire model. A directory of .md files with YAML frontmatter. The format is deliberately minimal: one required field ( type ), optional metadata ( title , description , resource , tags , timestamp ), and a free-form markdown body. A concept document looks like this: --- type : table title : " orders" description : " One row per customer order. Source of truth for revenue reporting." resource : " postgresql://prod-db/ecommerce/orders" tags : [ revenue , core , sla ] timestamp : 2026-06-15T10:00:00Z --- # orders The `orders` table records every purchase event. It is the join root for all revenue queries. Do not filter on `status = 'complete'` unless you specifically want to exclude in-flight orders from the count. ## Key columns - `order_id` - UUID primary key - `custom

OpenAI Agents SDK 0.13 → 0.17: Three Breaking Changes You Will Hit The OpenAI Agents SDK moved from 0.13 to 0.17 in five weeks (April 9 – May 19, 2026). That's 19 releases, including three breaking changes. Two of them are silent — they change runtime behavior without raising an error at upgrade time. If you're running agents on 0.13.x, this is the migration guide you need before you upgrade. The Three Breaks Break #1: ModelRefusalError (v0.15.0) — Silent to Loud The change: Model refusals used to return empty-string output. Now they raise an exception. What this means: If your code treated refusals as output == "" or checked for empty responses, you were silent-failing gracefully. That behavior changed. A model refusal now raises ModelRefusalError and will crash your agent run unless you handle it. What you need to do: Register a "model_refusal" error handler before upgrading past 0.15.0: from openai.agents import Agent , RunConfig def handle_refusal ( error ): print ( f " Model refused: { error . reason } " ) return " Model refused to respond. Try rephrasing. " agent = Agent ( model = " gpt-4 " , tools = [...], ) config = RunConfig ( on_error = { " model_refusal " : handle_refusal , } ) response = agent . run ( " ... " , config = config ) Without the handler, you'll see: ModelRefusalError: model declined to process this request This is an intentional change — OpenAI wants refusals to be explicit, not silent. But it means your error handling has to change. Impact: Any agent that doesn't add a refusal handler will crash on a refusal. High-risk if your agents field user input directly. Break #2: Default Model Changed (v0.16.0) — Silent Model Switch The change: The default model switched from gpt-4.1 to gpt-5.4-mini . What this means: If you created an agent without explicitly setting model= , you were running on gpt-4.1. On upgrade to 0.16.0+, that same code silently switches to gpt-5.4-mini. This isn't just a version bump — gpt-5.4-mini ships with different defaults

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