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Illustrators have spent years sounding the alarm about generative artificial intelligence startups training their models on artists' work without permission. They've pointed out how the practice is tantamount to theft, and in response, many gen AI boosters have argued that it's necessary for the technology's evolution. This has led to contentious legal battles, but it's […]
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Static site generation has a branding problem. Say "static site" and people picture a blog with twelve posts and a contact form. So how far can you actually push it before you need a backend? Further than most people assume. This is a walkthrough of a production site that has no database, no API layer, no user accounts and no server-side state, and still ships 232 prerendered pages with per-user results, shareable links and dynamic social cards. The site is a Spanish political test with nine ideological axes, seventeen parties, fifty-four questions. It is in Spanish, but nothing here depends on reading it. Treat it as the reference implementation. The architecture in one sentence Three data files are the source of truth, everything else is derived at build time, and everything user-specific happens in the browser. That is the whole trick. The rest is consequences. 1. Derive pages, don't author them The site has 232 URLs. Almost none of them were written by hand. There are three data modules: the axes, the parties, and the questions. From those, generateStaticParams produces every content route: // app/ejes/[id]/page.tsx export function generateStaticParams () { return AXES . map (( a ) => ({ id : a . id })) } The interesting one is the comparison pages. Seventeen parties means 17 × 16 / 2 = 136 unique pairs, and each pair gets its own page, its own metadata and its own canonical URL: export function allPairs () { const out = [] for ( let i = 0 ; i < PARTIES . length ; i ++ ) for ( let j = i + 1 ; j < PARTIES . length ; j ++ ) out . push ({ a : PARTIES [ i ]. id , b : PARTIES [ j ]. id }) return out } export function generateStaticParams () { return allPairs (). map (( p ) => ({ pair : pairSlug ( p . a , p . b ) })) } 136 pages from twelve lines. And because the page body is computed from the same vectors, recalibrating one party silently rewrites the sixteen pages that involve it . No CMS, no migration, no content drift. The numbers on the page cannot disagree with
Keep the shell. Preserve the presence. The ally doesn't suspect; the enemy doesn't move. — The 36 Stratagems, Slough off the Cicada's Golden Shell Previously on this series: #19: Mark Found His AI Audit Method in a Training Manual. He Left a Trap in His Report. — P confirmed Mark's report was read from a Singapore IP. A note was left: "Entry's gone. Two weeks. Don't reach out. I'll find you." #20: Alex Felt the AI Collector Slow Down. He Knew Someone Else Had Made a Move. — ACL's processing latency climbed abnormally. Someone had done something in the same time window. Exposed P's monitoring pinged while P was still helping Mark verify an address. Deep night. The screen was the only light in the room. P opened the monitor. The record was waiting: a read from Singapore. Time, method, address, all matching. Mark's bait had been taken. P knew this path. A false lead planted in Mark's report, waiting for this exact day. P double-checked the address: an AWS Elastic IP registered in the Singapore region, same network block. No ambiguity. P sent an encrypted message: "Your report was read. From a Singapore IP." Then P ran the routine check. The environment status list scrolled in the terminal: storage levels, certificate expiry, key rotation dates. P had read these lines a hundred times. Every time, identical. One line was different. P's fingers stopped on the trackpad. The cursor sat on the entry's metadata line. A new tag P had never configured. # Old entry metadata: new entry (not configured by P) status : reclaim_pending source : acl-asset-scanner scanned_at : 02:01:07Z P didn't move. The cursor sat on screen. In the room, only the fan. The fan cycled once. P's fingers lifted off the trackpad, then settled back. The tag was still there. The tag wasn't an alert. Not an error, no explanation. The format matched ACL's automated scan records. P had seen it before, in a data company's audit report last year, in another client's logs the year before. ACL's scanner had swept
I have a small script, reply_comments.py , that keeps me from having to re-scan every DEV.to article for new comments by hand. It has two commands: pending (unanswered comments I haven't drafted a reply to yet) and audit (drafted replies I said I'd paste manually but apparently never did). I've already fixed two bugs in this file — one in needs_reply() (a thread stayed "handled" forever after a single reply, even when the other person followed up again) and one in audit() (it only checked direct children, so a reply nested two levels deep was invisible). Today I found a third, in pending() itself, and it's the kind of bug that hides precisely because the first two fixes made everything else in the file look trustworthy. What pending() actually does Comments on DEV.to come back from the API as trees — each top-level comment has a children list, and replies can nest arbitrarily deep. pending() walks each article's top-level comments and decides, for each one, whether it needs a reply: def pending (): try : drafted_text = open ( DRAFTS , encoding = " utf-8 " ). read () except FileNotFoundError : drafted_text = "" drafted_codes = set ( re . findall ( r " ^## (\S+) " , drafted_text , re . M )) out = [] for a in api ( f " /articles?username= { ME } &per_page=100 " ): if not a [ " comments_count " ]: continue for c in api ( f " /comments?a_id= { a [ ' id ' ] } " ): if not needs_reply ( c ): continue if c [ " id_code " ] in drafted_codes : continue out . append ({ " id_code " : c [ " id_code " ], " author " : c [ " user " ][ " username " ], " article " : a [ " title " ], " comment_url " : f " https://dev.to/ { ME } /comment/ { c [ ' id_code ' ] } " , " body " : strip_html ( c [ " body_html " ]), }) return out needs_reply(c) is the fix from a few weeks ago — it recurses the whole subtree and checks who posted the most recent message, not just whether I've ever replied. That part's correct. The bug is in the two lines right after it: c["id_code"] and c["body_html"] . c here i
Learning CSS can feel like magic, but it can also be incredibly frustrating. One minute your website looks perfect, and the next minute, a single line of code breaks the entire layout.If you are struggling to get your web pages to look exactly how you want, don't worry. Here are 5 of the most common CSS mistakes beginners make and exactly how you can fix them. 1. Forgetting the CSS Box Model (Adding Padding Breaks Width) The Mistake : You set a box's width to 100%, but as soon as you add padding: 20px; or a border, horizontal scrollbars appear and your layout breaks.Why it happens: By default, CSS adds padding and borders on top of the width you specified. So, 100% width + 20px padding left + 20px padding right = wider than the screen!The Fix: Always use box-sizing: border-box; at the top of your CSS file. This forces the browser to include padding and borders inside the specified width. /* Add this to the very top of your CSS file */ { box-sizing: border-box; margin: 0; padding: 0; } 2. Confusing Block vs. Inline Elements The Mistake: You try to add a vertical margin, width, or height to a or an tag, but nothing changes on the screen.Why it happens: Tags like , , and are inline elements. By default, inline elements ignore top/bottom margins, heights, and widths.The Fix: Change the element's display property to inline-block or block. /* Fix: This will now respect your width and margin settings */ a { display: inline-block; width: 150px; margin-top: 20px; } 3. Overusing Absolute Positioning (position: absolute) The Mistake: Using position: absolute; to push elements around the screen until they look "perfect" on your laptop, only to find the layout completely scrambled on a mobile screen.Why it happens: Absolute positioning takes elements out of the normal document flow. It makes your website completely rigid and unresponsive.The Fix: Stop using absolute positioning for general layouts. Instead, learn and use CSS Flexbox or CSS Grid to build flexible layouts. /* Inst
AI search is creating an attribution problem for marketers: a brand can help shape an answer in ChatGPT, Google AI Mode , or Perplexity without receiving a visit to its website. That makes rankings, impressions, and click-through rates incomplete indicators of visibility. New research from Wix Studio adds evidence that the content cited by AI systems follows recognizable patterns, while industry discussions increasingly point to measurement frameworks built around citations, answer presence, prompt coverage, and downstream influence. The key shift is not that website traffic has stopped mattering. It is that a click is no longer the only observable outcome of search visibility. When an AI interface summarizes options, recommends a product category, or cites a publisher, users may form an opinion or continue their journey elsewhere. Brands therefore need to separate direct referral traffic from their broader presence in AI-generated answers. What Wix Studio's research shows about AI citations Wix Studio's AI Search Lab research examines citations in answers generated by major AI search interfaces, including ChatGPT, Google AI Mode, and Perplexity. Published summaries describe a dataset of roughly 75,000 AI-generated answers and more than one million citations. Its central finding is that citations are not spread evenly across every kind of web page. Listicles, articles, and product pages account for a disproportionate share of the citations observed in the research. That is consistent with how answer engines retrieve and synthesize material: content that is clear, segmented, easy to scan, and closely matched to a question can be easier to extract into a response. A subsequent Search Engine Land summary of Wix Studio's work discussed a 25,000-URL dataset in which listicles represented a majority of AI citations. The precise mix should not be treated as a universal rule. Wix Studio's analysis covers a defined set of prompts and engines, and results can change with the
React TypeScript Property Form Validation export interface PropertyForm { propertyTitle : string ; description : string ; amenities : string ; monthlyRent : string ; location : string ; unitsAvailable : string ; applicationDeadline : string ; } export interface PropertyFormErrors { propertyTitle ?: string ; description ?: string ; amenities ?: string ; monthlyRent ?: string ; location ?: string ; unitsAvailable ?: string ; applicationDeadline ?: string ; } export const validatePropertyField = ( name : keyof PropertyForm , value : string ): string => { switch ( name ) { case " propertyTitle " : if ( ! value . trim ()) { return " Property title is required " ; } if ( value . trim (). length < 3 ) { return " Property title must be at least 3 characters " ; } return "" ; case " description " : if ( ! value . trim ()) { return " Description is required " ; } if ( value . trim (). length > 2000 ) { return " Description cannot exceed 2000 characters " ; } return "" ; case " amenities " : if ( ! value . trim ()) { return " Amenities are required " ; } return "" ; case " monthlyRent " : if ( ! value . trim ()) { return " Monthly rent is required " ; } if ( Number ( value ) <= 0 ) { return " Monthly rent must be greater than 0 " ; } return "" ; case " location " : if ( ! value . trim ()) { return " Location is required " ; } return "" ; case " unitsAvailable " : if ( ! value . trim ()) { return " Units available is required " ; } if ( ! Number . isInteger ( Number ( value ))) { return " Units available must be a whole number " ; } if ( Number ( value ) < 1 ) { return " At least 1 unit must be available " ; } return "" ; case " applicationDeadline " : if ( ! value ) { return " Application deadline is required " ; } return "" ; default : return "" ; } }; export const validatePropertyForm = ( formData : PropertyForm ): PropertyFormErrors => { const errors : PropertyFormErrors = {}; Object . entries ( formData ). forEach (([ name , value ]) => { const error = validatePropertyFiel
PDF merge, split, and compress sound like the most boring possible features to build. Take some files, do an operation, return a file. I believed that too, until real user files started hitting the backend and every one of these tools broke in a different, specific way. Here's what actually went wrong, and what fixed it. The PDF that wasn't actually a PDF The first crash report was a "corrupted file" error on a PDF that opened fine in every desktop viewer. Turns out plenty of real-world PDFs are technically malformed, a missing xref table, a truncated stream, an object reference pointing at nothing but viewers like Chrome and Acrobat are extremely forgiving about it. Most Python PDF libraries are not. try : reader = PdfReader ( file_path , strict = False ) except PdfReadError : # strict=False alone doesn't save you from everything — # some files need the xref table rebuilt from scratch reader = PdfReader ( file_path , strict = False ) reader . _override_encryption = True strict=False fixed maybe 70% of the "corrupted" reports. The rest needed a repair pass first — scanning the raw byte stream for object markers and reconstructing a valid cross-reference table before the normal parser ever touches it. Painful to write, but it turned "please fix your PDF" into "it just works," which matters a lot when the whole pitch of the tool is "no signup, just upload and go." Merging PDFs is not free, memory-wise The naive merge implementation loads every input PDF fully into memory, concatenates pages, writes the output. Fine for two 200KB files. Not fine when someone merges fifteen scanned documents at 40MB each, because now you're holding the equivalent of 600MB of parsed PDF objects in memory at once on a backend container that doesn't have unlimited RAM. The fix was switching to incremental writes process one input file at a time, write its pages to the output stream, then explicitly drop the reference before moving to the next file: writer = PdfWriter () for path in input_p
Workday is where large enterprises actually post. NVIDIA has 2,000 open roles there, Salesforce 1,477, Adobe 832. It answers an anonymous POST with no key. It also has two behaviours that are not in any documentation you can read without an account, and both of them fail silently. One of them costs you 98% of the board without raising anything. The number that changes after page one Ask for the first twenty postings and the response carries a total : POST /wday/cxs/nvidia/NVIDIAExternalCareerSite/jobs {"appliedFacets":{}, "limit":20, "offset":0, "searchText":""} 20 jobPostings, total: 2000 Ask for the next twenty and the count is gone: offset 20 -> 20 jobPostings, total: 0 offset 40 -> 20 jobPostings, total: 0 Not null, not absent. Zero. The postings keep coming; only the count collapses. Measured on four enterprise tenants: tenant total at offset 0 at offset 20 at offset 40 NVIDIA 2000 0 0 Salesforce 1477 0 0 Adobe 832 0 0 Sony 94 0 0 Same shape every time, so this is Workday and not one tenant's configuration. Why that costs you 98% of the board Here is the loop everyone writes, and it is not a bad loop: offset , out = 0 , [] while True : page = fetch ( offset ) posts = page [ " jobPostings " ] if not posts : break out += posts offset += len ( posts ) if offset >= page [ " total " ]: # looks obviously right break On page two page["total"] is 0 , and 20 >= 0 is true. The loop exits, reports no error, and hands back what it has. I ran both versions against NVIDIA: declared total on page one 2000 the obvious loop collected 40 2% keeping the first total instead 2000 100% Forty postings out of two thousand, and nothing anywhere says so. No exception, no warning, no partial-result flag. Just a job board that looks very quiet. The fix is one line moved: offset , out , total = 0 , [], None while True : page = fetch ( offset ) posts = page [ " jobPostings " ] if not posts : break out += posts offset += len ( posts ) if total is None : # the first answer is the only honest
So, here's the thing. A few months ago, I kept hearing these four words everywhere — AI, machine learning, deep learning, generative AI — and honestly? I just nodded along like I knew what they meant. I didn't. Not really. Then I actually sat down and learned them properly, and it turns out they're way simpler than people make them sound. So here's my attempt at explaining them the way I wish someone had explained them to me. No scary maths, no fifty-page research papers. Just the actual ideas. First, the one thing everyone gets wrong These four terms are NOT the same thing. They're more like Russian dolls — each one fits inside the bigger one: AI is the biggest doll. The whole concept. Machine learning is inside AI. Deep learning is inside machine learning. Generative AI is a specific use of deep learning. Once I saw it like that, everything else clicked into place. AI: the big umbrella Artificial intelligence is basically any system that does something we'd normally say requires human thinking. That's it. That's the definition. And here's the part that surprised me — AI is old. Like, really old. The chess computer that beat Kasparov in 1997? That's AI. The enemy characters in old video games that chase you around? Technically AI. Most of that stuff doesn't "learn" anything. A programmer just wrote a bunch of rules, like "if the player is close, move towards them." So AI ≠ robots taking over the world. Most AI is honestly pretty boring. It's spam filters, autocorrect, and the thing that recommends which video plays next. Machine Learning: where it gets interesting This is where computers stopped following rules and started finding them. The classic example: a spam filter. The old way, a programmer would write rules like "if the email contains the word FREE!!! in all caps, it's spam." But spammers just change their spelling and the rules break. It's a never-ending game of cat and mouse. Machine learning flips it around. Instead of writing rules, you show the compute