Jamie Aliperti

[Micro/M43 @MicroM43]

The people leading AI initiatives aren’t looking for more AI content.

They’re looking for people who have actually built something.

I’ve had a few conversations recently with people in charge of AI at large companies. Enterprise brands, big 4 consulting, venture capital.

One thing keeps surprising me.

Many of them know the headlines, know where the money is being spent, see the business demands.

Few of them know anyone actually effectively building.

And that’s not a criticism. It’s just where we are in the cycle.

Most AI conversations today fall into three buckets:

Consumers
Specialists
Operators

Consumers use the tools.

Specialists understand the technology.

Operators are trying to answer a different question:

“How does this create value in the real world?”

That’s where things get messy.

Data.
Workflows.
Incentives.
Adoption.
Governance.
Customers.
People.

The technology is often the easiest part.

What’s interesting is that some of the ideas many of us explored in Web3 weren’t wrong.

Many were simply early.

Identity.
Provenance.
Ownership.
Trust.
Context.
Reputation.
Relationships.

The implementation may have missed or are a work in progress.

The underlying problems never went away.

My background has always been around product serialization and lifecycle management.

For years I’ve looked at systems through a simple lens:

Physical Thing
↓
Digital Record
↓
Ownership
↓
Behavior
↓
Relationship

The more I work with AI, the more I realize the pattern hasn’t changed.

The language changed.

The pattern didn’t.

A lot of people saw NFTs as JPEGs.

Some saw them as relationship infrastructure.

A lot of people see AI as chat interfaces.

I suspect the bigger opportunity is relationship infrastructure again.

Products.
People.
Communities.
Experiences.

Connected through identity, memory, and context.

We’re still very early.

[Conduit @ConduitIO]

The @Conduitio team recently launched a Performance Benchmarking tool to help them understand exactly how much Conduit can handle and what it might take to break it. @haris88 documented what the team has learned thus far. Read the blog post to learn more: https://t.co/O2vISeAp5w

[DeVaris Brown(devaris.eth) @devarispbrown]

As part of our commitment to delivering a better developer experience for data, we released two new features today. Better CI/CD with feature branch deploys. Also we made our Kafka connector public. Now it’s easy to pour data via code into a @apachekafka or @confluentinc stream

[DeVaris Brown(devaris.eth) @devarispbrown]

Building and operating complex architecture is trivial with @meroxadata's Turbine SDK. Bring your business logic and leave the orchestration to us. You can even use us alongside @databricks/@ApacheSpark for your advanced stream processing needs.

[DeVaris Brown(devaris.eth) @devarispbrown]

Pull up and see how you can roll your own real time anomaly detection with @meroxadata and @thatDotinc. If you’re going to do all the implementation and pay that big ass bill for @splunk you should attend this webinar for a better option.

[DeVaris Brown(devaris.eth) @devarispbrown]

Today is Black Women's Equal Pay Day. Take an hour out of your day to make sure that the Black Women on your staff are properly leveled and are paid the same as their peers. Shoutout to @IdalinBobe for ensuring days like this are on our company calendar with the bank holidays!

[Meroxa @meroxadata]

What if, instead of throwing it away, you could use categorical data to detect dangerous anomalies? What if you could do it in real-time & at scale? With Turbine & @thatDotinc Novelty Detector, you can. Register now and join us on August 17th to learn how! https://t.co/JHuYpR894Q

[DeVaris Brown(devaris.eth) @devarispbrown]

I built data app that does real time data enrichment using @clearbit. We use this app in our new user signup form and in @salesforce for leads. Regular @golang. No additional point solutions required. Take a look!

[DeVaris Brown(devaris.eth) @devarispbrown]

@AstasiaMyers I agree and believe as the tooling gets better, you'll see more realtime use cases. Having access to realtime data gives you more granularity with your data. Batch is just snapshots in time. We've been all in on realtime from day 1 with @meroxadata.

[Conduit @ConduitIO]

We just added a 🆕 deployment guide to the docs. If you want to see how to set up Conduit on AWS EC2, check it out:
https://t.co/wzEEDyjPT7

[Meroxa @meroxadata]

Designing real-time systems to move data between data infrastructures can be challenging. Join the @algolia #LiveCodingSession tomorrow and learn how to easily keep your @algolia index in sync with your application data in real-time using Turbine. https://t.co/UxP08M7NxO

[DeVaris Brown(devaris.eth) @devarispbrown]

Cat’s out the bag. @meroxadata is a recipient of DoD APFIT funding helping the Guardians of @SpaceForceDoD monitor aircraft health in real time. https://t.co/xKI95h1zZ5

[DeVaris Brown(devaris.eth) @devarispbrown]

We've been busy making high-quality, performant CDC connectors for our OSS Kafka Connect alternative, Conduit.

@SnowflakeDB @confluentinc @Zendesk @stripe @MaterializeInc @salesforce @AdobeMarketo @elastic @FireboltHQ @algolia @googlecloud @Azure and more coming!

[Meroxa @meroxadata]

Unprecedented amounts of data are produced daily. Developers have turned to data apps to help manage the complexity. But what are data apps and how do they differ from other apps? @allegedlysimon (VP of Engineering) breaks it down in his latest blog post. https://t.co/2IbvjQoWuU

[Sam Ramji 🇺🇦 @sramji]

Are you prepared for the Heroku of data?

Congratulations to @devarispbrown and the @meroxadata team for today’s launch. And may it enhance your calm.

[DeVaris Brown(devaris.eth) @devarispbrown]

That’s high praise. @heroku revolutionized web app development and ushered in a wave of successful startups. I hope we can do the same for data apps.

[Algolia @algolia]

How do you send and continuously sync data to Algolia? 🤔 🗯️

@anaptfox shows us the basics of how to build a data application and deploy to @meroxadata — ultimately moving data from your PostgreSQL database to your Algolia index.

🧠✨ Learn how ↓ https://t.co/Gw5V7W6dc2

[David Kowal @dak970]

Scuba Analytics is the fast and scalable event-based analytics solution to answer critical business questions about how customers behave and products are used. DM me for details. https://t.co/JG1QQhaWNi

[Jamie Aliperti @jaliperti]

How to Perform Retention Analysis To Grow Your Revenue https://t.co/cuq09tmrAP

[Scuba Analytics @Scuba_Inc]

Interana offers Amplitude customers a discounted path to multi-data-source analytics with full transparency to the query language. https://t.co/OeB2ThKbpC #analytics Data > Opinion