I don't use AI. At all. And honestly, I would like it gone for everyone. The world would be better off without it.

I use a custom Daemon. I tried OpenClaw and Letta and it was like emailing your AI. I have a 64gb Mac m1 Max and the largest model I could run in openclaw was a 27b because its not built for local AI, it's built for API. I wanted something that you could chat to, collaborate with, build, evalutate the code, make corrections, build, repeat. I couldn't find anything that didn't come with massive latency so I just made my own. Now I'm running a 125B A3B model at 30 tok/s using SSD Streaming and my agent is instant with no latency and can do everything I ask. I use Aider for programming, exa for searching.. I'm happy with it.

I'm using a cli I wrote to orchestrate per-project VMs for sandboxing using libvirt with COW shared golden images to minimise the disk overhead. The VMs get "rebased" periodically onto new golden images for the rootfs, but keep a persistent home FS for longer. I'm going to clean-up the documentation and open source it at some point soon.

I was using oh-my-pi, but currently playing around with Hermes as an alternative.

Exclusively using open router at the moment, mostly on principle around ZDR, and freedom to experiment with harnesses - but financially I'll probably have to compromise on this soon. GLM, Kimi and DeepSeek are the main models I'm using, with some OpenAI usage.

Tmux for remote access and session durability. I'd prefer to find a setup where I could access the same session through a web interface and seamlessly switch back to TUI, ideally spawning sessions through a web hook or similar. I'm going to take a look at Hedr to see if this can improve this.

Probably the two major things I'm planning to do next are looking at adding a bifrost VM that will hold all API keys, and centralise management of MCP configuration, potentially use it's skill repo stuff as well.

I also want to start controlling egress more - probably iron proxy, though tbh I'd like a solution that was relatively liberal whilst still mitigating exfiltration and prompt injection risks. Maybe I can find a curated allow list to use as a base.

Finally, I make a lot of use of open-webui, but it's disjoint from the agentic setup currently - it'd be nice to find a better way to share information/research from it into the agentic sessions.

I have had a $20 codex subscription. I initially just used codex cloud but more recently have been using opencode with the open chamber ui over the top of it.

Codex usage limits have really gone down hill so I also now have a $10 opencode go subscription on which I pretty much just use DeepSeek 4.1 flash. I’ve found DS to be a great model - probably somewhere just below GPT 6.1 Sol but at a fraction of the price.

In terms of workflow, I discuss features with chat gpt (because it’s free and it has access to my project’s repo, read only view of prod logs etc, so can make informed decisions). Once we’ve agreed on how a feature should look I ask it to create a spike github issue. I then get another agent in opencode (usually DeepSeek flash or Luna) to break it up into implementation tickets.

When I’m ready to build the feature I can give open chamber a list of tickets to work through using the task plugin, so each ticket gets a fresh context. I try to build directly on the main branch and try to ensure we can always deploy to prod even if a feature is half finished (using feature flags etc to switch it off until it’s ready).

Once a feature has been implemented I get GPT Sol 6.1 to do exploratory / regression testing with a real browser against a test environment. This usually teases out a lot of edge cases I hadn’t considered and also gives me a chance to improve usability etc.

It often takes a few iterations of this (exploratory testing, fixing issues that are found, exploratory testing again…) before a feature is ready to release.

I can’t believe how far things have come since August 2025 when I first tried out codex.

My day job has a Claude account, but for personal projects, I have been playing with different models and environments on my home server. My current setup is Qwen 3.8 Flash Next, Strata, and OpenCode/OpenChamber.

I already had a bunch of my projects on the server with with code-server (VS code in a browser) set up to access it, so switching between code-server and OpenChamber is just switching tabs.

However, I've mostly been managing it from my phone - the OpenChamber app is awesome. I still switch back to my laptop occasionally - it's a better interface for reading and writing code, and also the desktop OpenChamber app has a built-in browser that it can control, which comes in handy. I can bring up documentation in one tab, log into a service in another, and then tell the agent to do something. For example, this was how I got it to connect my Nest thermostat to my Home Assistant instance after I had tried and failed several times on my own.)

Oh, and separate from all that, I have Facebook muse set to automatically lower prices on a couple things I have on FB marketplace.

I use the Zed IDE with Opus 5.5 pro subscription. I use to have access to Fable with the max plan and I miss it, despite what everyone is saying I still find that Opus does weird things. Just yesterday I pointed it to a queue suddenly being twice slower to be processed in prod. A select SQL query called thousands of times per minute became slower because of a data increased. Opus proposed several non trivial ways to optimize the query by forcing some execution plan. I just kept the result in a variable for one minute, as the data don't change often. x1000 perf with 3 lines of code. I would have expected it to say that this query didn't have to be executed that many times in the first place. It codes well when you tell it what to do, but it is still dumb about what is the real problem (and so the real solution). That's worrying me a bit because Opus 5.5 clearly is SOTA, so better than any open weights models, and I really want to be able to run smart models locally on my laptop for independence and privacy reasons. So, if I still find Opus dumb now, when will I have an open weight model better than 5.5 that can run on my Ryzen 7 AI 350 with its Radeon 860m and 32GB of RAM? ... This summer with all the releases of DeepSeek and Qwen I was hopeful I could have it for Christmas, but it looks like I will have to wait much more...

1. Claude Code (Ultra) + Codex (Plus) 2. Yes, but not for coding. In my experience Opus 5.5 is the only model I'd code with now, but other tasks (especially when not subsidized / part of product) use many other models. Latest deepseek checkpoint is very solid 3. CC + extensive config / hooks 4. Usually supervising myself w/ 5-6 sessions, if orchestrating more than that it's a hand-rolled swarm tool 5. Tried NanoClaw, and I have access to Claude Duet, but between getting on and off the waitlist for Duet, I hand-rolled a personal agent that does what I want.

I built a Qt GUI-based harness, Humbug, that runs on MacOS, Linux, and Windows (I have reasons to use all 3). Has only 3 runtime dependencies other than the Python standard library: https://github.com/m6r-ai/humbug

Usually have 3 or 4 instances running at a time and lets me use LLMs from 9 different providers (and not using their libraries either). The low dependency approach was slightly limiting early on but now makes it very easy to extend and makes it very easy for an LLM to integrate new features or optimize existing ones.

I typically use Claude Sonnet for document-related tasks, and currently DeepSeek 4.1 Flash is my current go to for coding (it's insanely cheap on ollama.com) and I find it's still fast and reliable at very large context lengths. Previously I used GLM 5.2 a lot and before that was Sonnet for most things.

Humbug lets one AI delegate tasks to another and it can choose different models/providers when it does, so works nicely for getting second opinions on things. I haven't yet found a need for an agent swarm though - more often the parallelism comes from working on multiple projects at the same time.

One of the biggest unlocks for me in the last few months is having AIs write ADRs to capture design thinking as a project progresses. It lets new AI sessions discover the intent behind the current work and surface any potential conflicts that might arise with new requests.

One fun feature is the AI can control pretty-much any UI surface the user can control too, and it's quite fun to watch it edit files (you can watch it scrolling around the file in an editor) or use a terminal. It's a nice visual way to see what's going on.

I'm on a Claude and a GPT plan, with occasional Deepseek, GLM and other models for testing.

I work on cross-platform apps, so need to run things on a PC as well as my Mac. To make it less faff to switch between them, I run Juggler (https://github.com/juggler-ai/juggler) on both. Juggler treats local and remote sessions the same way, so I have both machines set up with the same two windows, one for the PC and one for the Mac. That means I can move seats between them and have the exact same two windows controlling each machine wherever I am..

Phasing out my usage of cloud based LLMs/harnesses.

Focus is pi + self hosted GLM Flash.

Pi is lightweight and easily extensible and creates minimal extra noise. the output I get is very close to what I want to see.

my workflow is focused on clear turns between planning, testing and implementation. Any useful/recurring tasks, like getting/filtering logs, checking statuses etc get regularly folded into a project cli and agent skills that are local to the project.

I spend time interactively iterating on the plan/design and acceptance tests, then let it do its thing. Usually I have multiple interactive sessions at once that I'm steering this way. 1-2 assigned projects, 1-3 side projects (additional automation, experiments/POCs, workflow/process efficiency etc.). I generally don't do sub-agents. When I need throughput I automate first, frontloading the "deterministic" parts.