DEV Community: Marko Korac

The Best AI Tools for Research and Writing: From Notes to a Finished Draft

Marko Korac — Thu, 23 Apr 2026 19:18:21 +0000

AI tools for research and writing have entered a new phase. It is no longer enough for a chatbot to produce a polished paragraph or shorten a long text. Today, a strong tool is expected to do much more: find sources, organize material, separate useful information from noise, preserve context, and ultimately help turn rough notes into a serious first draft.

That matters because the hardest part of writing is rarely just the writing itself. A huge amount of time goes into collecting material, comparing sources, extracting the key points, and trying to shape all of that into a coherent flow. This is exactly where AI is becoming useful — not as a replacement for the writer, but as a working layer between chaos and structure.

That is why the real question is no longer “which AI writes best,” but “which AI supports the whole process best.” Some tools are better for web research, some for working with your own documents, some for structuring ideas, and others for refining the final text.

A good AI research tool is not measured only by the quality of its text

The biggest mistake is judging every tool by how impressive its response sounds in a single prompt. That is part of the experience, but for serious work it is often not the most important part. What matters much more is how the tool gets its information, whether it can work with your own sources, how well it preserves context, and how smoothly it lets you move from research to drafting.

In other words, a polished answer is not the same thing as a strong research workflow. A tool can sound convincing while handling sources poorly. On the other hand, some tools may seem less flashy at first, but are much more useful when you need to process multiple documents, extract key ideas, or keep an entire project in one place.

That is why AI tools for writing are increasingly starting to look like work platforms rather than simple chatbots.

ChatGPT and Claude are strong when research and drafting need to work together

When the goal is to move from an open question to a structured draft, the strongest impression comes from tools that combine research, organization, and iterative writing. ChatGPT and Claude stand out here.

Their advantage is not just that they can generate text, but that they increasingly function like working partners. They can help gather information, summarize material, create structure, reshape tone, and refine drafts through multiple steps. That makes them useful for writers who do not want just “one answer,” but an entire workflow from idea to usable first version.

In practice, these tools are most helpful when you already know what you want to write, but need help turning research into a clear narrative. In those situations, they are excellent for outlines, working headlines, paragraph restructuring, and condensing large sets of notes into a coherent framework.

NotebookLM follows a different logic, and that is exactly why it is useful

Unlike general-purpose chat tools, NotebookLM becomes especially interesting when you are working with your own sources. Its greatest strength is not “creative writing,” but the fact that it stays grounded in the materials you provide and builds the workflow around those sources.

That makes it especially useful for students, analysts, long-form writers, and anyone working with PDFs, notes, presentations, or internal documents. When you already have a pile of material and need to extract meaningful conclusions, this kind of approach often matters more than a general chat interface that jumps between topics.

In practice, NotebookLM may not be the strongest tool for final polish or elegant phrasing, but it is very strong where source organization and material comprehension matter most. That is why it often works best as a middle layer in the process: first organize and understand the material, and only then move to full drafting in another tool.

Gemini is strongest if you already live inside the Google ecosystem

Gemini becomes especially useful when research is part of a broader workflow inside Google’s environment. If you already write in Docs, store files in Drive, work with spreadsheets, and manage projects in Workspace, the advantage is not just the AI output itself, but the fact that the tool is close to where the work already happens.

For many users, that matters more than having the “smartest” model. Not everyone needs the most spectacular answer. Many people simply need a tool that can quickly assist with writing documents, improving drafts, extracting context from existing files, and speeding up everyday work without forcing them to jump across too many tabs.

That is why Gemini makes sense for users who want a practical writing assistant inside an existing workflow, rather than a separate research platform.

Perplexity is excellent for quickly mapping the terrain

When the goal is to enter a topic quickly, see what is being discussed right now, and get a source overview without much setup, Perplexity remains one of the most useful options. Its main strength is speed.

That makes it especially valuable in the early stage of a project: defining the topic, building an initial source map, checking terminology, getting a basic market overview, or identifying relevant articles before diving deeper. It may not be ideal as the central place for a full writing workflow, but it is highly effective as a first step.

Put simply, Perplexity often helps you move quickly from “I do not know where to start” to “now I know which directions are worth exploring.”

For the writing itself, specialized tools still have their place

Even though large AI assistants now do many things, specialized writing tools have not disappeared. Their role has simply changed. Instead of being the main “text generator,” they increasingly handle the final layer: tone, clarity, concision, grammar, rephrasing, and stylistic consistency.

That remains especially important in professional writing, where it is not enough for a text to be informative. It also has to be readable, polished, consistent in tone, and adapted to its audience. This is where tools like Grammarly and similar assistants still make sense, especially when the overall structure of the draft is already in place.

In other words, the best workflow is often not one tool, but a combination: one for research, another for source-based work, a third for drafting, and a fourth for final polish.

So which are the best AI tools for research and writing?

If we look at them by role in the workflow, the most logical breakdown looks like this:

For web research and initial topic mapping: Perplexity and ChatGPT
For working with your own sources and notes: NotebookLM
For longer workflows that move from research into draft: ChatGPT and Claude
For users who live in Google Docs and Drive: Gemini
For final language polishing and style refinement: specialized writing tools like Grammarly

That does not mean there is one universally best choice for everyone. The right tool depends on whether you are starting from scratch, whether you already have your own sources, whether you work alone or in a team, and whether research, structure, or final tone matters most in your process.

Conclusion

AI tools for research and writing become most useful when we stop treating them like instant-text machines. Their real value lies in shortening the path from messy notes to a serious first draft.

That is why the goal today is no longer to find the tool that “writes the best,” but the one that best matches the way you work. For some, that will be a research-first tool. For others, it will be a document-first system. And for many, it will be a flexible assistant that connects several steps in one place.

At least for now, the best result usually does not come from one magical AI product, but from a smart combination of tools, each solving a different part of the job.

Originally published on InfoHelm Tech:

https://tech.infohelm.org/en/ai-tools/ai-research-writing

Token Unlocks: The Hidden Factor That Moves Crypto

Marko Korac — Sat, 11 Apr 2026 16:35:04 +0000

Most crypto investors watch charts.

But few pay attention to one of the most important factors behind price movement: token unlocks.

And that’s often where the real story is.

What are token unlocks?

A token unlock is the moment when previously locked tokens become available for trading.

These tokens usually belong to:

early investors
team members
advisors
treasury funds

They’re released over time through a vesting schedule.

The idea is simple: avoid flooding the market all at once.

But the effect on price can be significant.

Why unlocks matter more than people think

When new tokens enter circulation, supply increases.

And in markets, supply matters.

If demand doesn’t keep up, price pressure appears.

That doesn’t mean every unlock causes a crash.

But it does mean:

👉 more tokens = more potential selling

👉 more selling = more volatility

And that’s something many investors ignore.

Cliff vs Linear unlocks

Not all unlocks are the same.

🔹 Cliff unlock

A large amount of tokens is released at once.

This can create sudden market pressure.

🔹 Linear unlock

Tokens are released gradually over time.

This is usually easier for the market to absorb.

Understanding this difference is key.

It’s not just about how many tokens

The bigger question is:

👉 Who is receiving the tokens?

Early investors → may take profits
Team → may hold or sell
Community rewards → slower distribution

The same unlock size can have very different outcomes depending on allocation.

The mistake most people make

Many investors only look at:

price
market cap
hype

But ignore future supply.

That’s how projects can look strong today…

while hiding significant dilution ahead.

How to track token unlocks

If you want to understand a project better, track:

upcoming unlock events
vesting schedules
allocation breakdown
percentage of locked supply

There are tools that show this clearly — and they’re worth using.

Final thought

Token unlocks don’t predict price.

But they change the conditions under which price moves.

And in crypto, that’s often the difference between reacting late and understanding what’s coming.

If you want a deeper breakdown, I wrote a full version here:
👉 [https://tech.infohelm.org/en/crypto-economy/token-unlocks]

AI Short Films Are No Longer Just Demos — A New Production Logic Is Emerging

Marko Korac — Mon, 23 Mar 2026 11:49:19 +0000

For a while, generative video mostly felt like a tech spectacle: a few impressive seconds, strong aesthetics, plenty of hype, and very little serious production value.

That is changing.

AI video tools are no longer being judged only by whether they can generate a beautiful shot. They are increasingly being judged by whether they can become part of an actual workflow: scene development, iteration, continuity, shot variation, sound work, post-production, and final delivery.

That is the real shift.

The first wave of generative video was about possibility.

The next wave is about production logic.

The important question is no longer can AI generate video?

It is now: can AI become part of a reliable creative pipeline that saves time, lowers cost, and expands what solo creators and small teams can realistically produce?

Why short films matter more than they seem

Short films are probably the most important testing ground for AI-native storytelling right now.

Why?

Because short-form narrative sits in the perfect middle zone:

small enough for experimentation
serious enough to expose workflow weaknesses
flexible enough to absorb stylistic instability
ambitious enough to show whether the tools are actually useful

Feature films demand strict continuity, large budgets, legal clarity, and very high production reliability.

Short films do not remove those requirements entirely, but they reduce them enough to make experimentation practical.

That is why AI short films matter. They are not just a niche art experiment. They are an early signal of where broader video production may be heading.

The real bottleneck is no longer a single good shot

A lot of people still evaluate AI video the wrong way.

They see one impressive scene and assume the medium is basically solved.

But filmmaking does not live or die on one frame.

The real bottleneck is continuity.

Can the system preserve the same character across multiple scenes?

Can it keep the same atmosphere, costume logic, and environment?

Can it support camera language instead of random visual variation?

Can it survive editing without feeling like disconnected clips glued together?

That is where the conversation gets serious.

A beautiful single generation is still a demo.

A sequence with continuity, visual logic, and editorial usability is the beginning of production.

What is actually improving

The biggest improvement is not just raw visual quality. It is the slow movement from generation toward control.

That includes things like:

stronger scene consistency
more reusable character logic
better iteration cycles
integration with editing and audio workflows
faster testing of tone, pacing, and visual direction

This matters because the economic value of AI video is not just “making something cool.”

It is compressing pre-production and iteration.

A solo creator or small team can now test:

visual tone
character design
scene mood
storyboard rhythm
alternate shot directions

much faster than in a traditional pipeline.

That does not eliminate editing, sound design, voice work, or manual correction.

But it can reduce the cost of reaching a usable creative direction.

The market signal is real, even if the category is still early

The AI video generator market is still relatively small compared to the broader generative AI space.

But that does not mean it is irrelevant.

What matters is that the category is growing fast enough to become a real creative infrastructure layer rather than just a novelty.

Here is the broad market picture referenced in the original analysis:

Category	Value	Interpretation
AI video generator market 2025	$716.8M–$788.5M	Early but fast-growing segment
AI video generator market 2026	$847M–$946.4M	Commercial adoption is expanding
Projection for 2033/2034	$3.35B–$3.44B	Strong signal of long-term commercialization
Main bottleneck today	Continuity	Characters, locations, and scenes across multiple shots
Biggest economic shift	Faster iteration	Lower cost of testing an idea or scene

The point is not that AI video is already a mature market.

The point is that it is no longer a fringe experiment.

Three phases of AI video

A useful way to think about the category is through three phases.

1. Demo era

This is the phase most people already know:

isolated shots
virality
visual shock
prompt-driven spectacle

2. Creator workflow era

This is where we are now:

Shorts
music visuals
teaser content
social video
experimental short-form narrative
creator-led iteration

3. Narrative workflow era

This is the phase that is only beginning to emerge:

stable characters
more directorial control
modular scene construction
partial sound integration
more serious post-production use

AI short films currently sit between phases two and three.

They are good enough to leave the lab.

They are not yet stable enough to frictionlessly carry long-form narrative work.

That in-between state is exactly why this moment is so interesting.

What this changes for creators

The biggest misconception is that AI storytelling will reward the people with the best prompts.

I do not think that is true for very long.

As access to generation tools becomes more common, the real advantage shifts elsewhere:

rhythm
scene structure
continuity management
editing sense
sound choices
narrative economy
taste

In other words, AI likely lowers the cost of execution while increasing the value of creative judgment.

That is a huge shift.

If more people can generate visually striking material, then raw generation stops being the differentiator.

The differentiator becomes the ability to shape material into something coherent.

Not a clip.

A film.

What happens over the next 1–3 years

The most likely path is not “AI replaces filmmaking.”

The more realistic path is a layered one.

Scenario 1: creator acceleration

Short-form narrative, teaser pieces, promo videos, music visuals, and stylized experimental clips grow the fastest.

This is where speed matters more than perfect continuity.

Scenario 2: hybrid production becomes normal

AI becomes part of traditional production rather than a total replacement for it.

It gets used for:

ideation
previs
scene testing
inserts
stylized sequences
background generation
transitional narrative blocks

Scenario 3: AI-native short films become their own category

Some creators will stop trying to hide the AI layer and instead build a deliberately AI-native visual language.

Short films are the perfect place for this because they allow more formal freedom than mainstream commercial production.

The biggest risks are still very real

The upside is obvious, but the risks have not disappeared.

The main ones are still:

unstable character continuity
limited directorial precision
uneven output quality
legal and licensing uncertainty
technically impressive but narratively empty results

This last problem may be the most important.

A lot of AI video still looks better than it thinks.

And filmmaking has always punished that gap.

Final thought

The most useful question is not whether AI will replace film.

The better question is:

Which parts of the filmmaking process will AI make cheaper, faster, and more accessible — and who will be first to turn that into a real authorial language?

That is where the future probably lives.

The next generation of AI short films will not belong to the people who simply know how to generate a shot.

It will belong to the people who know how to turn generated shots into cinema.

Originally published on InfoHelm:

https://tech.infohelm.org/en/ai-tools/ai-short-films-analysis

Stablecoins and tokenization in 2026: blockchain is moving from speculation to infrastructure

Marko Korac — Sun, 08 Mar 2026 16:28:00 +0000

Crypto is still often discussed like a market story: price cycles, hype, volatility, regulation, crashes, recovery.

But in 2026, the more interesting story is infrastructure.

The strongest signal is not coming from memecoins or even from Bitcoin itself. It is coming from stablecoins and the early growth of tokenized real-world assets.

That is where blockchain starts to look less like a speculative ecosystem and more like financial software infrastructure.

The key distinction: liquidity layer vs asset layer

A useful way to read the market right now is this:

Stablecoins are becoming the liquidity layer
Tokenized assets are trying to become the asset layer built on top of it

That distinction explains a lot.

Stablecoins already operate at scale. As of March 2026, they represent roughly $301.06B in market value.

By comparison:

On-chain RWA (excluding stablecoins): about $26.47B
Tokenized stocks: about $1.01B

So the structure is obvious: digital money is already here at scale, while tokenized versions of traditional assets are still early.

That does not mean tokenization is weak.

It means the infrastructure is developing in a logical order:

First, you build digital liquidity
Then, you build digital financial instruments on top of it

That is a very normal pattern for platform shifts.

Why stablecoins matter from a systems perspective

Stablecoins are important because they make value transfer programmable.

They can move 24/7, across borders, through APIs, into trading systems, payment flows, smart contracts, treasury operations, and on-chain applications.

From a software perspective, that is a big deal.

Traditional finance is full of delays, intermediaries, settlement windows, regional fragmentation, and operational friction. Stablecoins do not solve everything, but they reduce some of the most obvious bottlenecks.

That is why they are increasingly better understood as financial rails, not just crypto tools.

And once something starts functioning like rails, regulators stop treating it like a niche curiosity.

Tokenization is smaller, but strategically important

The tokenized asset market is still much smaller than the stablecoin market, but it is already relevant.

Why?

Because this is the layer where traditional assets become more software-like.

Tokenized treasuries, funds, private credit, and stocks point toward a model where financial instruments can become:

easier to distribute
easier to fractionalize
easier to integrate into digital systems
potentially cheaper to settle

That does not mean blockchain replaces brokers, banks, exchanges, or custodians overnight.

It means some parts of the existing stack may become more programmable over time.

And that is a much more realistic thesis than “everything moves on-chain at once.”

Regulation is now part of the product story

This is where things get interesting.

The ECB has warned that wider stablecoin adoption could weaken monetary policy transmission and pull funds away from bank deposits. That is a serious macro concern, because bank funding structures still matter to the real economy.

Meanwhile, the US has recently signaled a more technology-neutral direction. On March 5, 2026, US banking regulators said tokenized securities should not face extra capital treatment just because they are tokenized.

That may sound technical, but it matters a lot.

Infrastructure adoption depends on rule clarity.

Institutions do not build serious products on top of regulatory ambiguity unless they absolutely have to.

So the next phase of this market may be shaped less by crypto ideology and more by boring but decisive things:

compliance
settlement architecture
interoperability
product integration
capital treatment
legal clarity

And honestly, that is how infrastructure wins.

What the next 1–3 years probably look like

The most likely scenario is not a total financial reset.

It is selective integration.

Here is the realistic path:

stablecoins keep growing as digital liquidity tools
tokenized treasury and fund products become more common
tokenized equities remain early, but continue proving the model
large institutions adopt where there is clear operational benefit
growth depends heavily on regulatory clarity and integration quality

The biggest risks are not only market volatility.

They are:

regulatory pushback
bank resistance
technical fragmentation
weak interoperability between on-chain and traditional systems

The upside is also not mainly speculative.

The upside is better financial infrastructure.

Closing thought

Stablecoins and tokenization are at different stages, but they are pushing in the same direction.

Stablecoins already look like infrastructure.

Tokenized assets still look early, but increasingly real.

If this trend continues, blockchain in finance will be defined less by speculation and more by whether it can become invisible, reliable plumbing for moving and managing value.

And that is a much bigger story than price action.

This article is based on the full analysis published on InfoHelm Tech, including the complete portal version with visuals and structured breakdown.

The Real Cost of Scaling AI Systems in 2026 (With Data)

Marko Korac — Tue, 03 Mar 2026 11:52:47 +0000

The Real Cost of Scaling AI Systems in 2026 (With Data)

Artificial intelligence is no longer just about model accuracy. In 2026, the real challenge is cost efficiency.

Training and deploying AI systems at scale requires serious infrastructure, and many teams underestimate how quickly expenses grow once a model moves beyond the prototype phase.

Let’s break down where the money actually goes.

1️⃣ Compute: The Largest Expense

Training modern AI models requires massive GPU resources. Even mid-sized models can consume thousands of GPU hours per month.

Here’s a simplified cost illustration:

Model Size	Estimated GPU Hours / Month	Estimated Monthly Cost
Small (≤1B params)	1,200	$8,000
Medium (1–7B params)	4,800	$32,000
Large (7B+)	15,000+	$110,000+

These numbers vary depending on region, cloud provider, and optimization strategy, but the pattern is consistent:

Scaling multiplies cost non-linearly.

2️⃣ Storage and Data Pipelines

Compute is only part of the story.

AI systems require:

Large-scale dataset storage

Continuous data ingestion

Backup and redundancy

High-speed retrieval

Data infrastructure costs can reach 15–25% of total system expenses in production environments.

3️⃣ Inference Costs at Scale

Training is expensive — but inference at scale can be even more costly.

When thousands or millions of users query a model daily:

Latency requirements increase

Redundancy is required

Auto-scaling becomes mandatory

Many companies realize too late that inference costs often exceed training costs over time.

AI Cost Growth Curve (Illustrative)

Visual illustration: InfoHelm

This simplified model shows how costs grow as usage scales. Notice that infrastructure expenses accelerate faster than user growth once real-time inference becomes dominant.

4️⃣ The Hidden Costs

Beyond raw infrastructure, scaling AI includes:

Engineering teams

Monitoring systems

Security layers

Model optimization cycles

Compliance and data governance

The total cost of ownership (TCO) is rarely visible in early-stage discussions.

What This Means for Teams in 2026

If you are building AI systems in 2026:

Budget for inference, not just training

Optimize early (quantization, batching, caching)

Monitor cost per request continuously

Avoid over-scaling before validation

AI is powerful — but financially sensitive.

Final Thought

In 2026, the question is no longer “Can we build this model?”

The real question is:

“Can we afford to run it at scale?”

Originally published on InfoHelm.