AI models are engines.
Data is fuel.
And raw fuel doesn’t take you very far.
The future belongs to those refining data before everyone else.
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Port3 Network
3.9K posts
Port3 Network
@Port3Network
AI Agents. Your Way. Always. Join the community https://t.co/WP5aPm2JhJ
Apple for AI Katılım Şubat 2022
556 Takip Edilen294K Takipçiler
Scaling data pipelines for AI workloads is foundational. Excited to see practical patterns for reliable, observable data infrastructure.
Databricks@databricks
As data volumes and complexity grow, data engineers need scalable ways to build, manage, and optimize pipelines. 📕 The Big Book of Data Engineering covers proven patterns for scaling ETL, orchestrating data and AI workloads, implementing observability, and managing pipelines with Lakeflow. You'll also see how organizations across Healthcare, Financial Services, Retail, and Entertainment are building intelligent batch and streaming data pipelines. databricks.com/resources/eboo…
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Open weights + full datasets + training recipes is how real progress accelerates. Data transparency like this strengthens the entire ecosystem.
Aman@Amank1412
NVIDIA just open sourced Nemotron 3 Ultra. > 550B parameters (55B active/token) > 1M token context > 47.7 on the AI Intelligence Index > 300+ tokens/sec > Open weights, datasets & training recipes Open source AI just got a serious upgrade.
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A lot of attention is going to AI governance right now.
Some believe governments should have a larger role.
Others think private companies should lead.
Should the people generating the data have more ownership in the AI systems built from it?
Curious to hear your thoughts.
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The internet gave AI access to information.
The next challenge is finding information worth learning from.
The gold rush has already started.
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Raw Data
→ Filter
→ Clean
→ Structure
→ Ready to Use
The value isn’t in collecting more data. It’s in making data useful.
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AI Agents are getting smarter every month.
But there is one problem that keeps showing up again and again — Bad data.
Port3 turns that complexity into structured, real time information that Agents can actually use. Because better outputs start with better inputs.
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Impressive step for dataset quality at scale. Cleaning synthetic noise is essential to prevent model collapse in training.
Avi Patel@avipat_
Introducing Kled-FD 0.1, the world's best fraud detection and dataset cleaning pipeline. The first all in one system capable of detecting AI generated content, near duplicates, stolen and plagiarized media, screenshots, manipulated and spliced content, NSFW and explicit material, minors and age sensitive content, sensitive and harmful content, and coordinated behavioral fraud rings. Kled-FD 0.1 has been battle tested across 1.2 billion uploads on Kled's data marketplace and is actively running quality checks on over 5 million uploads per day across image, video, audio, and text. Public benchmarks will be released soon. This is the first real step toward making data quality enforcement a humanless process.
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The biggest unlock for AI right now is not more models but better data foundations. Messy inputs hold everything back.
We are fixing it with verified structured sources that let agents reason clearly. This image captures the vision perfectly.
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Big move by Blackstone & Google on AI capacity. Real-time decentralized data annotation will be crucial to make these TPUs truly intelligent.
The Transcript@TheTranscript_
Blackstone & Google launch $5B TPU cloud venture to bring 500MW of AI data center capacity online by 2027. "This joint venture ...helps meet growing demand for TPUs" - Google Cloud CEO: $CRWV: -5% PM $BX: +1% PM $GOOGL: +1% PM
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Everyone talks about smarter AI agents but forget they run on the data you give them. Garbage in still means garbage out even in 2026.
Build with verified decentralized sources and everything levels up. Your agents deserve better fuel
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AI data centers are growing crazy fast but the real bottleneck isn’t compute its clean reliable training data. Scattered sources kill progress.
One unified layer fixes that and lets agents actually scale. Feels like the missing piece everyone needs.
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More than just data
→ Context
→ Patterns
→ Timing
→ Execution
Everything working together.
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Everyone talks model architecture but the real game is in the data trenches. Cleaning, mixing, synthesizing the right stuff decides if your model actually gets smarter or just louder. Most courses skip this because it’s messy and unglamorous.
(((ل()(ل() 'yoav))))👾@yoavgo
The big dilemma with teaching an "LLM course" is that it is really easy to get drawn into teaching the various technical things like efficiency tricks, attention variants, PPO vs GRPO, etc etc. But the real "meat" is not there, but in the data: data for pre-training, for mid-training, for SFT, for RL and for "reasoning", synthetic data, curated data, annotated data... cleaning, evaluating, improving, mixing, ... lots of stuff. but "data" is so much harder to teach: it is not "mathematic" or "algorithmic" like the technical things, and it is not clear what is the teachable thing there. it is also a lot less transparent than the technical topics, both because it is semi-secret, and also because it is also not appealing for publishing, for roughly the same reasons it is not appealing for teaching. so, what would you teach about data? what are the key lessons and insights one should know? any good papers or resources? good existing classes? blogs? hit me with what you have
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People think more data means better results. But most of the time it just adds confusion.
If the data is messy, scattered, or unclear. You are building on top of chaos.
Once you fix the structure. Everything starts to click.
Same data, totally different outcome.
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