Steffen Hoellinger

AI & Data Streaming | Field CTO @confluentinc

[martin_casado @martin_casado]

Mythos appears to be the first class of models trained at scale on Blackwells. Then will be Vera Rubins. Pre-training isn't saturated. RL works. And there is *so much* computing coming online soon.

Buckle your chin strips. It's going to be fucking wild.

[Chamath Palihapitiya @chamath]

Many of the coding wrapper companies have recently changed their monetization to move almost exclusively to usage based.

It’s a necessary step for these companies to be GM+ but it will also accelerate the economic incentive for the underlying Foundational Models to “move up the stack” as well. Otherwise, the FMs risk being disintermediated by agents/tool calls.

Based on the recent valuations of the FMs, it’s unlikely that they can afford to let this happen.

Next few quarters will be really interesting.

[Steffen Hoellinger @shoellinger]

@martin_casado @levie Fully agree. Yet the early agent frameworks seem to be overloaded with complexity and the experience suffers from too many inefficient handovers.

[Steffen Hoellinger @shoellinger]

@andreasklinger @pmoe Well deserved!

[Steffen Hoellinger @shoellinger]

💡 Real-time AI agents are only as intelligent as the data they process. I joined the "Life Is But A Stream" podcast with Joseph Morais (@thedatagiant) to talk about how real-time data streaming powers agentic AI and intelligent copilots.

📺YouTube: https://t.co/jNkIDOrGF1
🎧Apple: https://t.co/v7faXmrfbL
🎙️Spotify: https://t.co/Y4oaPsjahj

[Confluent @confluentinc]

💡 Real-time AI agents are no longer just an idea...they’re here.
Tune into the latest Life Is But A Stream episode with Joseph Morais (@thedatagiant) and Airy's Steffen Hoellinger as they dive into how real-time data streaming powers agentic AI and intelligent copilots.

📺 Watch it on YouTube: https://t.co/XhJayIr1Aa
🎧Listen Here: https://t.co/ixwB9zhyE2

[Steffen Hoellinger @shoellinger]

@martin_casado "There was never any more inception than there is now." (Walt Whitman, 1892)

[Steffen Hoellinger @shoellinger]

Full Talk: https://t.co/G9WOevq2ce

[Steffen Hoellinger @shoellinger]

Summary of my recent #FlinkForward talk "Building #Copilots with #FlinkSQL, LLMs & Vector DBs":
🤖 AI Assistants & self-serve AI/BI for streaming & batch data
🧠 AI-powered schema intelligence
🔄 Natural Language to generate Flink SQL jobs
⚙️ Continuous monitoring for agentic AI

[Steffen Hoellinger @shoellinger]

@martin_casado Just the beginning. Wait until your AI starts purchasing its own dev tools!

[Steffen Hoellinger @shoellinger]

That's why you should ♥️ Logs.

[Stephan Ewen @StephanEwen]

𝐄𝐯𝐞𝐫𝐲 𝐒𝐲𝐬𝐭𝐞𝐦 𝐢𝐬 𝐚 𝐋𝐨𝐠

An idea on how to drastically 𝐫𝐞𝐝𝐮𝐜𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 𝐚𝐧𝐝 𝐜𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐢𝐨𝐧 in distributed apps.

A fun way to look at this is similar to the idea of Turning the Database Inside Out - like Turning the Microservice Inside Out.

The core idea is: Applications coordinate with many systems (DBs, queues, locks, schedulers, ...) which are all logs underneath. But each log independently maintains its ground truth. We lack common order and conditions.
By letting all those logs build on top of a common log, we can make a lot of distributed systems problems in apps and microservices virtually disappear.

But how is this practically usable, given that our DBs and queues aren't built like this? How do we strike a sweet-spot balance between this model with its great resilience and consistency, and maintaining healthy decoupling and separation of concerns?

We wrote about that design pattern, how it inspired our work on @restatedev, and how you can adopt this for your architecture.

👇

[Stephan Ewen @StephanEwen]

𝐄𝐯𝐞𝐫𝐲 𝐒𝐲𝐬𝐭𝐞𝐦 𝐢𝐬 𝐚 𝐋𝐨𝐠

An idea on how to drastically 𝐫𝐞𝐝𝐮𝐜𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 𝐚𝐧𝐝 𝐜𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐢𝐨𝐧 in distributed apps.

A fun way to look at this is similar to the idea of Turning the Database Inside Out - like Turning the Microservice Inside Out.

The core idea is: Applications coordinate with many systems (DBs, queues, locks, schedulers, ...) which are all logs underneath. But each log independently maintains its ground truth. We lack common order and conditions.
By letting all those logs build on top of a common log, we can make a lot of distributed systems problems in apps and microservices virtually disappear.

But how is this practically usable, given that our DBs and queues aren't built like this? How do we strike a sweet-spot balance between this model with its great resilience and consistency, and maintaining healthy decoupling and separation of concerns?

We wrote about that design pattern, how it inspired our work on @restatedev, and how you can adopt this for your architecture.

👇

[Steffen Hoellinger @shoellinger]

@euaccofficial Important one.

[Elon Musk @elonmusk]

Success is uncertain, but entertainment is guaranteed! ✨

[Steffen Hoellinger @shoellinger]

@martin_casado Would love to join again!

[Steffen Hoellinger @shoellinger]

@chrija @nathanbenaich Clear case for a multi-agent LLM system. Let's just have a virtual meeting where the notary AI reads out the financing documents via text-to-speech and all stakeholders' agents transcribe it back, process and re-negotiate if necessary. Best deals negotiating last minute changes!

[Steffen Hoellinger @shoellinger]

@andreasklinger @nathanbenaich @skagarroum Great news! Still sad that Europe lacks private companies making transformative AI data center investments—yet.

[Steffen Hoellinger @shoellinger]

@andreasklinger The magic word is “Bezugsurkunde” - instead of unproductively sitting around for hours (or even for days), rather have the notary read everything out to his/her own employees; then just sweep in for a 5 minute chat to give your blessing to the sacred ritual that had happened. 🚀

[Tomasz Tunguz @ttunguz]

The major areas of AI innovation automate white-collar work. Reviewing the BLS’ data on employment for white collar work, I aggregated the data to these categories.

It’s striking that most of them already have a significant number of AI startups pursuing their ambitions to change workflows.

Software engineers were the first to benefit with Copilot. Today, there’s a panoply of different kinds of AI software for developers, including test generation, code refactoring, code generation, & security analysis.

Within education, The promise of computer adaptive testing and an individualized tutor and the style of Alexander the Great & Aristotle is possible.,

Automated document ingestion & reconciliation for closing books is changing accounting. AI powered financial due diligence rips through public filings, private placement memoranda, & compliance automation.

Legaltech has surged automating demand letters for personal injury, writing briefs for attorneys, & chomping away paralegal work.

We could keep iterating through the list. But if a founder wanted a list of jobs to automate, the BLS’ white collar jobs boards is a wonderful place to start. AI will change these workflows & capture a meaningful fraction of the labor spend.

It’s a roadmap for the White Collar Revolution.

https://t.co/q2nTVldCGR

[Steffen Hoellinger @shoellinger]

@sarahcat21 We give it stream processing as a powerful tool; works with weather data as well. 😉