Donna — predictability and controllability for your agents
Looking for early adopters for my agent utility.
I named the tool Donna — https://github.com/Tiendil/donna
With it, your agents will generate state machines while executing state machines created by other state machines.
Donna allows agents to perform hundreds of sequential operations without deviating from the specified execution flow. Branching, loops, nested calls, recursion — all possible.
Most other tools just send meta-instructions to agents and hope they won't make mistakes. Of course, agents make mistakes: they mix up steps, skip operations, misinterpret instructions. Donna truly executes state machines: it maintains state and a call stack, controls an execution flow. Agents only execute specific commands from Donna.
However, Donna is not an orchestrator, but just a utility, so it can be used anywhere, no API keys, passwords, etc. needed.
The core idea:
- Most high-level work is more algorithmic than it may seem at first glance.
- Most low-level work is less algorithmic than it may seem at first glance.
Pricing model at the start of Feeds Fun monetization
Right after I started working on the pricing for Feeds Fun users, I realized I should do it in a blog post: it's almost the same amount of work, it's the ideologically right thing to do, and it should be interesting. Anyway, I was going to write an RFC — the question is purely about publicity. I'm also taking the opportunity to conduct a retrospective on the project for myself.
What is Feeds Fun
Feeds Fun is my news reader that uses LLM to tag each news item so users can create rules like elon-musk + mars => -100, nasa + mars => +100. That effectively allows filtering the news stream, cutting it down by 80-90% (my personal experience) — no black-box "personalization" algorithms like in Google or Facebook; everything is transparent and under your control.
So, meet a free-form essay on monetization of a B2C SaaS dependent on LLM — couldn't be more relevant :-D
Results of 2025 for me and my blog
Blog metrics for 2025.
The New Year is near, so it's time to sum up the results of the year. Let me tell you what I was doing in 2025, how my plans for the past year went, and what my plans are for the coming year.
LLM agents are still unfit for real-world tasks
AI agents show their work to a programmer (c) ChatGPT & Hieronymus Bosch.
This week, I tested LLMs on real tasks from my day-to-day programming. Again.
Want a cool open source project in your portfolio?
A plea to the universe for a humane auth proxy (c) ChatGPT & Hieronymus Bosch.
As a mid-journey summary of my struggles with OAuth2/OIDC, I can say this: I haven't seen such amount of abstraction leaks and half-baked implementations, as in modern open-source authentication proxies, for a very long time — if ever.
Sure, it's great that such proxies exist at all and that there's something to choose from. It's also clear that they were made by enterprise developers to cover their very specific enterprise pains — most likely as side projects alongside their main products. But still… damn.
If you ever wanted a cool open source project to show off in your portfolio, grab Rust or Go and build a small auth proxy with OIDC and OAuth2 support that simply works. Something not aimed at corporations with Kubernetes clusters, but at small companies and indie developers who need to quickly plug a functionality gap without touching their app code. The situation where you have to modify backend code just to make the proxy work is pure madness.
People will bow to you :-) Especially now, when OAuth2 has suddenly become even more essential, since it's required by the Model Context Protocol.