标签：#AR

You scan one to pay at a sari-sari store, pull up a restaurant menu, or board a flight. The QR code has quietly become one of the most universal pieces of interface design on the planet. But it was never meant for any of that. The QR code was invented in 1994 to solve a very specific problem on a Japanese car factory floor, and the engineering decisions made under that constraint are exactly why it later conquered the world. A barcode problem on the assembly line In the early 1990s, Toyota's manufacturing arm had a data problem. Tracking thousands of distinct components through production meant scanning barcodes, and barcodes are stingy: a standard one-dimensional barcode holds roughly 20 characters. Workers were ending up with parts plastered in ten or more barcodes just to encode enough information, and each one had to be scanned separately. It was slow, and on an assembly line, slow is expensive. Masahiro Hara, an engineer at Denso Wave, a Toyota subsidiary, took on the challenge of designing something better. He wanted a code that could hold far more data, be read much faster, and tolerate the dirt, smudges, and odd angles of a real factory rather than a clean lab. Designing for speed and any angle The breakthrough was going two-dimensional. By encoding data in a grid of black and white squares rather than a single row of lines, Hara's team could pack in thousands of characters instead of a few dozen. The name they chose, QR for "Quick Response," was a direct promise about scanning speed. The most recognizable feature of a QR code, the three large squares in its corners, solves the hardest part of the problem: letting a scanner instantly find the code and work out its orientation no matter how the part is turned. Hara's team analyzed printed material to find a black-and-white sequence that almost never occurs naturally in text and images, and settled on a ratio of 1:1:3:1:1 for those corner markers. Because that pattern is so rare in everyday print, a scanner ca

Despite continued benefits, anti-vaccine rhetoric has driven down vaccination.

Swap out your creaky old box fan for a new model that lights up, mists, or even follows you around the room.

Don’t be fooled by the compact size of this soundbar. It’s a solid option for smaller TVs or spaces without having to sacrifice sound quality.

This is an adapted excerpt from a guide in my AI Knowledge Hub. The full interactive version is linked at the end. Agentic design patterns are named control structures for arranging LLM calls and tools. This post gives you the decision rule for picking one, the exact shape of each pattern, and the cost each adds — so you can match a task to the minimum structure that solves it. Everything here is model-agnostic and grounded in Anthropic's Building Effective Agents and the Claude Agent SDK. Workflow vs. agent: the split that decides everything Anthropic divides all agentic systems into two categories, and the split decides every downstream tradeoff: Category Definition Control lives in Use when Workflow LLMs and tools orchestrated through predefined code paths Your code You can pre-map the decision tree; want accuracy, control, lower cost Agent LLMs dynamically direct their own processes and tool usage , maintaining control over how they accomplish tasks The model Open-ended task where you can't predict the number of steps Every pattern composes one unit: the augmented LLM — an LLM enhanced with retrieval, tools, and memory. It generates its own search queries, selects tools, and decides what to retain. If a single augmented LLM call solves the task, stop — no pattern required. The escalation rule is the whole game: find "the simplest solution possible, and only increasing complexity when needed" — which "might mean not building agentic systems at all." Agentic systems trade latency and cost for better task performance, so only escalate when a specific failure mode forces it. The agent loop: gather → act → verify → repeat For open-ended tasks, every agent runs the same four-beat loop: Gather context — read files, run agentic search ( grep / find / tail to pull relevant slices instead of whole files), or delegate to subagents with isolated context windows. Take action — execute via tools: bash, code generation, file edits, MCP servers. Verify work — check the result b

Anyone who's wired an autonomous agent into email and calendar the traditional way knows the identifier sprawl: an OAuth client ID, a refresh token per user, a Gmail-specific message ID format, a Microsoft Graph calendar ID, and a webhook subscription ID for each — all with different lifetimes, all able to break independently. Half your "integration" code is really identifier bookkeeping. Nylas Agent Accounts collapse all of that into one value. When you create an account (the feature's in beta), the response hands you a grant_id , and that single string is the handle for everything the agent does — mail, calendar, contacts, attachments, and the webhooks reporting on all of them. One ID, the whole surface Every operation addresses the same path family, /v3/grants/{grant_id}/* : POST /v3/grants/{grant_id}/messages/send # send mail GET /v3/grants/{grant_id}/messages # read the inbox GET /v3/grants/{grant_id}/threads/{thread_id} # full conversation GET /v3/grants/{grant_id}/attachments/{id}/download POST /v3/grants/{grant_id}/events # host a meeting POST /v3/grants/{grant_id}/events/{id}/send-rsvp # respond to one GET /v3/grants/{grant_id}/contacts GET /v3/grants/{grant_id}/rule-evaluations # audit trail This isn't an abstraction invented for agents — an Agent Account is literally just another grant, the same primitive used for connected Gmail and Outlook accounts. The supported endpoints reference puts it as "same endpoints, same auth, same payloads." Anything you built for connected accounts works against an agent's grant unchanged. And the resources behind the ID are real: six system folders provisioned automatically ( inbox , sent , drafts , trash , junk , archive ), a primary calendar that speaks standard iCalendar, and outbound messages capped at 40 MB total. Webhooks route by it too The inbound side completes the picture. You subscribe once at the application level, and every notification — message.created , event.updated , message.bounce_detected , and the rest

Week one: a single test mailbox on a trial domain, provisioned by hand from the dashboard. Week twelve: a fleet of agent mailboxes spread across customer domains, each sending real mail with its own quota and reputation. The API calls are the same at both scales — what changes is everything around them: how you provision, how you share configuration, and how you keep one bad sender from pausing the fleet. Here's what the path from one to thousands looks like with Nylas Agent Accounts , which are currently in beta. Provisioning is a loop, not a ceremony There's no OAuth dance to scale around. Creating a mailbox is one POST with "provider": "nylas" — no refresh token, no consent screen — so a fleet provisioner is just iteration: curl --request POST \ --url "https://api.us.nylas.com/v3/connect/custom" \ --header "Authorization: Bearer <NYLAS_API_KEY>" \ --header "Content-Type: application/json" \ --data '{ "provider": "nylas", "workspace_id": "<WORKSPACE_ID>", "settings": { "email": "agent-0042@agents.yourcompany.com" } }' Two scaling-relevant details in that request. First, the domain: one application can manage accounts across any number of registered domains, and the docs explicitly recommend splitting high-volume outbound across multiple domains ( sales-a.yourcompany.com , sales-b.yourcompany.com ) so reputation damage on one doesn't contaminate the rest. Second, the workspace_id : passing it at creation is how each account picks up its configuration, which brings us to the part that makes fleets manageable. Configure workspaces, not grants At fleet scale, per-grant configuration is a non-starter. The model here is indirection: policies and rules attach to workspaces , and every account in a workspace inherits them. One policy object — daily send quota, storage cap, retention windows, spam sensitivity — governs a thousand mailboxes, and updating it updates all of them at once. The recommended carve-up is one workspace per agent archetype: outreach agents get a work

Every Agent Account you create lands in a workspace — the only question is whether you picked it or the platform did: curl --request POST \ --url "https://api.us.nylas.com/v3/connect/custom" \ --header "Authorization: Bearer <NYLAS_API_KEY>" \ --header "Content-Type: application/json" \ --data '{ "provider": "nylas", "workspace_id": "<WORKSPACE_ID>", "settings": { "email": "test@your-application.nylas.email" } }' That top-level workspace_id is optional, and what happens when you omit it is one of the more under-read parts of the Nylas Agent Accounts docs (the feature's in beta, so read with that in mind). Workspaces aren't cosmetic grouping — they're the carrier for every policy limit and mail rule that governs your agents. Getting placement wrong means an agent running with the wrong send quota, the wrong spam settings, or no inbound filtering at all. The placement algorithm When a new account is created, placement resolves in this order: Explicit workspace_id on the request — the account goes exactly where you said. The target workspace must belong to the same application; ownership is validated and mismatches are rejected. Auto-grouping by domain — if a workspace has auto_group enabled and its domain matches the new account's email domain, the account joins it automatically. The default workspace — everything else lands here. That third bucket is the interesting one. What the default workspace actually is Every application gets exactly one default workspace, created and managed by the platform. You can't delete it, and you can only update two of its fields: policy_id and rule_ids . Everything else is managed for you. It's easy to read "default" as "throwaway," but it's better understood as your safety net. Any account you forget to place explicitly — a quick test from the CLI, a provisioning script missing the workspace parameter — inherits whatever the default workspace carries. Which leads to a practical recommendation: attach a conservative baseline policy and

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. When I landed in Seoul after a grueling 12-hour flight from San Francisco, I walked through an unmanned immigration checkpoint, where a machine scanned my face and passport. On the subway home,…

Series Final. The four mechanisms covered across this series — knowledge graph, Auto Review, Self-Healing, Recurrence Prevention — plus the non-engineer-PR application that sits on top of them, all hang off a single conviction: AI isn't something to trust; it's something to design. The 'I don't trust AI to fill in the blanks for me' framing this lives inside isn't doubt about generation quality, but the clear-eyed acceptance that AI has no idea what context wasn't handed to it, and that 'ideal behavior with no spec given' is a fantasy. The starting point goes back to 2025, when I was trying to figure out how to make AI actually understand a large codebase — and ran into walls on both context window scaling (lost in the middle, attention dilution) and learning-based approaches (machine unlearning, destructive interference). GraphRAG + MCP became the way out: hand AI only the facts it needs, when it needs them, so it doesn't have to infer. From code-graph (which I burned two months on and threw away) to the current product-graph (cpg). This piece is the philosophy and the trial-and-error behind the whole series: harnesses confine where hallucinations are allowed to happen, design is translating principles into your own use cases, and Coverage 90% as a solo target breaks the implementation.

A blunt playbook for devs who don’t want to turn into autocomplete zombies. The first time an AI wrote code for me, I felt like I had unlocked cheat codes for real life. I typed a half-baked function name, hit enter, and suddenly I had a block of code that looked legit. It was magical. The second time, though? It suggested something so catastrophic basically the programming equivalent of pulling the fire alarm that I realized: this thing is less “mentor” and more “overconfident intern who thinks they know pointers but actually just broke prod.” That’s where most of us are right now. AI is everywhere: in our IDEs, our docs, even sneaking into PR reviews. Some days it feels like rocket fuel; other days it feels like an autocomplete with a drinking problem. The tricky part isn’t whether AI is “good” or “bad.” The tricky part is how we, as developers, use it without becoming lazy, dependent, or worse complacent. Because here’s the uncomfortable truth: AI won’t replace you, but bad AI habits absolutely will. TLDR : This article is a survival guide for developers in the AI era. We’ll break down why AI feels both magical and mid, the five switches that make AI actually useful, when to trust and when to verify, how to use AI as a research assistant (not a code monkey), the dangers of autocomplete brain, and a playbook for building a healthy workflow. Why AI feels both magical and mid Every dev I know has had that moment with AI. The first time it autocompleted a function and nailed it, you probably thought: “Wow… this thing just saved me half an hour.” It’s the same dopamine hit as discovering ctrl+r in bash or realizing you can pipe grep into less . Pure wizardry. But the honeymoon ends quickly. The same tool that wrote a clean utility function also happily hallucinates imports that don’t exist, invents APIs, and will confidently explain things that are flat-out wrong. It’s like pair programming with someone who sounds senior but has never actually shipped code. The magic-

I had a write that spanned two physically separate databases. A rename that had to propagate across several tables in one database and a couple of tables in another, and the two had to stay consistent. No distributed transaction coordinator was available to me. So I did the obvious thing: opened a transaction on each, did the work, and committed them one after the other inside a try/catch with rollbacks on both sides. It felt safe. It compiled. It passed tests. Then I drew the failure on a whiteboard, and the safety evaporated. The window that ruins everything Here's the structure, simplified: await using var txA = await dbA . Database . BeginTransactionAsync (); await using var txB = await dbB . Database . BeginTransactionAsync (); await DoWorkOnA ( dbA ); await DoWorkOnB ( dbB ); await txA . CommitAsync (); // <-- succeeds await txB . CommitAsync (); // <-- what if this throws? Two transactions do not make one atomic operation. CommitAsync is a point of no return, and there are two of them. Between the first commit returning and the second one starting, there is a window. If txB fails in that window — the connection drops, the process is killed, the database hiccups — then A is permanently committed and B never happens. Your rollback in the catch block is useless: you can't roll back txA , it's already durable. The two databases now disagree, and nothing in your code will heal that on its own. This is the dual-write problem , and it's not a bug you can fix by being more careful with try/catch. The atomicity you want simply isn't available from two independent commits. Ordering them, nesting them, wrapping them — none of it closes the window, because the window is inherent to having two commit points. Why "it's never failed" isn't reassurance The seductive thing about this pattern is that the window is small, so in practice it almost never triggers. You can run it for a year and never see an inconsistency. That's exactly what makes it dangerous: it trains you to tr

A story about an AI alert model that cut false alarms from 60% to 7% — and what happened when...

Honestly, bootcamp Grad Dives Into Google vs OpenAI API Pricing When I finished my coding bootcamp three months ago, I thought I understood what an API did. I mean, you send a request, you get a response back, right? What I did not understand was how dramatically the cost could vary depending on which model you picked. I had no idea that a single line of code change could mean the difference between paying pennies and paying hundreds of dollars at scale. That is the rabbit hole I fell down last week, and I want to walk you through everything I learned. This is the post I wish I had read before I burned through my first $50 in API credits. Why I Started Looking At Pricing In The First Place I was building a small app that takes user reviews and summarizes them. Pretty straightforward. I figured I would just plug in the most popular model and call it a day. That model, if you have been paying attention to the news, is GPT-4o. So I wired it up, ran a few tests, and everything looked great. Then I did the math. GPT-4o charges $2.50 per million tokens on input and $10.00 per million tokens on output. I did not even know what a "million tokens" really meant in practice. So I tested my app with maybe 50 reviews and watched my credit balance drop. It was not catastrophic, but it was enough that I started wondering if there was a cheaper way. I was shocked when I found out how big the gap actually is. The Pricing Table That Changed My Whole Plan I stumbled onto a platform called Global API, and honestly, the pricing chart there blew my mind. They give you access to 184 different AI models, with prices ranging all the way from $0.01 to $3.50 per million tokens. Compare that to the GPT-4o output price of $10.00 per million tokens, and you start to understand why I panicked a little when I saw my early numbers. Here are the five models I ended up comparing side by side: Model Input Cost Output Cost Context Window DeepSeek V4 Flash $0.27 $1.10 128K DeepSeek V4 Pro $0.55 $2.20 20

Armed with a ton of new upgrades, Ferrari came to Spain full of confidence.

Casey Harrell has had a set of electrodes embedded in his brain for almost three years. Harrell, who has amyotrophic lateral sclerosis (ALS) and is paralyzed, first used his brain-computer interface (BCI) to “speak” sentences with the help of a research team in 2023. Since then, Harrell has clocked thousands of hours of use. He…

Indian IT services company HCLTech is investing $150 million in the Bengaluru startup.

CSS functions, the alpha() function, Grid Lanes, some things about Dialog that you might not know, CSS Wordle, and more — this is What’s !important right now. What’s !important #13: @function, alpha(), CSS Wordle, and More originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

NewCore argues the next challenge in enterprise security will be managing AI agents, not people.

You've heard of Bitcoin. You've maybe heard of the Lightning Network. But what exactly is LND, and why should developers care? Let's break it down — technically, but from the ground up. The Problem: Bitcoin is Superb but Slow Bitcoin's base layer — the blockchain itself — is intentionally slow. Every transaction must be broadcast to thousands of nodes, verified, and bundled into a block that gets mined roughly every 10 minutes . The network handles about 7 transactions per second (TPS). Compare that to Visa's ~24,000 TPS and you quickly see the problem. Bitcoin in its raw form isn't built for buying coffee, splitting a bill, or paying a freelancer in real time. But there's a solution — and it lives on top of Bitcoin. Enter the Lightning Network The Lightning Network is a Layer 2 (L2) payment protocol built on top of Bitcoin. Instead of recording every single payment on the blockchain, it lets two parties open a private payment channel, transact off-chain as many times as they want, and only settle the final balance on-chain when they're done. Think of it like running a tab at a bar: Opening the tab = one blockchain transaction Each round of drinks = instant off-chain payment Closing the tab = one final blockchain transaction The result? Near-instant payments, near-zero fees, and massive throughput — without sacrificing Bitcoin's security. What is LND ? LND stands for Lightning Network Daemon. It's the most widely used implementation of the Lightning Network protocol, built and maintained by Lightning Labs. Key facts for developers: Written in Go 🐹 Exposes a gRPC API (port 10009) and a REST API (port 8080) Controlled via a CLI called lncli Uses macaroons for authentication (think JWT, but for Lightning) Connects to a Bitcoin node (bitcoind or btcd) as its source of truth Other Lightning implementations exist — like Core Lightning (CLN) and Eclair — but LND has the largest developer ecosystem and is the best entry point. How LND Fits Into the Stack Here's the architec