The rules are simple: two real people need to have a real conversation that can be clearly understood. But in practice, there are a few common mistakes that can make an otherwise long call unusable for an AI dataset.

This guide explains what makes a Hive Call eligible, what can cause a call to be rejected, and what you can do to give your conversation the best chance of being approved.

Make Sure Two Real People Are Actually on the Call

This sounds obvious, but it is one of the most important requirements.

A qualifying Hive Call must involve two different people speaking to each other in real time. Both participants should be part of the conversation and respond naturally to what the other person is saying.

One person using two phones, switching between devices, changing their voice, or attempting to create both sides of the conversation is not considered a valid two-person dialogue.

The same applies to prerecorded material or AI-generated voices. Hive Calls is designed to collect real human interaction, so the conversation needs to happen live between the participants.

Best practice: call someone you would normally have a conversation with and simply talk to each other as you usually would.

Have a Real Two-Way Conversation

A long call does not automatically mean a useful call.

Both participants should actively contribute. One person speaking for most of the call while the other only occasionally says “yes” or “okay” may provide very little useful conversational interaction.

You do not need to divide speaking time exactly 50/50. Real conversations are not that structured. One person may tell a longer story while the other asks questions, reacts, comments, and then takes the conversation in another direction.

What matters is that there is a genuine back-and-forth exchange.

A useful dialogue contains responses that are connected to what the other person just said. Ask follow-up questions, share your own experiences, disagree, explain something, tell a story, or naturally move from one subject to another.

Do not try to make the conversation sound like a recording task.

Just talk.

Keep the Conversation Meaningful

You can talk about almost anything on Hive Calls.

Daily life, travel, movies, relationships, hobbies, technology, work, food, sports, plans for the weekend, places you have visited, things you have recently learned, or stories from your life can all create useful conversations.

What matters is that the dialogue has meaning.

Repeating random words, reading unrelated sentences, continuously repeating the same question and answer, or trying to fill time without actually communicating may cause the call to be marked as not eligible.

A simple way to think about it is this:

Would two people realistically have this conversation outside Hive Calls?

If the answer is yes, you are on the right track.

Important Note: Please Avoid Conversations About DataHive, Hive Calls, and Earning Points

First, we want to say thank you.

We genuinely appreciate how interested many of you are in DataHive, Hive Calls, our missions, points, and how the platform works. We have seen many users spend their calls discussing the app, asking each other how to earn points, explaining missions, or talking about DataHive itself.

However, these conversations are not particularly useful for the AI companies that use conversational datasets.

The goal of Hive Calls is to capture natural conversations about the wider world, not conversations created specifically around the platform collecting them.

Because of this, from now on, calls focused primarily on DataHive, Hive Calls, points, rewards, how to earn points, mission mechanics, or how to use the app will be marked as not eligible.

You can still learn about the app through our guides, FAQ, and Welcome Hive Call, where you can speak with AI and ask questions about how Hive Calls works.

For calls with other users, please choose another topic.

Talk about something you would normally discuss with a friend, colleague, or family member. Those conversations are much more valuable for building real-world conversational AI datasets.

You Can Speak Any Language

Hive Calls is not limited to English.

You can speak in any language that both participants can naturally communicate in.

You do not need to switch to English just because you think it will make the call more likely to qualify. In fact, speaking in the language you normally use with the other participant can make the conversation more natural.

The important part is not which language you choose. It is whether the dialogue is real, understandable, and meaningful.

Audio Quality Still Matters

Hive Calls is designed for real-world conversations, so you do not need studio-quality audio.

However, both voices still need to be understandable.

Our processing systems, including automatic speech recognition, need to be able to distinguish the words being spoken. If the recording contains extremely loud music, traffic, wind, television, other people speaking nearby, heavy distortion, or very unclear speech, parts of the conversation may become unusable.

Try to avoid environments where you have to shout to hear each other.

You should also make sure your microphone is not covered, your connection is stable enough for conversation, and the phone is positioned normally while you speak.

Some background sound is completely natural. The problem begins when the background becomes louder or clearer than the conversation itself.

Don’t Leave the Call Running Just to Increase the Duration

Only genuine conversation is useful.

Long periods of silence, phones left connected while nobody is speaking, or deliberately stretching the call without meaningful interaction do not make the recording more valuable.

The goal is not simply to generate more minutes.

The goal is to capture real conversational minutes.

If your conversation naturally ends, it is better to finish the call than to leave it running.

Don’t Read Scripts or Play Prepared Material

Hive Calls is different from a scripted voice-recording mission.

You are not expected to prepare sentences in advance, read an article aloud, play a video, or follow a written dialogue.

The value of these calls comes from spontaneity: one participant says something, the other hears it, thinks about it, and responds.

Reading prepared material removes much of that interaction.

If you are struggling to continue the conversation, ask a new question or move naturally to another subject instead of reading something to fill the time.

React to the Other Person

One of the easiest ways to make a call more natural is also one of the most obvious: listen to the person you are talking to.

If they tell a story, ask about something they mentioned. If you disagree, explain why. If something is funny, react naturally. If you remember a similar experience, share it.

Real conversations contain interruptions, follow-up questions, changes of direction, laughter, pauses, and spontaneous reactions.

You do not need to manufacture any of these things. They appear naturally when two people are actually paying attention to each other.

That is exactly the kind of interaction Hive Calls is designed to capture.

Before You Start Your Next Call

You do not need to memorize a complicated list of rules.

Just remember the fundamentals:

• Call another real person.
• Both people should actively participate.
• Have a meaningful, natural conversation.
• Speak in any language you both feel comfortable using.
• Choose almost any topic, but avoid conversations focused on DataHive, Hive Calls, points, rewards, or how to earn on the platform.
• Make sure both voices are clear enough to understand.
• Do not use prerecorded audio, AI voices, scripts, or one person speaking from two devices.
• End the call when the real conversation ends.

Every call goes through processing, quality, authenticity, and eligibility checks before it can be approved.

Following these recommendations does not mean you need to make your conversation perfect. In fact, natural speech is rarely perfect.

People hesitate. They interrupt. They change their minds. They laugh. They pause to think.

That is exactly the point.

The best Hive Calls are not the ones that sound professionally recorded.

They are the ones that sound like two real people actually talking to each other.

That is why we created Hive Calls, a new mobile app from DataHive built specifically around real human conversations. Hive Calls allows DataHive users to call each other directly, talk naturally about the subjects they choose, and contribute qualifying conversations that can be used to build datasets for training, testing, and improving modern voice AI systems.

Instead of asking people to perform for a microphone, Hive Calls is designed around something much more familiar: simply having a conversation.

Why Voice AI Needs Real Conversations

For years, many speech datasets followed a relatively simple format: give a speaker a sentence, ask them to read it aloud, record the result, and attach a transcript. This type of data remains extremely useful for technologies such as automatic speech recognition and text-to-speech. But conversational AI requires something more.

A real dialogue contains information that is difficult to recreate with scripted recordings. Speakers react to each other in real time, leave pauses of different lengths, interrupt, correct themselves, use short responses such as “yeah” or “mm-hm,” change their tone, and adjust their speaking style depending on the conversation. Two recordings can contain almost identical words while communicating very different things because of timing, emotion, emphasis, or context.

This matters increasingly as voice technology moves beyond the traditional pipeline of speech recognition → text response → synthesized speech. New speech-to-speech systems and real-time voice assistants are expected not only to understand what a person says, but also to know when to respond, when to wait, how to handle interruptions, and how to maintain the natural rhythm of a conversation.

To build systems like these, AI companies need more than clean sentences. They need real conversational speech data.

Hive Calls was created to help collect exactly that.

How Hive Calls Works

Hive Calls makes contributing conversational audio simple. After installing the app, users can connect with other eligible DataHive users and start a voice call directly from their phone. The conversation does not need to follow a fixed script or a predefined list of questions. Friends can talk about travel, work, hobbies, entertainment, daily life, personal experiences, or any other subject that leads to a genuine two-way discussion.

With the consent of everyone participating in the call, qualifying conversations may be recorded and processed by DataHive. Calls are then checked for factors including duration, audio quality, eligibility, authenticity, and other quality drequirements before they can be accepted.

The key requirement is simple: the call must contain a real conversation between real people. Silence, leaving a call running without meaningful interaction, prerecorded audio, or AI-generated voices do not count as valid conversational data unless a specific task explicitly allows otherwise.

Eligible users can currently receive rewards for up to 40 qualifying minutes per day, and both participants can earn when the call meets the required conditions. Hive Calls also allows users to invite people they already know, making it easier to have conversations that feel natural rather than staged.

From a Phone Call to an AI Training Dataset

A conversation collected through Hive Calls can contain much more useful information than a transcript alone. The audio preserves not only the words being spoken, but also the relationship between the speakers: who responds first, where pauses occur, how quickly people react, when their speech overlaps, how their tone changes, and how the conversation develops over time.

These signals can be valuable for training and evaluating voice assistants, conversational AI, speech-to-speech models, automatic speech recognition systems, and other voice technologies.

For example, imagine two people discussing a holiday plan. One speaker begins explaining an idea, the other interrupts with a question, both laugh, and then the conversation changes direction. A transcript can capture the words, but much of the interaction exists in the audio itself. The timing, interruption, hesitation, laughter, and change in speaking style are all part of how humans understand one another.

This is one reason conversational datasets are becoming increasingly important for the next generation of voice AI. Systems that are expected to communicate naturally need training data that reflects how conversations actually happen outside a recording studio.

Approved datasets created from qualifying Hive Calls conversations may be licensed to AI and technology companies under data-use agreements. Account information such as phone numbers, email addresses, and payment information is not normally included in customer datasets. The focus is on the speech and conversational data itself.

Capturing the Way People Actually Speak

Another advantage of collecting real conversations is variability. People do not speak the same way in every situation. The same person may sound completely different when speaking with a friend than when reading a prepared sentence. Vocabulary changes, sentences become less structured, words get shortened, speakers hesitate more often, and emotion becomes part of the interaction.

This variability is particularly important for multilingual voice AI. Real conversations may include different accents, regional vocabulary, slang, code-switching, borrowed words, or pronunciation patterns that rarely appear in scripted datasets. The more conversational data reflects these real-world behaviors, the better it can represent the environments in which future voice systems will actually be used.

Hive Calls therefore expands the types of speech data DataHive can collect. Traditional recording missions remain useful for controlled tasks, but conversational calls introduce another layer: the interaction between speakers.

Instead of collecting only individual voices, we can now capture how those voices respond to one another.

Why Authenticity Matters

A larger dataset is not automatically a better dataset. For conversational AI, the usefulness of a recording depends heavily on whether the interaction is genuine.

If two people simply read lines from a script, leave a phone connected in silence, or play prerecorded speech, the resulting audio may be long but contain very little useful conversational information. A real dialogue is valuable because neither participant knows exactly what the other person will say next. Each speaker has to listen, understand, react, and continue the conversation.

That unpredictability is part of the data.

It creates natural pauses, spontaneous responses, corrections, interruptions, and shifts in tone that are difficult to reproduce artificially. Hive Calls is designed around preserving these characteristics while applying quality and fraud-prevention checks to submitted conversations.

The goal is not simply to collect more audio hours.

It is to collect better examples of how humans actually communicate.

A New Way to Contribute to DataHive

Hive Calls represents a new direction for DataHive. Until now, many contributions to speech datasets have naturally focused on individual recordings: reading sentences, responding to prompts, describing a topic, or completing other structured voice tasks.

Hive Calls adds another format entirely.

Users can now contribute by having ordinary conversations with other people, while helping create the kind of conversational datasets that modern voice AI increasingly needs. For participants, that means less time following scripts and more freedom to simply talk. For AI developers, it can mean access to speech data containing the dynamics of real communication rather than only isolated utterances.

As the Hive Calls community grows, the same infrastructure can help capture a wider range of languages, accents, conversation styles, devices, and real-world speaking conditions. That creates the potential for increasingly diverse conversational datasets for training and evaluating future voice systems.

Download Hive Calls and Start Talking

The next generation of voice AI will need to do more than recognize words. It will need to understand conversations: when people pause, how they react, when they interrupt, how their tone changes, and how meaning develops between multiple speakers over time.

That requires better conversational data.

Hive Calls is available now.

Install the app, invite someone you know or connect with another eligible DataHive user, and start a natural conversation. Your calls can become part of the datasets helping voice AI learn how people actually speak to each other.

Real conversations. Better voice AI.

Generic speech-to-text — and even much larger or paid systems — breaks down on dialectal, emotional Arabic. We show the fix is data, not model size: a targeted 4-dialect corpus, added to public data, roughly halves error on the covered dialects, with a controlled ablation isolating the corpus as the cause — and the gain is largest exactly where off-the-shelf models are weakest. We benchmark honestly against Meta’s 7B omniASR and the Deepgram API: on the speech we serve, our model leads; on generic data, frontier systems are competitive. The differentiator is the right data plus rigorous, non-cherry-picked evaluation.

The problem: where Arabic ASR breaks

Arabic ASR is not one problem. Modern systems handle Modern Standard Arabic (MSA) — broadcast, formal speech — reasonably well. They fail on spontaneous, dialectal, emotional speech, which is most of how people actually talk. Three things make it hard:

• Distance from MSA. Dialects like Moroccan Darija diverge sharply from MSA in vocabulary, morphology and phonology; MSA-trained models have little to draw on.
• No standard orthography. Dialects have no agreed way to be written. The same word is spelled several ways, and clitics (و/ب/ال) agglutinate onto words. This wrecks word-level error metrics (§6).
• Emotion shifts the acoustics. Angry, sad or happy speech changes rate, pitch and articulation — further off-distribution for models trained on neutral, read speech.

On our held-out target test (proprietary, speaker/script/text-disjoint), stock Whisper-large-v3 scores WER 0.589 / CER 0.226 — unusable for production. On Moroccan Darija it is far worse.

The corpus: built to test the hypothesis

To find out whether targeted data — not a bigger model — is what closes the gap, we needed speech that off-the-shelf systems have never seen and that public datasets simply do not cover: dialectal, emotional, conversational Arabic. So we built it ourselves. Through DataHive’s data-collection platform we recruited native speakers of each target dialect and ran a structured recording pipeline — scripted prompts across ten everyday domains (customer service, healthcare, finance, education, travel, emergency, family, daily life, shopping, technology), each performed in four emotional registers, with multiple takes and per-clip quality control.

The result is our proprietary 4-dialect × 4-emotion corpus — native-speaker recordings balanced across emotions and ten everyday domains, spanning 11,000+ distinct scripts, with speaker-, script-, and text-disjoint splits so the numbers later in this paper measure genuine generalization to unseen speakers and sentences, not memorization. And because it comes from a repeatable collection platform rather than a one-off dataset, it is an asset we extend to new dialects, domains, and emotions on demand — the coverage grows to fit the use case.

The approach: it’s the data, not the model

We fine-tune Whisper-large-v3 with LoRA on a data mixture, and run a controlled ablation to isolate what drives quality. Two recipes differ by exactly one thing:

• Recipe A — public only: open Arabic datasets (SADA + MASC).
• Recipe B — public + ours: the same public data plus our proprietary 4-dialect × 4-emotion corpus.

The public mixture is held byte-identical between A and B, so any A→B difference is our corpus, nothing else. Every model is scored on the same held-out target test — only the training data changes.

Results: the corpus is the driver

Headline, target test, leaderboard normalizer:

Public data alone does not solve the dialectal problem — adding more public Arabic (A) leaves word error at stock and only trims character error modestly. On the target domain the corpus is what moves the needle: B cuts WER by 56% and CER by 66% versus stock, and the corpus contribution itself — B − A, public mix held identical — is large and significant on every dialect (overall −0.127 CER / −0.340 WER; −0.094 / −0.226 under CAMeL). This is a target-domain result — §5 shows it does not hold on neutral, out-of-domain Saudi data.

The value of targeted data scales with where models fail

Target-test WER by dialect (lower = better). The corpus’s biggest wins land on Moroccan Darija — exactly where off-the-shelf collapses — and smaller wins on the MSA-adjacent Saudi dialects, where stock is bad but not catastrophic. Note the overall figures are micro-averaged and Moroccan + Najdi are 77% of the clips; the unweighted macro-average is a touch milder (stock WER 0.519 → B 0.237, −54%).

How we stack up against the field

We compared our corpus-tuned model against two strong baselines on the same target test: Meta’s omniASR-LLM-7B (a 7-billion-parameter open model) and Deepgram (a commercial paid API with dialect-specific Arabic models).

Overall WER on our target test — vs the field

Both baselines beat stock Whisper — omniASR-7B and Deepgram are genuinely strong — yet our corpus on plain Whisper beats them by a wide margin (overall −0.198 WER vs omniASR, significant).

Deepgram is a competent Arabic recognizer, but on our domain the corpus-tuned model cuts its error on every dialect by roughly a third to a half depending on dialect and normalizer (about −36% overall on CER under the convention-robust metric, up to ~50% on the leaderboard metric).

The honest caveat: domain, not universal superiority

This is the speech we serve — our dialects, our convention. Step outside it and the wins narrow or vanish, and we report that plainly:

• Neutral third-party data (Meta’s public Omnilingual). The corpus advantage concentrates the same way — large on Moroccan (B CER 0.219 vs stock 0.398, CAMeL) — but near-zero on MSA-adjacent Saudi: under the convention-robust metric public-only training (A) matches or slightly beats the corpus (B) there, and B’s edge over stock is small.
• External Moroccan. We beat omniASR-7B on adiren7 (YouTube Moroccan), but lose to it on MDER-MA — another external Moroccan set — by a wide, significant margin (B CER 0.660 vs 0.514, CAMeL), and roughly tie it overall on the neutral Casablanca benchmark (a shade behind on its Moroccan subset). The corpus makes Whisper better on our Moroccan speech, not Moroccan in general.

Our advantage is specialization for the target use case, not a universally better Arabic model. Saying so plainly is the point (§6).

How we measure (and why you can trust it)

Dialectal Arabic ASR is unusually easy to measure badly. We make three deliberate choices.

We report CER, not just WER. Because dialects agglutinate clitics and have no standard spelling, word-level WER swings with segmentation and orthography, while character-level CER is robust. We report both and read CER as the trustworthy signal.

We account for orthographic convention. Each system writes its own spelling. Scored against references in our convention, our model has a home advantage; against a third party’s convention it is penalized. Re-scoring under a convention-robust normalization (CAMeL-standard) shows that a sizable part of any model’s apparent lead is spelling, not quality — and the effect cuts both ways.

Under a stricter, convention-robust metric — the lead shrinks but holds

Overall CER. Everyone’s number drops once spelling is folded away — but the ranking holds and B stays lowest. Our CER lead over omniASR-7B goes from −0.063 to −0.039 (still significant); over stock it barely moves (−0.149 → −0.134): that gap is real quality, not orthography.

We don’t cherry-pick. We report the comparisons where we win and the ones where we don’t, under more than one metric and normalizer. For a buyer evaluating a vendor, that transparency is the signal: the numbers are real.

What this means for you

• If your use case is dialectal or emotional Arabic, off-the-shelf and even paid/frontier ASR will under-deliver — most on the hardest dialects.
• Targeted data is the moat. A well-collected, on-target dialectal corpus produces gains that model size and mountains of public data do not — quality and targeting, not raw volume, move the needle.
• The gain is measurable and targeted: it is largest on the dialects the corpus covers, and concentrated where generic models are weakest.
• DataHive brings both the proprietary data and a rigorous, honest evaluation method to prove the lift on your domain.

Appendix — methodology & notes

• Model: Whisper-large-v3 + LoRA (PEFT). Corpus share 40% of the training mixture.
• Ablation: Recipe A redistributes the corpus slot to public data, holding the public mix byte-identical to B; the only variable is our corpus.
• Test: held-out, speaker + script + text-disjoint; ~3,354 clips across four dialects. Headline “overall” figures are micro-averages over clips (Moroccan + Najdi are 77% of the test); we also report the 4-dialect macro-average, which is less sensitive to the speaker mix.
• Metrics: WER + CER under the Open Universal Arabic ASR Leaderboard normalizer and a CAMeL-standard convention-robust normalizer; 95% CIs from paired bootstrap.
• Baselines: stock Whisper-large-v3; public-only fine-tune; Meta omniASR-LLM-7B; Deepgram. External models are not scored on the Omnilingual dialect sets (omniASR’s own training data). Decoding note: Deepgram was given dialect-specific language codes (ar-SA / ar-JO / ar-MA); omniASR-7B ran through its standard fairseq2 pipeline without per-clip dialect language hints — a variable we did not fully control.

These aren’t arbitrary rules – they’re requirements of the AI datasets we’re collecting. Every recording is validated before payment, and recordings that don’t meet the project’s requirements cannot be accepted.

We’ll continue updating this guide as we launch new missions and learn from common mistakes.

Be Honest About Your Language Skills

Some missions require native speakers, fluent speakers, or people from specific regions with particular accents.

Please answer these questions honestly.

Every submission goes through a validation process. If we determine that you intentionally provided false information about your language skills or region to qualify for a mission, your submission will be rejected and your account may be permanently banned.

Being honest helps us match you with missions you’re actually eligible for – and gives you the best chance of getting approved.

Check Your Microphone Before You Start

Before recording, you’ll be asked to test your microphone.

Take a few seconds to make sure:

• Your microphone is working properly.
• Your voice is clear.
• There are no crackling sounds or technical issues.
• The recording volume isn’t too low or distorted.

A good recording starts with good audio quality.

Bad examples that will not be accepted

Pay Attention to Background Noise

Each mission explains whether background noise is expected.

• If the mission specifically asks for environmental or background noise, record in the appropriate environment.
• If the mission doesn’t mention background noise, record in a quiet place.

Unnecessary noise from TVs, music, conversations, traffic, or wind is one of the most common reasons recordings are rejected.

Bad examples that will not be accepted

Follow the Script Exactly

Please say exactly what’s written.

Do not:

• add extra words,
• skip words,
• change the wording,
• or improvise.

Our datasets are carefully aligned with the expected transcript. Even small changes can make a recording unusable.

Bad examples that will not be accepted

“Not really interested.”

“Is this an AI?”

If you make a mistake

Don’t continue speaking or repeat the sentence in the same recording.

Instead:

• Stop the recording.
• Start a new one.
• Record the sentence again from the beginning.

Unfortunately, we can’t manually edit every recording, so recordings with multiple attempts in a single audio file cannot be accepted.

Bad examples that will not be accepted

“I’m—honestly, I am not interested in that at all.”

“Uh, sorry, what company did you say again? I missed it the first time.”

Speak Naturally When the Mission Requires It

Some missions aren’t about reading text – they’re about sounding like a real person in a real situation.

For example, in our recent Robo Calls project, participants needed to answer the phone naturally, as if someone had actually called them.

A natural response sounds like something you would say in everyday life. It can be mumbled, bored, tired, or irritated. Or maybe you just answer the phone really fast because you’re completely out of time.

A rejected response often sounds like someone reading from a screen.

If the mission asks for natural speech, try to imagine the situation before you start recording. Natural speech makes a huge difference.

Today, we’re excited to share everything you need to know about staking SOL with DataHive AI. By delegating your SOL to our validator, you earn standard Solana staking rewards and Data Points that will contribute to our upcoming $DATA token airdrop. It’s a simple, powerful way to earn while supporting decentralized AI infrastructure.

Why Stake SOL with DataHive AI?

When you stake with us, you get the best of both worlds:

1. Reliable SOL Staking Rewards
2. $DATA Points for the Airdrop – Every epoch, you accumulate points based on your staked amount. These points directly contribute to your allocation in our future $DATA token airdrop.
3. Ecosystem Boost – Staking unlocks a higher multiplier on your Hive points for platform activity. It also allows you to connect more devices to maximize your overall point farming across the DataHive AI network.
4. Support the Mission – Your stake supports the growth and maintenance of the Solana network, and also contributes to the development of ethical data collection for AI.

We designed this system so that whether you stake a small amount or a larger position, you can meaningfully participate in both yield generation and our points program.

How $DATA Points Are Calculated

Points are awarded per Solana epoch (roughly every 2 days) and scale with the amount of SOL you stake. We use a straightforward formula:

y = kx + b

Where:

• y = $DATA Points per epoch
• x = amount of SOL staked
• k and b = coefficients

Important note: The coefficients k and b may be adjusted over time as we optimize the program based on network conditions and community feedback. The rates below reflect current parameters.

Here’s what you can expect at different stake levels (yearly estimates based on ~182 epochs per year):

Solana has updated its staking requirements, raising the minimum amount to 1 SOL. We will maintain point accruals for all users who staked 0.5 SOL prior to this update. Regrettably, new users are now unable to stake less than 1 SOL.

These points add to those you earn from running our extension and app, creating a powerful synergy for active community members.

How to Start Staking SOL on DataHive AI (Step-by-Step)

Getting started is quick and secure:

1. Visit the Staking Page on Dashboard.
2. Connect Your Wallet — Click “Connect Wallet” and choose your preferred Solana wallet (Phantom, Solflare, Backpack, etc.).
3. Choose Your Amount — Enter or slide to the amount of SOL you want to stake. A small one-time rent fee of ~0.0023 SOL will be required to create the stake account.
4. Review & Confirm — Double-check the details (your SOL will be delegated to our validator: `2ve7kgjvaDZhMPq2nXhvGLno8sPJ8BAEdCvza384PyC8`). Approve the transaction in your wallet.
5. Track Your Progress — Once active, monitor your staked SOL, earned $DATA Points, and projected rewards directly in your DataHive AI dashboard.

You can unstake at any time following standard Solana epoch timing.

Join the DataHive Community

Staking SOL with DataHive AI is more than just earning yield — it’s a way to actively participate in building the decentralized data layer for AI while positioning yourself for our $DATA airdrop.

We’re proud to offer this feature to our community and can’t wait to see the impact it will have as more of you join.

Ready to start earning?

Stake your SOL now on Dashboard.

**Questions?
**Drop them in Discord. We will be happy to help!

True intelligence is born where patterns end and “Black Swans” rare, boundary, and structurally complex events begin.

The Trap of Predictability: Why Models Stop Learning

To understand how AI learns, we have to look at the Error Gradient.

Think of a neural network as a system of billions of “levers” (weights). Training is the process of adjusting these levers until the output is correct.

• The Pattern Trap: When a model sees a typical example, its prediction aligns perfectly with reality. The “Loss” (error) is near zero. Since there is no error, the algorithm decides nothing needs to change. The levers stay put. The model isn’t learning; it is just confirming what it already knows.
• The Anomaly Spike: A rare event creates a massive error. This sends a high-voltage signal through the Backpropagation algorithm. This shock forces the system to recalculate connections even in the deepest layers of the network.

The takeaway: A rare event is the only moment a neural network truly wakes up and evolves.

Decision Boundaries: Mapping the Edge of Reason

In machine learning, the Decision Boundary is the invisible line in the model’s mind that separates one concept from another.

Imagine a map where thousands of dots representing “Safe Driving” are on one side and “Collision” on the other. If you only provide the model with “clean” data, it will draw a very crude, straight line between them.

However, if you add Edge Cases (those rare dots that sit right on the fence), the AI is forced to draw a surgical, nuanced boundary.

• Rare events act as anchors that map the complex landscape of reality.
• Without them, a model remains “brittle.” Any slight deviation in the real world will cause a catastrophic failure because the model never learned where the true limits lie.

The Long Tail and the Robustness Problem

The real world does not follow a simple bell curve; it lives in the Long Tail.

• The Head: Common situations that make up 95% of data.
• The Tail: The 5% of rare cases such as extreme weather for autonomous vehicles, rare medical pathologies, or hyper-specific linguistic dialects.

The irony of AI is that mistakes in that 5% “tail” are often the most expensive or dangerous. If a model has not seen the tail during training, it lacks robustness (the ability to remain stable in chaos). A high-quality dataset should not just be large; it must be mathematically balanced so the model treats rare cases with the same gravity as the norm.

From “Big Data” to “Smart Data”: The DataHive AI Perspective

For companies building SOTA (State-of-the-Art) models, the focus has shifted from raw volume to Data Curation. Raw data scraped from the web is often saturated with noise and “empty calories” (redundant information that adds no value to the gradient).

At DataHive AI, we bridge the gap between raw information and model intelligence. The value of a modern dataset lies in:

• Identifying High-Entropy Examples: Finding the specific Edge Cases that actually force a model to improve.
• Precision Labeling of Anomalies: Ensuring the model understands the “why” behind the exception, not just the rule.
• Balancing the Distribution: Artificially augmenting the “Long Tail” so that models are prepared for the 1% of events that matter most.

Summary

A model’s intelligence is not measured by how many patterns it can repeat, but by its accuracy at the points of maximum uncertainty. One professionally curated dataset with a high concentration of “hard” examples is more effective than a petabyte of uniform logs.

What Is the DataHive AI Referral Program?

The DataHive AI Referral Program allows users to earn a percentage of rewards every time they invite others to join DataHive AI. It’s simple — share your referral link, and when your friends start contributing data through the DataHive AI app or browser extension, you’ll automatically receive a share of their earnings. The Hive has no limits. Invite your network, grow the swarm.

The Reward Structure

You can earn up to 35% in total referral rewards across three levels:

• Level 1 — 20%: Earn 20% of the Data Points earned by people you directly invite.
• Level 2 — 10%: Earn 10% from the Data Points earned by users your referrals invite.
• Level 3 — 5%: Earn 5% from the Data Points earned by the next level of the network.

This tiered structure rewards long-term community builders who help the Hive expand sustainably.

In addition to percentage-based rewards across three levels, you can also earn fixed Hive Point bonuses:

• 1,250 points — for installing the DataHive AI browser extension
• 1,250 points — for installing the DataHive AI mobile app
• 2,500 points — when your direct referral becomes Active (100 hours)
• 1,250 points — when a referred user activates their own account (only if they joined via a referral link)

These bonuses help new users start earning instantly and reward long-term contributors who expand the Hive.

Example: You invite a friend who earns 10,000 Data Points. You receive 2,000 points. If your friend invites another user, you’ll also earn 10% from that person’s Data Points — and 5% from the next level.

Pending Referral

Pending Referrals are users you’ve invited who already bring you rewards from their earned Data Points but haven’t yet reached full activation. They remain pending until they keep their extension or mobile app active for 100 hours.

Once they hit 100 hours of work time, they become Active Referrals, and you receive an additional 1,250 points for each directly invited user who reaches this milestone.

What are DataHive AI points?

DataHive rewards users through two main earning systems: Data Points and Hive Points, both designed to value your real contribution to the network.

• Data Points: Earned for the amount of data your device helps collect. Each Job represents a data task completed by your device. For example, scraping a web page, video, or other content. The Jobs you complete automatically convert into Data Points, allowing you to track your daily contribution to the Hive. Simply keep your extension or mobile app active, and the more data your device processes, the more you earn.
• Hive Points: Earned for staying online and keeping your browser extension or mobile app active. The longer you stay connected, the more Hive Points you accumulate rewarding your uptime and helping the Hive stay strong.

How to Get Started

Getting started takes less than a minute:

1. Getting started takes less than a minute:
2. Sign up or log in to your account.
3. Install the DataHive AI browser extension and receive 1,250 points.
4. Install the DataHive mobile app and receive another 1,250 points.
5. Share your referral link and invite your network.
6. Track your rewards as your Hive grows.
7. Your referral earnings, activation bonuses, and installation rewards are calculated automatically.

Your referral earnings are automatically calculated and credited to your account.

Track Everything in Your Dashboard

Your Referral Dashboard gives you a complete overview of your network’s activity and rewards in real time. Each column helps you understand your Hive’s performance at a glance:

• Place: shows your referral’s ranking based on total Data Points earned.
• Description: displays a partially hidden email.
• Created: indicates when the referral joined DataHive using your link.
• Status: shows whether the referral is Pending or Active (after 100 hours of activity).
• Tier: displays which level the referral belongs to — Level 1, 2, or 3.
• Data Points: the total amount of data collected by that user.
• Your Reward: the share of Data Points you’ve earned from this referral’s activity.

By default, the table is ranked based on your maximum earned reward. The dashboard makes it easy to track your progress, understand your earnings, and see how your Hive grows day by day.

How can you boost your Data points?

You can increase your Data Points by staying active and engaging with the Hive beyond simply running the extension or app. Participate in data labeling and creation tasks. This improves dataset quality and boosts your earnings. Complete daily quests to earn extra rewards for consistent activity. Join special events that offer limited-time multipliers and exclusive bonuses. The more you contribute to the network, the faster your Data Points and your impact will grow. Read more here.

Why Join the Referral Program?

The Referral Program is one of the easiest ways to boost your rewards and help DataHive AI grow at the same time. By inviting new users, you expand the network’s capacity to collect and label high-quality data for AI and you get a share of every Data Point they earn. The more active contributors you bring in, the faster your own rewards grow. It’s a simple way to turn your community into a source of passive income while supporting the decentralized data ecosystem that powers the next generation of AI models.

Frequently Asked Questions

Who can join the referral program?

Anyone with a DataHive account can share their referral link and start earning rewards.

How are referral rewards calculated?

You earn a percentage (20%/10%/5%) of the Data points that your referred users generate by contributing data.

Do I get points for installing the extension or mobile app?

Yes.
1,250 points for installing the DataHive browser extension.
1,250 points for installing the DataHive mobile app

Do referrals get activation bonuses?

Yes.
Your direct referral earns 2,500 points when their account becomes activated (after 100 hours of uptime), but only if they joined using someone’s referral link.
You, as the referrer, receive 1,250 points when your referral reaches 100 hours and becomes Active.

When will I receive my rewards?

Rewards are distributed automatically and reflected in your dashboard in real time.

Is there a limit to how many people I can invite?

No limits. The more users you bring into the Hive, the more you can earn.

How can I track my network’s activity?

You can view your referral table, earnings, and user activity etc. from your Referral Dashboard.

A new data bottleneck has emerged. Compute is abundant. Open-source models evolve quickly. What is scarce now is high quality, fresh, hard-to-reach data. This shift is creating the next major wave in AI infrastructure: decentralized data layers powered by users.

The Limits of Traditional Data Scrapers

Centralized crawlers were not designed for the world of TikTok videos, infinite scroll pages, and fast-changing content. Their limitations are becoming more obvious every year.

1. Limited access to dynamic content
Most cloud scrapers cannot execute scripts at scale or simulate real user behavior. This means they miss data that loads dynamically, such as social feeds, short videos, or content behind interactions.

2. Expensive infrastructure
Cloud scraping at scale requires heavy server resources. As models demand more training data, infrastructure costs rise exponentially.

3. Fragile against blocking
Single IP ranges and centralized traffic patterns are easily detected and rate-limited by websites, which stops data collection or makes it inconsistent.

4. Shallow datasets
Static HTML snapshots no longer reflect how users see content. Modern AI models need richer context than centralized scrapers can provide.

Why Decentralized Data Layers Are the Future

Decentralized networks flip the model completely. Instead of relying on a few servers, data is collected by thousands of user devices that provide bandwidth, local execution, and real user-like access. This architecture solves core problems that cloud scrapers cannot overcome.

1. Real-world execution
User devices can load dynamic content exactly as real people do. This enables reliable collection of TikTok videos, short-form feeds, Amazon logged-in reviews, Instagram content, and other high-value data.

2. Massive scale
A distributed network grows naturally with every user who installs an extension or mobile app. This unlocks web-scale data without needing more centralized hardware.

3. Lower cost per dataset
User-powered networks remove the need for expensive servers. This reduces dataset costs by 10 to 20 times, making large-scale data acquisition economically sustainable.

4. Higher content diversity
Different locations, devices, and browsing environments produce richer datasets. This diversity is crucial for reducing model bias and improving generalization.

5. Resilience to blocking
Decentralized traffic looks identical to normal user behavior. This makes the network far harder to block and allows consistent access to data that cloud crawlers cannot reach.

Beyond Collection: Labeling at Source

The next evolution of decentralized data layers is not just about capturing content. It is about preparing datasets directly at the source through integrated labeling.

Traditional labeling pipelines require outsourcing, manual work, and repeated processing steps. A decentralized network can label and validate data in real time, turning raw content into ready-to-train datasets with much less overhead.

This approach brings AI teams closer to a unified data engine that combines:

• collection at scale
• filtering and deduplication
• labeling and annotation
• delivery in ready-to-train formats

It also significantly shortens the time from data request to model training.

How DataHive AI Enables This New Model

DataHive AI has built a fully decentralized data layer designed for AI companies. The platform uses browser extensions and mobile apps to collect real-world web data at massive scale, including dynamic and multimodal sources. It then cleans, deduplicates, labels, and prepares datasets that are aligned with how AI labs train models.

The Coming Shift in AI Infrastructure

The AI market is entering a phase where high quality data is the primary competitive edge. Teams that rely on traditional cloud scrapers will increasingly fall behind. Dynamic content, short-form video, multimodal formats, and behind-login data are quickly becoming the new standard for model training.

Decentralized data layers will become essential for AI companies that want to stay competitive. They offer scale, quality, cost efficiency, and access to data that centralized systems cannot reach.

The shift is already happening. Over the next few years, decentralized networks will replace outdated scraping architectures and become foundational infrastructure for AI development.

FAQs

What are decentralized data layers?

Decentralized data layers are networks where thousands of user devices collect, process, and label data. Instead of relying on centralized servers, they leverage distributed bandwidth and real user‑like access to capture dynamic, multimodal content at scale.

Why are cloud scrapers becoming obsolete?

Traditional cloud scrapers struggle with dynamic content such as TikTok videos, infinite scroll feeds, and behind‑login reviews. They are expensive to run, fragile against blocking, and produce shallow datasets that no longer meet the needs of modern AI models.

Why is high‑quality data more important than compute power today?

Compute resources and open‑source models are abundant. What’s scarce is fresh, hard‑to‑reach, high‑quality data. Access to such data is now the primary competitive edge for AI companies.

What role does labeling play in decentralized data layers?

Beyond collection, decentralized networks can label and validate data at the source. This transforms raw content into ready‑to‑train datasets in real time, shortening the pipeline from data request to model training.

How does DataHive AI enable decentralized data collection?

DataHive AI provides browser extensions and mobile apps that gather dynamic, multimodal web data at scale. The platform cleans, deduplicates, labels, and delivers datasets aligned with AI training needs.

What is the future of AI infrastructure?

The industry is shifting from centralized scraping to decentralized data layers. Over the next few years, distributed networks will replace outdated scraping architectures and become foundational infrastructure for AI development.

Unlike private data, which is protected by passwords or paywalls, public web data is open for everyone to see. However, being visible does not always mean it is free for any kind of use. Some websites have clear rules on how their content can be reused.In simple words, public web data means data that is publicly available online and can be viewed freely. Still, its use requires careful attention to laws and ethics.

Where public web data comes from

Public web data can appear in many forms and places. Here are the most common sources:

1. Government websites – Many governments publish data about budgets, population, and services. These are examples of public data that can help researchers, journalists, and businesses.
2. Company websites and blogs – Firms share product descriptions, pricing, and announcements online. These pages are open to the public and form an important part of public web data.
3. Forums and social media – Public discussions and reviews on websites like Reddit or Twitter can also be considered public web data. Even so, this area needs careful handling because it can include personal information.
4. Research databases and news sites – Universities and research institutes often publish open papers, reports, and surveys online. They are another valuable public source of reliable information.

Why public web data is important

Public web data is the foundation for many modern technologies and decisions. Businesses use it to understand markets and improve their strategies. Researchers use it to study social trends. Journalists use it to verify facts.

Artificial intelligence models also depend on it to learn from large sets of information. When used responsibly, public web data helps improve decision-making, transparency, and innovation.

How public web data is collected

1. Manual collection – People can copy and organise information manually when the volume is small. This is the simplest and safest way.
2. Web scraping – Tools can automatically gather information from websites. Scrapers can extract text, numbers, or images.
3. APIs – Some websites offer APIs, which are safe and structured ways to collect data. APIs usually include clear usage rules, making them one of the most ethical options.
4. Data aggregators – These platforms collect and organise public web data for others to use. They help ensure data quality and compliance with local and international regulations.

Challenges in using public web data

Working with public web data sounds simple, but it brings real challenges.
The most common problems include:

• Outdated or incomplete information
• Different data formats
• Duplicates or inconsistent entries
• Biased or context-lacking data

Cleaning and verifying the data often takes more time than collecting it. To make good use of public web data, users must build systems to check accuracy and freshness regularly.

Future of public web data

Public web data will continue to grow as more people publish information online. Artificial intelligence and research will depend on it even more in the future. Governments and companies are now discussing new rules to balance openness with privacy.

The focus will increasingly be on transparency and ethical use, not just access. The best organisations will be those that respect both the data and the people behind it.

DataHive AI and data scrapping

DataHive AI approaches web data in a fundamentally private and ethical way. Instead of directly collecting full datasets from users or websites, the system gathers only small, fragmented pieces of publicly available information through a distributed network of nodes. These fragments are anonymized and later aggregated into larger, structured datasets that can be used for AI training.

This means no personal or sensitive data ever leaves a user’s device in identifiable form. Each dataset is the result of many small, privacy-preserving contributions that are then cleaned, deduplicated, and labeled collectively — turning ethically sourced fragments into powerful training data for AI models.

FAQs

What does public web data include?

Public web data includes any open information found on the internet: text, images, videos, prices, reviews, and statistics that are available without login or payment. It’s often used for research, AI training, and market analysis.

Is it legal to use public web data for AI or business purposes?

Yes, if data is collected from publicly accessible sources and complies with website terms of service, privacy regulations (like GDPR), and ethical standards. The key is to respect content ownership and avoid scraping protected or private areas.

How is public web data different from open data?

Open data is usually published intentionally by organizations for free use under open licenses. Public web data is simply visible online, but may still have restrictions on reuse.

Can companies collect public web data safely?

Yes. Responsible platforms like DataHive AI collect only small, anonymized fragments of public data through decentralized networks. This prevents direct access to private or sensitive information and ensures compliance.

Why is public web data important for AI?

AI models learn from diverse, real-world information. Public web data provides the variety and scale needed to train systems that understand text, images, and behavior patterns accurately.

How can I ensure ethical web data collection?

Follow three principles: use only publicly accessible content, check website rules (robots.txt, terms), and anonymize data to remove personal identifiers. Platforms like DataHive.ai integrate these safeguards by design.

Is public web data the same as user-generated content?

Not necessarily. User content (like comments or reviews) can be public but still tied to personal identity, so it requires extra care and anonymization before use.

How can businesses benefit from public web data?

Companies use it to track competitors, monitor prices, analyze markets, and feed AI systems with real-world signals all while staying compliant with data ethics and privacy laws.

What trends shape the future of public web data?

Automation, AI labeling, and decentralized collection networks like DataHive AI are redefining how data is gathered shifting the focus from mass scraping to privacy-preserving, transparent aggregation.

DataHive is a decentralized data factory for AI — a platform that collects, cleans, and labels real-world web data at scale, delivering it ready for model training.

What kind of datasets can DataHive deliver?

We provide large-scale, domain-specific datasets such as: text, image, video, and audio, сollected from real-world sources. For example:

• E-commerce: product details, reviews, and pricing data from sites like Amazon or Walmart
• Video: TikTok and YouTube datasets for multimodal and generative models
• Audio: speech samples, podcasts, and user-generated audio data for voice and LLM fine-tuning
• Real Estate: Millions of media files related to residential and commercial properties including images, panoramas, floor plans
• Q&A / Knowledge: structured data from platforms like structured data from specialized in programming, system administrations, etc.
• Custom domains: upon request, we build datasets tailored to your model requirements

How is DataHive different from other data providers?

Most current providers rely on centralized crawling or manual scraping, both limited in scale and costly to maintain. DataHive’s distributed model offers:

• Scalability: no central bottlenecks, easy to scale across geographies
• Lower cost: decentralized infrastructure cuts dataset costs by 10–20x
• Dynamic content: capable of accessing JavaScript-rendered or infinite-scroll data that traditional crawlers miss
• Ethical and compliant sourcing: we collect only from vetted websites and publicly accessible sources, ensuring legal safety for enterprise clients

In short: we deliver hard-to-get web data, ethically and efficiently.

What is the process to get a dataset?

We start by understanding your model’s needs: the domain, structure, and scale of data required. Then we deliver a free pilot dataset to validate quality and structure. Once confirmed, full-scale collection begins, with options for ongoing updates and labeling pipelines integrated directly into your ML workflow. The dataset will be delivered either in an industry-standard format or in a custom format tailored to your specific requirements.

How do you ensure data quality?

Every dataset goes through multi-step validation:

• Collection filtering: removing duplicates, irrelevant pages, or low-quality content.
• Cleaning and normalization: ensuring consistent structure and metadata.
• Human-in-the-loop labeling: distributed annotators verify and label complex data.
• Benchmarking: internal testing against client-specified metrics before delivery.

What’s the business model?

We offer datasets as a service: priced by scale, complexity, and labeling requirements.
Clients can choose from:

• Pre-collected datasets (ready-to-train and already validated)
• Custom dataset collection (based on domain requests)
• Labeling-only services for in-house data teams

Our decentralized model allows cost savings that we pass directly to clients enabling enterprise-grade data at startup-friendly prices.

Who’s behind DataHive?

The DataHive team previously founded Profitero, big data for eCommerce company that processed 400–600 TB of data daily and was acquired by Publicis Group for $210M.

We’ve raised $3.5M from top-tier investors including 6th Man Ventures, Solana Ventures, and Wave GP, and we’re now focused on building the world’s most efficient decentralized data infrastructure for AI.

How can my company get started?

Simply reach out via datahive.ai or request your free pilot dataset. We’ll help you identify the right data domain, validate quality, and integrate it into your model pipeline.

The Hidden Value of Your Device’s Data Contribution

Your device sits idle much of the time, but it could be helping build the next generation of AI models. By installing the DataHive browser extension or mobile app, you join a network that collects small pieces of publicly available web data like product pages, reviews, videos, and Q&A content. This data fuels AI training ethically and transparently.

In return, you earn Data Points and Hive Points, which position you for future crypto rewards. And the best part? We never access or see your private data. Your device acts as a secure relay node, processing only encrypted, anonymous fragments of public information.

Privacy and Security: Our Core Commitment

We know trust is earned, especially when rewards seem too good to be true. That’s why DataHive integrates top-tier privacy and security measures into every aspect of our platform. We’ve designed the system to protect you while you contribute to the decentralized data ecosystem.

Privacy

DataHive collects only the bare minimum to get you started and keep the network running smoothly:

• Minimal Personal Data: Just your email for account creation and a referral code if you choose to invite others. No phone numbers, addresses, or other identifiers required.
• No Tracking or Spying: The extension or app doesn’t monitor your browsing activity, read your tabs, cookies, or access personal files, messages, or history.
• Anonymous Operations: All data is public, never tied to your identity, and aggregated from thousands of nodes to prevent any individual tracing.
• Secure Storage: We don’t store raw data on your device, only transfer small, encrypted fragments. Personal info is never sold or shared see our Privacy Policy for details.

Unlike many apps that harvest vast amounts of user data, DataHive keeps it simple and user-focused. We avoid collecting non-essential info like device specs beyond basic network support, ensuring your privacy remains intact.

Security

Security is woven into DataHive’s decentralized fabric:

• End-to-End Encryption: Every data request and connection is fully encrypted, protecting against intercepts.
• Decentralized Architecture: No central database means no single point of failure or target for hacks. Devices function as independent relays, handling random data portions without storing anything locally.
• Ethical and Legal Compliance: Data undergoes cleaning, filtering, and aggregation to remove any potential identifiers, aligning with global laws like GDPR.

We partner with industry standards to monitor for vulnerabilities, ensuring a safe environment for all users.

Your Data Stays Safe: What We Don’t Do

DataHive only collects public web data, nothing private or user-specific. Think of it like lending a hand to gather openly available info without anyone peeking into your personal space. We can’t see your browser activity, extensions, passwords, or any entered data. There’s no capability or intention to access your accounts, files, or messages. The network is fully anonymous: Even the DataHive team can’t link activity to individuals.

Why Decentralization Matters for Privacy and Security

Traditional data collection piles everything into centralized servers, creating honeypots for breaches and misuse. DataHive does it differently by distributing the workload across thousands of nodes like yours.

This approach delivers:

• No Single Point of Failure: Fragments can’t be exploited in one place.
• Built-In Anonymity: Data pieces are random and untraceable to users.
• Greater Transparency: Track your contributions directly in the app dashboard.

Decentralization not only secures the network but empowers you with control over your participation.

How DataHive Ensures Ethical Collection

We’re committed to doing things right. All data comes from vetted, public sources: no bypassing logins, paywalls, or restricted sites. Post-collection, datasets are scrubbed to eliminate personal identifiers, guaranteeing compliance with best practices for AI data creation.

Join DataHive Today: Secure, Private, Rewarding

Our mission is to democratize AI data while prioritizing your security and privacy. Sign up, install the extension or app, and start earning in the background. Join thousands already growing the Hive where innovation meets unbreakable protection.Ready to contribute safely? Sign up now at datahive.ai and reclaim the value of your device’s downtime.

FAQs

Does DataHive access my private information?

No. DataHive cannot access your personal files, messages, browser history, or any sensitive data. Your device only processes encrypted requests for public web content.

Can anyone see what my device is doing?

No. The network is fully anonymous. Each node operates independently, and no one—including DataHive AI—can link your activity to your identity.

Is the data encrypted?

Yes. Every connection is encrypted end-to-end, and your device never stores raw data locally.

Will DataHive slow down my computer or phone?

No. The app and extension are optimized for low resource use. You can adjust performance settings at any time.

Can I stop participating?

Yes. You can pause or uninstall DataHive whenever you want. Once removed, your device immediately stops contributing.

What data does DataHive actually collect?

Only public web data such as product listings, video metadata, and Q&A pages. No private, personal, or user-specific data is ever included.