DEV Community: baiwei

Inside the Marketing Workflow Powered by GPT Image 2

baiwei — Mon, 01 Jun 2026 06:24:25 +0000

Last week, I helped an e-commerce team diagnose their marketing process. They needed to produce 40 product images every week. Their designers were working until 2 AM, and the revision rate was still 60%. I asked if they had tried AI image generation. They said yes — "the text is always garbled, and the backgrounds are never right."

This isn't an isolated case. For the past two years, marketing teams have viewed AI images as "impressive but impractical."

Then GPT Image 2 arrived.

On April 21, 2026, OpenAI released this model. Five weeks later, it topped the Artificial Analysis text-to-image leaderboard with an Elo score of 1338. But the ranking isn't the point — what matters is that, for the first time, "marketing image generation" has become viable for production workflows.

This article will show you what GPT Image 2 can actually do, where it stands in the 2026 competitive landscape, and how you can start using it.

Core Capabilities of GPT Image 2 Text Rendering: From "Good Enough" to "Actually Usable" OpenAI's release page showcases multilingual examples in Chinese, Japanese, Korean, Arabic, and Devanagari. The Cookbook explicitly states that gpt-image-2 delivers "reliable text rendering with crisp lettering, consistent layout."

But stay rational: as of May 29, 2026, OpenAI's public documentation only emphasizes "improved / reliable" — there's no publicly reproducible "99% character-level accuracy" report. For marketing teams, the safer approach is to build your own evaluation: use 10 samples each of bilingual posters, packaging, menus, infographics, and UI designs, calculate error rates with OCR, then manually score whether the layout maintains hierarchy, spacing, line breaks, and logo positioning.

Resolution and Speed: Layered Workflows Are Key
gpt-image-2 supports any size within its constraints, with a maximum edge length of 3840px. Common 2K is the recommended reliable ceiling; 4K/UHD is labeled as experimental. Meanwhile, quality: "low" is ideal for fast drafts and iterations, and square images typically generate fastest.

"4K + high speed" don't come together by default — you trade them with a layered workflow: drafts at 1K/2K, finals at 4K.

Pre-Generation Reasoning: The Most Underestimated Change
OpenAI Help clearly states: Images with thinking will "plan and refine image outputs before generating them." The release page examples also directly demonstrate "thinking mode search capabilities."

This isn't a fully public "self-verification mechanism" in the academic sense, but it at least shows the system has shifted from single-prompt responses to a "plan first, generate later" approach. For marketing, this is crucial: when you need event posters, explanatory charts, UI-style layouts, or multi-scene storyboards, what you're really saving isn't one round of generation time — it's countless rounds of "prompt and pray" rework.

Multi-Turn Editing: Goodbye to the "Prompt and Pray" Loop
The Cookbook's practical advice: explicitly restate which elements must remain unchanged in each round to reduce drift; use "character anchor" examples to demonstrate consistency across multi-turn image continuation. Generate an image, then request specific changes — "swap the background to a kitchen counter," "remove the person on the left," "make the title bigger" — and the model preserves everything else.

If you want to try these capabilities yourself, there are now several platforms that give you direct access to GPT Image 2. For example, gpt-image2ai.net lets you use it without setting up your own API — just register and start generating.

The 2026 Image Generation Competitive Landscape If you only look at public blind-test preferences, the current landscape is clear:

The easiest wrong conclusion here is: "Since GPT Image 2 ranks first, I should go all in." Reality is the opposite. Nano Banana 2 excels at low latency, 4K, and multilingual grounding; Nano Banana Pro is better for complex charts and high-precision mockups; Seedream 5.0 Lite's strengths are deep thinking, online search, and Chinese business contexts; FLUX.2 is the only route that truly puts self-hosting, weight control, and LoRA training in your hands.

The 2026 image generation market isn't "whoever's strongest wins everything" — it's "who's most cost-effective, stable, and controllable for your specific needs." Running multiple models in parallel isn't a luxury; it's risk management.

What GPT Image 2 Can't Solve Even though OpenAI officially classifies GPT Image 2 as the recommended model for "brand-sensitive creative" and "identity-sensitive editing," the Cookbook still reminds you: product image processing requires keeping backgrounds opaque — if you need transparent layers, you'll need downstream matting. Product mockup success depends on edge quality and label completeness. And you need to repeatedly emphasize "only change X, everything else stays the same" to reduce drift.

The API reference is also very direct: gpt-image-2 does not support transparent backgrounds. This means that for brand packaging, SKU variants, or generating 100 scene images of the same product, it can handle "early proposals and intermediate drafts" — but it's not yet an "unattended pipeline."

This is exactly where LoRA has real value.

LoRA's principle is to freeze the main model and only train a small set of low-rank adaptation parameters, significantly reducing trainable parameters and memory requirements. By 2026, this approach has clearly entered image model foundations: BFL's official documentation positions FLUX.2 [klein] Base directly as a starting point suitable for LoRA and full fine-tuning.

From a cost perspective, LoRA isn't as expensive as many teams imagine. fal's FLUX.2 LoRA Trainer charges $0.008/step, so 1000 steps cost about $8. Following BFL's recommended 1500–2500 steps, a round of style LoRA training costs roughly $12–20, and character LoRA about $12–24.

But LoRA also carries clear risks: data rights risks, overfitting risks, brand risks, and licensing risks. For marketing teams, LoRA should be treated as a "brand asset layer," not a "filter you casually tweak."

In Practice: A Complete Marketing Image Workflow The optimal 2026 marketing team configuration: GPT Image 2 as the primary creative and refinement engine, Nano Banana 2 / Pro or Seedream 5.0 Lite for search and localization support, and FLUX.2 for self-hosted LoRA brand locking.

Three Scenarios Worth Starting With
Scenario 1: E-commerce New Product Listing
Upload white-background product shots and packaging references. Use GPT Image 2 for clean white-background images and scene drafts, then switch to high-quality mode for hero images. For batch generation with different backgrounds and material styles, move to FLUX.2 product LoRA. Finally, run everything through OCR and geometry quality checks.

Scenario 2: Global Ad Localization
Use GPT Image 2 or Nano Banana Pro to produce the master key visual. Then use Nano Banana 2 or GPT Image 2 for language translation and localized cultural adaptation. Finally, use OCR and human review to verify copy, currency, dates, and place names.

Scenario 3: Annual Brand Campaign Visual Consistency
Collect 20–50 approved campaign visuals, clean them, and write good captions. Train a style LoRA with 1500–2500 steps. Connect the LoRA to FLUX.2 for batch variant generation, then use GPT Image 2 for a small number of high-fidelity final touches.

Three-Layer Quality Control
Machine Proofreading: Use OCR to verify Chinese, English, and numeric copy
Rule Checking: Use image similarity or detection rules to verify product geometry, logo placement, and primary color deviation
Human Final Review: Handle brand tone, compliance language, and copyright boundaries

Conclusion and Action Items For marketing decision-makers, the most important judgments come down to three:

First, position GPT Image 2 as the primary engine for marketing image production — not the only engine. It's strong enough to handle text-heavy visuals, creative drafts, conversational refinement, and mid-to-high-frequency marketing assets. But it hasn't publicly proven that "99% text accuracy" naturally holds in your business, and transparent backgrounds and batch product standardization aren't its strengths yet.

Second, the priority order should be: pilot first, build quality checks second, train LoRA third. Start by bringing GPT Image 2 into real briefs to measure pass rates, revision rates, text accuracy, and production cycles. Then bring in Nano Banana / Seedream for search and localization capabilities. Only last, introduce FLUX.2 LoRA for high-repetition, high-value brand assets.

Third, the two most dangerous mistakes in 2026 are blind faith in a single model and blind faith in a single prompt. The former ignores lifecycle, cost structure, and privatization control. The latter ignores that what truly improves stability is "stateful iteration + explicit invariants + automated quality checks."

GPT Image 2 transforms marketing workflows not by replacing creative teams, but by freeing them from "repeatedly producing execution images" so they can spend their time on strategy, templates, brand rules, and final judgment.

If you haven't tried GPT Image 2 yet, you can start right now — gpt-image2ai.net provides a direct online entry point. No API setup needed; register and generate your first image. Run a real brief and see if it can bring your revision rate down.

Why Is AI Making Us More Tired Instead of Less? A New MIT Study Says We’re Using It Wrong

baiwei — Wed, 27 May 2026 14:44:17 +0000

“Why do I feel more tired after using AI?”

A friend of mine was venting about this the other day: “I used to write a proposal by sitting down, thinking it through, and finishing it in half a day. Now I ask AI to help. Sure, it spits out a huge draft in 10 seconds — but then I spend the next 30 minutes checking the facts, fixing awkward sentences, and correcting all the nonsense it made up with total confidence. This isn’t artificial intelligence. It’s like hiring a clueless intern who needs constant hand-holding.”

I’m guessing a lot of people can relate.

For the past two years, we’ve been surrounded by nonstop AI hype. We’ve been told AI can do everything at the push of a button. But in the reality of everyday work, AI often feels less like a productivity boost and more like a flow-breaking nuisance. So what’s really going on? Are we just bad at using AI — or is AI not as smart as we’ve been told?

A new paper by researchers from MIT, Yale, and Microsoft — Chaining Tasks, Redefining Work: A Theory of AI Automation — gets right to the heart of the problem.

Its central argument is blunt: if we only use AI to improve the efficiency of individual tasks, we’re dramatically underestimating its real value.

So let’s skip the academic jargon and talk about the hard truth this paper reveals in plain English: why AI often feels frustrating in practice, and how we should actually be using it.

How do most people use AI?

Take writing an article as an example: Outline the structure (human) ➔ generate a draft (AI) ➔ revise and polish it (human) ➔ proofread it (AI) ➔ format and publish it (human/AI).

See the problem? In that workflow, the work keeps bouncing back and forth between the human and the AI.

The paper points out that this task-by-task way of using AI hides a massive sinkhole: coordination and verification costs.

Every time work gets handed back from AI to a person, we have to read it, review it, verify it, and adjust it. That process breaks our train of thought and drains our attention. In many cases, the time people spend cleaning up after AI already cancels out whatever time AI saved in the first place.

That’s why using AI so often feels like managing an intern. We’ve chopped the work into pieces that are too small, and the cost of communication, supervision, and correction skyrockets.

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The Real Magic Is Task Chaining
So if one-off task assistance isn’t the answer, what is? The researchers introduce a crucial idea: task chaining.

Instead of seeing work as a collection of isolated tasks, we should treat it as a full pipeline. The real opportunity — for companies and individuals alike — is to bundle adjacent tasks together and let AI run through the entire chain from start to finish without interruption. Humans should step in only at the end to review the final output.

The paper uses a great example: lecturing versus tutoring. At first glance, both are forms of teaching. But their potential for AI automation is completely different.

Lecture-based teaching: A teacher’s prep work is continuous — review source material ➔ build slides ➔ generate classroom examples. These tasks are adjacent and tightly connected. You can hand that whole chain to AI and have it deliver a complete teaching package, with the teacher reviewing only at the end. The efficiency gains can be enormous.
Tutoring: Tutoring is a live, interactive loop — teacher explains ➔ student asks a question ➔ teacher adjusts based on the student’s response. In that setting, the work is constantly interrupted by human interaction. Because it can’t easily become one continuous task chain, AI’s automation value is much more limited.
That leads to a work-design principle that overturns a lot of conventional thinking: How tasks are arranged matters just as much as whether the tasks themselves can be automated. In other words, whether AI can truly transform a job often depends on whether we can stitch together the parts it’s good at into one seamless chain.

A Real Example of an End-to-End Automation Pipeline
Even before reading this paper, I had already come to the same conclusion through hands-on experimentation: if you want to unlock AI’s real power, you have to eliminate the high-friction handoffs between humans and machines.

Based on that insight, I designed and open-sourced a workflow for generating Xiaohongshu/RedNote image-and-text posts. In the past, making a single post meant doing everything in fragments: I would gather source material myself, ask AI to write the copy, manually format the post, then go back to AI again for images. It was the textbook definition of a high-friction workflow.

To eliminate that friction, I broke the process down into the smallest possible set of steps and built a system with one controller plus four independent execution modules:

Source collector: Scripts lock down the crawling logic and focus only on pulling content from the web and extracting useful information. That keeps AI from hallucinating fake news or invented facts.
Business writer: Once it gets a clean source draft, AI focuses on one thing only — turning that material into strong headlines and catchy social copy, complete with emojis and the right internet-native tone.
Image generator: The mechanical parts — calling the image API and saving files — are hard-coded into the workflow. AI only handles the part it’s best at: ideation, visual concepts, and writing prompts for beautiful images.
Review-and-storage module: Finally, scripts automatically package the text and images together and push everything neatly into a Feishu/Lark database for review.
In this pipeline, one controller script runs those four modules in sequence. The output of one module automatically feeds into the next. There’s no need for a human to jump in halfway through. The repetitive, mechanical work gets locked inside SOPs and code, while AI’s strengths — creativity and interpretation — get amplified.

At that point, all I have to do is drop in a link at the front end, then show up at the finish line with a cup of coffee and approve the final result in Feishu/Lark. That is exactly the kind of system-level efficiency the paper is talking about: eliminate friction, reduce handoffs, and let the chain run.

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At this point, some people will ask the obvious question: Does every single step in that pipeline actually outperform a human? For example, is the copy AI writes always more on-brand or more internet-savvy than what an experienced editor would produce?

Of course not. And that leads to one of the most counterintuitive — and most important — findings in the research:

System Efficiency Beats Local Perfection
A lot of managers and perfectionists instinctively push back here. They’ll say, “AI isn’t precise enough. My veteran employee is much better than AI at collecting data. Why would I hand the whole chain over to AI?”

The answer is this: AI does not need to outperform humans at every individual task in order to create enormous value.

Why not? Because the moment you insert that employee into the middle of the chain, the chain breaks. Once that person finishes the data collection, you now have another human-to-AI handoff — which means another round of transfer, verification, coordination, and correction.

The paper makes this point very clearly: even if humans are better than AI at certain intermediate steps, it can still make more sense to give the entire task chain to AI.

Why? Because when AI handles the whole thing from beginning to end, you remove the friction, reduce the handoff cost, and dramatically speed up total output — even if the quality at one step drops a little. What you’re saving is not just time on the task itself. You’re saving one of the most expensive resources in modern work: human coordination time.

It’s a lot like Henry Ford’s assembly line. Individually, a worker on the line might not have the craftsmanship of an old-school master artisan. But as a continuous system, the assembly line was overwhelmingly more powerful. In the age of AI, system-level efficiency beats task-level perfection.

Work Itself Is Being Redefined
Historically, we defined jobs by grouping tasks together in ways that matched human physical and mental limits. AI is now rewriting that equation.

The paper points out that in the early stages of AI adoption, the payoff may not even cover the cost. You have to buy tools, learn prompting, tolerate messy handoffs, and absorb all the coordination overhead. That’s the well-known productivity J-curve.

But once you cross that threshold — once you stop treating AI like a glorified typing tool and start redesigning the entire workflow around it, the real gains begin to show.

As that happens, traditional roles built around typing, formatting, and routine execution will be compressed. The future of work will be less about who can code faster or make prettier slides, and more about who can design better systems, spot better opportunities, and exercise better judgment.

If we can connect routine tasks into end-to-end AI task chains, we can free ourselves up to do the higher-value work that still depends on humans: complex judgment, strategic decisions, and emotional resonance. One person, paired with a rigorous automated workflow, can now do the work that used to require a team.

So maybe the question is no longer: “How can AI help me finish this task faster?”

The real question is: “How do I redesign this workflow into an AI pipeline that can run with little to no human intervention?”

From factory motors to wall outlets, the logic of technological progress has never really changed. The world is changing fast. AI is already powerful enough. What’s been too small isn’t the technology, it’s the role we’ve assigned it.

Over 10 Billion Views! What Exactly Are ‘Fuse Beads’ That Young People Are Crazy About?

baiwei — Tue, 07 Apr 2026 06:58:10 +0000

Recently, if you frequently scroll through Xiaohongshu, Douyin, TikTok, or Instagram, you will definitely be flooded with colorful little plastic beads—yes, these are "Fuse Beads" (or Perler Beads). This wave of enthusiasm is like a whirlwind, instantly sweeping through the social circles of young people globally!

Just how popular are fuse beads? Almost anyone who browses their phone can feel this craze. Not only are Asian craft bloggers rushing to post fuse bead tutorials, but Gen Z in Europe and America are also addicted to using them to create Y2K-style accessories and pixel art.

What's even more exaggerated is the objective data: in the "Top Ten Commodities of 2025" officially released by China's Taobao, the search volume for fuse beads surged by nearly 500% year-on-year, directly jumping to second place on the list! On Douyin and TikTok, the total playback volume of videos with the tags "Fuse Beads" and "Perler Beads" has exceeded tens of billions. This craze has directly ignited the global supply chain—in the toy industrial belts of Yiwu and Jinhua, factories have to pack over 50 tons, or about 2.5 billion fuse beads every day, continuously shipping them all over the world.

Open social media, and the headlines of those media outlets are more shocking than the last: "The Cross-Stitch of the New Generation of Young People," "The Spiritual Antidote Saving Working People," "The Highly Profitable Craft Spawning a Tens of Billions Industry Chain"...

These combinations of words seem to tell us that if you don't know about fuse beads, you are completely OUT!

So, what exactly are these "fuse beads" that make countless young people stay up late working on them? In a fast-paced society that pursues absolute speed and instant gratification, why are people willing to waste dozens of hours on this seemingly inefficient manual labor?

Let's uncover these "fuse beads" wrapped in trending searches together and see what their true face really looks like!

The sudden popularity of fuse beads is not just because the artworks themselves are beautiful, but it is the result of social spread, pixel aesthetics, and a low-barrier sense of participation amplified together.

The Essence of Fuse Beads: From "Nursing Home Rehabilitation Tool" to "Global Trendy Social Currency"

Contrary to what many people imagine, fuse beads are not a fresh product of the internet age at all.

Tracing its past and present, you will find an extremely outrageous cross-dimensional evolutionary path: as early as the 1970s, these small plastic tubes with holes were invented in Sweden, Northern Europe, but its initial positioning was extremely serious—it was a medical tool for the elderly in nursing homes to do fine motor skills and neurological rehabilitation training!

Later, the American brand Perler discovered its potential in color combination and transformed it into a children's educational toy popular in Europe and America. For a long time after it was introduced to Asia, it also just took root in children's art classes, teaching children to recognize colors.

So how did it suddenly become popular in the adult circles globally?

Behind this is a globally synchronized "Kidult" cultural revival. Facing the dual pressures of academics, the workplace, and the economy today, people in their twenties desperately need an emotional outlet. Whether it's young people in Asia going crazy over the fuse bead merchandise hand-made by stars in the hit drama "Love Game in Eastern Fantasy", or Gen Z in Europe and America obsessed with retro Pixel Art and Y2K aesthetics, this aesthetic resurgence combining top-down and bottom-up forces has allowed fuse beads to quickly cross borders and transform into the universal social currency of young people worldwide.

Why Are Today's Young People Getting More and More "Hooked" on Fusing?

Seeing this, friends might wonder: isn't it just putting colorful plastic beads on a pegboard and then ironing them with an iron? Why would such a boring and mechanical action make people addicted?

Good question! Let me uncover the real reasons behind the "addictiveness" of fuse beads for you from psychological and behavioral perspectives.

It is the Most Perfect Brain "Sleep Pod" (Flow Experience)

Modern people are bombarded by massive amounts of information every day, and the "Default Mode Network (DMN)" in the brain responsible for anxiety and rumination almost never rests. And the hours-long, mechanical repetitive actions of fuse beads (selecting colors, picking up beads, aligning) happen to effectively suppress this anxiety.

When you stare intently at the pattern, you actually enter a state of "Flow" in psychology. The brain secretes dopamine and endorphins, making you feel extremely calm. Overseas psychologists have also pointed out that this kind of manual labor can bring a "sense of time freezing" similar to deep meditation. Many players say: "Once I start doing fuse beads, I feel like time stands still and all my worries are gone."

The Real World is Out of Control, But Fuse Beads Give You 100% "Certainty"

In real life, working hard overtime doesn't necessarily mean a promotion or salary increase, and putting your heart into a relationship doesn't necessarily yield results. This "sense of losing control" is the root cause of anxiety for contemporary young people.

But fuse beads are different! It is an absolutely closed micro-universe with a 100% clear causal relationship. On this pegboard, as long as you put a red bead in a red grid, you will inevitably get a perfect pattern in the end. This sense of certainty that "as long as you pay, there will be a return" is the most scarce emotional antidote in contemporary society, perfectly aligning with the psychological demands of young people globally to "return to the inner self and reshape the sense of control."

When the external world is full of noise, what fuse beads provide is a small, controllable, repeatable, and completable space of order.

The Ironing Step: The Ultimate "Blind Box" Psychological Game

If you think fuse beads are just peaceful and quiet, you are completely wrong! The fuse bead process is divided into two steps: arranging the beads in the early stage, and high-temperature melting with an iron in the later stage.

The margin of error in the early stage is 100%; if you place it wrong, just pick it up and place it again. But what decides life and death is the short few minutes of "ironing" at the end. Didn't control the temperature well? Uneven heating? The entire artwork could be instantly ruined!

You've invested dozens of hours of "sunk costs" earlier, all betting on this one iron. This unpredictability greatly stimulates an exciting experience similar to "opening a blind box." If ironed perfectly, the sense of accomplishment bursts; if it fails, the huge unwillingness will immediately drive you: "No, I have to do it again!" This is the impeccable addiction loop of fuse beads.

Unique "Social Currency" and Circle Identity

Fuse beads are not only a tool for self-healing but also a "social business card" for finding like-minded people. In the Geek circles of Europe and America, players are keen on using fuse beads to replicate classic game characters from the 8-bit era (like Super Mario, Zelda, Pokémon); while in the Fandom circles of anime and idols in Asia, everyone likes to make exclusive support items and ACG (Anime, Comic, and Games) merchandise. Giving a hand-fused idol keychain or pet coaster to a friend is not only a sincere handmade gift but also a perfect carrier to express personality and circle identity.

The Commercial Paradigm Shift Brought by Fuse Beads: The $2.3 Trillion Global "Kidult" Market

At this stage, from e-commerce platforms to offline business districts, the money-making ability of fuse beads is indeed impressive. In 2025, the online sales of fuse beads on core e-commerce platforms in Asia approached 300 million RMB, and globally, it is leveraging a much larger "experience economy" and "emotional economy" market.

According to statistics, the scale of the global "Kidult" toy market, spawned by adults pursuing childhood fun, has reached as high as $2.3 trillion. The popularity of fuse beads actually marks a brand new consumption trend: young people are shifting from "buying results" to "buying a sense of participation."

In the past, we spent money to buy a ready-made trendy toy ornament; now, young people are more willing to spend money to buy a period of "self-immersive experience time," buying a "social capital" that can be shown off on social media. In Asia, physical fuse bead experience stores have sprung up like mushrooms after rain. Spending a little money to sit for an afternoon has become the best place for low-burden socializing; in Europe, America, and other regions, fuse beads are deeply bound with ACG merchandise and retro trends, deriving a rich IP co-creation ecosystem.

At the same time, besides the popularity of the physical fuse bead materials, more and more fuse bead peripheral industries have also started to take off. For example, specialized fuse bead gift customization services, fuse bead pattern trading markets, etc. It is particularly worth mentioning that with the development of AI technology, intelligent generation tools like BeadPattern Fuse Bead Image Generator (beadpattern.net) have begun to become widely popular in the player community.

Imagine this: you no longer need to struggle everywhere to find patterns. Just upload a cute photo of your pet, a group photo with friends, or a favorite anime screenshot, and AI can convert it into an accurate pixel-style fuse bead pattern with one click! These tools can even automatically match the bead color codes of global mainstream brands (such as Perler, Hama, Artkal) and directly generate a purchasing list. This combination of "AI + Craft" has completely broken the barrier for beginners to create, allowing everyone globally to easily customize unique and exclusive artworks, further pushing up the penetration rate of fuse beads.

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AI lowers the barrier to creation, while handcrafting retains the sense of participation and emotional value. Only when the two are superimposed is the commercial potential of fuse beads truly amplified.

Of course, while this track is growing wildly, the shortcomings and risks remain obvious. For example, the infringement risks brought by a large number of unauthorized anime and celebrity IP patterns, and the harmful gases that may be emitted when inferior plastic beads are heated. As global environmental and safety standards for toys and crafts continue to tighten, the entire industry will inevitably usher in a major reshuffle.

Written at the End

In this era where algorithms are getting smarter and the pace of life is getting faster, the global explosion of fuse beads is not accidental, but inevitable.

It shows us that humanity not only needs efficient AI tools to help us work, but also needs a seemingly "inefficient" physical craft to settle our souls. Whether it is for chasing stars, for socializing, or just to get a moment of peace, fuse beads have successfully built a warm spiritual refuge for young people globally in this anti-efficiency era.

If you also feel anxious, why not put down your phone, pick up the tweezers, and try to piece together a small world of your own!

💬 Interactive Topic: If you were to make your first fuse bead artwork in your life, what pattern would you choose to make? (Your own pet? A classic game character? Or a gift for a friend?) Welcome to tell me in the comments section, or just go try converting your photo into a pattern!

How Many Languages Should a Website Support?

baiwei — Fri, 12 Dec 2025 13:03:21 +0000

A few days ago, I read an article written by one of the most accomplished indie developers in China. In it, he described how his AI product, Raphael AI, gained a massive SEO boost after localizing the website into 32 languages.

What really caught my attention wasn’t the number itself — it was the result.

Looking at Google Search Console, he noticed something unusual: traffic was no longer coming only from English or a handful of major languages. Search queries in Arabic, Hebrew, Portuguese, and many other languages started to appear — languages he didn’t speak, didn’t target intentionally, and in many cases didn’t even recognize.

Yet those queries were bringing in steady, organic traffic.

By launching 32 localized versions, the product didn’t just “go global” in name — it began capturing search demand from dozens of language markets that would otherwise be completely invisible.

That raised a serious question for me.

⸻

Does This Mean More Languages Are Always Better?

If adding 32 languages can unlock so much organic traffic, does that mean every website should just keep adding languages endlessly?

And for solo developers — people without large teams, localization budgets, or dedicated SEO staff — how many languages actually make sense?

Even with modern AI making translation easier than ever, is launching 30+ languages really a smart move?

To answer this, I started digging deeper, comparing real-world cases and asking AI tools a simple question:

For a mature website, how many languages are actually the rational choice?

The answer surprised me.

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The Short Answer: There Is a Sweet Spot

For indie developers and small teams, more languages is not always better.

In fact, most successful international websites fall into a clear range:
👉 around 10 to 15 languages.

Once a site goes beyond 20 languages, something interesting often happens. The site looks very international — but efficiency drops sharply.

Why?

Because internationalization only works if three conditions are met:
1. Users can find you through search
2. They can understand what you’re offering
3. They are realistically able to convert

Languages that don’t meet all three usually add complexity, not growth.

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The Core Languages Almost Every Global Site Needs

Regardless of niche — tools, SaaS, or content — most global websites can’t avoid these foundational languages:
• English – the default language of global search
• Spanish – Spain + all of Latin America
• Portuguese (Brazil) – a massive, often underestimated market
• French – Europe and large parts of Africa
• German – high purchasing power and strong ad value
• Chinese – enormous population and search volume

Just these six already cover the majority of meaningful global search demand.

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The High-ROI Expansion Tier Most People Miss

After the basics, the next layer is where things get interesting — and surprisingly efficient.

Some language markets don’t look obvious at first, but consistently deliver strong long-term traffic with manageable competition:
• Japanese & Korean
High-quality users, strong search behavior, high expectations
• Indonesian & Turkish
Fast-growing markets with real demand and less saturated SERPs
• Russian
Large search volume and a relatively independent ecosystem

At this point, a website is already operating as a mature international product. Beyond this tier, marginal returns drop quickly.

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Why Too Many Languages Can Hurt SEO

This is the part many people underestimate.

Every new language version increases SEO complexity:
• More hreflang relationships to manage
• More content depth required per language
• More risk of thin or low-quality pages
• More chances for machine-translated content to fail quality checks

Low-quality language pages don’t just underperform — they can drag down the perceived quality of the entire site.

In practice, especially for solo builders, a website with 10 well-maintained, high-quality language versions almost always outperforms one with 30 shallow translations.

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A Practical Upper Bound (Not a Checklist)

If you want a realistic reference point — not something you must copy — this range is usually enough:
• English
• Chinese (Simplified / Traditional)
• Spanish
• Portuguese (Brazil)
• French
• German
• Japanese
• Korean
• Indonesian
• Turkish
• Russian
• Italian

Anything beyond this should be driven by real data, not ambition.

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Final Thought: Internationalization Is About Precision, Not Coverage

The goal of going global is not to “do everything.”

It’s to do the right things well.

If your product, content, or monetization model isn’t stable yet, aggressively adding languages often increases operational complexity without expanding real growth.

Truly professional international websites don’t look impressive because they support 40 or 50 languages.
They look impressive because every language they support stands on its own — for users and for search engines alike.

Don't Let the "Nonsense" Label Obscure the Real Struggles and Strengths of the ADHD Community

baiwei — Fri, 12 Dec 2025 12:25:54 +0000

Recently, a statement by Oscar-winning actor Anthony Hopkins has caused a stir on social media. He publicly stated that conditions like ADHD, OCD, and Asperger's are "trendy labels" and advised people not to "believe in anything." This remark was immediately met with harsh criticism from numerous ADHD charities and patient groups.

This is no overreaction. When a respected public figure dismissively calls a widely researched and confirmed neurodevelopmental disorder "nonsense," it not only exposes his personal misunderstanding but also deeply hurts countless individuals and families struggling with it. Hopkins's comments represent a deep-rooted and extremely harmful prejudice in society: viewing ADHD as a fashionable label rather than a real and complex condition.

Today, we must take this opportunity to clear the fog, re-understand ADHD, and explore how we can better help the ADHD community overcome the reading gap in this age of information explosion.

Why Hopkins is Wrong: ADHD is Far More Than a Simple Label

First, we must be clear: ADHD (Attention-Deficit/Hyperactivity Disorder) is not a trendy label that can be casually applied or removed. It is a well-documented neurodevelopmental disorder originating from the brain. It is not an excuse for laziness or lack of willpower, but a real medical condition involving brain neurobiology.

According to the latest scientific consensus, ADHD involves challenges in three core dimensions:

Inattention: This is not the same as occasional daydreaming. People with ADHD have chronic difficulty sustaining attention, following instructions, and organizing tasks. Their brain is like a constantly scanning radar, bombarded by various external stimuli and internal thoughts, making it difficult to focus on a single task for an extended period, especially an activity requiring high concentration like reading.
Hyperactivity: This manifests as persistent, inappropriate physical activity, such as fidgeting or constant small movements of the hands and feet. In adults, this hyperactivity may manifest as inner restlessness or an unstoppable stream of thoughts, i.e., mental noise.
Impulsivity: This refers to the tendency to act without sufficient thought. This can lead to interrupting others in conversation, making hasty decisions, or having difficulty with delayed gratification.

These symptoms do not exist in isolation but are intertwined, profoundly affecting a person's daily life. Depending on the presentation of symptoms, ADHD is mainly divided into three clinical types: predominantly inattentive presentation, predominantly hyperactive-impulsive presentation, and combined presentation. Many girls and adult women have symptoms that are primarily inattentive and internal, so they are often overlooked or misdiagnosed.

So, what is the root of all this?

Scientific research indicates that ADHD is closely related to the function of specific brain regions, especially the frontal cortex. This area is responsible for so-called executive functions—including planning, decision-making, working memory, and impulse control. In the brains of people with ADHD, the transmission system of neurotransmitters (like dopamine) is abnormal, leading to reduced communication efficiency in these key brain areas. It's like the conductor of an orchestra has temporarily left their post; although the various sections are still playing, they can't coordinate effectively.

Moreover, ADHD has a very high heritability, with studies showing a heritability rate of up to 75%. This means that if a parent has ADHD, their child's risk of having it is significantly increased. It is rooted in our genes and brain structure, and it's not a problem that can be solved by simply being told to "focus."

Therefore, when Hopkins flippantly dismissed it as "nonsense," he ignored the vast body of scientific evidence and the real, daily struggles of countless patients. The occasional distraction of an average person is worlds apart from the chronic, persistent, and life-impacting difficulties experienced by people with ADHD.

And among these difficulties, reading, as a fundamental way to acquire knowledge and engage in deep thinking, often becomes a particularly insurmountable mountain for people with ADHD.

When ADHD Meets Reading: An Uphill Battle

Imagine trying to read, but the words on the page seem to dance, and your thoughts uncontrollably drift to the bird singing outside, last night's game, or tomorrow's work... You try to pull your focus back, but you fail again and again. This is the daily reality for many people with ADHD.

The core reasons why reading is so difficult for them are:

Inability to Sustain Attention: Difficulty keeping focus on static text for long periods, leading to skipping lines, missing words, or rereading the same paragraph without comprehension.
Working Memory Deficits: Forgetting what was just read, making it hard to build a complete context and logical chain in their minds, which leads to difficulty with comprehension.
Internal Distractions: The never-ending "noise" of thoughts in their brain makes it hard to immerse themselves in the world of reading.

Therefore, well-intentioned advice like "just calm down and focus" is often ineffective and can even worsen their frustration. The root of the problem is not their attitude, but the way their brain works.

Let the ADHD Reading Plugin Lend a Hand

Fortunately, there are now reading tools designed specifically for the ADHD community!

I must strongly recommend a free Chrome extension: ADHD Reading. It's like a thoughtful reading companion that uses the power of technology to precisely address the core pain points of people with ADHD when reading.

It has several "god-tier" features:

Smart Text Highlighting: It automatically bolds the beginning of each word, acting as a visual guide to help your eyes scan the text more quickly. This utilizes the principle of artificial fixation points, providing a visual anchor for a wandering gaze, which significantly reduces line-skipping and word-skipping.
Focus Assistant: It highlights the line you are currently reading and dims the rest of the page. This is like shining a spotlight for you, allowing you to stay focused on the current content and block out irrelevant distractions.
Layout Optimization: With one click, it can adjust the font, line spacing, and paragraph spacing of a webpage, instantly making a chaotic layout clear and easy to read, greatly reducing visual fatigue and cognitive load.

These features are not just imaginary; they are based on a deep understanding of the cognitive characteristics of ADHD. It doesn't force the brain to change but cleverly alters the presentation of information to suit the brain's needs.

From Understanding to Empowerment: What We Really Need

Returning to Anthony Hopkins's comments. What the ADHD community needs is not flippant dismissal and ignorant labels, but science-based understanding and effective support.

Tools like ADHD Reading are the embodiment of this understanding. They show us that neurological differences should not be a barrier to acquiring knowledge. Through technological empowerment, we can pave the way for people with ADHD to roam freely in the ocean of knowledge.

If you or someone you know is also struggling with reading, give this free tool a try. This might be the first step to improving your reading experience and rediscovering the joy of reading.

Let's work together to replace misunderstanding with understanding, and blame with support, to build a more inclusive and friendly society for neurodiversity. Because every brain deserves to be seen and respected.

Sora2 Is Dead, Burn Paper for It

baiwei — Mon, 20 Oct 2025 08:05:10 +0000

Just over half a month in, OpenAI must honestly face an awkward reality: Sora2, the video generation platform they once had high hopes for, is slowly turning into a flashy junk-video generator.

When Sam Altman promised the world a revolution in the video field, he probably didn't expect that what would ultimately be delivered was just a high-end toy that users would get tired of after five minutes of novelty.

When Sora2 first appeared, it amazed everyone. With just a piece of text or an image, it could generate surreal short films comparable to actual footage, igniting the dream in countless people of becoming a great director. The media frenzy and the tech circle's hype further pushed it onto a pedestal.

However, as excited users flocked to the platform, the beautiful dream quickly collided with cold, hard reality.

In fact, the so-called intelligent creation is just a frustrating gacha game. Basketballs hang weirdly in the air, characters' clothes change color seamlessly between shots, and text renders into a pile of gibberish, not to mention the stiff facial expressions and awkward speaking voices.

This is not filmmaking at all; this is a long war of wits against the randomness and bugs of AI.

The professional film production team Shy Kids revealed that they need 300 generations to get a few usable clips, a number that means its creative efficiency is hundreds of times lower than the most traditional manual shooting.

Other users also quickly discovered that Sora2 only gave them a random button full of bugs. It cannot stably realize your creativity; it can only randomly reward you with some bizarre and motley clips.

I. The Dream of an AI-version of TikTok

If the birth of Sora2 was a splendid firework, then its rapid silence stems from three unforgivable sins in its product design. When we compare it horizontally with the true short video hegemons like TikTok and YouTube Shorts, the shattering of this dream becomes incredibly clear.

1. How to Retain Users with Ephemeral Content?

The content of mainstream short video platforms is rooted in people, a kaleidoscope of real life. Whether it's street interviews on Douyin, rural life on Kuaishou, or talent challenges on Reels, the core is always lively people and things that can resonate with others.

Sora2's content, on the other hand, is 100% synthesized by AI, essentially a visual spectacle show. Users might be amazed at first by a cyberpunk-style "Along the River During the Qingming Festival," but this amazement is short-lived. When the threshold for visual stimulation is constantly raised, what remains is emptiness and boredom. Because it lacks the core anchor of "people," the content loses its ability to continuously generate emotional connections.

As one netizen said: "On Douyin, I can see a hundred kinds of real life; on Sora2, I can only see one kind of fake excitement." In the end, the space for Sora2 users to be creative is severely compressed, making it naturally difficult to produce diverse and viral content.

2. A Community Without Social Connections is Just a Video Website

Social is the underlying operating system of all content platforms. TikTok not only has algorithmic recommendations but also a complex social network based on follows, friends, comments, and challenges, which constitutes strong user stickiness.

Sora2 is almost a desert in this regard. Since the content is detached from real-life personalities, the connections between users are very fragile. You might like a video, but it's hard to follow a person behind a virtual work. The so-called follow system is also a mere formality due to the high homogeneity of the content.

A community without strong social connections is essentially just a read-only video website, not an interactive social platform. Users on Sora2 are lonely; they are just passive consumers of information, not co-builders of the community.

As the novelty of the new AI tool fades, this lonely carnival naturally comes to an end.

3. No Bread, Who Will Generate Power for You?

This is perhaps the heaviest straw that broke Sora2's back.

Platforms like YouTube, TikTok, and Instagram have long established mature creator economy ecosystems: ad revenue sharing, creator funds, live streaming rewards, e-commerce... Creators' talents can be directly exchanged for tangible income, which drives a positive feedback loop.

In contrast, Sora2 is more like a "power-for-love" club. Creators here have no ad revenue sharing, no tipping tools, let alone commercial monetization. OpenAI's business model is still API fees for B2B and subscription fees for B2C. The free labor of creators merely provides nutrients for the model's evolution and earns eyeballs for OpenAI.

A platform that cannot let creators make money is like an engine without fuel. No matter how exquisitely designed, it will eventually become a pile of cold, useless iron.

II. Why Are AI Giants Obsessed with the Pipe Dream of a Content Ecosystem?

Sora2's defeat is not an isolated case. From text to video, we have seen too many AI companies trying to build a closed content platform driven purely by AI. However, whether it's Pika, Kling, Dreamina, or countless other fleeting applications, none have truly succeeded on this path.

Behind this is the original sin of AI content that cannot be fixed by technological iteration: it lacks the soul of human creation—real emotions, unique personal experiences, and groundbreaking originality. After a brief period of curiosity, users are faced with a cold, repetitive, and unrelatable algorithmic spectacle.

Trying to build a social platform with pure AI content is like trying to build a city in the desert without a water source—it is doomed to fail.

The lesson of Sora2 is also a question for the strategic direction of the entire industry. For current AI companies, instead of spending huge sums of money chasing a pipe dream of platform traffic, it is better to be a super AI tool in a down-to-earth manner.

On this point, Google's strategy appears more sober and pragmatic. It did not try to use Gemini to create an AI version of Quora or an AI version of YouTube out of thin air. Instead, it integrated its powerful AI capabilities as an underlying infrastructure into core products such as search and workspace suites, empowering billions of users worldwide. This is the duty of a tool, and it is also the role that AI should play at this stage.

Before envisioning the endgame of artificial general intelligence, it is better to be down-to-earth, return to the essence of a tool, and think clearly about how to solve specific problems for each specific person.