mochi VS secai

Totally agree. It is why we're building Mochi, https://github.com/mochilang/mochi a small language that treats AI, datasets, and graph queries as first-class citizens, not just targets for code generation.

It's inspired by the evolution you mentioned: early compilers generating Assembly, now AI tools generating Python or SQL. Mochi leans into that by embedding declarative data queries, AI generation, and streaming logic directly into the language. Here is how it looks:

  type Person {

Repo: https://github.com/mochilang/mochi

I'm building Mochi, a small programming language with a custom VM and a focus on querying structured data (CSV, JSON, and eventually graph) in a unified and lightweight way.

It started as an experiment in writing LINQ-style queries over real datasets and grew into a full language with:

* declarative queries built into the language

- FFI bindings to expose `fact`, `rule`, and `query` to scripts

Example: [https://chatgpt.com/share/cd\_684d3e3c59c08191b20c49ad97b66e...](https://chatgpt.com/share/cd_684d3e3c59c08191b20c49ad97b66e0...)

Total implementation was \~250 LOC. Genuinely amazed how effective the LLM was at helping bootstrap a real logic layer in one go.

The PR is here https://github.com/mochilang/mochi/pull/616

Your question is “how to test opaque nondeterministic databases”. I test my agents deterministically, because I know how to IoC. Check out this code [0] and follow the usage. In the rest of cases, you assert with embeds. Good luck.

[0] https://github.com/pancsta/secai/blob/74d79ad449c0f60a57b600...

It's nice to see that BEAM is still alive. If you're into actor model / state machine agents, I can recommend secai, which is in Golang [0]. It does have a form of goroutine cancellation. Do you happen to have some screenshots of your devflow in beam? How do you debug?

[0] https://github.com/pancsta/secai

Thanks, terminal UI is an important design choice - it's fast, cheap, and runs everywhere (like the web via wasm / ssh, or on iphones with touch). The LLM layer is still fresh, and I personally use it for web scraping, but the underlying workflow engine is quite mature and ubiquitous - it was used for sync engines, UIs, daemons, network services. It shines when faces complexity, nondeterminism, and retry logic - the more chaotic the flow is, the bigger the gains.

The approach is to shape behavior from chaos by exclusion, instead of defining all possible transitions. With LLMs, this process could be automated and effectively an agent would be dynamically creating itself using a DSL (state schema and predefined states). The great thing about LLMs is being charged by tokens instead of a number of requests. We can just interrogate them about every detail separately and build a flow graph with transparent (and debuggable) reasoning. I also have API sketches for proactive scenarios (originally made for an ML prototype) [0].

[0] https://github.com/pancsta/secai/blob/474433796c5ffbc7ec5744...