In my opinion these discussions should include MREs (minimal reproducible examples) in the form of prompts to ground the discussion.

For example, take this prompt and put it into Claude Code, can you see the problematic ways it is handling transactions?

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The invoicing system is being merged into the core system that uses Postgres as its database. The core system has a table for users with columns user_id, username, creation_date . The invoicing data is available in a json file with columns user_id, invoice_id, amount, description.

The data is too big to fit in memory.

Your role is to create a Python program that creates a table for the invoices in Postgres and then inserts the data from the json file. Users will be accessing the system while the invoices are being inserted.

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In my opinion, attempting to hold the hand of the LLM via prompts in English for the 'last mile' to production ready code runs into the fundamental problem of ambiguity of natural languages.

From my experience, those developers that believe LLMs are good enough for production are either building systems that are not critical (e.g. 80% is correct enough), or they do not have the experience to be able to detect how LLM generated code would fail in production beyond the 'happy path'.

If you are unfamiliar with the various ways that naive code would fail in production, you could be fooled into thinking generated code is all you need.

If you try to hold the hand of the coding agents to bring code to a point where it is production ready, be prepared for a frustrating cycle of models responding with ‘Fixed it!’ while only having introduced further issues.

Unfortunately, it seems investors now think that all paid software will be replaced by AI generated software, somehow open source projects laundered through generative AI models should finally convince enterprise customers to go with free.

LLMs for replacing work in its entirety seems to be a stretch of the imagination at this point, unless an academic breakthrough that goes beyond the current approach is discovered, which typically has an unknown timeline.

I just don't see how companies like Anthropic/OpenAI are drawing these conclusions given the current state.

Has Databricks recently open sourced additional Delta table features that were previously only available with a paid license? I can't seem to find a relevant announcement.

We are looking for senior DevOps engineers based in South Africa to join our team.

You should have a detailed understanding of building end-to-end systems on Linux/Kubernetes, including:

- 2+ years work experience in DevOps (especially Kubernetes and AWS)

- Experience in managing, scaling and migrating data in the context of relational database (preferably PostgreSQL)

- Able to use a major programming, preferably Java/Go

- Experience building/operating highly available relational database clusters

- Experience using Linux

- Combination of deep technical skills and business savvy enough to interface with all levels and disciplines within an organization

- Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment

Please get in touch at jobs@kubevisor.com