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Creating a website is more than just writing code. After building a project with HTML, CSS, or JavaScript, developers also need to consider how the website performs, how secure it is, and how easily users can access it. As a young developer, I think these aspects are important to learn through practice. In this article, I want to share my experience exploring Tencent EdgeOne Makers, along with some technical considerations and best practices that I found useful. Getting to Know Tencent EdgeOne Makers Tencent EdgeOne Makers provides an interesting environment for developers to experiment with their projects and explore modern web services. For beginners, one of its advantages is the opportunity to experience the complete journey of a web project. We can start by creating a project locally, testing its features, and then making it accessible online. This process gives developers a better understanding of what happens beyond the coding stage. A website that works on a personal computer still needs to be tested in a real environment before it can provide a good experience for users. My Technical Review Easy Project Experimentation One thing I like about EdgeOne Makers is the opportunity to experiment. Developers can start with a simple project and gradually improve it. This is useful for students and beginner developers because learning does not always have to start with a large or complicated application. A small website can already teach important lessons about structure, configuration, performance, and accessibility. Performance Matters A website should not only work correctly; it should also feel comfortable to use. Large images, unnecessary JavaScript, and unused resources can affect loading times. Because of this, developers should review their project before making it available to users. Some simple practices include compressing large images, removing unnecessary files, and keeping the website structure clean. Performance is especially important because users may

ChatGPT.com reached about 1.09 billion monthly US visits in July 2026, a 48.38% year-over-year increase, according to Semrush Traffic Analytics data. In the same comparison, Bing.com traffic fell about 50.43%. The contrast does not show AI replacing conventional search overnight. Google and YouTube still led the US dataset by a wide margin. It does show that the places where people first discover information are changing, while many website analytics setups remain poorly equipped to show the full effect. The July 2026 snapshot puts ChatGPT ninth among the leading US sites measured by Semrush. Businesses that still view organic discovery mainly through Google rankings and familiar referral reports risk missing a growing part of the customer journey: a user may ask an AI assistant for options, follow a recommendation, and arrive on a website without a cleanly identifiable source in Google Analytics 4. The underlying Semrush US Trending Websites data compares July 2026 traffic with July 2025. It is a view of US web traffic in Semrush's ranked-site dataset, not a count of every search or AI interaction. Still, the scale of the movement makes AI-assisted discovery a practical measurement issue, not simply a trend to monitor. What the traffic shift means for SEO measurement The key implication is not that businesses should abandon established search channels. Google recorded roughly 25.31 billion monthly US visits in the July snapshot, while YouTube recorded about 10.27 billion. Those figures underline how large the established platforms remain. What has changed is the need to distinguish where discovery happens from the source that ultimately appears in analytics. ChatGPT's growth can create new paths to content, products and services. But referral details may be unavailable when users move from an AI interface to a website, particularly when the originating referrer is stripped. In GA4, those visits can be grouped as Direct or remain otherwise difficult to classify. Web

Google is changing how manual actions under its Site Reputation Policy affect search visibility by region. From August 30, 2026 , the ranking impact of these actions will not apply to search results shown to people in the European Economic Area (EEA), while results shown outside the EEA may still be affected. For site owners with international audiences, that makes region-specific SEO monitoring more important than a single global view of performance. The change does not remove Google's Site Reputation Policy or its effort to address site reputation abuse . Instead, it changes how the consequences of a manual action are experienced in EEA search results. Google announced the update in its official Site Reputation Policy update , linking the regional change to its ongoing discussions with the European Commission and considerations related to the Digital Markets Act. What Google changed Google introduced its Site Reputation Policy in 2024 to address situations in which third-party content takes advantage of a host site's established ranking signals. The policy targets content that is published primarily to exploit a site's reputation in Search rather than to provide value consistent with the host site's purpose and oversight. The policy remains in place globally. What changes is the effect of a manual action for people searching from the EEA. Google says that when it applies a manual action under this policy, the impact will not apply to results displayed to users inside the EEA. Outside the EEA, the manual action can continue to affect the relevant site's search results. Enforcement consideration Search results in the EEA Search results outside the EEA Impact of a Site Reputation Policy manual action Does not apply to results shown to users in the EEA May continue to apply Potential treatment of the affected site portion Google may separate it in its systems so it can rank independently over time Google's announcement does not describe an equivalent regional change S

Google is preparing to make Search more agentic: instead of only returning results for a query typed by a person, persistent AI agents will be able to monitor information, evaluate options and take certain actions on a user's behalf. For businesses, that raises a practical question. Is the information on your website clear enough for an AI system to understand, compare and potentially act on? In its official announcement on a new era for AI Search , Google outlined Search agents that can work in the background around user-defined criteria. The company says the first category, information agents, will monitor topics across blogs, news and social content in real time, then provide updates and trigger potential actions. Google also described agentic tasks such as booking local experiences and services, including calls to businesses on a user's behalf. This is a confirmed product direction and rollout plan, not merely a prediction about how search might evolve. It does not mean traditional search results disappear. It does mean that a growing share of discovery could be mediated by systems that do more than retrieve links. They may identify a need, gather relevant details, compare available options and move a task toward completion. What Google is rolling out Google says information agents will launch first for Google AI Pro and Ultra subscribers in summer 2026. Wider availability in the United States is planned later in the season. The agents are intended to operate continuously, rather than only when a person opens Search and enters a new prompt. The initial use case is information monitoring. A user could define a topic and criteria, then have an agent follow relevant material across the web and report back when conditions change. Google also described a broader path toward actions, including booking and transactions. Shopping is part of that path, with Google saying it intends to expand agentic capabilities so actions can be completed through providers. Search capab

AI search is creating a new measurement problem for website owners: it can be harder to see how, where, and why content appears in an answer-led search experience. The concern is not a confirmed Google policy change or a universal loss of transparency. It is a credible industry signal that AI Overviews, AI Mode, and similar experiences may make traditional SEO visibility and attribution more difficult to verify. The discussion is timely because AI search is becoming another route by which people discover information, brands, and products. Search Engine Land's 2025 AI search optimization survey coverage provides useful context for the growing focus on GEO and AEO , terms often used to describe efforts to improve visibility in generative and answer engines. Google, meanwhile, continues to document AI-enabled Search experiences and related controls through its AI in Search materials. What remains uncertain is how consistently publishers will be able to connect AI answer visibility to traffic and commercial results. Why AI search changes the measurement question Traditional SEO has never offered perfect visibility, but it has established signals: rankings, impressions, clicks, landing-page visits, and referral data. AI-generated results can complicate that model because a search experience may synthesize an answer, cite selected sources, prompt follow-up questions, or satisfy a user without a visit to a publisher's site. This does not mean conventional SEO measurement is obsolete. It means teams should avoid treating a familiar metric as a complete picture of search performance when AI features are involved. The central question shifts from "Where do we rank?" to a broader one: Are we being represented accurately and usefully in the search journeys that matter to our customers? The practical challenge has several parts: Visibility can be contextual. An AI-generated response may differ by query wording and the information selected for the answer. Attribution may be weake

A valid robots.txt file does not necessarily mean a website is accessible to crawlers. Requests can still fail when a web application firewall , CDN, hosting configuration, rate limit, or other delivery layer returns an HTTP error such as 403 Forbidden or 429 Too Many Requests . End-to-end crawler testing addresses that gap by checking what happens when a crawler requests real pages, then comparing the result with server-side evidence. This is a useful operational practice rather than a newly announced SEO framework. The central idea is straightforward: robots.txt communicates crawl directives, but it does not guarantee that the infrastructure serving a page will allow the request through. For website owners, the practical goal is to find the specific layer that is preventing access before relying on an SEO dashboard's crawl report alone. Google's robots.txt documentation explains how Google interprets robots.txt and addresses situations in which the file is unreachable or HTTP responses affect access. That guidance matters because crawler access is shaped by both robots rules and the HTTP behavior a crawler encounters while requesting a site. robots.txt Is a Directive File, Not an End-to-End Access Test robots.txt is an important control point. It can tell compliant crawlers which paths should not be crawled. However, it operates separately from systems that decide whether an HTTP request may reach a page. A site can have an apparently permissive robots.txt file while a security or delivery layer blocks a request before useful content is returned. That distinction becomes clearer when crawlability is viewed as a sequence: a crawler must retrieve robots.txt where applicable, request the target URL, receive an acceptable response, and be able to access the intended content. A failure at any point can affect the practical result. Check What it can show What it cannot establish on its own robots.txt review Whether stated crawl directives permit or disallow paths Whethe

Fervo's deal with Google could expand to 1 gigawatt of geothermal power, enough to supply a very large AI data center in Utah.

A Google Business Profile can be a major source of calls, website visits, directions, bookings and customer confidence for a local business. That makes a suspension, reverification request, ownership problem or other loss of profile access more than a support-ticket inconvenience. It can interrupt a meaningful part of the lead pipeline. A Search Engine Land continuity-planning guide for Google Business Profiles , published on August 24, 2026, argues that businesses should prepare for this possibility before it happens. Its central point is practical: recovering a profile matters, but so does maintaining lead flow while recovery is underway. This is not an argument for abandoning Google Business Profile. A complete, accurate profile remains an important local discovery asset. The risk comes from treating it as the only dependable route between prospective customers and a business. If access is disrupted, recovery can take time and may involve lost profile content, reviews or historical performance data. A continuity plan gives the team a defined response instead of forcing it to improvise under revenue pressure. The four-part Google Business Profile continuity framework The framework is built around four connected actions: Preserve, Recover, Replace and Reduce . Together, they cover both immediate response and longer-term resilience. Preserve ownership, evidence and profile records Preparation starts with control. Businesses should ensure that the right people have ownership or access to the profile and that account responsibilities are clear. They should also retain the documents likely to be needed for verification or an appeal, such as business registrations, licences, utility bills and other evidence relevant to the business. It is also sensible to maintain copies of important profile information and keep NAP data consistent. NAP means the business name, address and phone number. Consistency across the website, directories and social profiles makes it easier for

The available material points to concern about spam-related disruption in Google Search results, but it does not provide a verified Google announcement, update date, scope, or confirmed ranking-impact data. That makes a precise assessment of any specific update impossible. The useful business response is not to assume a particular cause for every ranking movement. It is to make SEO operations more evidence-led, so teams can distinguish genuine site problems from normal search volatility. Spam enforcement can affect visibility unevenly. A page that loses rankings may have a technical issue, a weaker match for the query, a change in competitors' performance, or a broader shift in Google's results. Equally, a ranking gain is not proof that a site has found a lasting advantage. Treating short-term movement as a verdict on every content or link-building decision can lead to rushed rewrites, unnecessary disavowal activity, and lost focus on useful work. A practical response to search volatility Start by recording what changed before deciding why it changed. Keep a dated log of major publishing activity, redirects, template edits, internal-linking changes, backlink campaigns, analytics configuration changes, and platform releases. When visibility moves, compare the affected URLs and queries with that log. This creates a practical audit trail rather than relying on memory or broad assumptions about an update. A disciplined review should focus on patterns. If a small group of pages declines, inspect those pages closely. If a category, template, or query type declines together, look for a shared issue. If the movement is sitewide, technical crawling, indexing, rendering, or major content changes may deserve attention before individual pages are rewritten. Useful checks include: Whether affected pages remain indexed and accessible to Googlebot . Whether title tags, headings, internal links, canonicals, redirects, or page templates changed recently. Whether pages clearly answer

Google completed its August 2026 spam update after a rollout that began on August 18 and finished on August 21. The update was a routine spam-enforcement release rather than a newly announced flagship policy change, but third-party tracking indicates that its ranking effects were substantial for some websites. For site owners dependent on organic search , the central message is straightforward: Google’s enforcement against spam remains active, and abrupt visibility changes can be severe when a site falls on the wrong side of its quality and manipulation assessments. Google recorded the release and completion of the rollout in its official Search Status Dashboard incident entry . The company listed the start time as August 18, 2026, at 09:27 PDT, and marked the incident complete on August 21, 2026, at 01:49 PDT. It was Google’s third announced spam update of 2026, following spam updates in March and June. The official notice establishes the timing of the rollout, not a detailed account of which sites or tactics were affected. That is where independent ranking data adds useful context. SE Ranking’s analysis, later reported by Search Engine Land, found that 16.71% of URLs that had ranked in the Top 10 dropped beyond position 100 for the same keyword during the August update. Its July baseline showed 9.2% making that same move. The August share was therefore roughly 82% higher than the baseline. What the ranking data shows A move from the Top 10 to beyond position 100 is not a minor fluctuation. It can effectively remove a page from the search results that most users see, with an immediate effect on clicks and leads for pages that previously generated traffic. The SE Ranking figures do not prove that every observed loss was caused by Google’s update, nor do they identify every affected site type. They do, however, provide a market-wide indication that the August rollout coincided with sharper movement than a normal July comparison period. Measure July baseline August 20

SEO hiring is increasingly centered on senior ownership rather than pure execution. A Semrush analysis of 3,900 US SEO job listings on Indeed, captured on November 25, 2025, found that 59% of openings were senior leadership roles . The category included Director, VP, Head, Chief, Lead, and Executive titles. The finding matters because it signals how employers are defining SEO work for 2026. Companies appear to place greater value on people who can set priorities, manage projects, connect SEO with other channels, and direct AI-enabled workflows . That does not mean junior SEO work has disappeared. It does mean that the available listings are weighted strongly toward people accountable for strategy and business outcomes. What the SEO job data shows Semrush's analysis of 3,900 SEO job listings describes a polarized US market. Senior leadership positions made up the majority of listed roles, while SEO Specialist jobs represented about 15% and SEO Manager jobs about 10%. Listing category or measure What Semrush found What it indicates Senior leadership roles 59% of listings Demand is concentrated in roles with strategic ownership. SEO Specialist roles About 15% of listings Specialist execution roles are a smaller share of the market. SEO Manager roles About 10% of listings Mid-level management roles are also less prevalent than leadership listings. Median pay About $130,000 for senior roles, versus about $71,630 for other positions Employers are placing a substantial pay premium on senior SEO responsibility. The study also found that approximately 31% of senior listings mentioned project management. Cross-channel responsibilities were another recurring theme, reinforcing the idea that SEO is being hired as a growth function that must work with content, marketing, product, and other teams. AI is part of this changing job description. Semrush found AI mentioned in 31% of senior listings, with roughly 10% specifically mentioning AI familiarity. These figures do not prove th

AI visibility is not a single score that a business can measure once and treat as settled. A cross-engine study of local-service searches found that ChatGPT and Gemini named the same top business in only 4.2% of identical queries . For small businesses trying to be discovered through AI assistants, that gap means a strong result in one engine may say very little about how another assistant presents the market. The research, published by Steady Demand in its AI Citation Ledger , examined 1,487 queries across 50 U.S. metropolitan areas and 10 service verticals. It focused on prompts such as “best plumber near me,” tracking the businesses named and the sources used to ground responses. Its central finding is practical: AI-driven discovery is fragmented by engine, source mix, location, and category . That does not prove that AI responses drive more leads than conventional local search. The study measures citations and top-name outcomes, not conversions or overall ranking quality. But it provides a useful baseline for marketers because it shows why checking a brand in one AI assistant is not enough to understand its broader AI visibility. What the cross-engine data shows The study compared how Gemini and ChatGPT answered the same local-business prompts. Their differences extended beyond the final recommendation. The systems often drew on different source ecosystems, which helps explain why they surface different businesses. Measure Gemini ChatGPT Exact top-business match between engines 4.2% of identical queries produced the same top business Typical citation mix About 60% of citations were business websites More reliance on Reddit and traditional directories Overlap in cited domains About 8% overlap Repeated-query source alignment About 40% alignment, described as grounding drift Top-result repeatability benchmark About 7% top-match stability in AI-generated results Not specified separately in the supplied research The contrast with Google’s local pack is notable. In th

Google has launched Gemini 3.7 Flash as a generally available model, extending it across the Gemini API, Google AI Studio, Vertex AI, Gemini Enterprise, the Gemini app, and AI Mode in Search. The August 13, 2026 release positions the model as the successor to earlier 3.5 and 3.6 Flash generations, with Google emphasizing stronger instruction following, improved understanding of user intent, and faster responses for coding, agentic workflows, and multi-step tasks. For enterprise developers, the significance is less about a single destination than a more consistent model layer across Google's consumer and business AI surfaces. Teams can evaluate the same model family for application development, managed enterprise use, and search-facing user journeys, while Google AI Pro and Ultra subscribers gain access through Gemini Spark as its rollout progresses. Google's official Gemini 3.7 Flash model documentation lists the GA model's specifications and launch pricing. It supports a 1 million-token context window , outputs of up to 64,000 tokens , and adjustable thinking levels. Those characteristics make the release relevant to workloads that need to process substantial source material, generate longer responses, or balance response speed against reasoning depth. What the Gemini 3.7 Flash rollout changes The core change is broad availability. Gemini 3.7 Flash is not limited to a standalone developer preview or one consumer product. Google is making it available through the Gemini API and related development environments, while also incorporating it into AI Mode in Search and the Gemini app. For AI Mode, Google says Gemini 3.7 Flash is replacing earlier Flash variants for many users in supported markets. The model's focus on following instructions and interpreting intent matters in a Search setting, where users often ask compound questions, refine requests, or expect a response to account for constraints stated in natural language. On the developer side, access spans Google AI

ChatGPT had the highest share of paid clicks among the leading Google destinations in iPullRank's Q3 2026 zero-click and paid-click analysis. The dataset found that about 4.75% of Google traffic landing on ChatGPT came from paid clicks , well above the corresponding shares reported for major destinations such as YouTube, Wikipedia, and Amazon. The result does not reveal OpenAI's advertising budget, bids, or total advertising activity. It does, however, show that paid placements represented a notably larger portion of observed Google referrals to ChatGPT than for the other leading destinations studied. That makes paid search an important part of the discovery picture for a widely used AI platform, alongside organic search, direct visits, and other referral paths. What iPullRank's data shows In its Q3 2026 zero-click behavior analysis , iPullRank examined roughly 200 million events to understand where Google clicks go and how often those clicks are paid. ChatGPT ranked around sixth among the leading destinations by Google clicks, behind destinations including YouTube, Google's own pages, Reddit, Facebook, and Wikipedia. That overall ranking is important context. ChatGPT is not the largest destination in the analysis by total Google clicks, but its paid-click proportion stands out . A 4.75% share means paid traffic accounted for a more visible portion of its observed Google arrivals than it did for the larger, more established web destinations used for comparison. Destination Paid-click share of Google traffic Context in iPullRank's analysis ChatGPT About 4.75% Highest share among the leading destinations analyzed YouTube About 0.2% Far below ChatGPT's reported share Wikipedia Effectively 0% Minimal paid-click contribution in the dataset Amazon Under 2% Below ChatGPT's reported share The measure is deliberately narrow. It counts paid Google clicks that land on ChatGPT, not every interaction a user may have with ChatGPT after searching, and not OpenAI's total ad spendin

AI referral traffic remains a small share of website visits, but it is becoming too important to dismiss. Conductor's 2026 AEO / GEO Benchmarks Report found that AI referrals accounted for 1.08% of total website traffic across 13,770 domains in 10 industries between May and September 2025. That is roughly one in every 100 visits, a modest channel today, but one growing at about 1% month over month during the study period. The more important lesson is not that AI has replaced search, social, or direct traffic. It has not. Rather, AI chatbots and AI answers and AI Overviews are creating an additional discovery layer where users can encounter brands, products, and publishers before they ever produce a measurable site visit. For marketing, editorial, and analytics teams, referral reporting alone can therefore understate AI's role in awareness and early research. Conductor's 2026 AEO / GEO Benchmarks Report provides a useful macro-level benchmark for interpreting this shift. The data supports a measured conclusion: AI referrals are real, growing, and context-dependent, but they are not yet a substitute for conventional traffic channels or a complete proxy for AI-driven discovery. Why AI referral traffic needs broader interpretation A referrer records a visit that arrives from a traceable source. That makes it useful for understanding the traffic that actually reaches a website. It does not, however, capture every way an AI answer may influence a user's decision. A person may see a brand cited in an AI response, conduct a later branded search, visit directly, or choose not to click at all after receiving enough information in the answer itself. This distinction matters because AI surfaces can shape visibility before the click . AI answers and AI Overviews may influence which companies, publications, or products users consider, even when conventional analytics attributes no visit to an AI source. Referral data should remain part of performance reporting, but it should not

Over the last couple of weeks, hackers have targeted and broken into the systems of several water plants in the United States. Here’s what we know and don’t know about this wave of attacks allegedly carried out by the Iranian government.

The massive Toba eruption seems to have had little climate impact.

Large language models appear to cite the beginning of a webpage far more often than its closing sections. A large-scale analysis led by Kevin Indig found that 44.2% of LLM citations came from the first 30% of page content , giving publishers a clear reason to put definitions, findings, and conclusions near the top rather than burying them in long narrative introductions. The research matters as AI-generated answers become another route through which people discover information. The central implication is not simply that content should become shorter. It is that pages need to make their most useful, supportable information easy to identify and summarize quickly, while still serving readers who need context and detail. According to Kevin Indig's Growth Memo analysis of how AI pays attention , the findings are based on 3 million ChatGPT responses and 30 million citations, with 18,012 verified instances analyzed. Search Engine Land also reported the study's citation-distribution figures. Together, the results offer a practical benchmark for teams working on SEO, generative engine optimization , editorial planning, and knowledge content. What the citation analysis found The study identifies a pronounced top-loading pattern. The first third of a page accounted for the largest share of citations, followed by the middle section and then the final third. At paragraph level , citations were most often drawn from the middle of a paragraph, rather than its opening or closing sentence. Content location Share of citations Editorial implication First 30% of a page 44.2% Place the core answer, key definition, or primary finding early. Middle 30% to 70% of a page 31.1% Use this section to provide the supporting explanation and context. Final third of a page 24.7% Do not rely on the conclusion alone to carry essential information. Middle of a paragraph 53% Keep the substantive statement clear within coherent, focused paragraphs. This does not mean every page should begin with a compr

SMX Advanced will hold two in-person conferences in 2027 , expanding the advanced search marketing event to the West Coast and East Coast for the first time in a single year. The conference is scheduled for San Diego from March 17 to 19, followed by Boston from September 20 to 22. The expansion gives the SMX Advanced community two distinct opportunities to convene around advanced SEO, PPC, AI, and GEO topics . Search Engine Land confirmed the plan in its official SMX Advanced 2027 announcement , describing it as the first year the event will run twice. For a conference long associated with practitioner-focused search marketing education, the change is significant because it turns SMX Advanced into a two-city annual schedule rather than a one-off gathering. The development also arrives as SMX Advanced marks its ongoing 20th anniversary. A two-city schedule for advanced search marketers The announced 2027 program consists of two separate events, not duplicate dates running at the same time. San Diego opens the schedule in March, while Boston follows roughly six months later in September. SMX Advanced 2027 event Location Dates Role in the expanded schedule West Coast event San Diego March 17 to 19, 2027 First of two in-person SMX Advanced events East Coast event Boston September 20 to 22, 2027 Second of two in-person SMX Advanced events The two-event structure expands the calendar without changing the core identity described for SMX Advanced. The conference programming is positioned around expert-led sessions, deeper discussions, and enhanced networking for professionals working across search and adjacent areas of marketing technology. The stated subject areas matter because search marketing teams are increasingly dealing with an overlapping set of disciplines. SEO and paid search remain central, while AI and generative engine optimization, or GEO, have become relevant parts of the broader conversation about how businesses are discovered and represented in search exper

Google AI Mode appears to be treating the passage, not the full web page , as a central unit of citation. A year-long Pillarbase analysis of 15.7 million AI Mode citations across 148 industries found that nearly half were scroll-to-text highlights. The study identified about 4.6 million unique highlighted passages from 2.7 million pages, with heavily reused passages appearing across hundreds of queries. That pattern matters because a citation can do more than point readers toward a domain. It can elevate a specific sentence or short section as the textual evidence behind an AI-generated answer. For publishers, the practical implication is that a strong page may not be enough on its own. Its individual passages need to be clear, self-contained, and useful in the context of a query. The evidence does not mean Google has publicly disclosed a new formal citation policy or changed the underlying mechanism in a documented way. Google’s May 2026 discussion of AI Mode focused on expanding usage and changing user behavior. But the available third-party research consistently indicates that AI Mode visibility is substantially shaped by extractable sections of content. What the research says about AI Mode citations Pillarbase’s findings provide the broadest view of the pattern. Scroll-to-text highlighting is designed to take a reader to a particular part of a page, rather than merely opening the page at its top. In the study dataset, the prevalence of these highlights suggests that AI Mode is frequently associating an answer with a discrete source fragment. A separate December 2025 analysis from SALT.agency reached a compatible conclusion from a content-structure perspective . The agency found that AI Mode citations often depend on descriptive subheadings and opening sentences , while finding no simple advantage for content positioned above the fold. Its AI Mode content-structure study is particularly relevant for teams trying to understand how a page’s organization can affect