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If you've been following the AI market lately, you already know the headline numbers: DeepSeek V4 costs about 3% of what GPT-4o charges per token. GLM-4 runs benchmarks competitive with GPT-4 at roughly one-twentieth the price. Qwen delivers multilingual performance that rivals Claude for a rounding error in your cloud bill. The spreadsheets look incredible. The problem is actually using these models. Signing up for each provider means navigating Chinese-language dashboards, topping up separate wallets, managing six different API key formats, and dealing with SDKs that don't follow any consistent convention. Most developers give up after the second integration. That friction is why, despite the economics being objectively absurd in 2026, most teams still default to a single Western provider and eat the cost. AIWave exists to kill that friction. One API key. One endpoint. Fifty-plus models across eight Chinese labs, all speaking standard OpenAI-compatible format. Zero code changes to switch between DeepSeek, GLM, Qwen, MiniMax, and everything else. This post covers how the platform works under the hood, what the request lifecycle looks like, and how to integrate it in any language that can speak HTTP. The Fragmentation Problem, Quantified Before getting into the solution, here's what the Chinese LLM landscape actually looks like as of June 2026: Provider Flagship Model API Format Auth Method SDK Language DeepSeek V4-Pro Custom (DS format) Bearer token + signature Python, JS Zhipu GLM-4.5 OpenAI-compatible-ish JWT with expiry Python, Java Alibaba Qwen-3-Max DashScope (Alibaba) AK/SK + HMAC Python, Java, Go MiniMax MiniMax-Text-01 Custom REST API Key + Group ID Python Moonshot Kimi-K2 OpenAI-compatible API Key Python, JS Baidu ERNIE 4.5 Qianfan (Baidu) OAuth 2.0 Client Cred Python ByteDance Doubao-Pro Ark (Volcengine) IAM AK/SK + SigV4 Python, Go 01.AI Yi-Lightning OpenAI-compatible API Key Python Eight providers, seven different authentication schemes, four distinct A

When it comes to our deepest thoughts, secrets, and mental health struggles, "the cloud" can feel like a very crowded place. In an era where data privacy is paramount, sending your private journal entries to a central server for analysis feels... risky. But what if you could have the power of a world-class LLM like Llama 3 running entirely on your MacBook? Thanks to the Apple MLX framework, local LLM execution is no longer a pipe dream—it’s a high-performance reality. By leveraging privacy-preserving AI and advanced Llama 3 quantization , we can build a personal mental health assistant that provides Cognitive Behavioral Therapy (CBT) insights without a single byte ever leaving your machine. 🚀 Why Apple MLX? 🍏 Apple's MLX is an array framework designed specifically for machine learning on Apple Silicon. It’s essentially "NumPy meets PyTorch," but optimized to squeeze every drop of power out of your M1/M2/M3 chip's Unified Memory Architecture. The Architecture: 100% Local Data Flow Here is how our private assistant handles your data. Notice the absence of any "External API" or "Cloud Storage" blocks: graph TD A[User Private Journal Entry] --> B{Local Python App} B --> C[Apple MLX Framework] C --> D[Quantized Llama 3 - 4bit/8bit] D --> E[CBT Sentiment Analysis] E --> F[Empathetic CBT Feedback] F --> B B --> G[Local Encrypted Storage] subgraph MacBook Pro / Air C D E end Prerequisites 🛠️ To follow this advanced guide, you’ll need: An Apple Silicon Mac (M1, M2, M3 series). Python 3.10+ . mlx-lm : The high-level library for running LLMs with MLX. Step 1: Setting Up the Environment First, let's create a virtual environment and install our dependencies. We are using mlx-lm because it handles the complexities of quantization and model loading seamlessly. mkdir private-mental-health-ai && cd private-mental-health-ai python -m venv venv source venv/bin/activate pip install mlx-lm huggingface_hub Step 2: Downloading & Quantizing Llama 3 Llama 3 8B is a powerhouse, but it's a bi

Gemini 3.5 Pro goes general-availability in late June 2026 with a 2-million-token context window and a Deep Think reasoning mode that positions it against the most capable frontier models currently live — at a moment when the field is unusually thin. Claude Fable 5 was disabled globally on June 12 under a U.S. export control directive. GPT-5.6 remains a release candidate in Codex backend logs under the codename kindle-alpha . As of June 19, 2026, Gemini 3.5 Pro is the next major frontier model with a confirmed launch window, and it’s already live for select enterprise customers on Vertex AI. This is what’s confirmed, what’s still unknown, and what developers should do before GA drops. The Timing Isn’t an Accident Google announced Gemini 3.5 Pro at I/O on May 19 with a June general-availability target. At the time, that framing put it in direct competition with Claude Fable 5 (released June 9 before the shutdown) and the anticipated GPT-5.6. That competitive calculus shifted on June 12 when Anthropic disabled Fable 5 for all customers worldwide following an export control order. Claude Opus 4.8 is still live — it hits 88.6% on SWE-Bench and is a legitimate coding workhorse — but its 200K context ceiling blocks the entire category of codebase-scale and multi-document workloads that Fable 5 had been handling at 200K. The gap Gemini 3.5 Pro steps into isn’t hypothetical. Teams that built agent pipelines around Fable 5’s coding accuracy have been on Opus 4.8 stopgaps or migrating to GPT-5.5 since June 12. Neither alternative offers 2M context. Neither has a Deep Think mode native to the same model. Gemini 3.5 Pro is arriving into the most favorable competitive opening Google has had at the frontier in 18 months. The 2M Token Context: Where the Ceiling Disappears Gemini 3.5 Flash shipped with a 1M-token context window, doubling Gemini 3.1 Pro’s 500K limit. Pro doubles Flash again. At 2 million tokens, a single API call can hold: A 2,000-file TypeScript monorepo at 200 lin

Anthropic’s Fable/Mythos shutdown is the first real model export-control shock The important AI story this week is not just that Anthropic launched bigger Claude models. It is that the US government then told Anthropic to switch two of them off for foreign nationals — and Anthropic says the practical answer was to disable them for customers while it works through compliance. That is a very different kind of platform risk than rate limits or pricing changes. If you are building on frontier models, model access can now move because of export-control decisions, safety claims, and geopolitical pressure. What happened Anthropic announced Claude Fable 5 and Claude Mythos 5 on June 9. Fable 5 was described as Anthropic’s most capable generally available model, with stronger performance across software engineering, knowledge work, vision, scientific research, and longer complex tasks. Mythos 5 was positioned above that: an upgrade to Claude Mythos Preview, with Anthropic calling out cyber-defence and life-sciences use cases. Three days later, Anthropic published a blunt update: the US government had issued an export-control directive requiring Anthropic to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States — including foreign-national Anthropic employees. Anthropic said the order arrived at 5:21pm ET on June 12, did not include detailed specifics, and that its understanding was that the government believed it had become aware of a jailbreaking method for Fable 5. Anthropic said access to other models was not affected, but the “net effect” was that it had to abruptly disable Fable 5 and Mythos 5 for customers to ensure compliance. Al Jazeera’s follow-up on June 19 frames the downstream effect clearly: allied countries and companies are now being forced to think harder about dependence on US frontier-model access. It also reports that Anthropic had granted roughly 200 institutions across 15 countries access to Claud

Written by Stephanie Dover, Software Engineer 10+ YOE, ex GitHub, Twitch, Microsoft. Creator of Klaussy. LinkedIn · GitHub · Klaussy Desktop · Klaussy Agents TL;DR klaussy-agents is a free, MIT-licensed CLI ( pip install klaussy-agents ) that makes the prose an AI coding agent writes, PR comments, review notes, commit messages, read like a person wrote them. It works in two layers: a humanization spec baked into the agent's skills so it writes clean prose up front, and a deterministic klaussy humanize pass that scrubs the output afterward. The scrubber is rule-based regex, not an LLM, and it never touches code. There's also a part I didn't expect going in: once the AI tells are gone, what's left can read curt and run long, so the spec also handles tone (don't be rude) and length (one sentence for a reply, one to five for a review comment). Repo: github.com/steph-dove/klaussy-agents. The problem You can spot AI-written text now. Everyone can. And the place it grates most is a code review comment or a commit message, where the prose sits next to your name in a thread your teammates read. The tells are consistent. The em-dash is the biggest one. Right behind it: filler openers like "It's worth noting that…" and "I wanted to point out that…", chatbot scaffolding like "Hope this helps!" and "Let me know if you have questions!", and stacked hedges like could potentially . An agent that leaves those in your PR reads like a bot, and people notice. The obvious fix is to tell the model not to do it. Add "don't sound like AI" to the prompt and move on. That helps, inconsistently, and it regresses silently the moment you change the model or the prompt drifts. Editing every comment by hand works too, but hand-editing every comment defeats the point of having an agent write them. I wanted something I could trust without rereading. Why "just tell the model" wasn't enough The honest answer to "why not just prompt for it" is: a prompt asks, it doesn't enforce. The model tries to com

For the last 30 years, stopping the flow of cybersecurity-related software has proven to be ineffective. It's unclear why it would work now with Anthropic’s cybersecurity model Mythos.

Every time I started a new AI chat session, it read my entire codebase. 50 files. Thousands of tokens. On every single message. Whether I was asking about authentication, database schema, or a single UI component — the AI read everything. I'm 16 and building AI-powered products. Token costs add up fast. Context windows fill up. The AI loses track of older files. Responses slow down. So I built something to fix it. The Problem When you work with AI on large projects, you face a choice: Give the AI too much context → burns tokens, hits context limits, slower responses Give it too little → AI misses important files, makes wrong assumptions There's no middle ground — or at least there wasn't. Introducing FolioDux FolioDux is a lightweight, open-source file-mapping standard for AI-assisted development. The idea is simple: instead of giving your AI every file, you give it a compact index that tells it where everything is and what it does . The AI reads the index first, identifies the relevant files, and reads only those. One file. Two rules. Any AI. It works with Claude, ChatGPT, Gemini, Cursor, Copilot — any tool that accepts a system prompt. How It Works You add one file — FOLIODUX.md — to your project root. # FOLIODUX · TaskFlow · v1.0 · 2026-06-18 · 17 files STACK: React19+TypeScript+Vite · Express+SQLite · JWT --- ## TASKS auth/login/register → AuthView.tsx, authService.ts, server.ts create/edit task → TaskForm.tsx, taskService.ts, server.ts, types.ts list/filter tasks → TaskList.tsx, taskService.ts database → db.ts, server.ts --- ## INDEX App.tsx | fe | root: routing, auth state, layout wrapper AuthView.tsx | fe | login + register forms, error display taskService.ts | svc | CRUD tasks, local cache, optimistic updates server.ts | be | Express: all routes — auth, tasks, projects, user db.ts | be | SQLite setup, schema creation, migrations on boot types.ts | typ | Task, Project, User, Status(todo|in-progress|done) --- ## GROUPS Frontend: App.tsx · AuthView.tsx · TaskLi

Go’s IPO — Japan’s biggest so far this year — has done more than provide a much-needed boost to the country’s languishing listing season. It has also supplied the taxi-hailing app with the capital required to address an existential issue: Japan’s shortage of drivers. Go, which went public Tuesday, plans to use the ¥88.6 billion […]

A small experiment of mine :) Happy to hear your thought about this submitted by /u/soupgasm [link] [留言]

Nothing's next budget phone is the latest victim of RAMageddon. As 9to5Google reports, Nothing co-founder Akis Evangelidis announced in a post on X that a follow-up to the CMF Phone 2 Pro won't be coming this year: We were working on a successor but with memory prices where they are right now, we can't build […]

The Runbook That Lied to Me at 3am The pager went off at 3:14am for a wedged OpenStack Neutron agent. I did what any tired engineer does: I opened the runbook. It told me to restart a service that had been renamed eighteen months earlier, pointed at a Grafana dashboard that 404'd, and assumed a network topology we'd migrated off of two quarters back. The runbook wasn't just unhelpful. It was actively lying to me, and I burned twenty minutes trusting it before I gave up and went to read the source. That's the real problem with documentation. It isn't that we don't write it. It's that the moment we finish writing it, it starts rotting, and the cost of keeping it fresh is high enough that nobody pays it until the document has already betrayed someone at 3am. A runbook your team doesn't trust is worse than no runbook, because no runbook at least forces you to think. This is where AI actually earns its keep in a platform org, and not in the way the marketing decks suggest. AI is not going to own your documentation. It's going to do the tedious first-draft labor — turning a resolved incident, a chunk of shell history, or a deploy diff into a structured skeleton — so a human engineer can spend their scarce attention on the part that matters: verifying the commands, marking what's unproven, and editing the robotic tone out so the team actually reads it. AI drafts. You verify and sign off. That distinction is the whole game. Why "Humanizing" AI Is the Job, Not a Slogan Let me be precise about what I mean by "humanizing AI," because the phrase gets abused. I don't mean making AI sound human to fool a reader. I mean keeping a human in the loop as the editor and owner of record, and doing the unglamorous work of turning a competent-but-soulless machine draft into something a colleague trusts. Two things break trust in AI-drafted docs, and both are fixable by a human pass: Unverified claims stated with confidence. An LLM will happily tell you to run systemctl restart neutron-l3-

Ekehi platform has moved from hand-written HTML/CSS/JS pages to a typed, component-driven React 19 client and a module-based TypeScript Express/Node.js API. 0. Where we started and where we landed Before. A static client built from per-page folders ( landing/ , contributors/ , login/ , signup/ , admin/ ), each shipping its own index.html , a shared styles.css , and vanilla ES module scripts under client/shared/ . The server was an Express API written in plain JavaScript. After. Layer Before After Client Static HTML + CSS + vanilla JS React 19 + Vite 8 + TanStack Router + TypeScript 6 Styling One global styles.css Tailwind CSS 4 with @theme design tokens Data fetch scattered per page TanStack Query over a typed lib/api client Server Express in JavaScript Express + TypeScript, module-per-domain Repo Two loose folders pnpm workspace with shared git hooks Quality gate None ESLint 9, Prettier 3, Husky, commitlint, Vitest The migration ran in two phases on separate branches: **Phase 1 — client rewrite. **Phase 2 — server rewrite. 1. Why this framework? Choice: React 19 , rendered as a client-side SPA through Vite 8 , routed by TanStack Router . TanStack Router was chosen rather than React Router because it gives fully type-safe routes, first-class search-param typing, built-in code-splitting, and file-based route generation that pairs cleanly with Vite. 2. The folder and component structure The client uses a feature-sliced layout: code is grouped by domain, not by technical type. client/src/ ├── components/ │ ├── layout/ navbar.tsx, footer.tsx │ └── ui/ button, input, modal, select, dropdown, ... (design system) ├── config/ env.ts, env-schema.ts, endpoints.ts ├── features/ one folder per domain │ ├── auth/ auth.query.ts, auth.service.ts, auth.types.ts, components/, pages/ │ ├── opportunities/ pages/ │ ├── resources/ pages/ │ ├── submissions/ pages/ │ ├── admin/ pages/ │ └── site/ pages/ (landing, contributors) ├── lib/ │ ├── api/ request.ts, errors.ts, refresh.ts, types.t

YINI Specification RC 6 is now released The YINI configuration format has reached Specification Release Candidate 6 . YINI is a configuration format designed to feel familiar if you like INI-style files, but designed to bring more explicit structure, clarity, useful data types, and predictable parsing rules to real-world configuration needs.. The short version: @yini ^ App name = "Example" version = "1.0.0" debug = false ^^ Server host = "127.0.0.1" port = 8080 ^^ Features enabled = [ "auth", "logging", "metrics", ] YINI tries to sit somewhere between classic INI, JSON, TOML, and YAML: More structured than traditional INI. Less punctuation-heavy than JSON. Indentation-insensitive unlike YAML. Explicit about parsing rules and validation behavior. RC 6 is an important release because it tightens several parts of the language and moves the format closer to a stable 1.0 specification. What changed in RC 6? RC 6 includes a number of syntax and behavior updates. The main theme is the same as before: Make the format clear for humans, but deterministic for parsers. Here are some of the notable updates. Clearer section markers YINI uses section markers to define structure. ^ App name = "MyApp" ^^ Server host = "localhost" ^^^ TLS enabled = true The number of section markers defines the nesting level. This keeps hierarchy visible without relying on indentation. The primary section marker is: ^ YINI also supports alternative section markers, but ^ remains the recommended default for most files and examples. Strict and lenient mode behavior is more clearly defined YINI has two parsing modes: Lenient mode — practical, forgiving, and intended as the default. Strict mode — validation-focused and intended for stricter tooling, CI checks, and production-sensitive configuration. A file can declare its intended mode: @yini strict or: @yini lenient RC 6 clarifies how these declarations should behave when the file is parsed in a different mode. The goal is to avoid silent surprises. If

Every developer who has ever muttered "there is a bug in this" is repeating a word with a surprisingly literal origin. On September 9, 1947, the operators of the Harvard Mark II, an early electromechanical computer, traced a malfunction to its source and found something they did not expect: a moth wedged inside Relay #70. They removed the insect, taped it into the operations logbook, and wrote a now-famous line beside it: "First actual case of bug being found." That page, moth and all, survives today in the collection of the Smithsonian's National Museum of American History. It is one of the best-loved stories in computing, and like most good stories it is a little more complicated than the popular version. Worth getting right, because the discipline it gave us is the same one behind every connected device we build. What actually happened in 1947 The Mark II was a room-sized machine built from relays, switches, and thousands of moving parts. When a moth flew into one of those relays, it physically interfered with the contacts and caused a fault. The technicians who found it had a sense of humor: calling it the "first actual case of bug being found" was a joke precisely because engineers had already been using "bug" for years to describe mysterious faults in machinery. Thomas Edison used the term in his notebooks back in the 1870s. So the 1947 moth did not invent the word "bug." What it did was give the term a perfect, photographable origin story, and it cemented the companion word that really matters: debugging. The act of removing that moth was, quite literally, de-bugging the computer. The Grace Hopper connection The story is almost always told with Grace Hopper at its center, and that deserves a small correction. Hopper, a pioneering computer scientist who later helped develop COBOL, was part of the Mark II team in 1947, but the evidence suggests she did not personally find the moth or write the logbook entry. What she did do was tell the story, brilliantly and o

Modern collaboration needs more than documents and chat messages. Teams need a shared visual space where ideas can be created, organized, and refined together in real time. That's why I built SyncCanvas — an AI-powered collaborative whiteboard that combines real-time multiplayer collaboration, infinite canvas drawing, and AI-assisted brainstorming into one modern workspace. The Problem Most collaboration tools focus on only one aspect of teamwork. Some are great for drawing. Others are excellent for documentation. AI tools often live in separate windows, disconnected from the creative workflow. I wanted a platform where teams could brainstorm visually while AI actively helped generate and organize ideas directly on the canvas. Introducing SyncCanvas SyncCanvas is an infinite multiplayer whiteboard designed for students, developers, teams, and creators. Key features include: Real-time collaboration with live synchronization Infinite canvas with pan and zoom Drawing tools, shapes, text, and sticky notes AI-powered content generation using Gemini Private room sharing Guest mode access PNG export support WiFi Rooms for local collaboration Real-Time Collaboration Collaboration is at the heart of SyncCanvas. Using Yjs and WebSockets, multiple users can work on the same board simultaneously while seeing updates instantly. Users can: View live cursors Track online participants Join private rooms using secure room codes Collaborate anonymously through guest mode This creates a seamless experience similar to working together in the same room. Infinite Canvas Experience The whiteboard is designed to be limitless. Users can: Draw freehand sketches Create rectangles and circles Add text elements Organize ideas with sticky notes Move freely across an infinite workspace Export workspaces as images The canvas is powered by Fabric.js, providing smooth rendering and flexibility for future enhancements. AI-Powered Brainstorming One of the most exciting features is the integration of G

A few months ago I was about to buy a flight. The page showed "Only 2 seats left at this price" in red letters. I hesitated, then refreshed the page out of curiosity. The counter said "Only 2 seats left" again. Same number. It had been resetting on every page load the whole time. That's when I started cataloguing every trick I'd seen on shopping sites and built a Chrome extension that flags them automatically, in real time, on any page. This isn't just my opinion — it's documented research In 2019, Princeton and University of Chicago researchers scraped 11,000 shopping sites and found dark patterns on more than 1,250 of them. The FTC has since fined several companies specifically for fake countdown timers and pre-checked subscription boxes. This isn't a grey area anymore — it's a known, studied, and increasingly regulated practice. The extension targets four categories that account for most of what's out there. The four patterns Fake urgency. Countdown timers that reset, "X people are viewing this" badges that never change, "only N left in stock" that's been static for a week. Trap checkboxes. A checkbox for "Yes, sign me up for the newsletter" that's pre-checked and styled to blend into the page so you don't notice it. Confirmshaming. The decline button reads "No thanks, I don't want to save money" instead of just "No thanks." Psychological pricing. Prices ending in .99 or .95 designed to register as a lower price bracket than they are. Why no AI this time My phishing detector used Claude because language and intent are genuinely ambiguous — you need a model that understands context. Dark patterns are different. They're structural. A countdown timer either resets on reload or it doesn't. A checkbox is either pre-checked or it isn't. That's a DOM query, not a judgment call. So this one runs entirely on regex and DOM inspection. No API calls, no latency, no cost per scan, works offline. Sometimes the boring solution is the correct one. Detecting the urgency pattern T

Your runbooks are out of date. I don't know your team, but I'd bet money on it. Most teams write runbooks once, in a panic after an outage, and then never touch them again until the next outage proves them wrong. How runbooks rot Steps reference deprecated tools. The grafana dashboard moved, the CLI command was renamed, the bastion host got retired. Nobody updated the runbook because nobody re-ran it during the calm months. The team that owned the system left. Three of the five engineers who wrote it are gone. The remaining two haven't actually run the runbook in 18 months because the outage type it covers stopped happening. Half-truths from the start. The original author skipped the obvious steps (because they were obvious to them) and the new on-call engineer can't reproduce the recovery. The result: at 2 AM during the actual incident, the runbook sends you down a dead end. Now you're improvising under stress. What working runbooks have in common I've seen exactly two patterns work: 1. Runbooks live with the code. Put them in the repo of the system they document. When the code changes, the runbook PR is part of the same review. Out-of-repo wikis die first because the cognitive distance is too great. 2. Runbooks are reviewed by people who weren't there. Have a junior engineer run through the runbook quarterly on a non-incident day. Every place they get stuck is a real bug in the document. Fix it then. The author will be too close to see the gaps. The three sections that matter A useful runbook has exactly three sections: Symptom : how do I know this is the right runbook? Concrete signals, with example screenshots if visual. First 5 minutes : what to do RIGHT NOW to stop the bleed. Not the root cause investigation, just the triage actions. Investigation : where to look, what queries to run, what to escalate. That's it. Anything else (architecture diagrams, history, philosophy) goes in a separate doc that the runbook links to. The cultural part The hardest part isn't

What’s the most cliche possible gift you can give a relative? A digital photo frame, displaying a rotating slideshow of family photos. Now Aura has completely refreshed this product space with its gorgeous Aura Ink frame, which uses e-ink to create a display that doesn’t even look digital. Digital frames have always been so popular […]

submitted by /u/stronghup [link] [留言]

I built a simple mobile World Cup Bingo game and would love some feedback. The idea is simple: - Create your own World Cup bingo card - Follow the matches during the tournament - Earn points as events happen - Compete against friends - Check the final leaderboard when the tournament ends There's a free version and an optional Pro upgrade (R$4.90) that removes ads, unlocks exclusive themes, allows unlimited cards and gives up to 3 free swaps per game. I built it mainly because I wanted something more casual and social than fantasy football apps. Would love to hear what football fans think: https://bingo-da-copa.vercel.app/ submitted by /u/No_Net_1962 [link] [留言]