A New Platform to Power the Human Side of AI.🔥. Ta-da platform is officially LIVE. And it’s designed to power the next generation of AI with verified, human-contributed data at scale. ▶️ What kind of tasks?.From voice, photos, videos, labeling, social media, and so much more to

Not all agents are created equal ❌. Some AI agents repeat. Some reason. Some adapt. What separates an average bot from a smart one isn’t just the model. It’s the context it carries. But context isn’t static. It evolves with the user. What’s the best way to build contextual

gm, DeFAI summer feels better with Ta-da. 🏖️

Gm fam! 👀. Quick story: We asked an AI to solve a problem. It made it worse. Turns out, the “training set” was 90% noise, 10% random internet data. Moral of the story: Don’t expect precision from models not trained on good human data. Better data = Better AI. Image credits:

All eyes here… 👀. A new task will be released soon on the @Ta_da_io platform. This time, we’re going physical. A global 3D object scanning campaign is launching shortly. And the best part?.You just need a phone and a few minutes. What you get:.✔️ Real money for real tasks.✔️

He’s a 10 but says “any data is good enough to train an AI model” on the first date.

Gm BUIDLers! . Building AI w/o context is guesswork. A model trained on incomplete data guesses rather than learns. Context distinguishes hallucination from relevance, and it comes from human understanding, not just more parameters. How are y'all planning to enhance context

gm 🏖️   . what's your favorite Ta-da feature?

❌ Myth: If the model works, the data must be fine. ✅ Fact: Good outputs don’t always mean clean inputs. AI can perform well even on flawed or biased data. Performance can mask problems. A model might generate fluent, relevant responses while still being trained on data that’s

Gm. AI isn’t just about how much data you have. It’s about how right that data is. What do you think: should AI models reveal where their training data came from?

AI is only as decentralized as the data behind it. We all love to talk about decentralization. But most AI models are still trained on centralized, closed datasets. If you’re building in Web3, your AI stack should reflect the same values: .→ Open contribution systems →

Everyone talks about making AI smarter. But how do we make it more trustworthy?. Most models are trained on data that no one double-checks. No humans. No context. No accountability. That’s where the problems begin. If you’re building or using AI, then how much do you trust the

The rise of AI agents is real. But there's something no one’s talking about enough: the quality of data they’re trained on. Every week, there’s a new model. Every week, a new app. @GoogleTrends also indicated a significant increase in interest for "AI Agents" and "Agentic AI"

RT @Liembriel: 🚨 AI needs one thing to grow: good data 🚨. AI is getting better really fast—but here’s the truth:.It can only be as smart as….

Gm, believe in DeFAI…

▬▬▬.◙.▬▬▬.═▂▄▄▓▄▄▂.◢◤ █▀▀████▄▄▄▄◢◤.█▄ █ █▄ ███▀▀▀▀▀▀▀╬.◥█████◤.══╩══╩═.╬═╬.╬═╬.╬═╬.╬═╬.╬═╬ just dropped down to say.╬═╬.╬═╬ your AI is only as good as your Data.╬═╬ ☻/.╬═╬/▌.╬═╬/ \.

if you’re in the blue area, you need to train AI with good human data…

Gm 🫡. Building AI in public isn’t just a trend, it’s the shift and most AI projects still build behind closed doors. But what if the edge lies in training in public? .> open feedback.> open datasets.> open incentives. Because ownership starts with transparency. So, should AI

Turn your FREE TIME into EXTRA INCOME! 🤩. Got a few minutes to spare during the day?. With our task platform, you choose when and how much you want to work. Whether it’s early morning or late night, your time is yours and every task gets you closer to your goals. No deadlines.

❌ Myth: AI is always correct. ✅ Fact: AI often sounds confident and still gets it wrong. A model can give polished, structured answers and still hallucinate. Accuracy depends on how the AI model was trained and what data it was fed. Don’t just trust the model. Trust the