Posted by eggbrain 1 day ago
Ask HN: What's your current AI agentic setup?
1) What AI subscriptions are you personally paying for? (Claude, ChatGPT/Codex, Cursor, etc)
- Which tiers?
- Have you purchased multiple subscriptions from the same provider (eg two Claude accounts)?
2) Are you using models outside (or instead) of those subscriptions? For example, pay-as-you-go APIs, OpenRouter, or locally hosted models.
- Which models and providers?
3) What coding harnesses and interfaces are you using? Are you sticking with the default CLIs or IDE integrations, or using alternatives like OpenCode, Pi, Aider, Muse Code, or custom-built harnesses?
- What made you switch harnesses (if you switched)?
4) Are you using any agent orchestration or swarm tools?
- For example, Munder Difflin, Gas Town, Ruflo, or something custom.
5) Are you using broader agent platforms like OpenClaw / Hermes Agent / Grok bot / Dots / Muse?
- If you are using them, what are your main use cases?
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I know my workflow has differed significantly from a year ago, and so it would be interesting to capture a snapshot today of what people are using and why.
How do people handle this?
Do you keep everything in the same conversation thread?
Have your agent maintain a task journal. It is like a more detailed commit history, that includes tribal knowledge gained during the task and additional context, eg.
X is incorrect but we cannot change it due to some other service depending
on it, don't use Y as it is deprecated in favor of Z.
Describe the current project or larger task in progress, then give a
breakdown of all the planned sub-tasks with status, explain any learnings,
then note what work is remaining. Write this in a file called HANDOFF.md
Ask AI to record in a projects directory memory: LOG.md, HANDOFF.md, DECISIONS.md, a plan folder. Reference code with absolute paths. Keep repeatable processes in skills. Version control it all, and use a dreaming process across multiple sessions to stop repeat mistakes.
That's what's worked for me, but there's many variations of this.
For big tasks/features, I'm always creating a new git worktree and I'm using a "status repo" skill that creates a sibling status git repository where everything is recorded.
It works quite well whenever I have to revisit something. It's quite token hungry but I have the full trace of what happened in the past.
https://gist.github.com/vkalintiris/3ad526e71a75bcba013aaadc....
Another approach I want to explore is using https://github.com/netdata/netdata (disclaimer, I work at Netdata) to send all OTel logs/traces of LLMs to it. The idea is to replace the status repo skill with something that will allow LLMs to retrieve the history of a given task/feature. It will probably require some "checkpointing" to make this more efficient.
> Try to keep context shoved in a conversation thread under a token limit to save tokens.
That is another solid tip. If using subagents, use the main thread for only the conversation + launching subagents. Tell it explicitly not to implement anything, and only support integration efforts.
Doing so keeps the worker subagent's context clean - so it can stay laser focused on the task.
Would you mind explaining what each file is, and how this works (do you create a skill such that AI is doing this on every prompt)
The context should be self-evident from the code alone, and if it is not, it should be in your head and you should write it down again so that you do not forget it yourself.
Being forced to re-explain stuff to the LLM is good, actually, as that way, one actually thinks about the architecture, choices and limitations.
I've been iterating on this approach for about a year—it makes switching between agents and harnesses trivial.
I still use emacs, I run agents in buffer or in different terminal tabs. I like pi but only claude is approved at work, so I mostly stick to that for consistency.
I'm use 1-3 agents at a time, not more than 2 on the same branch. I have some pipelined workflows but I don't run swarms.. I'm on budget!
My employer pays for copilot, business plan. It also pays for Gemini.
2) Are you using models outside (or instead) of those subscriptions?
Gemini, company-wide. I mostly use it for general research, making clip art, and occasional programming questions / technical questions. I don't use it everyday, but when I use it, I really like it.
I no longer use ChatGPT or Claude's general-purpose chat bots.
3) What coding harnesses and interfaces are you using? Are you sticking with the default CLIs or IDE integrations, or using alternatives like OpenCode, Pi, Aider, Muse Code, or custom-built harnesses?
IDE integration with Visual Studio, and Copilot code reviews on Github. I mostly use agent mode; sometimes I use ask mode, and sometimes I use plan mode. (One very important thing is to instruct copilot, in .github\copilot-instructions.md, to only use its edit tool and not use Powershell to edit files. Otherwise you need to babysit it and approve cryptic find and replace commands.)
For Copilot in Visual Studio, I almost always use Claude Opus 5.5. Some of the GPT models "appear" to work well, but they leave the code harder to maintain.
We now do a round of Copilot reviews with our pull requests in draft mode before asking others to do the review; and we're not spending as much time "reading" code when looking at pull requests.
We don't (yet) have agents running unattended with high-level instructions and then making pull requests.
4) Are you using any agent orchestration or swarm tools?
No
5) Are you using broader agent platforms like OpenClaw / Hermes Agent / Grok bot / Dots / Muse?
No
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One important thing: I'm after my boss to dedicate time (1-2 weeks) for me to set up and learn more of the hands-off tools. I love working with co-pilot, but it's best for work where I need to be hands-on. I really need to be able to offload simple work, or work where lower-quality results are okay, to a bot that I don't need to supervise.
- DeepSeek Chat for reasoning about some idea
- DeepSeek Flash as pay-as-you-go API provider
- Maki as my harness
It is cheap, and I feel like it is a sustainable workflow, both financially and cognitively.
- opencode 2 as the harness it works well for pretty much any specific task
- openrouter to use deepseek flash 4.1 … insanely cheap, works amazingly well and fast
- orca as the orchestrator to manage all the work (self hosted). There’s a mobile app to orchestrate afk too
And yep, vscode to browse repos etc
Full disclosure: I’m the creator of the project.