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CAI.com — a custodial @cai.com email, multi-chain stablecoin wallet, and MCP-installable agent API

CAI.com — a custodial @cai .com email, multi-chain stablecoin wallet, and MCP-installable agent API A custodial email, a stablecoin wallet, a credential vault, and an agent-ready API — all at one @cai.com address. This post walks through what CAI is, what you get when you sign up, and how to wire the agent side into any MCP-compatible host. What you get at cai.com/app A free @cai.com email comes with four product surfaces, all under one account: A real inbox at @cai.com . Send and receive mail like any other address. The signup gives you the address; the dashboard gives you the SMTP/IMAP credentials if you want to use a desktop client. A custodial multi-chain stablecoin wallet. Built in. Six chains. External wallets supported. MoonPay for fiat on-ramp (partial-live, third-party KYC and region limits apply — see cai.com/capabilities.html ). A user vault for site credentials. Store website logins and passwords. The agent you build retrieves them when needed, with your explicit confirmation. The vault is for your site credentials, not the agent's API key. An API key for the agent you build or use. Free tier covers read scopes; pay and full scopes may require verification. The key is in the account dashboard. How the signup works The signup at cai.com/app is four steps. About 2 minutes. Go to cai.com/app . Pick "Apply for @cai.com email." Enter your name. That's the only field on the first screen. CAI emails a 6-digit verification code to the address you provide. The code expires in 15 minutes. The email has a one-time link, not the code — copy the code from the email and paste it into the form. Enter the code, create a password, and you're done. At the end you have: A @cai.com email address. A custodial multi-chain stablecoin wallet. A user vault for site credentials. An API key for the agent you build or use. No card. The email is free. The agent side (for the technical reader) For the technical reader, the agent side is the reason to look at CAI. The install is one c

2026-07-04 原文 →
AI 资讯

I Spent 20+ Years in Industrial Maintenance. Now I’m Learning to Build Software.

I spent over 20 years working in industrial maintenance as a boilermaker. Most of that time was in refinery shutdowns and turnarounds—high-pressure environments where systems either hold or fail. There is no “mostly working” in that world. That experience has shaped how I approach software development. ⸻ I’m not just “learning to code.” I’m building systems. I’m currently working on transitioning into web development, but I’m not approaching it as a tutorial exercise I’m building real projects from day one—and documenting the process as I go. Not theory. Not exercises. Actual systems that are meant to run. ⸻ What I’m building right now A portfolio site that behaves like a system (kmwebdev.me) This isn’t a “personal website” in the usual sense. It’s a live system under controlled change. I treat it like industrial maintenance work: versioned updates instead of redesigns small, controlled changes only tracking what changed and why stability over aesthetics Nothing gets changed without intent. ⸻ A production-focused email framework (Skeleton Framework) Alongside the portfolio work, I’m building a separate system for HTML email development. Email is one of the most constrained environments in web development. Rendering is inconsistent, standards are partial, and modern CSS support is unreliable across many clients. So instead of fighting those constraints, I’m building a framework specifically designed around them. The focus is simple: predictable rendering in real-world email clients It’s still early, but it’s being developed with production use in mind—not experimentation. ⸻ The way I work hasn’t changed—only the tools have In industrial maintenance, you learn a few hard rules: don’t assume—verify don’t scale chaos don’t change more than you can test document everything that matters So I carry that directly into development: versioned releases (v1.0, v1.3.6, etc.) controlled incremental changes explicit documentation of limitations real-world testing across environmen

2026-07-04 原文 →
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The square-ish phone that I wanted to love

The Ikko MindOne Pro is delightfully small. I keep calling it a square phone, which isn't quite right; the screen is square, but the phone itself is slightly rectangular. The camera flips up so you can use it for selfies - you can even open it partway to use as a stand or a kind […]

2026-07-04 原文 →
AI 资讯

Chasing the Light: How the June Solstice Game Jam Turned One Prompt Into a Hundred Different Games

Every game jam lives or dies by its theme, and this year's June Solstice Game Jam handed developers something deceptively simple: the longest day of the year. What emerged from that single prompt wasn't a wave of near-identical sunrise simulators — it was a scattershot of genres, mechanics, and emotional registers, all orbiting the same core idea of light and time. One Theme, a Dozen Interpretations The solstice lends itself to more than one reading, and jam entrants leaned into that ambiguity. Some treated "longest day" literally, building puzzle games where a slowly arcing sun becomes a physical obstacle — light that reveals hidden platforms, burns away fog, or casts shadows players must dodge or exploit. Others went abstract, using the solstice as a metaphor for endurance, building narrative pieces about characters pushing through their hardest, brightest, most exhausting day. Sci-fi submissions reframed the concept entirely: distant planets with artificial suns, space stations timing their orbits to a 24-hour light cycle, or crews racing against a ship's failing life-support "day" before darkness means death. Meanwhile, a handful of more grounded, historically-minded entries used the solstice as a backdrop for ritual and tradition, drawing on centuries of human fascination with the year's turning point. Light and Time as Game Mechanics What makes this jam interesting from a design standpoint is how consistently teams turned an atmospheric theme into an actual mechanic rather than just window dressing. Light became a resource to manage, a weapon, a timer, or a stealth tool. Time compression and dilation showed up frequently too — some games squeezed an entire day-night cycle into a five-minute play session, forcing players to make fast decisions as shadows visibly crept across the map in real time. This is a common jam trick: constraints breed creativity. When a 48- or 72-hour deadline collides with a theme built around a literal clock, developers naturally start

2026-07-04 原文 →
AI 资讯

wa.me/username doesn't work yet — I verified it two ways

wa.me/username doesn't work yet — I verified it two ways, here's what to use instead If you've tried to build a "share my WhatsApp" link using a @username instead of a phone number, you've probably assumed wa.me/username (or wa.me/u/username ) works the same way wa.me/15551234567 does. It doesn't — at least not yet, as of writing this. I wanted a definitive answer instead of trusting blog posts or AI chatbot answers (more on that below), so I tested it two independent ways. Test 1: server response curl -I https://wa.me/u/some_real_reserved_username Every username path I tried — including a certified-real, currently-reserved username — 302-redirects to: api.whatsapp.com/resolve/?deeplink=...&not_found=1 Compare that to the phone-number path, which redirects to: api.whatsapp.com/send/?phone=...&type=phone_number Different resolver, different outcome. The server-side route for usernames exists, but every lookup currently resolves as "not found" — even for real, live usernames. Test 2: real device Server response alone doesn't rule out Universal Links / App Links intercepting the URL client-side before it ever hits a server — curl can't see that. So I also opened all three link variants ( wa.me/username , wa.me/u/username , and a redirect through my own domain) on a real phone with WhatsApp installed. None of them opened a chat. Why this matters if you're building anything around WhatsApp usernames WhatsApp has rolled out @username handles as a real, user-facing feature — but it hasn't published a public deep-link spec for opening a chat from one, the way it has for phone numbers for years. If you're building a tool, a profile page, a business card generator, anything that assumes wa.me/username "just works," it doesn't, for anyone. One more data point: I asked Meta AI directly about this, with the counter-evidence above in hand. It kept asserting the link already works and didn't engage with the evidence when pushed. That's a useful reminder that chatbot answers about

2026-07-04 原文 →
AI 资讯

Data structures your CS degree kind of glossed over

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. Star git-lrc to help devs discover the project. Do give it a try and share your feedback. Every CS program hammers the same seven into you. Arrays, linked lists, hash tables, stacks, queues, graphs, trees. You could probably recite their Big O complexities in your sleep at this point, and honestly, for 90% of the code you'll ever write, that's plenty. But every now and then a system hits a wall that none of the seven basics can handle gracefully, and someone had to invent a weirder tool to patch the gap. I went down a rabbit hole recently looking at a handful of these, and I liked them enough that I wanted to write them up properly instead of just leaving forty open tabs to rot. Fair warning, there is some depth here. Get a drink. When your hash table can't promise you a fast answer: Bloom filters Normal hash tables are great until you need to ask "have I possibly seen this before" across a dataset way too big to store in memory. Think a crawler checking billions of URLs, or a database deciding whether it's even worth going to disk to look for a row. A Bloom filter solves this by giving up on certainty in one direction. It's a fixed array of bits, plus a small handful of independent hash functions. Adding an item flips a handful of bits on. Checking for an item hashes it the same way and checks whether those same bits are on. If any single bit is off, that item was never added, full stop, no ambiguity. If they're all on, the item was probably added, but two unrelated items can accidentally light up the same bits, so you might get a false alarm. The asymmetry is the entire design. Zero false negatives, occasional false positives. It's the data structure equivalent of a metal detector at a stadium gate. It'll never wave through someone with a knife, but it might beep at your belt buckle and make you empty your pockets for nothing.

2026-07-04 原文 →
AI 资讯

👾 🧚🏼‍♀️Maximizing Fable for Life Admin

TLDR: The most powerful AI on the planet, only a few days of access. Maximize it. I'd first like to give credit where it's due: @trickell - Thank you for sharing Network Chuck's youtube video with me. The reference video is found here guys if you missed it: Network Chuck's Video on Fable I first started by creating a nice template for tech documentation for personal use. It created a beautiful piece of work in about 5 minutes - something I could easily expand on in the future. Here is what it generated for me with after a one or two careful prompts: Clean UI, Easy Navigation! Created this personal reference guide for studying for CCNA (Network Chucks Summer of CCNA) Wanna see it? It lives here: Techdocs But after learning about the true span of Fable's power, I started asking the serious questions, the ones that are life-changing. How can I increase my quality of life based on my resume, experience, and current life circumstances? I wrote about 2 pages of life issues that needed fixing - you know the stuff that slowly eats away at your soul, like student loan debt and people that are challenging to work with? Yes - I told it my biggest issues and instructed it to give me actionable plans that are free or low-cost. Even fable told me that this was a lot. 😅 Getting Organized Knowing the scope of my own problems I knew that my thoughts and processes had to be organized. Luckily for me, I remembered I had a good place to do that. A place that Fable could connect to and place documentation in place for me with checklists, notes, summaries and actionable plans. That app is called Notion, and some of you may have heard of it. No one is going to organize your life for you, no one, except for AI I couldn’t think of a better place for lightning fast critical life-admin documentation on the spot. And I can tell you, this integration works like a charm, and I highly recommend it. For a busy person with a million ideas, this is great. Anxiety Relief I had a tremendous amount of

2026-07-04 原文 →
AI 资讯

Is Your Unity Game's Physics a Hidden Bottleneck?

Is Your Unity Game's Physics a Hidden Bottleneck? Unlock CPU Power with Jobs and Burst Introduction It's 2026, and player expectations for high-fidelity, responsive game worlds have never been higher. Yet, for many Unity developers, the pursuit of complex physics, intricate AI, or large-scale simulations often runs headlong into a critical bottleneck: the main thread. If your Unity game still relies primarily on MonoBehaviour.Update() for computationally heavy tasks like custom collision detection, advanced pathfinding, or sophisticated flocking behaviors, you're inadvertently sacrificing precious frames and player experience. The sequential nature of Update() becomes a severe limitation, preventing your game from fully utilizing modern multi-core CPUs. The solution isn't just an optimization; it's a fundamental architectural shift. Unity's Jobs System and Burst Compiler are no longer esoteric tools reserved for DOTS (Data-Oriented Technology Stack) purists. They are immediate, essential allies for extracting raw, predictable, and highly performant power from your CPU cores. By embracing these systems, you can transform your game's performance, delivering unparalleled fluidity and scalability. Code Layout and Walkthrough: Embracing Parallelism The core problem with MonoBehaviour.Update() is that it executes serially on the main thread. While fine for simple per-frame logic, complex calculations involving many entities quickly become a single-threaded choke point. The Jobs System, coupled with the Burst Compiler, offers a robust alternative. 1. The Power Duo: Jobs System and Burst Compiler Jobs System: This framework allows you to break down heavy computations into small, independent units of work (Jobs) that can be scheduled to run in parallel across multiple CPU cores. It handles the complexities of thread management, allowing you to focus on the logic. Burst Compiler: This incredible technology takes your C# code written for Jobs and compiles it into highly optimi

2026-07-04 原文 →
AI 资讯

How to Compress Images in the Browser with Canvas API (No Uploads, No Server)

How to Compress Images in the Browser with Canvas API Every image you upload to a "free" online compressor is sent to a server — often without you knowing what happens to it afterward. For a tool that processes your private photos, that's a terrible design. Here's how to build (or use) an image compressor that runs entirely in the browser using the HTML5 Canvas API. No uploads, no server costs, and unlimited file sizes. The Core Technique: Canvas toBlob() The key API is HTMLCanvasElement.toBlob() : js const canvas = document.createElement('canvas'); const ctx = canvas.getContext('2d'); const img = new Image(); img.onload = () => { canvas.width = img.naturalWidth; canvas.height = img.naturalHeight; ctx.drawImage(img, 0, 0); canvas.toBlob((blob) => { const url = URL.createObjectURL(blob); }, 'image/jpeg', 0.8); }; img.src = 'your-image.jpg'; The second parameter is the MIME type (image/jpeg, image/png, image/webp, image/avif). The third is quality (0–1). Step-Down Resizing for Large Images If you're compressing a 6000×4000 px photo, drawing it at full resolution onto a canvas can eat 70+ MB of memory. Step-down resizing halves the dimensions repeatedly: function stepDownEncode(img, maxDim, quality) { let w = img.naturalWidth; let h = img.naturalHeight; let src = img; while (w > maxDim * 2 || h > maxDim * 2) { w = Math.floor(w / 2); h = Math.floor(h / 2); const temp = document.createElement('canvas'); temp.width = w; temp.height = h; temp.getContext('2d').drawImage(src, 0, 0, w, h); src = temp; } const canvas = document.createElement('canvas'); canvas.width = w; canvas.height = h; canvas.getContext('2d').drawImage(src, 0, 0, w, h); return new Promise((resolve) => { canvas.toBlob((blob) => resolve(blob), 'image/jpeg', quality); }); } This prevents memory crashes and actually produces better quality (step-down preserves more detail than a single jump). Comparing Real-World Results Format Avg Original Avg Compressed Avg Savings JPEG → JPEG (Q80) 3.2 MB 0.8 MB 75% PNG → We

2026-07-04 原文 →
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Qi fan fan

Despite my initial skepticism, I'm now sold on wireless Qi chargers that add integrated fans to keep your phone cool while charging. I figured they'd be too loud, or too weak, or too gimmicky, but I'm a convert after spending a week with the new $59.99 Kuxiu D5 Qi2.2 charging dock. Its active cooling system […]

2026-07-04 原文 →
AI 资讯

Bruno — API Client แบบ Git-Native ที่เก็บทุกอย่างเป็นไฟล์

Bruno — API Client แบบ Git-Native ที่เก็บทุกอย่างเป็นไฟล์ เวลา dev team ต้องเทส API — เครื่องมือที่ทุกคนนึกถึงคือ Postman กับ Insomnia แต่ปัญหาคลาสสิกที่เจอกันแทบทุกทีม: "Postman collection อยู่ไหน?" — "ใน account ผมไง" "ขอ invite หน่อย" — "เดี๋ยวส่ง link ให้... เอ๊ะ หมด free tier แล้ว" นี่คือ pain point ที่ทำให้คนจำนวนมากมองหาเครื่องมือใหม่ — และหนึ่งในนั้นคือ Bruno Bruno คืออะไร Bruno เป็น API client แบบ desktop app (มีทั้ง macOS, Linux, Windows) ที่มีแนวคิดแตกต่างจาก Postman โดยสิ้นเชิง: Postman Bruno เก็บข้อมูลที่ไหน Cloud account ไฟล์ใน project (Git repo) ต้อง login ไหม ✅ ต้อง ❌ ไม่ต้อง Collection format JSON (binary-ish) Plain text (Bru files) Collaborate ผ่าน Postman cloud ผ่าน Git (PR, diff, review) Open source ❌ ✅ (GitHub: 45K+ stars) Offline ไม่ค่อยได้ ✅ ทำงานออฟไลน์ได้เต็มที่ หัวใจของ Bruno คือ "API Client ไม่ใช่ Platform" — มันคือเครื่องมือธรรมดาที่เก็บข้อมูลเป็นไฟล์ — เหมือนที่ dev ทั่วไปเก็บโค้ด จุดเด่น 1. Collection คือไฟล์ — เก็บใน Git ได้ my-project/ ├── src/ ├── bruno/ │ ├── users/ │ │ ├── GET users.bru │ │ ├── POST create user.bru │ │ └── DELETE user.bru │ ├── auth/ │ │ └── POST login.bru │ └── bruno.json └── .git/ ทุก API request เป็นไฟล์ .bru — plain text — diff ได้, PR review ได้, merge ได้ — เหมือนโค้ด meta { name: GET users type: http seq: 1 } get { url: https://api.example.com/users body: none auth: bearer } 2. ไม่มี Cloud — ข้อมูลอยู่กับคุณ Bruno ไม่เคยส่งข้อมูลขึ้น server — ทุกอย่างอยู่บนเครื่องคุณ ทั้ง request, response, environment variables สำหรับทีมที่ทำงานกับข้อมูล sensitive (banking, healthcare, government) — ข้อนี้สำคัญมาก 3. ใช้ Git เป็น Collaboration Tool แทนที่จะ "invite teammate เข้า workspace" (แบบ Postman) — คุณแค่: git add bruno/ git commit -m "add user API collection" git push เพื่อน git pull → เปิด Bruno → เห็น collection เดียวกันทันที 4. Environment Variables — แบบเดียวกับที่ dev ใช้ # environments/production.bru vars { base_url : https : //api.production.com api_key : {{ PROD_API_KEY }} } เปลี่ยน environment ด้วยการคลิก —

2026-07-04 原文 →