标签：#ci

Every GEO ("generative engine optimization") tool, including ours until recently, sells some version of the same pitch: fix your robots.txt, add Schema.org markup, write FAQ schema, and AI engines will cite you more. We build one of these tools — Causabi scans sites for AI-crawler readiness and generates fix files (robots.txt, llms.txt, JSON-LD, FAQ blocks). As part of validating our own scoring weights, we ran the numbers on whether the score actually predicts getting cited. Short version: it mostly doesn't, once brand prominence is in the picture. What we measured We scored 44 domains on a 6-category on-site readiness algorithm: robots.txt (AI bots allowed or blocked) Schema.org (Organization/LocalBusiness JSON-LD completeness) FAQ schema (FAQPage markup, 3+ entries) content depth/structure brand/NAP signals freshness (dateModified, recency) Then we checked how often each domain actually got cited by an AI engine (Claude, via its web-search tool, one measurement window, a fixed prompt set per domain). What we found On-site score vs. citation rate: Pearson r ≈ -0.08, Spearman ρ ≈ -0.03. Functionally no correlation — if anything, a very slight negative one, which is more likely noise than a real inverse relationship at this sample size. 86% of the 44 domains got zero citations in the window, independent of their score. The domains that did get cited clustered almost entirely by brand prominence — well-known domains got cited at a noticeably higher rate (~0.16 of prompts) than everyone else (~0 for the rest of the sample), regardless of how well-optimized their markup was. Why I'm not overselling this n=44 is small. This is an internal validation exercise for our own product, not a peer-reviewed study, and I don't want it read as one. Specific caveats: Single engine (Claude) this round. Citation behavior differs meaningfully across ChatGPT, Gemini, Grok, and Perplexity — we haven't run the same check across all four yet. One time window, no longitudinal before/after.

Bryan, a food broker from Michigan, wasn't sure if he'd be able to make it to urgent care in time. He started feeling off on Thursday, and by Saturday, he was having to use the bathroom every 15 to 30 minutes. "It's no joke about the explosive diarrhea," Bryan, who asked that his last name […]

Babies are tremendous learning machines, and key advances for AI may soon be found in the architecture of their little brains.

Inkling, a 975-billion-parameter open source model, was trained to understand video and audio. It could help Thinking Machines establish itself among competitors like Anthropic and OpenAI.

OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other models boost their defenses against cyberattacks. Last week the company released the latest version of its flagship LLM, GPT-5.6. OpenAI says that training it against GPT-Red made the model its most robust release yet. GPT-Red automates…

Argentina’s Lionel Messi was supposed to be done years ago. Now, sports science is helping soccer’s biggest stars rewrite the rules of aging.

Risk stratification sounds like a data-science buzzword until you have to build the thing. For a Medicare Advantage plan, it's a concrete pipeline: take a population of members, score each one's clinical and financial risk, and rank them so care management and documentation teams know who to touch first. Here's how I'd architect it. The core idea Population health risk stratification = scoring + segmentation. You compute a per-member risk signal, then bucket members into tiers (e.g., rising-risk, high-risk, catastrophic) so finite resources go where they move outcomes and revenue most. The mistake teams make is treating it as a single ML model. In practice you want a layered signal: a stable, explainable base (RAF + chronic conditions) plus optional predictive overlays. Explainability matters because care managers won't act on a black-box score, and auditors won't accept one. Step 1: Build the member feature record { "member_id" : "SYNTH-77310" , "age" : 73 , "hccs" : [ "HCC37_1" , "HCC85" , "HCC18" ], "raf" : 1.842 , "gaps" : [ "a1c_overdue" , "no_pcp_visit_180d" ], "utilization" : { "ed_visits_12m" : 3 , "inpatient_12m" : 1 } } The RAF here is your defensible, model-grounded risk anchor under CMS-HCC V28. Everything else is supplemental signal. Step 2: Score and tier def risk_tier ( member ): base = member [ " raf " ] util = 0.15 * member [ " utilization " ][ " ed_visits_12m " ] \ + 0.30 * member [ " utilization " ][ " inpatient_12m " ] score = base + util if score >= 3.0 : return " catastrophic " if score >= 1.8 : return " high " if score >= 1.0 : return " rising " return " stable " Keep the weights transparent and tunable. The point isn't a perfect model; it's a defensible, reproducible ranking your operational teams trust. Step 3: Make "rising-risk" actionable The tier that quietly drives the most ROI is rising-risk — members trending toward high cost who still have open documentation and care gaps. Surface their specific gaps (overdue labs, undocumented chroni

The US is getting closer to observing daylight saving time year-round. On Tuesday, the House advanced the Sunshine Protection Act on a 308 to 117 vote, which would turn clocks one hour ahead permanently, as reported by CBS News. President Donald Trump wrote on Truth Social in May that the bill would save the "hundreds […]

The app is designed for people who want to create social content, but find traditional video editing tools too complex or time-consuming.

Gwen Shapira shares how teams are scaling AI features using PostgreSQL for mission-critical apps. She explains how to leverage Postgres's multi-modal capabilities - including JSONB parsing and high-recall HNSW vector indexing - to deliver deterministic and semantic context to LLMs. She also discusses vector quantization to speed up queries by 4x and strategies for managing agentic memory. By Gwen Shapira

Hey everyone, I recently had an "aha!" moment regarding how React handles updates under the hood, and I wanted to share it because I realize a ton of developers (including myself, until recently) trip over this exact concept. The common mental model is that React Reconciliation compares the Virtual DOM directly to the Real Browser DOM and surgically updates only what changed. But that’s fundamentally incorrect. React never reads or directly compares the real DOM during the diffing process. It actually splits the process into two entirely separate phases —The Render Phase and The Commit Phase —which creates a massive distinction between Re-rendering and Re-painting. Here is the exact breakdown of what happens when a single state change affects just 1 out of 100 divs in a component: The Render Phase (Pure JavaScript) When state changes, React calls your component function. It doesn't know which of your 100 divs changed yet, so it has to evaluate the entire JSX block. The Scope: React re-renders all 100 virtual divs in memory. The Process: It builds a brand-new Virtual DOM tree and compares it to the previous Virtual DOM tree (JavaScript object vs. JavaScript object). The Outcome: It spots that 99divs are identical, but 1 div has an update. It flags that single virtual node with an "Update" tag. Because this happens purely in-memory as JavaScript, it is incredibly fast and cheap. The Commit Phase (The Real DOM Update) This is where Reconciliation does its primary job. It acts as a shield to protect the browser from doing unnecessary work. The Scope: React completely ignores the 99 unchanged elements. The Process: It surgically targets the single real browser div associated with the flagged Virtual DOM element and updates only its modified property (e.g., element.textContent = "New Value"). The Outcome: The browser repaints only 1 single div on the screen. The Conclusion: Reconciliation isn't about stopping React from re-rendering (re-running JS to calculate the UI). It

Gidi Littwin's new AI startup, Hemispheric, makes diagnostic brain scans for conditions like depression, PTSD, and Parkinson’s. He wants the technology to be as cheap and easy as a blood test.

Private buyers are increasingly outbidding museums for fossils.

Spotify founder Daniel Ek's body-scanning startup, Neko Health, is setting its sights on the United States after raising $700 million from a star-studded group of celebrities, entrepreneurs, and investment firms. It plans to open its first clinic in New York this year before expanding rapidly across the country. Neko operates private clinics offering full-body scans […]

Companies’ increasing reliance on AI chatbots isn’t making the customer service experience smarter. It’s just making it more infuriating.

Official case counts likely capture only a fraction of US cyclosporiasis infections, and the outbreak is likely to get worse before it gets better.

OpenAI employees have donated more than $215,000 to a political effort opposing Leading the Future, a group backed by the company’s president, Greg Brockman.

SpaceX's latest residential dish - the Starlink V5 - is now available in "select areas." It's notably smaller and lighter than the V4 dish with improved power efficiency. It'll be available in more places as SpaceX ramps up production to meet global demand. The company notes that Starlink V5 is not intended for in-motion use […]

Series: Master OOP for Software Engineering Interviews Introduction Ask ten beginner developers: "What is abstraction?" Most answers sound like this: "Abstraction is the process of hiding implementation details and showing only essential information." Technically, that's correct. But if I ask the next question: "Why was abstraction invented?" or "Can you explain abstraction using an Inventory Management System?" or "How is abstraction different from encapsulation?" many candidates struggle. That's because they memorized the definition instead of understanding the idea behind it. In this article, we'll learn abstraction the way experienced software engineers think about it—not by memorizing definitions, but by understanding why it exists, what problem it solves, and how it appears in every modern software system. 🎯 Learning Goals After reading this article, you should be able to: Explain abstraction without memorizing a textbook definition. Understand why abstraction exists. Identify abstraction in everyday life. Recognize abstraction in software systems. Confidently answer beginner interview questions. Build a strong mental model that makes future OOP concepts easier. Before We Learn Abstraction... Let's ask an important question. Why do programming languages even provide OOP? Imagine writing software for an e-commerce company. The system contains: Products Customers Orders Warehouses Payments Delivery Partners Notifications Discounts Reviews Thousands of features. If every developer had to understand every implementation detail before writing code, software development would become impossible. We need a way to reduce complexity. That solution is called abstraction. The Problem Abstraction Solves Imagine buying a new car. You sit inside. You: Press the accelerator. Turn the steering wheel. Shift gears. Press the brake. Simple. But underneath the hood, hundreds of complex operations happen every second. The engine burns fuel. The pistons move. The gearbox changes tor

The restrictions, which can be turned off, will include a crackdown on “addictive” app features and will be in addition to a total ban on children under 16 accessing platforms like TikTok and YouTube.