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Everything that makes an app or agent real happens after it works on your machine. Locally coding an app is a joy: hot reload, a seeded database, a mocked API key. Then you go to ship it, and "deploy" quietly expands into a Dockerfile that behaves in CI, somewhere to actually run the container, a database it can reach, TLS, secrets wired into the environment, and a pipeline to hold it all together. The feature took an afternoon. The plumbing takes the rest of the week.

Software used to be built by engineers. Not any more. Low-code tools brought in domain teams. AI copilots brought in everyone else. And now agents are building and acting alongside humans; marketing, finance, HR and legal are all shipping the apps they used to file tickets for. The number of people (and things) building on your data has exploded, and it isn't slowing down. And there's no single, standardized way to build, there likely never will be.

Ask a simple question "who are our most profitable customers?" and things fall apart. The data lives in six systems, nobody agrees which table is canonical, the column called profit is actually revenue, and the business rules that matter live in someone's head or a Confluence page nobody's touched since 2023. Now point an AI agent at that same mess.

TL;DR Katja Rantala thought her dream job involved international relations and diplomacy. Turns out leading people and companies through change is what she was meant to do. “Some days everything is on fire and it's a catastrophe. The important question is whether the big picture moved.” That is Katja Rantala's description of what it is like to work in an environment that keeps changing shape.

Lovable is fast at the part that used to take a week. Describe an application, and you have a working interface in minutes. Lovable already has an answer for the backend. Its built-in Cloud backend is enabled by default and runs on Supabase's open-source foundation, and you can connect your own Supabase project instead. Both are reasonable places to start. Neither puts the database in an account you already own, in the cloud and region you picked, next to the rest of your data platform.

New pricing for Aiven for Apache Kafka is now live for all new customers on AWS, GCP and Azure. For our existing customers: nothing changes. When choosing your cluster you are guided by three things: And you pay for three things.

TL;DR Aiven contributed GCP support and private networking to the open source OpenSearch Migration Assistant, previously an AWS-only tool. Delivered via five Terraform-based PRs, the changes add a GKE deployment path and let regulated industries migrate without using the public internet - giving Elasticsearch, OpenSearch, and Solr users on GCP a supported route to Aiven for OpenSearch.

If you’re using Apache Kafka for data streaming, then you’re likely to want to take data from one topic and write an amended version of that data to another topic, for instance: for filtering (give me the items that have been delivered), enrichment (add some more information to messages, from another data source) or anomaly detection (show me potential signs of trouble). The premier solution in this space is the Apache Kafka Streams library.

Let me start with something most Kafka contributors think but rarely say out loud: nobody understands all of Kafka. I'm not a core committer and have only contributed a few times, but those contributions I have made have been in part thanks to using coding assistants. There are some issues with this approach though, the Apache Kafka project is huge. It's split into many parts: the core, the server, the client libraries, the streams engine, the storage layer, the consensus code, and more.

Aiven for ClickHouse 26.3 is now available in Early Availability. This upstream Long-Term Support release makes full-text search generally available, enables asynchronous inserts by default, introduces materialized common table expressions, and brings a wide range of JSON and query-performance improvements. The upstream 26.3 release includes 27 new features and 40 performance optimizations.