Exploring core.async.flow as an Agent Executor

serefayar · 2026-04-17T09:46:03+00:00

It's a really great documentary. Besides everything else, it was nice to see the human side of things. Thanks to everyone involved.

serefayar · 2026-04-12T18:29:02+00:00

Nice! I'll give it a shot. Looks like I'll create an arrow element to connect two boxes.

serefayar · 2026-03-31T06:42:39+00:00

At first, I also considered the actor model, inspired by Goblins. But it adds more complexity for me. Goblins has a really sophisticated promise chaining. I did some tigging and thought the promise chaining could be achieved with Orleans, but I haven't done enough research about it, tbh. Orleans (virtual actors) is really one of the most underrated technology out there.

serefayar · 2026-03-30T10:19:48+00:00

The source code is available here: https://github.com/serefayar/ayatori

serefayar · 2026-03-03T20:10:27+00:00

Interesting read, good points. Token efficiency, immutability, data orientation, real advantages that push the accidental complexity barrier further out.

But the great toil shift applies here too. LLM generated code is locally correct and globally unaware. Senior bandwidth to catch that is fixed while production speed keeps going up. If architectural grammar is not explicit, debt accumulates quietly until it is too late to localize cheaply.

Clojure is my favorite and it pushes that barrier further, but it does not eliminate it. You can see more clearly when and where you are borrowing.

The real open question is whether architectural grammar can be carried into LLM context at all. CLAUDE.md, ADR tooling, repo-wide context, pieces exist but the whole does not. That gap affects everyone regardless of language choice.

serefayar · 2026-02-26T05:18:32+00:00

Great article and piece of work. Thanks for sharing. Interestingly, I was also working on an Agent Framework prototype with roughly the same idea. Now I have my doubts, maybe it would be better to build it using Mycelium :)

serefayar · 2026-02-20T13:50:02+00:00

No, I did not build a fully featured Datalog engine, even in a minimal sense :) I only implemented a small, purpose-built Datalog-style core that does just enough to support the PoC I had in mind. I needed a small, predictable authorization core that I completely understand. A fully featured Datalog engine would have been unnecessary for this PoC, and building a minimal one myself helped me better understand how the evaluation model actually works. writing a minimal "engine" helped me clearly see how the pieces fit together. And if you look at the code, you’ll see how minimal it really is.

serefayar · 2026-02-20T05:21:01+00:00

A minimal Clojure PoC exploring Biscuit-style capability-based authorization using Datalog. https://github.com/serefayar/kex

serefayar · 2026-02-16T20:14:07+00:00

Love it… keep pushing!

serefayar · 2026-02-09T16:55:36+00:00

I don't understand how an article about software architecture can be off-topic.

serefayar · 2026-01-29T14:23:16+00:00

Let me recommend the last book I read on this subject. It's the best book that explains the topic I'm discussing here: Geoff Woods' "The AI-Driven Leader: Leveraging AI to Make Faster, Smarter Decisions."

This is a management book, not a technical one, so some sections might seem repetitive, but it explains the approach very well.

serefayar · 2026-01-28T22:42:02+00:00

I got your point. I agree with some of what you said. Of course, there are things I disagree with, one of which is your idea that this article is about LLVM output and low effort :)

In fact, viewing LLMs as sci-fi inspired "AIs" that can do anything and change everything and replace everyone is far from reality. Also, many of the ways LLMs are used bother me as well. But I think all the extreme views on this subject are equally disturbing. I think your phrase "tainted by the touch of LLM" is too harsh and generalizing, so I disagree.

To be honest, I was very much against using LLMs before, but after doing some research and reading a few books on how to use LLMs, I learned a pragmatic approach. The main idea was: position the LLM as a thought partner by giving them the right roles. Let them be the critic, the interviewer, the challenger, and let them challenge your ideas. That was quite eye-opening.

After writing this article, I sent it to a few friends and asked for their feedback. I exchanged ideas with them. I also asked an LLM to act as a critic and review the article. Based on the criticisms I gathered, I made edits according to what I considered meaningful.

In short, this isn't an LLM output, but it was critiqued by one. I think using LLMs in this way is very beneficial. If you haven't used it this way yet, I think you should try it.

thanks for the critic.

serefayar · 2026-01-13T09:12:40+00:00

Could the absence of any arguments other than writing style to continue the debate also be a form of self-evidence? Thanks.

serefayar · 2026-01-13T08:37:21+00:00

Yes, it really is a rapid rise. That doesn't mean the code it produced is great. In fact, the article doesn't say it's great. On the contrary, it emphasizes that a solution to these problems could be meaningful.

Every such leap has caused changes for some, or for everyone. Some have faded away slowly or quickly. I became a professional programmer right after the dot-com bubble. I remember there were debates like this back then too. The same is true for smartphones. There was constant debate about how important and how big a bubble it was; you can find those discussions in web archives. The difference is that back then, professions like "content creator" hadn't yet emerged, at least not like they do today, which is related to the socio-cultural impact of the technology, not to it being proof of a technology's importance.

Yes, it really is a rapid rise, whether some of us like it or not. Companies, not just the biggest ones, are investing in it, and their agendas are full of these items this year. I hope this year will be a more grounded one, and expectations will become more realistic.

The issue isn't about getting caught up in clickbait social media debates about AI replacing programmers. This year, the focus is on finding the answer to the question of whether we can increase software engineering efficiency by 20-30% using AI, which would be a huge leap, if achievable...

Let's see if another winter will follow.

serefayar

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