Last week, we released several improvements to production based on your reports.

Beta Participation Disclaimer

Some users could not play the video, and a few UI elements were not displaying correctly on smaller screens. These issues are now fixed, and the user experience should be standardised for all.

News Block Improvements

Thanks to your reports, we have made multiple upgrades:

• Increased coverage so more queries show news results
• Improved article sources for better quality
• Removal of duplicated stories
• Improved relevancy

It’s a work in progress, but your previous feedback has already helped us improve the news block.

Autocomplete

A new version is now live. It still needs more data to reach the quality we have set for ourselves and you, but these changes should already show a difference.

A big thank you to everyone who contributed (you know who you are) for providing some of the most helpful feedback.

We are currently working on improvements for:
Spellchecker
WilsonAI
Light Mode

Stay tuned, and please keep the feedback coming. Together we are building something truly special.

What “human-centric” looked like at MyData 

The core idea is direct and practical: people should see where their personal data goes, decide who can use it, and change their mind over time. That’s the heart of the MyData approach. This year’s gathering marked ten years of pushing that idea forward, which the conference site captured well. 

Across keynote halls and hallway chats, two words kept surfacing. Empowerment. Interoperability. For services to earn trust, consent must be meaningful and portable, and systems must work together by default. Although Timpi takes the different path, and never collects any data, we realize in certain industries that is impossible. That’s why the definition of valid consent remains the anchor. It must be freely given, specific, informed, and unambiguous. 

Highlights from stages and hallways 

There were many strong sessions. The most useful moments were the unscripted ones. Networking and open conversations sharing experiences and forming relationships. 

Transparent AI needs trustworthy data 

AI systems inherit the strengths and weaknesses of their data. Provenance, auditability, and bias mitigation came up in several discussions. The throughline was simple. High-integrity inputs produce more reliable outputs. An independent, manipulation-resistant view of the web is not a nice-to-have. It is critical infrastructure for responsible AI, and that is exactly what Timpi will soon provide for the organizations and individuals through our Data API 

Policy and the path to adoption 

Policy conversations are finally converging with deployable tech. Accountability requirements are meeting architectures for consent, portability, and governance. The remedies phase in the United States antitrust case around search underlined the point. Markets need competition and accountability, not default lock-in. See the Department of Justice’s remedies update and this Reuters recap. 

Why this matters for search and AI 

The web’s current incentives have rewarded surveillance and narrow control for a very long time. Recent enforcement and policy work are opening the door to a healthier model. At the same time, Europe’s data-space initiatives show a way to scale trustworthy, governed data flows that respect rights and still support innovation. For builders of AI, that means better inputs. For people, that means services that compete on value, not on how hard they make it to leave. 

How does this link to Timpi’s mission 

Timpi exists to challenge Big Tech’s grip on discovery by delivering a privacy-first alternative: 

• Independent, manipulation-resistant index. Our decentralized index reduces single-point control and the incentives that lead to opaque curation. People should see the web as it is. 

• Privacy-first business model. We reject surveillance ads and data harvesting. The product is the search experience, not the user. 

• Data services for builders. Developers and researchers can access clean, high-integrity web data through API. That supports responsible AI and analytics without compromising privacy. 

This is the blueprint we are building toward in beta as we prepare for wider access. 

Where the industry is heading 

Signals from MyData and the broader market point in the same direction. 

• From platform silos to governed interoperability. Cross-domain data spaces and portable consent are moving from pilots to practice. The European vision for interoperability in data spaces is an early read on how that scales. 

• From opaque ranking to auditable discovery. People are asking tougher questions about provenance, ranking incentives, and training data. Independent indexes and transparent inputs are gaining ground. 

• From surveillance economics to trust economics. As expectations rise and enforcement catches up, services that earn consent and respect choice will outperform those that rely on friction and defaults. 

Timpi × MyData: what’s next 

We will continue to work closely with MyData and its members. The goal is clear. Help move the internet toward a human-centric model. That includes staying active in the community, sharing what we learn from building an independent index, and collaborating where our privacy-first approach can turn principles into production. 

Closing 

The takeaway from Espoo is straightforward. Human-centric data has moved from idea to implementation. The next phase is about doing the work at scale. Consent that moves with data. Interoperable rails that earn trust. Products that deliver real value without surveillance. That aligns with Timpi’s mission and the kind of web we want to help build. 

Ready to help shape a human-centric web? Join the Timpi beta waitlist for early access and updates. 

Artificial intelligence is transforming how we interact with information online. At Timpi, we’ve built Wilson AI Chat—an experimental feature designed to push AI conversations beyond what mainstream tools can deliver. While still in pre-beta, Wilson AI Chat represents the next step in our mission to give people information that is transparent, decentralised, and unmanipulated.

What is Wilson AI Chat?

Wilson AI Chat is Timpi’s conversational AI system, built to let you do more than just search. Unlike traditional search engines—or even standalone AI assistants—Wilson Chat combines:

• Contextual memory for ongoing conversations,
• Real-time information retrieval from the open web, and
• Decentralised intelligence powered by Timpi’s independent index and Synaptron nodes.

It is currently being tested as an experimental feature, with a branded user interface replacing the early developer-only tool.

How Does It Work?

At its core, Wilson AI Chat functions like a conversation. You ask questions, it provides answers, and it remembers the flow of your discussion. But behind the scenes, several advanced techniques make it different from other AI tools:

1. Contextual Memory

Wilson keeps track of your recent exchanges using a sliding memory window. This allows it to respond naturally in conversation, even across multiple questions.

2. Retrieval Augmented Generation (RAG)

When you ask about current events or information not in its core training data, Wilson goes to the internet, retrieves fresh information, and integrates it into its response.

3. GraphRAG – Smarter Connections

Wilson goes beyond a simple keyword search. Using graph-based retrieval, it builds a web of connected concepts—like an investigator connecting pins and strings on a board. This helps uncover relationships between topics that aren’t immediately apparent.

4. Timpi’s Data Advantage

Unlike Big Tech models, Wilson is powered by Timpi’s independent, decentralised search index. Combined with RSS and news feeds, this ensures access to unbiased, transparent data sources.

The Future of Wilson AI Chat

The first release will feel familiar—text-based chat similar to ChatGPT. But Wilson is built for much more:

• Multimodal interaction: interpreting images, video, audio, and diagrams.
• Agentic intelligence: combining multiple AI techniques for more advanced reasoning.
• Decentralised consensus: instead of relying on a single answer, Wilson will consult multiple Synaptron nodes and provide the best (or multiple) perspectives. This protects against bias and manipulation.

Why Wilson AI Chat Matters

Mainstream AI tools are powerful but centralised, opaque, and often biased. Wilson AI Chat is designed to be different:

• Decentralised by design – no single point of control or manipulation.
• Transparent in sources – built on Timpi’s independent index and trusted feeds.
• Resilient against bias – multiple Wilsons can provide multiple perspectives.

By combining privacy-first search with conversational AI, Wilson AI Chat is a glimpse into the future of ethical, open, and decentralised AI.

In the few short years of AI’s exponential growth, some have been quick to announce the demise of traditional search. But have we been too quick to write the epitaph for search engines?

The real question isn’t whether generative AI will kill search or whether search engines will constrain AI. It isn’t even a battle between search and AI, Google vs ChatGPT.

It’s about whether we can build a future of web search where we can harness the benefits of both AI and traditional search to create a truly transformational knowledge engine.

Key takeaways

• AI is changing how we access information, but that doesn’t mean search is dead. While generative AI tools like ChatGPT and Perplexity are becoming more popular, most people still rely on traditional search engines and may even use both tools in tandem.
• AI chatbots struggle with ambiguous queries, context collapse, hallucinations, and citing reliable sources, limiting trust and traceability. Search promotes diversity and user agency while AI risks homogenisation, obscuring debate and amplifying bias.
• Traditional search infrastructure remains the backbone of AI. Language learning models (LLMs) depend on indexed web data from search engines. Without a strong, transparent search infrastructure, AI models can’t provide accurate, up-to-date information.
• The real opportunity lies in combining AI and search. A hybrid model merges AI’s usability with search’s transparency, enabling more intuitive, traceable, and ethical knowledge discovery.

“Nobody Searches Anymore” — But Is That True?

Our digital landscape is undergoing a seismic shift as generative AI reshapes how we access information online.

Many have wondered whether we’re looking at the greatest digital battle of the 2020s: Google vs AI.

After all, the writing may be on the wall for the search monopoly. For the first time in a decade, Google’s search market share dropped below 90% at the end of last year — and it’s stayed there.

A Gartner report predicts that traditional search engines will drop in volume by 25% by 2026, losing ground to AI chatbots. And at a Sydney conference this year, Australian Association of National Advertisers Chief Josh Faulks predicted that ChatGPT will surpass Google search within 4 years.

None of this should come as a surprise, considering 61% of Gen Z and 53% of Millennials already prefer AI tools to traditional search engines, according to a recent Vox Media survey.

AI-powered assistants like ChatGPT, Perplexity, and Claude have undeniably changed how we find information, particularly among younger demographics — and who can blame them?

The curated, conversational responses of generative AI often feel more intuitive than sifting through pages of search results. And that’s not to mention the instant gratification that comes with immediate responses.

We see this in the rise of platforms like Perplexity, which blends AI-powered summarisation with source attribution, or the ubiquitous presence of ChatGPT, functioning as a powerful Q&A engine.

But the numbers oversimplify a complex evolution. Classic search engines remain widely used — at least, for now.

A 2025 survey of 1,500 Americans found that nearly 80% of those questioned still prefer Google or Bing for general information queries.

In the UK, an Online Experiences Tracker (OET) survey of user behaviour found that 90% of British adults still visited search engines in May 2024.

So we’re not seeing a wholesale abandonment of search engines. Instead, most people use AI to complement traditional search, not replace it — and that is where the opportunity lies.

Search isn’t dead. But the interface to knowledge gathering is evolving.

What AI Is Good At — And Where It Fails

AI assistants can summarise vast amounts of information, offer creative suggestions, and even manage contextual reasoning that feels remarkably human — tasks far beyond the scope of traditional search.

Need a quick overview of a complex topic? AI can distil it. Looking for a new recipe with specific ingredients? AI can whip one up (though you may want to proceed with caution).

Generative AI thrives in scenarios requiring rapid summarisation or iterative dialogue, such as brainstorming business ideas or translating complex texts.

However, its current limitations are equally pronounced.

Accuracy remains a persistent challenge

“Hallucinations” — fabricated information presented as fact — are a common occurrence. A Cornell University study found that while ChatGPT performs well on straightforward factual questions, it struggles with complex ‘how’ and ‘why’ queries.

Context collapse is rife

Generative search results on Google have had their own issues, generating what Mike Caufield calls context collapse, “where the different use contexts (jokes, movies, recipes, whatever) get blended into a single context”.

In their book Verified, Mike Caufield and Sam Wineburg argue that neither search nor AI can read your mind — they don’t understand the intent behind your search query.

If you type in an ambiguous query — for example, “how many rocks to eat a day” — you might be after a joke, a debate, the lyrics of a song, or, in this case, a famous satirical Onion article.

A traditional search engine will likely present a diverse list of results, covering most of these search intents. From there, it’s essentially a choose-your-own adventure, matching the right result to your intent.

But AI attempts to interpret the question and provide an answer in summary, with a risk of flattening nuance, obscuring debate, amplifying bias, and delivering misguided advice to serve ‘gravel, geodes, or pebbles with each meal’.

Perspectives are narrowed

Search engines serve as democratic equalisers, granting visibility to niche websites and emerging voices.

On the other hand, AI’s tendency to prioritise popular or synthetically generated content risks creating algorithmic monocultures, where homogenised outputs drown out diverse perspectives.

At its best, search offers neutrality. An ethical, well-designed search engine presents multiple perspectives and lets you choose a path. It’s inherently interactive. AI, on the other hand, assumes authority — often without disclosing where that authority comes from.

And that’s the other limitation at play here: source traceability.

Sources and processes are buried

Sources of AI outputs are often opaque, making it difficult to fact-check the information provided.

And perhaps more critically, the underlying mechanisms that make AI produce answers are frequently what we’ve come to call ‘a black box’. Users have little to no insight into how the generative AI arrived at its conclusions.

AI opacity erodes trust

Misformation, hallucinations, and the lack of transparency all undermine user trust.

According to an Ofcom Online Nation 2024 report, the most popular reason for using a generative AI tool was to find information or content, yet just 18% of UK users aged 16 and above found AI search results reliable (the number being only marginally higher for those aged 8–15 years).

These are fundamental limitations for information gathering.

Search Is Still the Backbone of Every “Smart” Answer

One rather important fact is consistently overlooked in all the noise about AI versus search: AI assistants can’t conjure knowledge out of thin air (although that doesn’t stop them from trying).

These models are trained on vast datasets typically scraped from the indexed web — the very domain of traditional search engines.

Behind every AI response lies the infrastructure of search: the crawling and indexing frameworks pioneered by search technologies. Perplexity, for example, continues to rely on a web crawling system, a retrieval engine, and ranking algorithms to produce its responses.

These are the fundamental systems that catalogue news reports, research, blogs, and forums. They’re what makes knowledge discoverable, verifiable, and current.

They’re essential for AI to function effectively since the accuracy of any AI-generated response hinges on the quality and breadth of the underlying search data.

Without a robust and impartial search infrastructure, even the most intelligent language learning models (LLMs) are severely limited and prone to bias.

The search framework is as important as ever. It’s just less visible. AI may be disrupting search’s front-end user experience, but under the hood, it’s fueled by the very thing it’s supposedly replacing.

The Future Isn’t AI or Search — It’s AI and Search

Despite first appearances, the contest between traditional search engines and AI isn’t a zero-sum battle. LLMs and search engines are the perfect partnership in the making.

AI exposes search’s problems with user experience: who wants to sift through dozens of links when you just need a quick answer? Search exposes AI’s reliability issues: what value do direct answers have when you cannot trust their accuracy?

The future of web search is a hybrid system, an ‘answer engine’ that combines AI’s contextual reasoning and natural language interaction with search’s comprehensive coverage and source transparency — all without compromising neutrality or user control.

It’s a new way of searching that feels more like a dialogue than a list of links, yet reveals the source material for traceability and context.

This fusion is already happening in limited ways.

Perplexity cites sources (though often of inconsistent quality). Google’s AI Overviews attempt to synthesise search results (though often poorly, as we’ve seen). Microsoft’s Copilot integrates web search with AI reasoning (though within a surveillance-heavy ecosystem).

AI and search desperately need what the other provides. AI enhances usability. Search ensures traceability. Together, they could redefine how we interact with knowledge.

In this shifting landscape, we have an opportunity to reimagine a new system — an AI-search partnership designed with privacy, ethics, and user agency at its core.

Human-Centred Search in the AI Age

The conversation around AI and search is often framed as a showdown, but at Timpi, we see a crossroads.

One path leads to more of the same: centralised control, opaque algorithms, and surveillance-capitalist models.

The other offers a radical rethink of how we discover and interact with information online — a model grounded in ethical design, decentralisation, and user sovereignty.

We’re not interested in replicating Big Tech’s race to the bottom. Our infrastructure is decentralised by design, built on a growing, independently maintained web index that can crawl 3 billion pages weekly.

This index powers the Timpi search engine, delivering unfiltered, accurate results through a clean, user-first interface free from intrusive ads and hidden agendas.

We also provide real-time data services that respect privacy and promote fair competition, enabling businesses, researchers, and AI developers to tap into unmanipulated, unbiased, and censorship-resistant datasets.

Crucially, in an era where personal data has become a commodity, we refuse to harvest or sell personal data. Instead, we’re out to prove that genuine intelligence doesn’t require intimate knowledge of people’s personal lives.

Our privacy-first approach ensures you can enjoy the benefits of AI-enhanced search without surrendering your digital rights or becoming targets of profiling and manipulation.

It’s not just about building a web index, dataset, or search engine. We’re building a movement to reclaim the internet as a transparent, equitable space where technology amplifies human values, rather than erodes them.

Make the switch to Timpi today for a more transparent and ethical way to search.

Ever since British mathematician Clive Humby declared that “data is the new oil” in a 2006 speech at a conference for the Association of National Advertisers, it has become dogma in tech circles.

It’s a memorable metaphor; data is powerful and valuable. It fuels innovation, just as oil did in the industrial age. Just as oil powered the industrial revolution, data powers the digital economy.

But like most catchy analogies, this one tells only half the story.

Oil is also volatile. It spills, ignites, and pollutes. And when it leaks, it causes damage that’s hard to undo.

The uncomfortable truth is that viewing data solely as a high-value asset is not just incomplete; it’s dangerously naive. Every byte collected, every user profile built, every behavioural pattern stored represents both opportunity and risk.

If data really is the new oil, it’s time we asked: why are there so many data leaks?

And more importantly, how do we stop them?

Key takeaways

• The “data is oil” analogy is dangerously incomplete. Sure, data fuels innovation in the same way oil powered industry. And like oil, data is volatile. But unlike oil spills, data breaches can impact millions of lives globally in an instant and leave permanent digital scars.
• We’re in the midst of a digital data disaster. High-profile breaches are just the tip of the iceberg. The average cost of a data breach is now nearly US$5 million globally, and public trust is eroding as people feel they have less control over their data.
• Data hoarding amplifies risk. Most companies collect and retain far more personal data than necessary, treating it as an asset to monetise rather than a liability to protect.
• Four principles can prevent ‘data fires’: data minimisation, transparency, governance, and trust.
• Real innovation comes from respecting user rights, building for consent, and prioritising trust. Search engines like Timpi are leading a movement towards a transparent, equitable, and human-first internet, moving beyond surveillance capitalism.

The “Data Is the New Oil” Analogy (and Where It Falls Short)

In his statement, Clive Humby intended to highlight how powerful data can be when it is collected, analysed and processed into insights.

But almost 20 years on,  AI and data expert Nisha Talagala warned that “if we only see data as fuel, we miss how often it spills and who gets burned.”

Unsecured data behaves exactly like oil: explosive when mishandled, toxic when it leaks, and capable of causing damage that lasts for years.

But there are crucial differences between oil and data. For a start, data is highly vulnerable to theft, surveillance, and manipulation.

When oil spills, it affects a specific geographic area. When data leaks, it affects individual lives across the globe instantaneously.

Though undoubtedly difficult, oil spills can be largely contained and cleaned up. Data breaches create permanent digital footprints that can never truly be erased.

When data ‘spills’, it doesn’t just contaminate the environment — it destroys reputations, erodes trust, and fuels what researchers call ‘digital wildfires’ that spread misinformation and chaos.

What’s worse, most companies hoard personal data the same way corporations hoarded oil: to control, monetise, and dominate. They don’t stop to ask: should we be collecting this at all?

We’re Drowning in Data Leaks

You don’t need to be a cybersecurity expert to see the pattern. We’re witnessing a full-scale environmental disaster in the digital realm.

Facebook’s Cambridge Analytica scandal exposed the data of 87 million users, fundamentally altering how we think about social media privacy.

Equifax’s 2017 breach compromised the personal information of 147 million Americans — nearly half the country’s population!

The 2023 MOVEit breach showed how interconnected our digital supply chains have become. By exploiting a single vulnerability in the file transfer software, the CL0P ransomware group compromised more than 2,500 organisations worldwide, from government agencies to major corporations.

These headline-grabbing incidents represent just the tip of the iceberg.

According to IBM’s Cost of a Data Breach 2024 report, the average cost of a data breach has reached US$4.9 million globally, with healthcare and financial services bearing the heaviest burden.

Each breach is a warning sign that:

• Our data infrastructure isn’t secure.
• Surveillance capitalism is fragile by design.
• Consumers are losing trust.

According to Pew Research, 79% of Americans feel they have little or no control over the data companies collect about them. Meanwhile, 71% believe their data is less secure now than it was five years ago.

The system is broken, not because data has no value, but because we’ve treated it like crude oil: extract, refine, and sell.

The Principles That Prevent “Data Fires”

In her Forbes article, Nisha Talagala offers a better framework. To reduce risk and increase trust, we need to flip our assumptions and adopt four guiding principles:

1. Data minimisation

Data minimisation means collecting only what’s essential, when it’s essential, and disposing of it responsibly when you’re done.

It’s a principle enshrined in the General Data Protection Regulation (GDPR) Article 5(1)(c):

“Personal data shall be adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed.”

2. Transparency

Users deserve to know what data is collected, how it’s stored, and who it’s shared with. This isn’t just about compliance; it’s about building trust through honest communication.

3. Governance

Ensure oversight and compliance with ethical data practices through active governance, documentation, and technical controls, such as clear data retention schedules and automated deletion processes.

Governance means implementing robust systems for data protection that build privacy and security into the foundation of digital infrastructure, not bolting it on as an afterthought.

4. Trust

Respect the user, not just the bottom line. People are becoming increasingly sophisticated about their digital rights and increasingly willing to abandon services that don’t respect them.

These principles are particularly crucial for search engines and advertising models. Traditional search platforms have built their business models on extensive data collection and user profiling.

But as surveillance capitalism researcher Shoshana Zuboff explains, this approach treats “private human experience as free raw material for translation into behavioral data”.

The result is a system that prioritises prediction and manipulation over user autonomy.

Instead, these principles should shape the tools we use every day — especially the tools we trust to access knowledge, like search engines.

Because if search is the gateway to the internet, then how it handles your data matters more than ever.

How Timpi Prevents Data Leaks at the Source

At Timpi, we don’t patch holes in the oil pipeline. We refuse to drill in the first place.

We’ve taken a fundamentally different approach to other search engines:

No Personal Data Collection

We don’t harvest, store, or sell your personal data because we don’t collect it in the first place. This isn’t just a policy choice — it’s built into our architecture. We can’t lose what we don’t have, and we can’t misuse what we don’t collect.

No surveillance ads

You won’t see behaviour-based ads. Timpi doesn’t monetise your clicks, searches, or attention span.

Fully decentralised infrastructure

Our web index is built on a decentralised network that can’t be controlled or censored by any single entity.

With the Timpi search engine, your searches are processed through our network of over 1,000 nodes across six continents. This distributed architecture means there’s no central honeypot for hackers to target, no single point of failure that could expose millions of users.

Anonymised by design

Our infrastructure is built with privacy as the default, not an afterthought. We don’t track, profile, or fingerprint users. Every query is processed anonymously and discarded after delivering results.

It’s a radical departure from the Big Tech norm, and that’s the point.

Where mainstream engines see users as data points, we see them as members of a movement: people who want a better way to search, discover, and connect without giving up their privacy.

A Better Future: From Extraction to Empowerment

It’s time to retire the “data is oil” analogy, or at the very least, rethink what it means. Real innovation doesn’t come from extraction, but from empowerment.

The future isn’t about hoarding data behind closed doors or building empires on behavioural surveillance. It’s about:

• Respecting user rights
• Designing for consent, not convenience
• Building trust, not exploiting it

At Timpi, we believe the internet can be transparent, equitable, and human-first. Our privacy-first search engine is just the beginning. We’re building a movement to reclaim the digital commons, one unbiased result at a time.

Wilson AI is the intelligent assistant behind Timpi, a decentralised search engine. When you ask a question on Timpi, Wilson AI helps you understand and explore answers in a clear, conversational way.

It works by combining powerful AI models with up-to-date search data from Timpi’s independent index, offering a fresh, less-biased perspective on the world’s information.

How does Wilson AI work?

At the core of Wilson AI are large language models the same kind of technology used by many modern AI tools. These models are trained to understand natural language and respond in a human-like way.

But unlike some AI systems that rely only on their internal training (which can quickly go out of date), Wilson AI combines that knowledge with real-time data from Timpi’s decentralised search index.

This technique is called retrieval-augmented generation (RAG). It means Wilson AI doesn’t just “guess” answers based on past data it looks up relevant, current information and uses that to help build a better response.

Where does the information come from?

Wilson AI pulls from Timpi’s unique search index. This index is built differently to traditional search engines, it’s decentralised and designed to grow in diversity over time.

That means Wilson AI can access a broader range of viewpoints, giving you answers that are more independent and less filtered than those you might find on mainstream platforms.

How is Wilson AI different from other AI tools?

Wilson AI has three key differences:

1. Up-to-date: It uses fresh data from Timpi’s search index to keep responses current — not just frozen in time like many older AI models.
2. Decentralised sources: Wilson AI doesn’t rely on a single platform’s view of the web. It draws from Timpi’s independent, growing, and more diverse data set.
3. Privacy-first: We don’t store your searches. What you type into Wilson stays private — we don’t track, save, or sell your data.

Your data stays yours

We believe your privacy matters. Wilson AI does not store, log, or share your search queries. Every interaction is processed in the moment and then forgotten — just the way it should be.

Introduction: More Than Just Another Search Engine 

If you’ve heard us say “Timpi is the world’s first decentralized search engine with its own index,” you might’ve raised an eyebrow. After all, what about Brave or DuckDuckGo? Don’t they protect your privacy too? Isn’t Presearch decentralized? 

Fair questions. But here’s the truth: most so-called “decentralized” or “private” search engines still rely on Big Tech indexes — and that changes everything. 

In this post, we’ll break down the real differences between Timpi and the rest. Because what’s under the hood matters — and Timpi is fundamentally built different. 

What Most “Search Engines” Really Are 

Before we get into why Timpi is unique, we need to clear up a big industry misconception: not all search engines are the same. 

Search Aggregators vs. Independent Indexes 

• Search Aggregators (like DuckDuckGo and Brave) don’t crawl the web themselves. They aggregate results from existing indexes like Google, Bing, or Yandex. 

• Web Indexes (like Google or Bing) do the hard work of crawling, storing, and ranking billions of pages. 

Why This Matters 

If your search engine relies on Big Tech’s index, it also inherits their biases, commercial agendas, and privacy tradeoffs — even if the interface looks different. 

Timpi isn’t a wrapper around someone else’s data. We’ve built our own independent index from scratch. And we’ve done it using a decentralized network, which no one else has done at scale. 

What Makes Timpi Different 

1. A Truly Independent Web Index

Only four companies in the world currently operate large-scale independent indexes:
Google, Bing, Yandex, and Baidu. 

Timpi is becoming the fifth — and the only decentralized one. 

That means: 

• No licensing dependencies. 

• No commercial control from competitors. 

• Full ownership of how we crawl, store, and rank the web. 

In past tests, our decentralized node network indexed over 5 billion pages in 48 hours — and that’s just the beginning. 

2. Privacy by Design

Many aggregators claim to be privacy-focused, but if they use Google or Bing’s index, your search data can still be exposed. 

Timpi does it differently: 

• No tracking. We never collect or sell personal data. 

• No behavioral profiling, ad targeting, or manipulation. 

• No surveillance — not even by default. 

We believe in search without exploitation. Your queries are your business. 

3. Decentralized by Infrastructure — and Governance

Other so-called decentralized search engines focus on ad delivery or front-end incentives. 

Timpi decentralizes the entire system: 

• Collector, Guardian, and GeoCore nodes power the infrastructure. 

• The Timpi Autonomous Protocol (TAP) coordinates crawling and indexing. 

• Governance is handled by TAG (Timpi Autonomous Government), where the community helps guide ranking logic and content standards. 

No single company controls the narrative. 

4. Unbiased, Unfiltered Results

The Search Engine Manipulation Effect (SEME) has shown how biased search results can sway elections and shape public opinion. 

Timpi doesn’t play favourites. We: 

• Deliver unmanipulated search results. 

• Expose hidden content other engines de-rank or suppress. 

• Let users influence rankings through transparent community input. 

We’re building a system that shows all sides — not just the ones that pay the most. 

5. Scalable, Sustainable, Low Carbon

Timpi’s decentralized model means: 

• Lower infrastructure costs 

• Smaller carbon footprint 

• More resilience and scalability 

Our nodes run on machines already in use by contributors — no massive data centers or power-hungry server farms. 

6. Rewards That Actually Mean Something

Like Brave and Presearch, we reward users. But with Timpi, rewards go deeper: 

• Users can earn $TIMPI tokens for reviewing or curating content. 

• Node operators are paid to help power the index. 

• Community members help shape the search engine’s future — and benefit from it. 

This is digital infrastructure owned by the people who use it. 

Why We’re Not Competing — We’re Collaborating 

Let’s be honest: 95% of global search traffic goes through Google. The rest of us? We’re building alternatives that give people real choice. 

At Timpi, we don’t see DuckDuckGo, Brave, or Presearch as enemies. In fact, we believe many of them could benefit from moving away from Google’s or Bing’s indexes — and instead plug into ours. 

Our API layer will allow others to use Timpi’s unbiased, decentralized index without building from scratch. 

Summary: Timpi at a Glance 

Feature	Timpi	DuckDuckGo / Brave / Presearch
Owns Web Index	Yes	No (aggregates from Bing/Google)
Decentralized Infrastructure	Yes	No
Decentralized Governance	Yes (via TAG)	No
Unbiased Search Results	Yes	Partial or Unknown
Real User Rewards	Yes ($TIMPI)	Yes (tokens/points)
Privacy-Preserving	Yes	Mostly
Low Carbon, Scalable	Yes	No (centralized infra)

Final Thought: Why It All Matters 

The web should belong to people — not corporations.
Search should empower, not manipulate.
And privacy should be a right, not a privilege. 

Timpi was built to deliver on these principles. And now, as we prepare for full public launch, we’re ready to show the world what decentralized search really means. 

Want to be part of the future of search?
Explore Timpi’s mission or join the community. 

The Persistent Presence of Cookies (and Advanced Tracking): 

Cookies, small data files stored by your browser, serve various purposes. First-party cookies enhance website functionality. However, third-party cookies, often from advertising networks, track your activity across different websites, building a profile of your interests. 

• The Clue: Using a browser extension to visualize cookies can reveal the extent of third-party tracking. A significant number of these cookies after browsing indicates widespread cross-site tracking. 

Beyond cookies, more advanced techniques are employed: 

• Local and Session Storage: These browser storage mechanisms can retain more data about your website interactions for longer periods. 

• IndexedDB: A more sophisticated storage system capable of holding substantial amounts of data, potentially including tracking information. 

• Canvas Fingerprinting: This technique analyzes subtle variations in how your browser renders images to create a unique identifier, even without cookies. 

• Audio Fingerprinting: Similar to canvas fingerprinting, this method analyzes your browser’s audio processing to generate a unique identifier. 

• IP Address Tracking: Your device’s internet address can be used to track your online activity, especially when combined with other data. 

• Device Fingerprinting: Collecting details about your device and browser (user agent, screen resolution, plugins) creates a unique signature for identification. 

• The Clue: While direct detection is difficult, unusual battery drain or increased CPU usage during browsing might suggest resource-intensive tracking scripts are running. Privacy-focused browser extensions aim to block these methods. 

The Trail of Personalized Advertisements: 

Seeing ads that directly relate to your recent online searches or browsing history is a strong indicator of targeted advertising. This personalization relies on tracking your online activity. 

• The Clue: Notice if the advertisements you encounter consistently reflect your recent online behavior. This suggests your data is being used to tailor the ads you see. 

Social Media Data Collection Practices: 

Social media platforms gather extensive data from your interactions within their sites and track your activity across the web through embedded elements on other websites. Also, some platforms might use your data for their other proprietary apps, just like Meta recently started sourcing explicit or implied consent for using your data for their AI models. 

• The Clue: Consider the amount of personal information requested during sign-up and the permissions you grant to connected apps. Pay attention to platform comms or related news in jurisdictions with more lenient privacy laws. More data shared allows for more comprehensive tracking. 

Search Engines and Data Logging: 

Search engines often log your search queries and browsing behavior to personalize results and deliver targeted ads. 

• The Clue: Comparing search results and advertisements in a regular browsing session versus an incognito session or a privacy-focused search engine can highlight the impact of personalized tracking. 

Location Awareness and Device Sensors: 

Websites and apps frequently request access to your location data for various services, but this data can also be used for tracking and targeted advertising. 

• The Clue: Review the location permissions granted to apps on your devices and be cautious about unnecessary location requests from websites. 

Beyond location, device sensors can be subtly employed: 

• Accelerometer and Gyroscope: Motion and orientation data can be analyzed to infer user behavior. 

• Magnetometer: This sensor can potentially be used for indoor positioning and tracking. 

• The Clue: Unexplained significant battery drain when not actively using location-based services could indicate background tracking using these sensors. 

The Possibility of Microphone Access: 

The phenomenon of seeing ads related to recent conversations has raised concerns about devices listening to our discussions. While the extent is debated, microphone access granted to apps could potentially be used for data collection. 

• The Clue: Review microphone permissions for apps and disable access for those that don’t require it for their core functionality. 

Understanding Privacy Policies: 

Privacy policies detail how websites and apps collect and use your data, often including information about their tracking practices. 

• The Clue: While lengthy, reviewing the privacy policies of frequently used services can provide valuable insights into their data handling and tracking methods. 

Steps to Enhance Your Privacy: 

While complete anonymity online is challenging, you can take steps to limit online tracking: 

• Utilize Privacy-Focused Browsers: Browsers designed with privacy in mind offer built-in tracking protection. 

• Employ Privacy-Enhancing Browser Extensions: These tools can block trackers, cookies, and other tracking mechanisms. 

• Consider a Virtual Private Network (VPN): A VPN encrypts your internet traffic and masks your IP address, making tracking more difficult. 

• Adjust Privacy Settings: Review and modify privacy settings on your online accounts and devices to limit data sharing. 

• Regularly Clear Browsing Data: Deleting cookies, cache, and browsing history removes stored tracking information. 

• Manage App Permissions: Carefully review and control the permissions you grant to websites and apps. 

• Opt-Out of Targeted Advertising: Many advertising platforms offer options to opt out of personalized ads. 

• Explore Privacy-Focused Search Engines: These alternatives do not track your searches. 

• Utilize privacy-focused search engines: These alternatives do not track your searches 

Understanding the methods and signs of online tracking empowers you to make informed decisions about your digital privacy and take steps to mitigate unwanted surveillance. Whether you like it or not, somebody’s always watching – and you might want to make their job more difficult. While the digital landscape presents inherent tracking possibilities, adopting privacy-conscious practices can offer a greater degree of control over your online experience. 

Definition:

An “unbiased” search engine is often described as one that presents information neutrally, without favoring any particular source, ideology, or commercial interest. However, in practice, search engines are complex socio-technical systems. Every layer—data collection, indexing, ranking, and presentation—embeds human judgments and trade-offs. 

Technical Reality: 

• Crawling: Search engines use bots (crawlers) to discover web pages. The selection of which sites to crawl and how often is not neutral; it’s based on heuristics (e.g., page popularity, domain authority). 

• Indexing: Not all content is indexed. Algorithms prioritize pages based on freshness, relevance, and technical accessibility (robots.txt, sitemaps). 

• Ranking: Ranking algorithms (like Google’s PageRank) use graph theory to evaluate the importance of a page based on inbound links. This inherently favors well-linked (often well-funded) sites. 

• PageRank Explained (Stanford) 

Verification Principle:
Every claim, source, and ranking signal should be critically evaluated for provenance, accuracy, and authority. Reliable search engines should reference established, peer-reviewed, or otherwise credible sources, and ideally provide transparent citations for users to verify independently[1][2]. 

2. Systemic Biases in Traditional Search Engines

Traditional search engines are subject to multiple, deeply embedded forms of bias—algorithmic, commercial, technical, and societal. These biases arise from both the design of algorithms and the data they operate on, with significant implications for fairness, representation, and user experience.

Algorithmic Bias

• PageRank Bias:
  Mathematically, PageRank assigns higher scores to pages with more inbound links:

                     PR(A)=(1−d)+d∑i=1n PR(Ti)C(Ti)PR(A)=(1−d)+d∑i=1n PR(Ti)C(Ti)

Where $ d $ is the damping factor. Sites with more links from authoritative sources get exponentially more visibility. 

• Personalization Feedback Loops:
  User click data is fed back into ranking models (e.g., via Reinforcement Learning), causing popular results to become even more prominent—a “rich get richer” effect. Commercial and Regulatory Bias

• Ad Placement:
  Paid results (ads) are interleaved with organic results. Eye-tracking studies show users often can’t distinguish ads from organic links, leading to commercial bias in perceived relevance. 

• FTC: Paid Search Disclosures 

• Legal Filtering:
  DMCA and GDPR requests trigger automated content removals. Search engines maintain “removal indices” to filter out URLs flagged for legal reasons. 

• Google Transparency Report

Infrastructure Opacity

• Black-Box Algorithms:
  Proprietary ranking functions and index structures are not publicly documented. This lack of transparency means users and researchers can’t audit or challenge the system’s decisions. 

3. Regulatory and Legal Interventions

• Antitrust Cases:
  In 2024, U.S. courts ruled that Google’s exclusive agreements and ad tech practices constituted illegal monopolization, requiring it to open its index to competitors. 

• Reuters: Google Antitrust Ruling 

• Right to Be Forgotten:
  Under GDPR, individuals can request URL removals from search results, which search engines must process algorithmically and log for compliance. 

• Wikipedia: Right to be forgotten 

4. Web3 Search: Decentralization and Community Governance

Tokenized Search Engines

• Presearch: 
  • Architecture: Uses blockchain to record searches and reward users in PRE tokens. 
  • Governance: PRE token holders vote on search providers, ranking logic, and policy changes via a Decentralized Autonomous Organization (DAO). 
  • Case Study: In 2022, the community voted to remove Google as a fallback provider, demonstrating user-driven governance. 
  • Presearch Blog

Decentralized Indexing Protocols

• Timpi
  • Open Protocol: Anyone can audit the crawling/indexing code or run a node(if you own the specific NFTs). 
  • Incentives: Node operators earn tokens for contributing resources and maintaining data integrity. 
  • Network: Distributed nodes (Guardians, Collectors) crawl and store web data using a peer-to-peer protocol.
  • Timpi Whitepaper 

Forkability and Open Source

• Open Governance:
  If the community disagrees with protocol changes, they can fork the codebase and launch a new network—a feature impossible in proprietary systems like Google. 

5. Centralized vs. Decentralized Search: Technical Comparison

6. Verification, Citations, and Source Reliability: Best Practices

Why Verification Matters:
In both traditional and decentralized search, the integrity of information depends on rigorous verification, transparent citation, and source reliability. Here’s how to ensure this in any context: 

• Cross-Reference All Claims:
  Every fact, statistic, or assertion should be backed by a credible source. Cross-check information across multiple reputable sources to confirm accuracy and consistency[1][2][3]. 

• Evaluate Source Authority:
  Prefer peer-reviewed, governmental, or institutionally recognized sources. Check the author’s credentials and the domain’s reputation—academic (.edu), government (.gov), or established news (.org, .com with a track record) are preferred[1]. 

• Transparency and Citations:
  Reliable systems provide clear citations and references. This allows users to independently verify claims and trace information to its origin[1][2]. 

• Use Reference Management Tools:
  Tools like Mendeley or EndNote help maintain consistency and completeness in citations, ensuring that every in-text citation matches a reference entry and vice versa[2]. 

• Apply Verification Frameworks:
  Use frameworks like the 5Ws (Who, What, Where, Why, How), SMART check, or CRAAP test (Currency, Relevance, Authority, Accuracy, Purpose) to systematically evaluate the credibility of sources[1]. 

• Peer Review and Reputation:
  Favor sources that have undergone peer review or are recognized for their accuracy and reliability. Check for transparency in editorial and review processes[1]. 

7. Why This Matters for Technologists and Activists

• Transparency: Open protocols and rigorous citation practices allow anyone to audit, verify, or challenge search logic and results. 

• Accountability: Community governance and transparent references shift power from corporations to users, making information ecosystems more democratic. 

• Resilience: Decentralized, auditable systems are less vulnerable to censorship, manipulation, or single points of failure. 

• Trust: Reliable search and information systems foster trust by making their data sources, logic, and governance open for scrutiny. 

References 

• Stanford Encyclopedia of Philosophy: Search Engines and Ethics 

• PageRank Explained (Stanford) 

• FTC: Paid Search Disclosures 

• Google Transparency Report 

• Reuters: Google Antitrust Ruling 

• Wikipedia: Right to be forgotten 

• Presearch Blog 

• Timpi Whitepaper 

• Collabwriting: How to verify information online [1] 

• Elsevier: How to write your references quickly and easily [2] 

• AIPRM: Reference list and citation checker [3] 

Bottom Line:
Even the most advanced search engines are shaped by technical, economic, and social forces. Web3 search platforms introduce transparency and community control at the protocol level—making search fairer, more accountable, and open to innovation. The reliability of any information system ultimately depends on robust verification, transparent citations, and rigorous source evaluation. 

• https://blog.collabwriting.com/how-to-verify-information-online-the-ultimate-guide/         

• https://scientific-publishing.webshop.elsevier.com/manuscript-preparation/how-to-write-your-references-quickly-and-easily/       

• https://www.aiprm.com/prompts/copywriting/research/1806717596498919424/    

But these seemingly harmless ads, have a more sinister side, silently inflating the prices you pay and limiting your choices without you realizing it. 

How you ask, well to understand the how we need to how retargeting works. 

How Retargeting Ads Actually Work 

Retargeting, or remarketing, is a digital marketing tactic used be advertisers to show ads to individuals who have previously shown interest in their products or services. 

“Interest” is a used very loosely here. Well executed retargeting feels, personal and, well targeted – its ads from product pages you have visited or items left in your Wishlist. But the reality is businesses are desperate to use any data they can to show you targeted ads that any visit to a site that is set up for retargeting results in those ads following you around the internet, often with products that you simply don’t want. 

Websites that are set up for retargeting carry cookies or tracking pixels placed to record your browsing behaviour, allowing ad networks, and by extension advertisers, to serve you ads tailored to your previous interactions. 

Retargeting as a tactic is so popular as it has significant impact on conversion rates, as targeted consumers are more likely to purchase items they’ve previously considered – and if that sounds a bit obvious, Well it is.

But what are the hidden costs of this approach to the end user? The answer to that lies in pricing. 

Hidden Costs: How Personalised Ads Affect Pricing 

Data is one of the most important cogs in the digital marketing machine. The ad network ecosystem (Google, Meta et al) trade on data, make money from data and change businesses money to access data to place ads. 

Retargeting appeal lies in the relative ease of data capture. Drive traffic to our site, track user behaviour and hey presto we can follow you around with (so-called) tailored adverts ad-nauseum. 

The hidden cost is far more than the anger you might feel from being followed online. When advertisers know exactly what you’re interested in, they can employ pricing strategies designed to capitalise on your demonstrated intent.  

Some of the most common pricing strategies include; 

• Price Discrimination: Different prices shown to different consumers based on their perceived willingness or ability to pay. Orbitz, for instance, once displayed higher-priced hotel options more prominently to Mac users, assuming they would pay more. 

• Selective Pricing: If you’re being retargeted after showing interest, you might never see special discounts or promotional offers that are available to other customers. Retailer often customise prices based on your browsing history 

• Reduced Competition: Targeted retargeting ads can monopolise your attention on a single retailer or brand, making it less likely you’ll shop around for better deals or alternatives. 

Case Studies: Real Examples of Inflated Prices 

• Orbitz and Mac Users: Orbitz steered Mac users towards more expensive hotels after discovering this demographic tended to spend more, subtly manipulating users into pricier options. 
• Staples’ Pricing Strategy: Investigations revealed Staples displayed different prices online depending on customers’ locations – higher prices appeared for users further from competitor stores – clear gouging of people located further away 

The rise of dynamic pricing 

Dynamic pricing, a strategy where prices fluctuate based on various factors, is prevalent in industries like airlines, accommodation booking services, and ticketing platforms is a hot button topic, and the bastard child of traditional retargeting and state of art data-led on site Behavorial targeting. 

These industries utilise user data, browsing behaviour, and retargeting techniques to adjust prices dynamically. 

In the airline industry, dynamic pricing algorithms analyse factors such as demand, booking patterns, and even competitor actions to set ticket prices in real-time. For instance, you start searching for flights, switching between airlines and comparisons sites. Yournbooking intent is tracked and prices adjusted accordingly. Similarly, hotel booking platforms adjust room rates based on occupancy levels, booking windows, and user behaviour, leading to significant price variations for the same room depending on when and how a customer searches 

Ticketing services for events also employ dynamic pricing, where ticket prices can escalate rapidly based on demand and user engagement. For example, concert tickets may initially be offered at a standard price but can surge to higher amounts as demand increases, sometimes resulting in significant price disparities among attendees. 

These practices highlight how dynamic pricing, combined with user data and retargeting, can lead to consumers paying more based on their online behaviour and perceived willingness to pay. This reinforces the importance of transparency and fairness in pricing strategies, as well as the need for consumers to be aware of how their data influences the prices they encounter online. 

How can you make more informed decisions about how your data is tracked and used? 

Transparency and Fairness: The Need for Change 

The introduction of Apple’s App Tracking Transparency (ATT) feature in iOS 14 marked a significant shift in user privacy awareness. This update required apps to obtain explicit permission from users before tracking their activity across other companies’ apps and websites. As a result, users became more conscious of how their data was being used, leading to increased scrutiny of online tracking practices.  

This heightened awareness has prompted other companies to adopt similar measures, such as implementing more transparent privacy policies and offering users greater control over their data. Consequently, users now frequently encounter prompts and messages on websites and apps, informing them about data collection practices and seeking consent for tracking activities.  

While this shift towards greater transparency is good, it still falls way short – who reads those consent prompts on a website anyway?  

I bet you are still seeing loads of a ads? 

What can you do to take action; 

• Clear Cookies and Trackers: Regularly delete cookies or use privacy-focused browser extensions. 

• Use a Privacy-Centric Search Engines: Timpi actively protect your data while providing the only unbiased web index. 

• Support Transparency and Regulation: Advocate for clearer online pricing disclosures and data privacy regulations. 

Search engines are often our starting point, directing us to the sites and solutiomns we seek. So they need to play a bogger role in unmasking how your data is used. Decentralized and privacy-focused search engines, like Timpi, offer users a different online experience. Without tracking user behaviour, Timpi delivers fair and transparent search results and pricing. Reults are contextutal – ads relevant only to your immediate query rather than your entire browsing history (Timpi does not support any form of retargeting) – leveling the playing field, ensuring everyone sees the same price. 

Empower Yourself, Protect Your Wallet 

Awareness is the first step in regaining control over your online shopping experience. Recognising how retargeting manipulates your purchase decisions and inflates prices empowers you to demand better. Choose setting that help control data, choose platforms that prioritise your privacy and transparency, and protect your wallet for data manipulated pricing. 

These algorithms operate by analyzing our search queries and comparing them to a web index containing pages and other internet elements like images. Initially relying on simple keyword matching, modern algorithms employ sophisticated natural language processing to better understand user intent. They aim to grasp the meaning behind our words, striving to provide results that truly answer our questions. 

Beyond keywords, algorithms assess content quality. Factors like expertise, authoritativeness, and trustworthiness (E-A-T) are considered, particularly for topics deemed important. Content depth, originality, and even readability play a role in how pages are ranked. Website authority, often judged by the number and quality of backlinks – essentially digital endorsements from other websites – also significantly influences visibility. User interactions, such as click-through rates and time spent on a page, provide feedback that algorithms use to refine their rankings over time. Furthermore, personalization, taking into account location, search history, and device, tailors results to individual users. 

A Closer Look: The Shaping of Our Information Landscape 

The impact of these algorithms on the information we encounter is considerable. The ranking of search results inherently assigns a perceived value and credibility to top entries. This can influence user perception, leading to a greater trust in and engagement with higher-ranking sites, potentially shaping understanding based on a limited set of visible sources. Less visible information, regardless of its potential value, may be overlooked. 

Personalization, while intended to improve user experience, carries the risk of creating “filter bubbles.” By primarily showing content aligned with past behavior, users may encounter a narrower range of perspectives, potentially reinforcing existing biases. The pursuit of high search rankings has also fostered the field of SEO. While ethical SEO aims to improve website visibility, the focus on algorithmic optimization can sometimes overshadow the creation of truly valuable and balanced content. This can lead to a situation where well-optimized sites gain prominence, potentially at the expense of less technically savvy but more informative sources. 

Moreover, algorithms face the ongoing challenge of combating misinformation. While designed to prioritize authoritative sources, the scale of online content and the evolving tactics of those seeking to spread false information make this a complex task. Even sophisticated algorithms can be susceptible to manipulation. Additionally, the way search results are presented, with features like featured snippets and knowledge panels, can subtly guide user attention and potentially influence understanding. The underlying mechanisms of these algorithms are often opaque, making it difficult for users to fully comprehend the forces shaping their online experiences. 

The Rise of Privacy-Focused Search 

In light of the data-driven nature of many mainstream search engines, a growing number of users are turning to privacy-focused alternatives. These engines prioritize user anonymity and minimize data collection, offering a different approach to information retrieval. But what are the specific benefits of this approach, especially when considering the algorithmic shaping of search results? 

• Reduced Filter Bubble Effect: Privacy-focused search engines, by design, limit the amount of personal data they collect and store. This means they have less information to use for personalization, which, as discussed earlier, can contribute to the creation of filter bubbles. By showing a more standardized set of results, these engines can expose users to a wider range of perspectives and information, potentially mitigating the echo-chamber effect. 

• Mitigating Algorithmic Bias: Algorithms are trained on data, and if that data reflects existing societal biases, those biases can be inadvertently amplified in search results. Because privacy-focused search engines collect less user data, they rely less on personalized profiles and historical search behavior. This can help to reduce the influence of biased training data and potentially lead to more objective search results. 

• Enhanced User Autonomy: When users are not being tracked and profiled, they have a greater sense of control over their online experience. They are free to explore information without the feeling their every click and query is being monitored and analyzed. This can lead to a more empowering and less manipulative search experience. 

• Less Susceptibility to Manipulation: The extensive user data collected by some search engines can be a valuable target for manipulation. Whether it’s for targeted advertising or to influence public opinion, the availability of detailed user profiles can make it easier to tailor and deliver manipulative content. Privacy-focused search engines, with their limited data collection, reduce this risk. 

• Long-Term Data Security: Data collection poses risks not only to the individual user but also to society. Large data sets can be compromised, misused, or exploited. Privacy-focused search engines, which collect minimal data, inherently limit the potential damage from such breaches or misuse. 

It’s important to note that privacy-focused search engines still employ algorithms to rank and display results. However, their algorithms often rely more on contextual factors and less on personal data. This can lead to a different search experience, one that prioritizes a broader range of information and reduces the potential for personalized manipulation. 

Search matters 

Search engine algorithms are dynamic systems, constantly evolving through advancements in AI, machine learning, and ongoing efforts to improve user experience and address the challenges of misinformation. Recognizing their fundamental role in shaping our access to information is a crucial step towards navigating the digital world more effectively and fostering a more informed online experience. 

Reclaiming Control in a Centralised World 

Blockchain promised decentralization. But somewhere along the way, many networks began to look a lot like the systems they were meant to disrupt—concentrated power, opaque governance, and closed ecosystems. 

Enter Neutaro: a purpose-built, independent blockchain designed to power ethical Web3 applications with real-world utility. From decentralized search to censorship-resistant infrastructure, Neutaro is building the foundation for a freer, more open digital future. 

Why We Partnered with Neutaro 

In the race to dominate Web3, many blockchains have prioritized speculation over substance. Networks are increasingly optimized for trading volume, yield farming, and hype cycles—leaving mission-driven builders to either compromise or be sidelined. 

Neutaro was created for a different purpose. It’s not just another Layer 1. It’s an enabler of systems that challenge Big Tech dominance, restore user privacy, and uphold information freedom. 

We needed an ecosystem where: 

• Data sovereignty is real, not theoretical. 

• Decentralized applications (dApps) aren’t bottlenecked by centralized validators. 

• Infrastructure decisions align with societal values—not VC roadmaps. 

So we Partnered with Neutaro. 

The Foundation: Cosmos SDK, Customized for Purpose 

Neutaro is built using the Cosmos SDK, known for its modular architecture and interoperability. But Neutaro has gone beyond the default. 

Neutaro is tailored to optimize: 

• Data ownership and transparency 

• Validator decentralization with meaningful governance stakes 

• Modular tooling that supports real-world applications like decentralized search, data access, and private identity systems 

This customization ensures that Neutaro isn’t a generic chain—it’s fit-for-purpose infrastructure designed to power value-aligned innovation. 

What Makes Neutaro Different 

1. Independence by Design

Neutaro is not controlled by a single company or foundation with opaque decision-making. Governance is decentralized, with real influence from node operators and stakeholders. 

This ensures: 

• No single entity controls the network 

• Policy decisions are transparent and community-led 

• The blockchain can evolve in alignment with its mission—not market trends 

2. Infrastructure-First, Not Token-First

Neutaro isn’t a token pump. Its native token, $NTMPI, is designed to power utility—not speculation. 

It underpins: 

• Node incentives for powering decentralized services (search, data access, AI infrastructure) 

• Validator staking to secure the network 

• In-app utility for decentralized applications built on Neutaro 

This aligns economic incentives with meaningful infrastructure contributions, not quick gains. 

3. Built to Power Timpi—and More

Neutaro was born to support Timpi—a decentralized search engine breaking free from Big Tech’s influence. But the possibilities don’t stop there. 

The blockchain is already enabling: 

• Privacy-focused search infrastructure 

• Reward mechanisms for decentralized data contribution 

• Transparent advertising models that don’t exploit personal information 

And its architecture is ready for other use cases: AI data integrity, content moderation, decentralized governance systems, and beyond. 

Governance That Respects Community 

Neutaro features an evolving on-chain governance model designed to reflect the values of its participants. Key features include: 

• Decentralized validator participation, with strong incentives for geographical and ideological diversity 

• Community proposal mechanisms, allowing stakeholders to propose protocol changes, funding initiatives, or ecosystem upgrades 

• Transparent voting records, ensuring accountability and auditability 

This isn’t governance theatre—it’s real, community-driven decision-making. 

The Role of $NTMPI 

$NTMPI is more than just a token—it’s the lifeblood of the Neutaro ecosystem. 

Its roles include: 

• Staking: Securing the network by delegating to validators 

• Gas fees: Powering transactions and dApp usage 

• Node rewards: Compensating participants for contributing compute, storage, and bandwidth 

• Access NFTs: Tied to node ownership and operations, issued via Timpi’s decentralized hardware network 

This utility-first approach to tokenomics ensures long-term alignment with infrastructure health and user value. 

Designed for Builders Who Value Freedom 

Neutaro is ideal for developers building applications that need: 

• Decentralized infrastructure without centralized bottlenecks 

• Custom governance aligned with community or nation-state values 

• Transparent access to data—search, analytics, identity—without compromising privacy 

If you’re building in Web3 and want your app to remain truly sovereign, Neutaro is your home. 

Built to Scale with Integrity 

Neutaro isn’t chasing vanity metrics, its focused on: 

• Quality over quantity of dApps 

• Long-term security and resilience of the network 

• Interoperability with meaningful partners—not fragmented hype ecosystems 

The infrastructure is ready. The principles are clear. The invitation is open. 

Explore Neutaro further at www.neutaro.com
Visit https://timpi.io to learn more about the search ecosystem and how to get involved.