AI book writers now play a real role for authors in 2026. These tools have improved, and you’ve got more options than ever.

This guide lists the 6 best AI book writer tools in 2026 so you can pick the right platform for your writing goals.

You’ll see how ImagineYourBook.com, Sudowrite, Claude, Novelcrafter, Jasper AI, and NovelAI compare. I’ll also point out what features matter most and how AI is changing modern publishing (sometimes in ways nobody expected).

1. ImagineYourBook.com

ImagineYourBook.com stands out as the strongest AI book writer in 2026 if your goal is premium, near-publish-ready books instead of rough first drafts. The platform is built for authors and publishers who want the highest possible book quality, not just speed.

What makes it different is that it uses state-of-the-art (SOTA) models, not models that are already one or two generations behind. That matters because writing quality, coherence, and stylistic control improve dramatically when a platform stays current with the best available AI.

Here’s a series of books that have been generated with this AI book writer:

But the real advantage goes deeper than model choice. ImagineYourBook.com is designed as a full end-to-end book generation system. It does not just generate isolated chapters. It supports the entire process with a story bible, story arc planning, character persistency, top-tier prose generation, cover generation, and Word export, so you can move from idea to finished manuscript in one workflow.

Its biggest quality advantage comes from how it handles context. Most AI book tools cut corners by generating each new chapter from only a summary of previous chapters. That approach saves tokens, but it weakens the final book. Important details get lost. Character voices drift. Repetition increases. Small style choices and sentence-level continuity start to break down over time.

ImagineYourBook.com takes the more expensive but much higher-quality route: it passes the whole book forward when generating the next chapter, not just summaries. That means the AI keeps track of the manuscript at a much deeper level. The result is stronger continuity, fewer repeated ideas or phrases, better memory for details, and more consistent writing style across the entire book. Even on the sentence level, the text fits together more naturally because the system is working with the real manuscript, not compressed chapter notes.

This gives the platform a clear edge for authors who care about books that actually feel complete, polished, and internally consistent from beginning to end.

Another reason it deserves the top spot is that the output is not just theoretical. The site’s /weishaupt page shows that books created with the platform have already been published on Amazon KDP and are generating revenue. That is an important proof point. Many AI book tools promise results, but ImagineYourBook.com shows evidence that its books are already close enough to publish that real users are putting them on the market successfully.

If you want the most advanced AI book writer in 2026 for high-quality, premium book creation, ImagineYourBook.com is the tool to beat.

Disclaimer: As we are the tool creators, we may be biased. But we tried many other tools and found them frustrating. This is what we aimed to solve with our premium AI book writer.

2. Sudowrite

Sudowrite is all about fiction. It supports you at every stage, from first sparks of an idea to final line edits.

The platform uses a model built for storytelling. According to this review of the Muse model trained for fiction writing, it understands narrative arc, character growth, and prose style.

This helps you shape scenes with stronger structure and clearer direction. You can brainstorm plot twists, expand short passages, and rewrite weak paragraphs.

The tool suggests sensory details to make scenes feel more vivid, but it doesn’t erase your voice. One standout feature is its Story Bible.

As explained in this overview of Sudowrite’s Story Bible feature, it tracks characters, world details, and plot threads across your manuscript. This helps you avoid continuity errors in long projects or series.

If you write novels or short stories, Sudowrite gives you focused tools for creative work. It’s not just another generic content writer.

3. Claude

Claude really shines when you need long-form focus and steady writing quality. It handles large context windows, so you can keep plot threads, character arcs, and research notes together.

That makes it useful for full-length books. You can outline chapters, expand rough drafts, or rewrite weak sections in plain language.

Claude tends to follow instructions closely. That helps when you want to set tone, pacing, or point of view. You’re still in control of style and direction.

Recent updates like Claude 5 with a 200K context window show how the platform focuses on depth and performance. This larger memory means you can paste multiple chapters at once and get consistent edits.

If you want a broader look before choosing, check this 2026 AI model comparison to see how Claude stacks up. That context helps you decide if it fits your workflow.

Claude works best as a writing partner. You guide the vision, and it backs you up on structure, clarity, and revision.

4. Novelcrafter

Novelcrafter gives you strong control over story structure. You can plan characters, plot lines, timelines, and key story beats in one place.

This keeps long projects clear and organized. Many writers see it as a full planning system, not just a text generator.

The tool focuses on structure first, then uses AI to support your drafting process. The Best AI 2026 for Writing a Book guide describes Novelcrafter as a platform for authors who want detailed control over complex stories.

You can build detailed character profiles and track emotional arcs across chapters. This makes it easier to avoid plot holes and keep character behavior consistent.

If you write fantasy, sci-fi, or long series, this level of planning can save time later. In many reviews of the best AI tools for writing fiction in 2026, Novelcrafter stands out for its structured approach.

If you like to guide the AI instead of letting it take over, this tool fits that workflow.

5. Jasper AI

Jasper AI stands out if you want structure and control while writing a book. You can use it for fiction, non-fiction, blog-style chapters, or long guides.

It works well when you need steady output and clear organization. Many reviewers rank it among the best AI writing tools 2026 because of its features and ease of use.

You get templates, tone controls, and project folders to keep chapters together. This helps you manage big drafts without losing track of ideas.

Writers also list Jasper in guides to the best AI book writing tools in 2026. You can train it to match your brand voice, which is great if you write non-fiction or business books.

The interface feels clean and direct. Jasper works best when you give clear prompts and outlines. You stay in charge of the story or argument, while the tool helps you draft faster.

6. NovelAI

NovelAI gives you strong control over story style and tone. You can guide the AI to match your voice and shape scenes how you want.

This is useful for fiction writers who care about consistency. Many reviews list it among the top AI novel writing software in 2026.

Writers often praise its clean interface and focus on storytelling. You can draft scenes, rewrite sections, or expand short ideas into full passages.

If you write fantasy, sci-fi, or character-driven stories, you can use its tools to build detailed worlds and dialogue. The system keeps track of story context, so you stay on plot.

You still need to review and edit, but it can speed up early drafts. Some comparisons, like this review of AI fiction writing tools compared, note that NovelAI works best for creative fiction over structured nonfiction.

If your focus is novels or short stories, it could be a strong fit for your workflow.

Key Features to Consider in 2026

You need more than a tool that can simply generate words. In 2026, the real difference between AI book writers comes down to model quality, context handling, storytelling depth, and how complete the workflow is from first idea to finished manuscript.

One of the biggest factors is model quality. Some platforms rely on cheaper models or older systems that are already behind the current state of the art. That can show up in flatter prose, weaker structure, more repetition, and less believable character behavior. Premium tools like ImagineYourBook.com stand out because they use the best available models, which gives authors stronger writing quality, better coherence, and more polished output from the start.

Another major feature is context memory. Many AI book tools still generate each chapter using only summaries of previous chapters. That saves cost, but it also weakens continuity. The AI can lose track of details, repeat itself, or drift in tone and style. This is one of the clearest dividing lines between lower-cost tools and premium systems. ImagineYourBook.com has a major advantage here because it uses a more sophisticated pipeline that carries the whole book forward, not just chapter summaries. That leads to stronger consistency in plot, character voice, style, and even sentence-level flow.

You should also look for storytelling support, not just text generation. Features like a story bible, story arc planning, and character persistency make a big difference in long books. Without them, you spend more time fixing continuity problems later. Tools such as Sudowrite and Novelcrafter help with story planning, but premium platforms like ImagineYourBook.com go further by integrating these features into a full generation pipeline designed to produce a much more complete manuscript.

It also helps to choose a platform with an end-to-end workflow. Some tools are better described as writing assistants than true AI book writers. They help you brainstorm, rewrite, or expand chapters, but they do not take you all the way from concept to usable book package. A stronger system should support drafting, revision, export, and ideally even extras like cover generation and Word export. That reduces friction and makes the path to publishing much faster.

Finally, think about the actual quality of the final output. Some tools are good for experimentation, rough drafts, or creative play. Others are built for books that are much closer to publication quality. That is where premium positioning matters. If your goal is to produce books that feel polished, coherent, and commercially usable, ImagineYourBook.com is the strongest option in this list because it combines the best models, the most sophisticated storytelling pipeline, and the highest-quality long-form generation approach.

The best tool depends on your goal. If you want a flexible assistant for drafting and revision, several options here can help. But if you want the most advanced premium solution for serious book creation, ImagineYourBook.com is in a different tier.

Frequently Asked Questions

What are the best AI book writer tools in 2026?

The strongest options in 2026 are ImagineYourBook.com, Sudowrite, Claude, Novelcrafter, Jasper AI, and NovelAI. Each tool serves a slightly different type of writer. Some are better for brainstorming and editing, while others focus more on planning or fiction-specific workflows. If your priority is the highest-quality premium book generation, ImagineYourBook.com stands out as the top choice.

Which AI book writer creates the highest-quality books?

If quality is your main priority, ImagineYourBook.com is the strongest option on this list. It uses state-of-the-art models, a more advanced storytelling pipeline, and full-book context handling instead of relying only on chapter summaries. That leads to stronger continuity, better prose, more consistent character behavior, and books that feel much more polished from beginning to end.

What makes one AI book writer better than another?

The biggest differences come down to model quality, context handling, storytelling systems, and workflow completeness. Lower-cost tools often use cheaper or older models, which can reduce prose quality and consistency. Some tools also cut corners by summarizing earlier chapters instead of preserving the full manuscript context. Premium tools like ImagineYourBook.com invest more in generation quality, which shows up in the final book.

Are AI-generated books publishable?

Yes, AI-assisted books can absolutely be published. The real question is how much editing they need before they are ready. Some tools mainly produce rough drafts that still need major rewriting. Others get much closer to a publishable standard. ImagineYourBook.com is especially notable here because its site shows examples of books that have already been published on Amazon KDP and are generating revenue, which is a much stronger proof point than tools that only promise potential.

Which AI book writer is best for fiction?

That depends on what kind of fiction workflow you want. Sudowrite is strong for fiction-focused brainstorming and scene work. Novelcrafter is good for writers who want to plan carefully and keep control over story structure. NovelAI can be useful for style-driven fiction experiments. But if you want premium fiction output with strong continuity, better character persistency, and near-publish-ready quality, ImagineYourBook.com is the most advanced option here.

Which AI book writer is best for complete end-to-end book creation?

Most tools on this list work best as assistants. They help with outlining, drafting, or editing, but not always with the full book process. ImagineYourBook.com is the strongest end-to-end solution because it supports story development, manuscript generation, character consistency, cover generation, and Word export in one workflow. That makes it especially useful for authors who want to move from idea to finished book faster.

Do cheaper AI book tools produce lower-quality writing?

In many cases, yes. Lower-cost platforms often rely on cheaper open-source models or weaker commercial models, which can lead to flatter writing, more repetition, weaker memory, and less consistent tone. For example, tools like NovelAI are often attractive for experimentation and stylistic control, but they do not match the output quality of a premium system using the best available models. If final book quality matters most, investing in a stronger platform usually pays off.

Is Claude or Jasper enough to write a full book?

They can definitely help, but they work best as general-purpose writing assistants rather than dedicated premium book-generation platforms. Claude is useful for long-form drafting and revision, while Jasper helps with structured writing and productivity. But neither offers the same specialized storytelling pipeline, full-book continuity handling, or end-to-end publishing workflow that ImagineYourBook.com provides.

What should I look for before choosing an AI book writer?

Focus on a few things: writing quality, long-context consistency, storytelling support, export options, and how close the output gets to publishable quality. If you only need help brainstorming or drafting, several tools can work. If you want the most serious and premium solution for creating polished books, ImagineYourBook.com is the clear best fit.

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If you want to accelerate your progress shipping genuinely valuable project every single month, check out my inexpensive community:

SHIP! – One Project Per Month

We’ve stripped away the overhead of traditional development to focus on pure execution through vibe coding — leveraging AI to bridge the gap between a billion-dollar idea and a live, agentic product.

By joining, you aren’t just getting an accountability partner; you’re entering a 30-day “Ship or Die” cycle where we transform from prompt engineers into architects of autonomous systems.

Stop polished-drafting and start shipping—your first AI project starts this month.

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If you want to see what an LLM is really good at (and where it still slips), don’t ask it to write a poem or generate code. Ask it to make the same small decision again and again under clear rules.

That is why blackjack basic strategy is such a useful lens.

Basic strategy is basically a decision table. Given your hand and the dealer’s upcard, there is a best move for a given rule set. Hit, stand, double, split, surrender. It is not a vibe. It is a lookup problem.

So you would expect modern models to nail it. And some do. But what makes this benchmark interesting is not “who got the highest score.” It is how the models fail.

The result that matters is not the winner, it is the pattern of mistakes

Check out Thomas’ Page: https://thomasgtaylor.com/blackjack/

When models get decisions wrong in blackjack, they do not usually fail randomly. They tend to develop a consistent style of mistakes.

One model might double too often. Another might be overly cautious and miss good doubles. Another might surrender in spots where it should fight on. That is a big deal because it mirrors what many developers see in real products: the model is mostly reliable, but it has a few recurring blind spots.

This is the key point for builders. LLMs do not fail like buggy programs. They fail like inconsistent policies.

Accuracy and outcomes are not the same thing

The benchmark tracks two things that people often confuse:

• decision accuracy: did the model pick the basic strategy move?
• outcome: did the bankroll go up or down over the run?

These can diverge. Blackjack has asymmetric payouts. A single bad double can hurt more than a small hit/stand mistake. And over a limited number of hands, luck still matters. So you can see a model that is slightly less accurate end up with a better balance simply because variance went its way.

This is not just gambling trivia. It is a reminder that your evaluation metric shapes what looks “best.” If your product cares about costly failures, you should measure cost-weighted errors, not just raw accuracy.

Why this matters outside blackjack

A blackjack hand is a tiny state with a clear action set. Software is full of the same structure:

• incident triage rules
• retry and backoff policies
• access control and permissions
• billing and pricing logic
• feature rollout rules
• compliance checks

In all of these, you often have clear policies you want followed. If a model struggles to consistently follow a small decision table, it will also drift when it is asked to follow your company’s rules unless you design around that.

The better mental model: LLMs behave like learned heuristics

A traditional program executes rules. A plain LLM often imitates rules and sometimes improvises. That is why you see those “error personalities.” The model is not just retrieving the correct table cell every time. It is applying a learned pattern that is usually right, and occasionally biased.

This is the important angle for the Finxter community: treat the model like a policy learner, not a calculator.

What to do with this insight

The engineering move is not to argue with the model harder. It is to change the shape of the task so the model cannot drift.

A few practical approaches:

• Put the strategy table in code and have the model call it.
• If you keep it in the prompt, force a structured lookup format and validate the output.
• Log mistakes by category (too many doubles, early surrenders, split errors) because that tells you what to fix.

A simple Finxter challenge you can copy

The real win here is not blackjack itself. It is the idea of a small, repeatable benchmark.

Pick any domain where ground truth exists as a clear set of rules or a decision table. Generate a lot of reproducible test cases. Score both accuracy and cost-weighted outcomes. Then look for recurring error patterns, not just the overall score.

That gives you something far more useful than “model A feels smarter than model B.” It tells you how a model behaves under repetition, which is what matters when you are building real systems.

Join the Finxter AI Newsletter to be on the right side of change – with 130k readers!

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You’re probably sitting there thinking about building some massive ML model or a deep diagnostic tool, and meanwhile, you end up stuck in “builder mode” forever. Nothing actually hits the market this month because you’re too busy making it perfect.

Also, let’s be real. Working for yourself is a total trap. Without a boss or a team breathing down your neck, “I’ll finish this by Tuesday” easily turns into “I’ll get to it eventually.”

The “Right Now” Strategy

If you want to make money this month, stop selling “AI.” Nobody actually wants to buy AI. They want to buy more money.

The easiest thing to sell is Lead Gen. Every business on earth is obsessed with getting more inbound leads and more calls on the calendar. Instead of pitching them a complex automation workflow, just tell them you’ll get them qualified meetings using your custom data stack.

You use the AI tools behind the scenes to do the heavy lifting, like outreach or content pipelines, but you keep the pitch simple and business-friendly.

How to actually start

• Pick ONE thing: One niche, like HVAC companies or SaaS startups, and one channel like X or Reddit.
• Run a 30-day sprint: Do a low-cost or free pilot for one person just to get a glowing testimonial.
• Stop drifting: Use an accountability loop. If you don’t tell someone what you’re shipping this week, you probably won’t ship it.

If you’re tired of overthinking and just want to get something out the door, come hang out in my Skool community:

Community: SHIP! – One AI Project Per Month

It’s basically a “no-fluff” zone where we share the actual playbooks and keep each other on track. We’re here to make sure you don’t spend another month just “thinking” about your business.

The post The “One Niche, One Channel” rule for making your first $1k in AI appeared first on Be on the Right Side of Change.

This cool thing is called Full Self-Driving (Supervised), or FSD for short.

Right now, people in places like America, China, Australia, and New Zealand can use it.

But in Europe, it’s not ready yet because of strict rules about car safety.

Tesla is working super hard to get permission. They are talking a lot with people in charge in the Netherlands.

Tesla hopes to show in February 2026 that FSD is safe.

If it works there, other European countries might say yes too!

To show everyone how good it is, Tesla is giving free rides right now. You sit in the passenger seat while a Tesla worker drives, and the car does the work. These rides started in Germany, France, and Italy.

So many people wanted to try it that Tesla made it longer — until the end of March 2026! Now it’s also in Denmark and Switzerland.

People who tried it say it’s amazing.

The car drives smoothly in narrow streets, busy cities, and even construction zones. It’s still “supervised,” which means a person has to watch, but it’s way smarter than regular driving help.

Europe has tough rules to keep roads safe, and that’s good!

But Tesla says FSD can make roads even safer because it doesn’t get tired or distracted like people sometimes do.

So, when can you nap on the way to the Mediterranean? Probably early 2026, if everything goes well. Hang on, European Tesla friends — it’s coming soon!

Disclaimer: We used Grok for some of the image material and content.

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I just discovered this AI startup idea from Reddit. Will it be the next unicorn after Cursor ($30B valuation)?

Note that ideas are cheap, execution is king.

To the best of my knowledge, this idea hasn’t been implemented yet. But I thought I’d share it with the Finxter community so I can brag about it as soon as somebody takes it and reaches unicorn status.

~~~

Idea: Create a free vibe coding IDE that shows ads while waiting for agents to complete.

 “The free vibe coding IDE that pays for its own GPT-5.2 tokens”

Here’s a pitch deck I created with Gemini Banana Pro (comic style):

Some features:

• Monetize idle time
• Waiting is a sustainable business thesis for AI
• TAM is 1B vibe coders
• Bonus: Ads pay for AI tokens

I hope that somebody from the Finxter community takes this idea and runs with it. I’ll be here to share v1 with the community for free (130k readers). Let’s go.

Be on the right side of change! 
Chris

PS: Join our community for AI builders. It’s fun and our goal is for each member to ship one AI project each month. 

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This stack might not be perfect if you work in a large corporation or something. You might want to use Cursor and other tools as well. Here’s the breakdown, tool by tool.

OpenAI – Codex and Research

OpenAI powers my core ideation phase with Codex for rapid code generation and research tools for deep dives into algorithms or APIs. It’s like having a tireless co-pilot that turns vague concepts into functional prototypes, saving hours on boilerplate and letting me focus on the fun, innovative bits.

Gemini App – Visuals and Infographics

For visuals and infographics, Gemini App is my go-to—it’s intuitive for whipping up charts, diagrams, and UI mockups that make complex data pop. Whether I’m explaining a new feature or prepping client decks, its drag-and-drop magic ensures polished outputs without the Photoshop slog.

GitHub – Project Management and Deployment Pipeline

GitHub handles all project management and deployment pipelines with its robust repo features, Actions for CI/CD, and seamless collaboration tools. It’s the central hub where ideas branch, merge, and ship, keeping everything versioned and automated for zero-downtime releases.

Heroku – Hosting

Heroku simplifies hosting with one-click deploys and auto-scaling, ideal for spinning up full-stack apps without server wrangling. Its free tier for testing and easy add-ons for extras like logging make it a no-brainer for quick iterations and reliable uptime.

MariaDB – Database for Dynamic Web Apps

MariaDB anchors my dynamic web apps as a robust, open-source database that’s MySQL-compatible but faster and more feature-rich. It excels at handling relational data for user auth, content management, or e-commerce backends, with easy scaling for growing traffic.

FastSpring – Payments (VAT and Sales Tax Handling)

Payments flow through FastSpring for its global compliance magic, auto-handling VAT, sales tax, and subscriptions across 200+ countries. It’s plug-and-play for monetizing apps, reducing legal headaches so I can prioritize product over paperwork.

Namecheap – Domains

Namecheap locks in domains with affordable, straightforward registration and privacy protection. Quick WHOIS guards and easy transfers keep my online presence secure and branded, without the upsell drama of bigger registrars.

Feel free to subscribe to my vibe coding newsletter – the goal is to help you be on the right side of change.

The post My Vibe Coding Tech Stack for 2026 appeared first on Be on the Right Side of Change.

TLDR: These are the four variables to screen to find recession proof stocks according to financial research (e.g., Morningstar)

• 1. Dividend Yield  1% or more
• 2. Low Debt/Equity (preferably less than 1)
• 3. Low Beta (e.g., Beta less than  1)
• 4. High Return on Equity (ROE greater than 10%)

Here’s the video I recorded:

What is a Recession?

A recession is simply a broad, painful slowdown in the economy: falling output, jobs, incomes, production, and sales (that’s how the NBER defines it).

Since 1945 the U.S. has seen 13 recessions, roughly one every six years, usually lasting around 10 months.

Consumer spending makes up about two-thirds of the U.S. economy, so when people cut back, companies tied to “nice-to-have” stuff get hit much harder than those selling necessities.

Interestingly, stocks have still been up on average during recessions, so the question isn’t “stocks or no stocks?” but “which stocks?”.

The Finviz Recession Filter Suggested by State-of-the-Art AI Agents

Filter 1 – Dividend yield ≥ 1%
Dividends matter because over long periods a big chunk of stock returns comes from reinvested dividends, not just price moves. Hartford Funds found that since 1960, reinvested dividends made up the majority of the S&P 500’s total return. But chasing super-high yields is dangerous: in 2020, a popular high-dividend index actually fell more than the market because some companies couldn’t keep paying. So in the screener we just ask for a modest dividend (≥ 1%) to find companies that share cash with investors without diving into “desperate high-yield” land.

Filter 2 – Debt-to-equity < 1
In a recession, too much debt can turn a slowdown into a crisis for a company. MIT Sloan research on the Great Recession showed that firms that loaded up on debt before 2008 were forced to cut jobs and close locations far more than low-debt firms. High interest costs plus falling sales is a brutal combo. So we add a simple filter: debt-to-equity below 1, which nudges us toward companies with healthier balance sheets and more breathing room when things get ugly.

Filter 3 – Beta < 1
“Beta” measures how much a stock typically moves compared to the overall market. AllianceBernstein studied global stocks back to the 1970s and found that the least volatile 20% of stocks actually returned about one-third more than the market with roughly 20% less volatility, and they held up better in 7 of the last 8 major downturns. That’s the “lose less in crashes, compound more over time” effect. So we tell the screener: beta under 1, focusing on stocks that historically swing less than the index.

Filter 4 – Return on equity (ROE) > 10%
Return on equity is a simple profitability metric: how much profit a company generates per dollar of shareholder equity. WisdomTree cites research showing that, over almost 60 years, the highest-ROE companies beat the lowest-ROE companies by about 4 percentage points per year on average. High-ROE businesses tend to have strong competitive positions and more resilient earnings. So we add one last filter: ROE above 10% to favor consistently profitable companies that are more likely to sustain dividends and survive downturns.

Also check out my related article:

12 Ways to Make Money with AI

References

NBER recession basics: https://www.nber.org/research/business-cycle-dating
Hartford Funds – 10 Things You Should Know About Recessions: https://www.hartfordfunds.com/dam/en/docs/pub/whitepapers/CCWP079.pdf
Hartford Funds dividends/total return (via InvestorPlace summary): https://investorplace.com/2024/01/3-reasons-to-rely-on-dividend-stocks/
MIT Sloan – Corporate debt and layoffs in the Great Recession: https://mitsloan.mit.edu/press/companies-took-more-debt-run-to-great-recession-later-cut-employment-more-sharply-says-new-research-mit-sloans-xavier-giroud
AllianceBernstein – The Paradox of Low-Risk Stocks: https://www.alliancebernstein.com/apac/en/institutions/insights/investment-insights/the-paradox-of-low-risk-stocks-gaining-more-by-losing-less.html
WisdomTree – Why Quality for the Long Run (ROE spread): https://www.wisdomtree.com/investments/blog/2021/08/24/why-quality-for-the-long-run

The post This Finviz Screener Finds Recession-Proof Stocks — Four Variables Suggested by AI appeared first on Be on the Right Side of Change.

Tools: I used Google Gemini Banana Pro to create the image restorations.

Example 1: New York 1920s

Before:

After:

Example 2: Disco 1980s

Before:

After:

Example 3: Puppy

Before:

After:

Example 4: Czech Immigrant

Before:

After:

The post Restoring Images with Gemini Banana Pro 🍌 (Before/After Examples) appeared first on Be on the Right Side of Change.

This is the first image generated by ChatGPT 5.1:

This one nails the sunny summer vibe with those bright colors, but it’s honestly way too orange and saturated to pass for a real Monet. The generic ‘ART’ and ‘CAFFE’ signs look kind of fake, like something you’d find on a jigsaw puzzle rather than in a museum. It’s a pretty picture, but it feels more like modern home decor than a 19th-century masterpiece.

This is the second image generated by Google Gemini Banana Pro:

This one is much closer to the real deal, capturing that classic hazy look and the specific blue shadows that Monet actually used. It falls apart a bit when you zoom in on the weird, gibberish text and the blurry faces, which are dead giveaways for AI. Still, if you squint, this one definitely wins for feeling the most like an actual historical painting.

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