Port3 Network

AI Agents. Your Way. Always. Join the community

[Port3 Network @Port3Network]

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.
https://t.co/Nvxjsl0MqQ

[Port3 Network @Port3Network]

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.

[Port3 Network @Port3Network]

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.

[Port3 Network @Port3Network]

The internet gave AI access to information.

The next challenge is finding information worth learning from.

The gold rush has already started.

[Port3 Network @Port3Network]

Raw Data

→ Filter
→ Clean
→ Structure
→ Ready to Use

The value isn’t in collecting more data. It’s in making data useful.

[Port3 Network @Port3Network]

GM Fam☕️☕️

Say it back to pump your bags

[Port3 Network @Port3Network]

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.

[Port3 Network @Port3Network]

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.

[Port3 Network @Port3Network]

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.

[Port3 Network @Port3Network]

Happy Bitcoin Pizza Day 🍕🍕

[Port3 Network @Port3Network]

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

[Port3 Network @Port3Network]

Right where it should be.

[Port3 Network @Port3Network]

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

[Port3 Network @Port3Network]

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.

[Port3 Network @Port3Network]

GM ☕️

[Port3 Network @Port3Network]

More than just data

→ Context
→ Patterns
→ Timing
→ Execution

Everything working together.

[Port3 Network @Port3Network]

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

[Port3 Network @Port3Network]

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.

[Port3 Network @Port3Network]

Caught in the light.

[Port3 Network @Port3Network]

Raw data is everywhere. Feeds never stop, dashboards keep growing.

But when you actually need something useful. Something you can trust and act on. It suddenly feels very limited.

The real edge is not more data. It’s knowing what’s worth using.