Software 3.1? – AI Functions

Good comp. Working with expensive materials and stuff that can explode while people are inside by necessity forces a greater scrutiny of good vs. bad ideas. You don't get that ideal balance between experimentation and wisdom when anyone can type anything into an editor at no cost.

It is a horrific idea because it takes the energy requirement of Python and multiplies it by a 1000. I guess they're looking for people they can fool.

"Code will be in constant flux and nobody but the allocated llm will understand it"

A lot of valid concerns against arb code execution in prod for security, performance, auditability etc.

However, I do resonate somewhat with the post if I think about some accounting processes.

Accounting is where I came from, and a lot of data processing we do is mostly determinstic, with some "smartness" or judgement sprinkled in. Take for example bank reconciliation, the basic process is to match bank statement lines with accounting entry lines. In practice, dates, descriptions, and amounts often mismatch between the 2 for various reasons (typos, grouped bookings, value date vs transaction date differences, truncated values). This impacts a lot of SME's and these basic accounting processes are still manual as you need eyeballing. You look at a typical back office excel spreadsheet and will understand this.

You can pre-program the matching rules up to a certain point until it becomes unmaintainable. Or you can use LLM to generate data-dependent matching logic on the fly. I think there is a space for the latter approach, if we keep the scope tight and well contained. As with all engineering, it's about the trade-offs.

Useful targets for LLM to generate can be subsets of sql statements (create views and selects) or pure functions (haskell?), where side effects are strictly limited and there is only data in - data out. I am toying with SQL idea myself (GH: https://github.com/spoj/taskgraph).

Part of the Strands team here. We hope AI functions will expand the tool belt of programmers. They aren't meant to replace regular Python, but to handle the inherently unpredictable parts of a pipeline that require an agent: parsing unstructured uploads, normalizing messy user input, research tasks, etc.

With AI Functions and post-conditions, we want to make this process more robust, ergonomic and cheaper: you don't always need a frontier model for ambiguous tasks. Smaller/faster agents can do the work if you have robust correctness checks.

On the roadmap: JIT-compiled functions that reuse previously generated code to cut costs, LLM-based backprop for learning/memory/prompt tuning, and strong remote sandboxing for code execution. We're focused on getting the DevX right before shipping these — happy to answer questions.