Dennis Gubsky · Maintainer of loomcycle — an Apache-2.0 agentic runtime in Go.
https://loomcycle.dev · https://github.com/denn-gubsky · @[email protected]
Recently I upgraded my TrueNAS box with Ryzen 7 8900 APU + 96GB DDR5. This config runs Ollama + loomcycle (my agentic Go runtime) + loomboard (the agentic UI), all self-hosted. I tired several models and Ollama configurations. For me is important to fit the whole model in GPU and run multiple agents+tool in parallel (see the blog for details: https://loomcycle.dev/blog/local-llms-on-truenas-and-the-fro...). Gemma4 is fast, but hallucinating a lot, especially when using tools. My current favorite is qwen3.6:27b. This is smart and reliable model, which can run tools in agentic configuration, do long researches, generate code, formulas and diagrams. On my hardware the qwen3.6 speed is about 9-13 tok/s, which is not high, but acceptable. The most tricky part is context size balancing for multiple model instances in limited memory. Ollama splits the context memory between model instances and may fail to run if agentic runtime allocates larger context than size than it is available in the context storage.
I'm doing a lot of QA in my startups. There are several layers:
1. One model (Opus) writes a code, another model (Sonnet, Qwen, Kimi) writes unit tests using requirements and code;
2. Full code review by me. Just to understand what is going on in the codebase;
2. Integration tests are running with PlayWright MCP. Another model validates UI per requirements;
3. Substantive runtime tests prepared by me or human QA team. All features should be included into testing, plus regression testing of existing features, plus edge cases.
QA in AI era becomes more important than coding skills. So keep it in a good shape.
In my experience, AI solves the implementation part well - if you know explicitly what should be implemented. If your engineering understanding is fuzzy, the implementation will be fuzzy too.
Engineering and architectural skills matter more now than before. Previously, you had time for rethinking and rearchitecting while coding. That timeline is collapsing - your ideas become real in hours, the bad ones included.
Verification matters more than implementation. When you ship 20 PRs to verify in a single day, every day, all month, how can you be sure the ideas are right, and the implementation is correct? You need ways to challenge your own system: integration tests, runtime tests, and load tests that make you confident in what you're building.
Three skills become non-negotiable in this AI era (until tomorrow, when the answer probably changes again):
1. System Architecture. Build correct framing for your ideas with room for future extensions. Requirements should be complete, precise, and extensible. Project documentation grows with the code and stays current.
2. Organizing AI agentic teams into verifiable flows. Self-evolving specialized agents that verify each other's output until you're confident the code matches the requirements you actually had.
3. Verification, verification, verification. Integration tests, runtime tests, load tests, experiments. The code an AI produces has to be confirmed correct across every dimension that matters.
Thanks for the article. It's super interesting for me, but I have not found a solution for agents running in runtimes without browser. For example on VPS. I'm using WebFetch/WebSearch now, but in some cases I need authorization and cookies. I'm wondering if there is GUI-less browser available, just for agentic usage?
I integrated the gbash and benchmarked it. gbash is not production grade yet. There are issues with tools implementation and compatibility (I added findings as issues) and performance issues (avg ~30% slower than system C utilities, grep is way slower). Here is full report: https://loomcycle.dev/blog/bashbox-in-process-shell-sandbox
Thank you for good advise. The agentic runtime I'm developing uses system Bash and has to spawn system processes. The pure Go is better match. Checking the repo.
Interesting library. I think I could use for agents running in virtual volumes. I maintain an agentic runtime written on Go. What is the best way to incorporate bashkit.sh?
I'm curious what to do if agent self-improvement goes a wrong way. How do you evaluate the agent quality? Which way do you return to the last good branch? How to automate this process?
Nice and very fast indexing tool. Installed by Claude code, and indexed by 3.5K nodes codebase in less than 2 seconds. Outstanding.
The graph looks good, but navigating with the trackpad is not easy. Would be more convenient if zooming centered on mouse position, not in the center of view. So user can select a node by mouse and zoom directly.
Anyways I installed the MCP and will see if code review will work faster now.
This is what was for looking for. I need a visual surface to plan agentic feature development workflow. Don't you mind if I make Paca integration with the AI agentic Go runtime I'm developing? your OpenHands concept with container per agent seem little heavy. I will start from WASM plugin, but in perspective, I would like to make a port replacing the OpenHands on services/ai-agent level.
Thank you for sharing this. I'm going to implement this judgement loop in my AI agenting runtime. Worth to try on local LLM with cloud high-end judge model in a loop + QA model to build and run tests as the second judge. I think both judge and QA agents should have an access to the initial RFC requirements. Seems to be a good approach to save API tokens iterating local LLM as code-writer.