标签：#AR

Why some records should be created by your system, not your users, and how to do it safely in .NET. A support ticket lands on your desk: "The Teams page is empty. I added a member, but no team shows up." You check the API. It's behaving exactly as written: { "items" : [], "totalCount" : 0 } Nothing is broken. And that's the problem. The system is faithfully returning nothing, because the row that the page reads from was never created. Somewhere in your design, you assumed a human would create it first. This article is about a small, recurring design decision that quietly causes empty dashboards, confused users, and "is this a bug?" tickets: who is responsible for creating derived records the user, or the system? and how to let the system do it without introducing duplicate rows or race conditions. The problem Let's use a fictional product: a collaboration tool called Loop . In Loop, the important entities are: An Organization (a paying customer). A Member (a person invited into an organization under a plan). A Team a grouping that members belong to, keyed by (OrganizationId, PlanCode) . The admin dashboard lists Teams . Each team card shows a member count. Here's the catch in the original design: creating a Member wrote a member row. Creating a Team was a separate, manual step an admin was expected to do first. If an admin invited members without first creating the matching team, the dashboard showed nothing even though the members clearly existed. From the user's point of view, they did everything right. From the system's point of view, a required row simply didn't exist. Why it matters The Team record isn't independent information. It is fully derivable from the first member invited under a plan. When one entity's existence is implied by another, forcing a human to create it manually is a design smell. It leads to: Empty states that look like outages. Users can't tell "no data" from "misconfigured." Support load. Every skipped step becomes a ticket. Silent data dr

Now that Google has changed how its usage quotas are tallied, you might not get as many AI responses as you did before.

Before a young Athenian took his full place in the city, he spent two years in the ephebeia. Training happened in the gymnasion, organized by tribe, the same tribes that would later send him to represent them in the Boule itself. Nobody handed him full standing first and hoped the judgment would follow. This should have been post seven. It’s showing up as sixteen because the gap only became visible once the room was real enough to test against. The Agora described what a Spec Session does. It never asked whether everyone walking into that room shares an accurate picture of what the agent can actually do. Most rooms don’t. Someone watched a demo and thinks the agent can do anything. Someone else got burned by a bad output three weeks ago and doesn’t trust it with anything real. Nobody’s intuition has been tested against the same tasks, and the spec that comes out of that room ends up too ambitious or too conservative depending on whose untested belief happened to speak first. That gap has a name in Athens. The gymnasion existed because nobody was handed a place in the city first and expected to develop judgment on the job. The ephebeia ran two years, training built around a specific fact. Physical readiness and civic judgment weren’t taught in separate places. They happened in the same space, under the same supervisors, organized by the same tribal groupings that would later structure how the city actually governed itself. The same word, gymnasion, ended up naming both the training ground for eighteen-year-olds and the buildings where Plato and Aristotle did their most serious thinking. Academy and Lyceum were gymnasia first. Nobody separated the trial from the reflection. The trial was how the reflection got earned. That’s the part worth taking seriously. Testing a tool and understanding a tool were never two different activities. The testing is how the understanding gets built. A team that reads documentation about what an agent can do has a description. A team tha

Balmuda has engineered a remarkably pleasant breeze with its new NatureWind Studio, but is it worth hundreds more than its competition?

A subscription audit should survive contact with a real app. So I started with mine. TurnTalk is a live iOS travel translator with in-app purchases. I operate the app and its RevenueCat implementation. That makes it a useful public example, but not a customer case study. I will not claim a conversion lift I have not measured. Here is the first finding I would put at the top of its audit. Evidence: the store page introduces several different jobs The subtitle makes a focused promise: Understand Any Guide, Live But the first screenshot sequence spreads attention across: AI travel translation Voice translation Photo translation Instant translation Each feature may be useful. The issue is not feature quality. The issue is that a visitor has to decide which product TurnTalk is before deciding whether to download it. A traveler who wants to understand a live tour guide is evaluating a specific job. A visitor comparing general translator apps is evaluating a much broader category. Those users arrive with different intent. Why I would rank this before a paywall redesign The paywall cannot repair ambiguous acquisition intent. If the store page attracts people for four different jobs, aggregate trial and purchase rates become difficult to interpret. A low conversion rate could mean: The paywall is weak The first session does not prove the promised value The visitor downloaded for photo translation but reached a live-translation flow The listing attracted broad curiosity instead of durable travel intent Changing the paywall first would alter one screen while leaving those explanations mixed together. That is not a clean experiment. P0 action: make the first three screenshots tell one story I would test a narrower opening sequence: Situation: You joined a tour, but cannot understand the guide Mechanism: Put in your existing earphones and start live translation Outcome: Hear the guide in your language without staring at the screen Photo translation and secondary conversation mod

Get ready to crush your coursework or scoot to class in style with these back-to-school gear picks.

“Context bombing” tricks malicious AI agents into shutting down before they can do harm.

SEO Automation for Small Businesses Can Work — But Only If You Automate the Right Things Running a small business and keeping up with SEO at the same time is genuinely exhausting, and most owners either ignore SEO entirely or throw money at agencies that deliver reports without rankings. SEO automation for small businesses offers a middle path: systematic, repeatable processes that handle the mechanical parts of search optimization without requiring you to be an expert or hire one full-time. The short version — automating keyword tracking, technical audits, internal linking, and content scheduling will save you real time; automating content creation wholesale, without human review, will almost certainly hurt you. The rest of this article is about making that distinction practically useful. The Tasks That Actually Benefit From Automation Most of SEO is repetitive in ways that humans are bad at — checking 200 pages for broken links, making sure title tags aren't duplicated, tracking keyword rankings week over week. These are tasks where consistency matters more than judgment, which makes them exactly what automation tools are built for. A local landscaping company I know of was spending roughly 4-5 hours a week on manual rank checking across about 60 target keywords. After setting up automated tracking through a tool like Semrush or AccuRanker, that time dropped to under 30 minutes — just reviewing a dashboard. The monitoring didn't change their rankings, but it changed how quickly they spotted a drop and responded. That gap between noticing and acting is where small businesses tend to lose ground quietly. Technical SEO audits are another area where automation earns its keep. Tools like Screaming Frog or Sitebulb will crawl your site and surface issues — missing alt text, redirect chains, slow page load times — that you'd never catch manually unless you happened to stumble across them. According to a Semrush industry report, sites with automated audit schedules fix te

Find the best SteelSeries coupon codes, promo codes, and discount codes for May 2026. Get deals on headsets, keyboards, and mice, including first-order and student discounts.

Suspect-condition workflows are deceptively simple to prototype and surprisingly hard to make defensible . Anyone can flag "this member might have HCC X." Building a system whose output survives a RADV audit is a different problem. This is a walkthrough of the three stages and the engineering decisions that matter at each. Stage 1: Identify Identification is pattern detection over a member's clinical record — labs, medications, prior diagnoses, utilization. Model it as a set of rules or features that emit candidate HCCs: def identify_suspects ( member ): suspects = [] if member [ " labs " ]. get ( " a1c " , 0 ) >= 9.0 and " insulin " in member [ " meds " ]: suspects . append ({ " hcc " : " HCC38 " , " trigger " : " a1c>=9 + insulin " }) if member . get ( " egfr " ) and member [ " egfr " ] < 30 : suspects . append ({ " hcc " : " HCC326 " , " trigger " : " egfr<30 " }) return suspects The temptation is to maximize recall here — flag everything. Resist it. Every unvalidated suspect you generate is downstream work and downstream risk. Stage 2: Validate (the stage that actually matters) Validation attaches evidence to each suspect and scores its defensibility. This is the difference between a documentation opportunity and an audit liability. def validate ( suspect , member ): evidence = collect_evidence ( suspect [ " hcc " ], member ) # labs, rx, prior dx suspect [ " evidence " ] = evidence suspect [ " confidence " ] = score_evidence ( evidence ) suspect [ " defensible " ] = suspect [ " confidence " ] >= 0.7 return suspect Key design rule: a suspect with an empty evidence array should never reach a coder. Make that a hard gate, not a soft warning. Under CMS-HCC V28 and current audit posture, a captured-but-unsupported diagnosis can be extrapolated across a contract into a real clawback — so "defensible by default" is the right engineering stance. Stage 3: Capture Capture routes validated suspects to the right human with the evidence inline, so the clinician or coder can

Big Howdy, I'm so tired of hearing all the hype about AI. Don't get me wrong, I think AI is pretty cool and I use it all the time for various tasks throughout the day, but I hate AI marketing. What do I mean by that? Whenever there is any hype, advertising, or corporate shilling about AI, it's always vague. Talks about agents, multi-agent systems, RAG pipelines, etc. are everywhere, and yet the specifics are always just out of reach. Even when I try to dig into deeper technical articles, the handwaving usually begins almost immediately. Several architecture diagrams and acronyms later, I still do not have a much better idea of what is actually happening (although I'm not really looking hard enough). I’m going to fix that. For myself, at least. First Stop: RAG My first target is RAG. It's been out for quite a while now and I've been hearing about it for forever. It also sounds like enough progress has been made that the problem has effectively been solved. Best practices have been established and it's pretty commonplace now, so it's a great place to start. But what is RAG? RAG stands for Retrieval-Augmented Generation , which is... not a helpful name if you are just learning about this for the first time like I am. However, if you use AI at all, you've already been using a form of it without even knowing it. RAG is simply a pattern: Retrieve relevant external information. Add that information to the AI model’s working context. Generate an answer grounded in it. That's it. When you use an AI assistant and it goes and searches the internet for relevant information to give you an answer, that's a form of RAG. That doesn't mean your AI assistant is a RAG system, but it can perform RAG functions. But this STILL sounds a bit hand-wavy to me. How does this work in real life? The Part I Kept Missing Context windows are massive these days, but I have seen technical documents that are literally more than 3,000 pages long and full of dense technical information. Even if one tec

You don't need to know anything about Go to read this. The game is just the fixed yardstick. The story is a hardware benchmark: the same program, the same problem, the same settings — only the machine changed, from a 2017 GPU cluster to a single 2026 graphics card. That makes it a rare clean measurement of one decade of progress. What "solving" means here There are two very different things a computer can do with a board game. It can play it well — that's what AlphaGo did. Or it can solve it: mathematically prove the outcome under perfect play from both sides, leaving no doubt. Solving is the hard one. You explore an enormous tree of "if I play here, they play there…" move sequences until you have an airtight proof. Each node in that tree is one position examined. The target here is a single 7x7 opening called JA . In 2023, a NeurIPS paper ( Game Solving with Online Fine-Tuning , Wu et al.) proved its verdict — the attacker cannot win — using a cluster of twelve GTX 1080Ti GPUs running 384 parallel workers. The solver is guided by a neural network that estimates how hard each branch is, and crucially that network is fine-tuned online — it keeps learning during the solve. I rebuilt that exact solver (same code, same problem, same initial model, same search settings) and ran it on one RTX 5090 . It reached the identical proof . Everything but the hardware was held fixed, so the two runs line up as a generation-vs-generation benchmark — and it doubled as a full shakedown of the new Blackwell workstation. The numbers 1x RTX 5090 (2026) 12x GTX 1080Ti (2017) ratio Worker slots 24 384 1/16 the parallelism Per-slot throughput 284 nodes/s 141 nodes/s 2.01x faster Search work to proof 1.01B nodes 1.73B nodes 0.59x (41% less work) Avg work per sub-job 4,189 nodes 6,136 nodes shallower proofs Live model updates 4,007 208 19.3x more Wall-clock time 41.4 h 8.9 h 4.64x slower Verdict loss (proven) loss identical The single card finished slower in wall-clock time (41 h vs 9 h) — b

The window is almost shut. On August 19, eight startups will take the stage at Stripe Tour Sydney in front of investors, global press, and the Australian tech community. One startup walks away with automatic entry into TechCrunch Disrupt in San Francisco — no application, no further competition, a guaranteed spot on the world’s most […]

From AI workflows to battery life and security, here's what it's really like to live with Vertu's luxury foldable every day.

Databricks has remade its image into an AI company and has published research on the cost savings of open weight AI models for coding.

India's smartphone slowdown highlights how the AI boom is reshaping consumer electronics, from pricing and demand to corporate strategy.

The potential deal highlights a growing trend of complex, multi-stage funding rounds that mask true entry prices.

"Missiles are being launched at the joint force every single day in [Operation] Epic Fury."

Pebble founder Eric Migicovsky says buyers of its new e-paper smartwatches should know what they're signing up for and trust Pebble to make things right if they run into issues, despite the short warranty. "I think the most important thing is trust," Migicovsky told me in an interview this week. "Do people trust the product […]

Don't eat Taco Bell lettuce in Indiana, Kentucky, Michigan, Ohio, or West Virginia.