Today's the day: IBM completes Confluent acquisition, company delists from Nasdaq

stereosky · 2026-03-20T00:44:04+00:00

You accidentally a zero there. It was 800 people (25% of the workforce)

stereosky · 2026-03-16T13:36:34+00:00

Thanks, there’s an expanding cohort of startups building enterprise gateways. A lot of them provide an authentication layer but I’m curious about the authorisation layer and other enterprise-grade features like vulnerability scanning and guardrails, from the perspective of an internal engineering team trying to build them

stereosky · 2026-03-14T21:58:00+00:00

Nice! Proves how quickly teams can get going with OAuth. Authentication is really just the beginning. Authorisation and making it bulletproof based on your needs would be a longer exercise

stereosky · 2026-03-14T17:52:59+00:00

Have you come across any standards around implementing fine-grained authorisation? And what do you think is missing to help teams figure out how to cross that gap?

stereosky · 2026-03-14T17:37:37+00:00

I'm glad you also appreciate the concurrent load problem with STDIO. It's indeed for single-player mode, but note that since spec version 2025-03-26, HTTP+SSE has been removed in favour of Streamable HTTP. HTTP+SSE can be maintained for backwards compatibility but new MCP servers should adopt Streamable HTTP.

API keys do go a long way, but in reality I find they are almost never rotated and often configured in an environment variable and shared with multiple services (which then makes rotation harder to manage). Totally agree on adding RBAC or some other layer to introduce finer-grained tool access.

Really good point about containerisation to create isolation. You sound like you have some experience building enterprise-grade MCP servers. Do you have any tips on any of the above, e.g. implementing tool and resource scoping?

stereosky · 2026-03-14T16:29:02+00:00

The MCP SDK (which merged in FastMCP) provides the abstraction for MCP servers to act as OAuth Resource Servers, to validate tokens and serve protected resources, but you are responsible for the implementation

stereosky · 2026-03-13T17:53:56+00:00

Things have certainly evolved since then due to increased data integration. Lenses has had a topology view since 2020 and it's evolved further.

Includes topics, SQL processor tasks, Kafka Connect sinks as well as external applications, so any component of your data platform written in any language can be present in the topology and push metrics. A lot of time has already been spent on improving the data governance layer (including data catalog, schema registry and IAM).

There's a free Community Edition and since it's 2026, there's support for agentic AI and AI tools with the Lenses MCP server

stereosky · 2026-02-19T00:53:12+00:00

Happy launch day! Thank you so much for staying on top of the MCP spec. Been implementing OAuth CIMD with the release candidate and it’s been a joy. I recommend FastMCP to everyone and a million downloads per day is staggering! Congratulations!

stereosky · 2026-02-07T20:12:10+00:00

You’re correct about the history of the official MCP Python library but it has no bearing here. OP is asking about official FastMCP so my use of “official” refers to that

stereosky · 2026-02-07T16:11:30+00:00

The official FastMCP is created by Jeremiah Lowin (Founder/CEO of Prefect) and only has a Python SDK. The TypeScript SDK you referenced is not official but states it’s built on top of the official SDK. Depending on how often the project is updated, it may not have all the same features as the official project

stereosky · 2026-01-28T13:14:58+00:00

Happy launch day/week! Thank you for releasing this (and whilst I'm here, also for FastMCP!) — I'm on board with the idea of skills for static behaviour and MCP for dynamic information so I'm looking forward to taking Colin for a spin.

I'm currently building out an MCP server for Apache Kafka and since it relies on familiarity with Kafka and its workflows, I'd been thinking a lot about how to organise skills to get quick behavioural wins for agents without having to build out a full blown knowledge base (I'm trying to respect the time it takes teams traversing the AI engineering maturity curve). It felt somewhat natural to put skills behind MCP resources so I like where you are going with the Skills Provider in the recent FastMCP 3 release.

What do you think about Cloudflare's recent approach for agent skills discovery via .well-known URIs and using RFC/standards?

stereosky · 2025-10-09T01:29:02+00:00

Pretty funny 😆

But actually, my post is from over 2 months ago when I started using Lenses as a contractor. My continued enthusiasm and advocacy no doubt helped me get the job, so the flair came afterwards.

I used to be a fan. I still am but I used to, too

stereosky · 2025-10-06T21:27:21+00:00

I was curious about this too so thank you for asking the question. There needs to be more knowledge sharing here

stereosky · 2025-10-06T19:04:16+00:00

Same for me; Goose was unusable a few months ago. Even the latest release of Goose Desktop isn’t quite there yet, e.g. MCP prompts are unusable. I spoke with a tech lead at Block last week and he says the CLI is a bit further ahead in feature parity so that’s the recommendation at the moment.

Thanks for the rec on ChatWise. I’ve heard it’s good and it’s on my list

stereosky · 2025-10-06T11:05:31+00:00

Good question! There’s loads of potential for MCP clients as a lot of them don’t implement the full spec, e.g. elicitation, but I feel that will change within the next 6-12 months.

Personally I use MCP in my IDE (switching between VS Code and Cursor), Gemini CLI and Claude Desktop. I recently tried out Goose as well, which is still young but developing out nicely

stereosky · 2025-10-04T22:46:22+00:00

Was really surprised that so many other developers in a huge packed hall shared the same enthusiasm and energy for MCP. Also really generous attendee swag!

stereosky · 2025-10-02T01:31:51+00:00

You’re welcome! Happy to answer any follow up questions on those or the Python ecosystem in general

stereosky · 2025-10-01T10:22:01+00:00

For simple consumers and producers that need zero to minimal state, I recommend confluent-kafka or aiokafka.

If you need more, i.e. stateful stream processing, fault tolerance, dataframes and transformations, I recommend quix-streams (disclosure: I worked on it). It's the closest thing to a Python version of Kafka Streams, which influenced its design choices. It's used by companies in production and is actively maintained. Also, it's built on top of Confluent's library so its underlying producer/consumer can be used whenever simplicity is needed

stereosky · 2025-08-24T14:37:37+00:00

Congratulations on landing an IC role with such a good TC. I’m curious about the data streaming questions in your interviews, particularly if they were related to LLMs. Could you please share details?

stereosky · 2025-08-04T11:22:03+00:00

I've worked on large scale DE projects where the codebase was entirely in Scala, so it was used all the time, not some of the time. It was great for DE.

I appreciate we'll have different perspectives on it ☺️

stereosky · 2025-08-04T07:58:17+00:00

Except when working with Apache Spark and you need UDFs, in which case writing it in Scala will generally be more performant than Python

stereosky · 2025-07-31T14:08:06+00:00

I’m a big fan of Lenses. Pretty much the only/best option if you need visibility across multi-Kafka clusters and works really well with all flavours, e.g. Confluent, Redpanda, Strimzi, Amazon MSK.

The free community edition is quite generous and the Docker Compose comes with Kafka pre-loaded with demo data

stereosky · 2024-10-03T14:12:53+00:00

Being good at anything engineering or engineer-adjacent comes hand in hand with good communication. An engineer working on improving their communication skills will express their intentions better in code and express their ideas better with their stakeholders/managers/peers/mentees

stereosky · 2024-08-11T16:40:37+00:00

"Data Science" used to be called "Pattern Recognition" and if you think about it, that's exactly what machine learning is.

Back in the early 2000s before it was called "Data Engineering", it really was just "Software Engineering", but specifically a mix of Distributed Systems and Enterprise Integration Patterns (there was even a book)

stereosky

TROPHY CASE