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From OpenSSL to One Click: Meet the Payneteasy Key Pair Factory

Connecting to a payment gateway rarely fails because of business logic. More often, it fails at the very first technical step: authentication. If you’ve ever worked with payment APIs, you know the drill. Before sending a single request, you need to generate a cryptographic key pair and sign every request correctly. Sounds straightforward—until you actually try to do it. The hidden hurdle in every integration To securely call the Payneteasy API, each request must be signed. That means: Generating an RSA key pair (usually via OpenSSL) Converting between PKCS#1 and PKCS#8 formats Building a correct signature base string Percent-encoding everything properly Signing with RSA-SHA256 or HMAC-SHA1 Assembling the Authorization header One small mistake—a missing character, wrong encoding, or incorrect format—and your request gets rejected. For teams without deep cryptography expertise, this step alone can turn a one-day integration into a week-long debugging session. If you’re curious, the full manual process is documented here: https://doc.payneteasy.com/integration/general_api_usage/request_authentication_methods/oauth.html#generating-key-pair The solution: Key Pair Factory We built the Payneteasy Key Pair Factory to remove this bottleneck entirely. Instead of dealing with OpenSSL commands and key formats, you can generate everything you need in just a few clicks. What it does: Generates a ready-to-use RSA key pair Ensures correct formatting for Payneteasy APIs Eliminates manual conversion and configuration errors Keeps the private key on your side Provides a public key for request verification No cryptography expertise required. The tool is open-source and available on GitHub: https://github.com/payneteasy/key-pair-factory Why this matters This is not just about convenience—it directly impacts integration speed and success. With the Key Pair Factory, you get: Faster onboarding Fewer integration errors Less back-and-forth with support teams A smoother developer experience I

2026-06-11 原文 →
AI 资讯

I went to the woods to drink surprisingly great espresso

As summer returns, I'm again reminded of my limits as I head into the great outdoors: I can put up with a heavy, uncomfortable backpack, bug bites, mud, and even bland dehydrated food, but I will not forsake my morning brew. I've tried every imaginable coffee gadget in my half-century of camping. These range from […]

2026-06-11 原文 →
AI 资讯

YouTube is introducing DMs (again)

YouTube is reintroducing private messaging after testing new ways for users to share videos and "have conversations about them" last year. In an announcement on its official blog, YouTube says it's now starting to expand the in-app video sharing and messaging feature to users in the US and "other global regions" who are 18 or […]

2026-06-11 原文 →
AI 资讯

If your agent touches health data, do the boring part first

I’ll say it plainly: the first health-adjacent agent workflow I’d trust is not an AI doctor. It’s a narrow pipeline that takes 6 months of Apple Watch sleep data, cleans timestamps, maps records into a fixed sleep-diary schema, flags broken rows, and stops for human review before anything reaches a clinician. That sounds unsexy. Good. That’s exactly why it’s the first version I’d trust. I landed on this after reading a post on r/openclaw where someone said they had their AI assistant turn months of Apple Watch sleep data into the diary their sleep clinic requested, and the data gotchas were brutal. That sentence contains the whole product. Not “AI healthcare.” Not “autonomous wellness.” Not a GPT-5 wrapper with a soothing UI pretending it understands sleep medicine. Just a very practical engineering problem: parse ugly export data normalize time boundaries fit it into a clinician-friendly format fail loudly on bad rows require a human to approve it That is a real use case. And if you build automations in n8n, Make, Zapier, OpenClaw, or Python, it should feel familiar: the hard part is not the final prompt. The hard part is the ugly middle. The hard part is ETL, not reasoning Most health-agent demos skip the only part that matters. They show the polished summary. They show Claude or GPT-5 saying something calm and articulate. They show a dashboard. I don’t think that’s the hard part. The hard part is ETL: extraction transformation loading For sleep data, that means dealing with stuff like: timestamps crossing midnight timezone normalization naps vs overnight sleep missing start or end times overlapping intervals gaps from the device not recording clinic-specific diary formats If you get any of that wrong, the model summary at the end is not helpful. It is actively misleading. That’s why I think the boring pipeline is the real product. The workflow I’d actually ship If I had to build this today, I would keep the architecture aggressively narrow. Apple Health export ->

2026-06-11 原文 →
开发者

Bluesky is getting ‘communities’

Bluesky will be getting "communities," which will function as smaller spaces where you can "go deeper and hang out with people who care about the same stuff" sometime this year, according to head of product Alex Benzer. They will be built on the decentralized AT Protocol that underpins Bluesky, with Benzer saying that "it's a […]

2026-06-11 原文 →
AI 资讯

Apple’s new Siri AI knows when to shut up

Apple's new Siri AI is finally here, and so far, it seems like it works. I have access and have been messing around with it, and my biggest impression so far is that Siri AI is quite curt - which I mean as a compliment. Many AI chatbots are cheery and wordy. While a more […]

2026-06-11 原文 →
AI 资讯

Mirror Therapy Without the Mirror Box: Treating Phantom Limbs in a Browser Tab

A 1990s Nobel-adjacent therapy, a webcam, and 21 hand keypoints — recreating the mirror-box illusion for phantom limb pain, no hardware required. A therapy built on an illusion In the 1990s, neuroscientist V.S. Ramachandran discovered something remarkable: amputees suffering phantom limb pain often felt relief just by seeing their missing limb move again. His apparatus was almost comically simple — a box with a mirror. Put your intact hand in, look at its reflection where the missing hand would be, and move. The brain, watching the "missing" hand obey commands again, often dials the pain down. The limitation was never the science. It was the box: a physical apparatus, used in clinics, hard to scale, impossible to measure. Replacing glass with keypoints A webcam plus real-time hand tracking can produce the same illusion with better properties: webcam frame → hand landmark model (21 keypoints, on-device) → reflect: phantom[i] = { x: 1 − x, y, z } → render real hand (solid) + phantom twin (ghost) on canvas The reflection is one line of math. Everything around it is what makes the illusion land: const phantom = real . map ( p => ({ x : 1 - p . x , y : p . y , z : p . z })); The visual treatment matters more than I expected. The phantom hand is rendered as a ghostly cyan skeleton with a translucent palm fill, a "breathing" glow that pulses on a ~3 second cycle, and a fading afterimage trail of its last few frames — it reads as present but ethereal , which is exactly the perceptual story mirror therapy needs to tell. A dashed mirror plane down the center of the frame makes the reflection relationship legible at a glance. The engineering details that matter Tracking : MediaPipe HandLandmarker (Google's pretrained model — credit where due), running via WebAssembly with GPU delegate. ~30 FPS on a laptop. Privacy by architecture : every frame is processed on-device. For a medical-adjacent application, "video never leaves your browser" isn't a feature, it's a requirement. Lazy

2026-06-11 原文 →
AI 资讯

Xbox warns of a ‘reset’ as it prepares for layoffs

Microsoft's Xbox division will be hit with significant layoffs next month, according to people familiar with Microsoft's plans. The company has been preparing for the layoffs internally for weeks, with Xbox CEO Asha Sharma hinting about "making hard choices" last month. Sources suggest the cuts could even involve a studio closure, or changes to the […]

2026-06-11 原文 →
开发者

Apple, Google add support for Thread 1.4

Apple and Google are updating their smart home streaming devices to Thread 1.4. As first spotted by Matter Alpha and 9to5 Google, the latest spec has arrived on compatible Apple TVs in the tvOS 27 developer beta and the Google TV Streamer through a software update. This lays the groundwork for these devices, which serve […]

2026-06-11 原文 →
开发者

Kalshi adds required employment verification for some prediction market bets

The CFTC is considering its first regulation for prediction markets, as arrests over "insider trading" on everything from military operations to Google Search data continue to stack up. As CoinDesk reports, a notice of proposed rulemaking says "the proposal would establish a structured framework for evaluating whether such contracts involve an activity enumerated in Section […]

2026-06-11 原文 →
AI 资讯

You can just tell the Instagram algorithm what you want now

Instagram is going to let you tweak what its algorithm shows you on your main feed. With the Your Algorithm feature, "you can now see the topics we think you're interested in, and change them, across all the major parts of Instagram," according to Instagram boss Adam Mosseri. Right now, the feature will only surface […]

2026-06-11 原文 →
AI 资讯

Amazon’s Echo speakers can now help kids wind down and fall asleep

Amazon has launched a new feature for its Echo and Echo Kids smart speakers called Sleep Studio that's designed to make the daily transition to bedtime more enticing for kids and less stressful for parents and caregivers. The feature uses a combination of bedtime stories, relaxing sounds, and guided meditations along with scheduling and customization […]

2026-06-11 原文 →