DEV Community: Hitendra

Why AI Search Should Be Visual, Not Just Text

Hitendra — Tue, 24 Mar 2026 05:50:47 +0000

A lot of AI search products still treat text as the final answer.

You search something, get a summary, maybe a few links, and that is supposed to be enough.

But the more I think about how people actually explore information, the more obvious it feels that text alone is not enough.

Some things are easier to understand when you read them.

Some things are easier to understand when you see them.

That is a big part of how I think about building Rixx.

Rixx is an AI search and research engine, and one of the areas I find most exciting is making search feel more visual, interactive, and useful beyond plain text answers.

Text is useful, but not always the best format

Text works well for a lot of things.

If you want a definition, a quick explanation, or a short summary, text is usually enough.

But many searches are not like that.

If someone searches for a country like USA, they usually do not want only a paragraph. They also want to see the place.

If someone searches for Mount Everest, visuals immediately add context that text alone cannot.

If someone looks up a person, movie, game, or famous place, images often help them understand the subject faster than a written answer.

That is why I think AI search should not stop at “here is some text.”

It should also help users see the topic.

Visuals make search feel more natural

A big reason traditional search works so well is that it gives people different ways to explore information.

You can read.

You can click.

You can compare.

You can open images.

You can move through different formats depending on what you need.

AI search should do the same, but better.

Instead of only generating a block of text, it should know when a topic would be more useful with:

Images
Charts
Graphs
Interactive visuals
Richer topic views

That is one of the directions we are pushing with Rixx.

Some topics are meant to be seen

This becomes really obvious with certain kinds of searches.

A few examples:

Countries like USA or Japan are easier to explore with images and visual context
Places like Mount Everest, Paris, or the Grand Canyon are naturally visual
People are often easier to recognize and understand with images
Movies and games benefit from posters, artwork, and visual identity
Trends, stats, and comparisons are often much clearer as charts

In all of these cases, plain text helps, but visuals make the search experience much better.

That is why I think the future of AI search is not only about better text generation. It is also about better visual understanding.

Charts and graphs can explain faster than paragraphs

This matters a lot.

Sometimes a chart can explain in five seconds what would take five paragraphs to explain in text.

The same is true for graphs and interactive data views.

If someone is trying to understand growth, comparisons, timelines, patterns, or relationships, visual outputs are often the fastest and clearest format.

That is another reason I think AI search needs to evolve beyond text-heavy answers.

With Rixx, one of the things we care about is making search more useful through interactive charts, graphs, and richer visual outputs, not just summaries.

Visual search makes exploration more engaging

Search should not feel like reading one final answer and leaving.

Good search should invite exploration.

You search one topic, then another.

You compare things.

You look deeper.

You move from answer to context.

You go from knowing a little to understanding much more.

Visuals help a lot with that.

They make search feel less flat and more alive.

That is especially true when the user is exploring things like places, public figures, movies, games, or broad topics where images and visual structure create instant clarity.

AI search should know when to show, not just tell

For me, this is the real point.

A good AI search engine should not only answer the query.

It should also understand the best way to present the answer.

Sometimes that means text.

Sometimes that means a chart.

Sometimes that means a graph.

Sometimes that means images.

Sometimes that means a richer visual layout that helps the user explore.

That is a big part of what makes this space interesting right now.

We are moving from search that only retrieves information to search that can present information in a more useful format.

That is a core part of the thinking behind Rixx.

Building a more visual search experience

As we continue building Rixx, one thing that feels increasingly clear is this:

better answers are important, but better presentation matters too.

If the answer is good but the format is wrong, the experience still feels incomplete.

That is why I think visual exploration should be a real part of AI search, not just an extra feature added on the side.

When someone searches a person, place, country, movie, game, or a data-heavy topic, the product should be able to respond in a way that feels natural to that subject.

That is the kind of experience I want Rixx to keep pushing toward.

Final thoughts

AI search is already changing how people find information.

But I think one of the next big improvements is simple:

search should not only tell you things. It should also show you things.

That means better visuals, better charts, better graphs, and a more interactive way to explore information.

That is one of the ideas shaping what we are building with Rixx.

You can check out what we’re building at Rixx.

Rethinking AI Search: What We’re Building with Rixx

Hitendra — Tue, 24 Mar 2026 05:38:51 +0000

Building Rixx: An AI Search Engine for Research, Visual Exploration, and Content Creation

A lot of AI search tools today are already impressive.

You type a question, get an answer in seconds, maybe see a few sources, and move on.

But after using these tools more seriously, I kept feeling the same thing:

search still feels incomplete.

In real workflows, people do not just want a quick answer. They want to dig deeper, verify what they are seeing, compare model outputs, save useful context, export results, share chats, and sometimes even turn a research session into a full blog post.

That is a big part of why I started building Rixx.

Rixx is an AI search and research engine built around a simple idea: getting an answer is only one part of the experience. The real value comes from helping people search better, verify more deeply, explore information visually, and turn research into something useful.

Why I felt this space still needed work

The current wave of AI search products has clearly changed how people discover information.

But a lot of them still feel focused on the first step only: answering the query.

That is useful, of course. But for many users, the next steps matter just as much:

Is this answer actually strong?
Can I verify it more deeply?
What happens if I use another model?
Can I export or share this easily?
Can I turn this into a blog post or useful output?
Can I understand this visually instead of reading only text?
Can the system remember context and become more personalized over time?

Those questions became the starting point for Rixx.

What we are building with Rixx

At its core, Rixx is an AI-powered search engine.

But the goal is not to stop at “ask a question, get a reply.”

We are building toward a richer experience that includes:

Better AI-generated answers
The ability to choose different AI models
Deep verification workflows
Personalized memory
Exports
Shareable chats
Visual exploration
Interactive charts and graphs
Turning sessions into blog posts
Workspaces for organizing research more deeply

For me, that is where AI search gets really interesting.

Because at that point, it starts becoming more than just a faster interface for finding information. It becomes a tool for actual understanding and creation.

Better answers matter more than just speed

Speed is nice, but better answers matter more.

A fast answer that misses nuance, skips context, or sounds confident while being weak is not actually that helpful.

That is why one of the main things we care about in Rixx is improving answer quality.

Sometimes users want a quick overview.

Sometimes they want a more reasoned answer.

Sometimes they want to go much deeper and verify what they are seeing.

Those are different needs, and AI search should be able to support them.

Model choice should be normal

One thing that feels increasingly obvious is that different models are good at different things.

Some are faster.

Some reason better.

Some write better.

Some feel stronger for deeper research.

So instead of treating one model as the answer to everything, I think users should be able to choose what works best for their task.

That flexibility is a big part of the thinking behind Rixx.

Verification is not optional anymore

This is probably one of the biggest things.

AI can sound convincing very easily. That is not the same as being right.

So a search product cannot just focus on generation. It also has to think seriously about verification.

That does not only mean showing information. It means helping users go deeper, check claims, and feel more confident about what they are reading.

That is a major part of how I think about building Rixx.

Search should feel more visual

Another thing I find interesting is how often plain text is not the best format.

Sometimes a chart explains something faster than paragraphs.

Sometimes a visual view of a place is more useful than a written summary.

Sometimes understanding a person, topic, or concept becomes easier when the information feels interactive.

That is why visual exploration is also part of the product direction.

I want Rixx to make it easier to not only read information, but also see it in a more meaningful way through things like interactive charts, graphs, and richer visual outputs.

Research should lead to something useful

This part matters a lot to me.

A good research session should not just end with “answer generated.”

It should lead somewhere.

Sometimes that means exporting the result.

Sometimes it means sharing the chat.

Sometimes it means continuing the work later.

And sometimes it means turning that session into a full blog post.

That is one of the things I find most exciting about Rixx: the idea that search and research can flow directly into content creation instead of forcing users to start over in another tool.

Workspaces are part of the bigger picture

We also see workspaces as an important part of where the product is going.

A lot of research is not one search and done. It is ongoing. It has context, follow-ups, useful outputs, and connected ideas.

That is where workspaces start making a lot of sense.

They are not the only part of Rixx, but they are an important part of the bigger vision, especially for users who want to organize research more deeply over time.

This is something we are continuing to build out further.

What I think the next phase of AI search looks like

For me, the next phase is not just:

“Here is your answer.”

It is more like:

Here is a better answer
Here is a way to verify it
Here is another model if you want a different approach
Here is the visual version
Here is the export
Here is the share link
Here is the blog version
Here is a workspace to continue from later

That is the direction we are pushing with Rixx.

Final thoughts

There is already a lot happening in AI search, and that is a good thing.

But I still think there is a lot of room to improve how these tools actually feel in real use.

Not just faster.

Not just more polished.

But more useful.

That is what I am trying to build with Rixx.

If you are building in AI search, using these tools heavily, or thinking about where this space is going next, I would genuinely love to hear your thoughts.

You can check out what we’re building at Rixx.

The Rise of AI Powered Search Engines: Which One Really Wins in 2025?

Hitendra — Tue, 25 Nov 2025 04:09:51 +0000

The rise of AI powered search engines. Which one really comes out on top in 2025.

Search is changing pretty quickly these days.

People are shifting from chasing keywords and clicking through those old blue links. They want direct answers right away. Summaries help too. Insights make sense. Citations add trust. Even full research reports show up now.

I took the last few weeks to try out the big names in AI first search engines. These are not just chatbots. They mix AI thinking with actual indexed results from the web.

Some things really caught my eye.

1. Perplexity

Perplexity stands as the most solid player in AI search at the moment.
It runs fast. Answers feel accurate. Citations back everything up.
This setup works great for quick overviews. Fact checks come easy.
It struggles a bit with multi step tasks. Deep research can fall flat.

Experience score sits at 8.5 out of 10.

2. AndiSearch

AndiSearch keeps things simple and focused on privacy. Speed surprises in a good way.
No tracking happens here. The interface stays clean.
It reminds me of DuckDuckGo. An AI boost layers on top.
Research does not fit well. Content creation workflows miss the mark. Complex questions trip it up.

Experience score lands at 7.8 out of 10.

3. You.com

You.com offers a lot of flexibility in AI search. Customization options help. Tool integrations tie in nicely.
The interface uses modular blocks for results.
Developers like it for tinkering around.
The UI sometimes overwhelms. Inconsistencies pop up now and then.

Experience score hits 7.9 out of 10.

4. Google AI Overviews

Google AI Overviews blends old school search with generative AI.
It draws from the biggest index in the world.
Broad topics work fine. Basic queries get handled.
The AI part feels added on. Not built from the ground up for this.
Hallucinations show up more than they should.

Experience score comes in at 6.5 out of 10.

5. Rixx

Rixx brings a new take on AI search engines. It suits serious research and creation work.
Rixx goes beyond a simple AI overlay on search.
It acts like a complete research setup. Search leads to analysis. Extraction follows. Collaboration joins in. Publishing wraps it up.
Several features set Rixx apart.
AI helps with results. Citations come included.
Upload options cover text. Images work too. Video and audio fit. PDFs upload easily.
Deep Research Mode layers queries. Extractors pull out details.
Users pick the model. Claude options exist. OpenAI shows up. Gemini and Mistral join the list.
Any search session turns into a shareable blog post. One click does it.
Follow up queries keep memory intact. Context stays preserved.
Collaborative workspaces help teams.
Rixx feels less like plain search. It serves as a thinking partner. A research console adds power. Publishing layers finish the job.

Experience score reaches 9.4 out of 10. This comes early. Impressions stay strong.

Status remains invite only right now.

Final thoughts on AI search.

This field goes past just grabbing answers.
Reasoning plays a key role.
Context sticks around over time.
Traceability keeps things reliable.
Building with results opens new doors.
Perplexity started the push forward.

Trained a CycleGAN Model for Real ↔ Painting Image Style Transfer Using PyTorch

Hitendra — Fri, 25 Jul 2025 05:43:56 +0000

I built and trained a custom CycleGAN model for real ↔ painting style transfer using PyTorch.

It transforms real-world photos into painting-style images — and back — using unpaired datasets.

🛠️ Key Features

ResNet-based Generator + PatchGAN Discriminator
Cycle-consistency, identity, and adversarial losses
Full training, validation, and testing pipeline
Inference on single images or full directories

🔗 GitHub Repo

👉 github.com/hitendraa/cyclegan

Includes full code, setup instructions, and example outputs.

📖 Based On

Original paper: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

Reference repo: junyanz/pytorch-CycleGAN-and-pix2pix

Let me know what you think or if you want to try extending it!