标签：#art

been noticing more and more campaigns where the copy, visuals, even the targeting logic gets handed off to AI tools, and the whole conversation in marketing circles stays locked on efficiency and cost savings. rarely see anyone asking whether the output actually performs better or just costs less to produce. there's a gap between what the tools claim and what the data shows. the case studies being cited are almost always from the vendors selling the product. i've looked for independent research on this and haven't found much. the part that bugs me most is the personalization pitch. personalization at scale sounds great until you realize every brand is using the same three AI tools to personalize, which means they're all producing weirdly similar content aimed at the same audience segments. that's kind of the opposite of standing out. the cost efficiency argument makes sense on paper, the same way it does with robotics or game development. cut headcount, ship faster, reduce spend. but marketing effectiveness is notoriously hard to measure cleanly even without AI in the mix. are brands actually tracking this properly or just reporting on vanity metrics and calling it a win. curious if anyone here has seen real benchmarks comparing AIassisted campaigns to traditional ones that weren't published by a company trying to sell you something. submitted by /u/SwordfishOverall4378 [link] [留言]

submitted by /u/Sumsub_Insights [link] [留言]

I'm a solo dev with zero users right now, and I just spent an afternoon on a decision most people would've hardcoded in five minutes. Here's the setup. NotebookBloom is my Chrome extension for Google's NotebookLM. At launch I don't want to charge for much — I want people to actually use it, tell a friend, leave a review. So the plan is: only cloud sync (Google Drive backup) is Pro on day one. Everything else — flashcard export to Anki, citation export, bulk import — free. But "free on day one" implies "not free forever." Once there are enough users, I want to flip some of those to paid, one at a time, watching what happens. And that's where I hit a wall that only exists for extensions: there is no server runtime. My extension runs in the user's browser. So if I write "is this feature paid?" as a hardcoded if in my code, then flipping it later means: edit code → rebuild → upload to the Chrome Web Store → wait for review[你查到的审核时长，如 "usually under a day, sometimes 3"]. Think about that. A pricing change — arguably the most business-critical lever I have — would be stuck in a review queue. That's absurd. So I stopped and rebuilt it as a switch. One file, features.ts , with a single decision function: canUse(feature, isPro, gates) → isPro OR the feature isn't currently gated Four flippable keys: cloudSync, ankiExport, citationExport, bulkImport. The default (compiled into the extension) is: cloudSync = paid, the rest = free. That's my day-one tiering. The switch values live in my Cloudflare Worker's KV. To flip Anki export to paid, I change one KV value — no rebuild, no store review. Every user picks it up within a day. The part I'm quietly proud of: it costs zero extra requests. The extension already calls /status to check "does this Google account have a subscription?" (you can't trust the client to self-report that — that's how you get pirated). I just piggybacked the switch values onto that same response. The paywall config rides along on a request I was already maki

Viral tales of good triumphing over evil are racking up millions of views. They’re almost entirely AI-generated clickbait.

The startup is building voice models designed to make AI phone calls pass the Turing test.

Dropbox has integrated Model Context Protocol (MCP) with its internal knowledge platform, Dash, to surface security design context during AI assisted code reviews. The system retrieves threat models and security requirements for pull requests, helping reviewers validate implementation against design intent. An InfoQ Q&A explores the architecture and key lessons learned. By Leela Kumili

If your Wi-Fi is a bit sluggish, there are some things you should check before upgrading your router.

Ellis AI announced Thursday its emergence from stealth with $10 million in seed funding.

Holly Cummins discusses why "nothing is new under the sun" in tech. She maps historical architectural tradeoffs to modern cloud, microservices, and AI hype cycles. She connects financial debt (post-ZIRP) and technical debt to epistemic and sleep debt, showing engineering leaders how to navigate shifts in assumptions, embrace sustainability, and revive proven engineering disciplines. By Holly Cummins

Two weeks ago I wrote here that I killed my SaaS subscription 7 days after launch and rebuilt it as a buy-once product. I ended that post with a promise written down before I could talk myself out of it: 3 real purchases in 14 days of relaunch, or I move on and leave UIPrompt in maintenance mode. Either way I would post the numbers. The 14 days are up. Here are the numbers. Purchases: 0. New organic signups during the window: 0. The last real signup was a free account three days before the relaunch even went live. They looked once and never came back. So by my own written bar, this is a move-on. UIPrompt goes to maintenance mode today. I want to be useful about why, because "it didn't sell" is a result, not a lesson. What I did in those 14 days Quite a lot. That turns out to be part of the problem. I shipped a real product. The buy-once model was clean: a free playground with no signup, one $39 price, and an AI Design System Pack export that survives a blind test (a fresh AI session got only the exported files and matched 34 of 34 specced properties, inventing zero colors). I bought a custom domain. I launched on Product Hunt with a video, posted a Show HN, cross-posted the pivot article, made a 20-second promo video in Remotion with licensed music, put it on YouTube and X, and submitted to Peerlist, Dev Hunt, Indie Hackers, SaaSHub, and a stack of directories. None of it produced a single sale. Not one. The lesson I did not want On the first launch I blamed pricing. I killed the subscription, and I was right that a burst-usage tool should not bill monthly. But here is the uncomfortable part: fixing the pricing changed nothing, because pricing was never the binding constraint. Demand was. Two different pricing models, same zero, should have told me the problem lived upstream of the checkout page the whole time. I was tuning the part of the funnel I could see and control (the offer) while the actual leak was at the top: not enough of the right people, with pain acute

submitted by /u/kindermaxi123 [link] [留言]

Tecno is teasing a truly bezel-less smartphone concept and it'll reveal it at IFA 2026.

Everything I vibe-coded kept coming out the same: purple gradient, three-card row, rounded-2xl everything, an italic serif hero I never asked for. The model fills any decision you leave unspecified with the average of its training data, and that average is the "AI look." So I catalogued the tells, then wrote them up as a drop-in rules file. Rename it to CLAUDE.md, .cursorrules, or AGENTS.md and your agent designs against the defaults automatically. It is phrased as "prefer a real decision over the reflex," not a blanket ban, because half these patterns are fine in the right place. You just don't want all of them at once by accident. Rules file: https://github.com/febbhav/signs-of-ai-design/blob/main/design-rules.md submitted by /u/SteepLikeAMountain [link] [留言]

In a review triggered by OpenAI’s Hugging Face incident, Anthropic discovered three of its AI models had breached real organizations during third-party evaluations.

If you just want the recommendation: for the ordinary AI-generated image an inference job hands back — a 2 to 8 MB PNG — do one plain object PUT into your S3-compatible storage and stop there, because multipart upload only earns its complexity when a single artifact is big enough that losing a transfer halfway through costs you real money to redo, which for my team starts somewhere north of 100 MB. Everything below is about that threshold, and about the operations bill you pick up the moment you cross it. I run the platform roadmap for a team that renders a few hundred thousand images a month, and I count pages before I count features, so read the rest with that bias in mind. Should I use multipart upload for large AI-generated images, or a single object PUT? Multipart solves two narrow problems: a payload too awkward for one HTTP round trip, and a transfer you refuse to restart from byte zero. A 6 MB PNG has neither problem. The shape of the flow is always the same wherever you run it. You start a multipart upload and get back an upload id, you push each part under that id, you collect the returned ETag and part number for every one of them, and you send the finished list back in a complete call that stitches the object together server-side. Parts have to be at least 5 MiB on Amazon S3 and on every S3-compatible store I've tested against, with the final part exempt, which already tells you the feature was designed for objects measured in hundreds of megabytes rather than for a batch of thumbnails. Where it genuinely pays off in an image pipeline is the long tail: a 4-gigapixel tiled upscale, a nightly ZIP export of a customer's whole render history, a raw latent archive somebody in research wants kept for a year. Those are the jobs where a dropped connection at 80% is a real incident and not a shrug. For everything else, one put is one line of code and one thing to monitor. There's a second cost that people underrate, and it's the one I'd argue about in a design re

Researchers fear AI is moving too fast, while Mark Zuckerberg is worried about who owns it. Plus: Inside Black Forest Labs’ push into robotics.

>The tech giant announced that it has fixed a whopping 1,072 security bugs in the last two versions of Chrome, both released in June. That is more than the number of bugs patched in the previous 23 versions released over the last two years, which totalled 1,036 fixes. submitted by /u/ControlCAD [link] [留言]

The last two versions of Chrome have included more patches than the previous 23 combined.

Google's next foldable doesn't look all that different, save for the camera bar.

Buying multiple lamps for different rooms can get expensive. Govee’s rechargeable Table Lamp Classic gives you one lamp you can use throughout your home instead of getting one for every room, and right now it’s down to $59 ($21 off) at Amazon. That’s just $4 shy of the all-time low set during Prime Day, and […]