Computer-use is a big limitation that my 2015 Macbook Pro cannot handle. I find the Codex cli says it looks at the end output artifact but so often it fails to refine it into acceptable form. If it could use my computer screen and visual inputs for review, it might be able to actually design documents/powerpoints/etc. I'm juicing everything I can out of the 11 year old laptop and I'm honestly impressed at what it can still do.
Same here. I find the design, architecture, system design discussion to be better on Claude, but after Opus 4.6 I switched over to Codex for actual coding and love the results. I use both via the CLI and generally tell Claude to output the result of our decisions as a markdown that will be easy to read and implement by an agentic coding tool. Then I fire up Codex and read said markdown as the input of the session and way to build all the appropriate context needed. I see this as a way to step into letting the agents go run on their own and interact with each other, but I still like to steer so I put these manual steps in the flow. Letting the agents go off on their own and one shot big chunks is not reliable enough yet imo.
Well said. Deciding what to build is derived by experience in the context of your problem space, and that is human centric. Makes me encouraged that as AI tooling increases in adoption, more people will build things to solve their problems. Instead of large corporations hoarding talent and then building things for people, I could see more small business success where they sit closer to the very things that need solving.
Interesting. I've been building around that MCP abstraction and have had some early success flying in Cosys-Airsim (and Gazebo before that): https://github.com/jakedcmp/droneserver . I am starting to realize I need to break apart the MCP interface from all the other pieces of the stack for cleaner architecture but thats pending work. My flow goes like this: LLM -> MCP tools -> droneserver -> MAVSDK/MAVLink -> PX4/Ardupilot -> Cosys-Airsim Software in the Loop testing. What is novel for me is not having to learn how to fly a drone and bringing that capability into already existing technologies like PX4 autopilot. I have been attempting to code my own mission planning so I will check out QGroundControl as that might already be a solved problem and not worth building from scratch. I have also built the foundation of video streaming back from the drone so I can run video/image perception. Once I get perception working I am hoping I can build intent level autonomy where images are analyzed according to high level mission plan and potentially re-task the drone based on that. For example, the user issues simple command to fly around the property and scan for broken parts of the fence. During flight if an image of a broken fence is perceived, then the drone stops its flight and goes closer to capture additional imaging/video and document a gps location. Still hacking things together towards a real demo so the code probably wont port over well but idk. To anyone in this thread who wants to discuss further or collaborate let me know, it seems we are all working in a similar domain but from slightly different angles. Exciting area to build, I know there is big demand for solutions in this space over the next decade.
Very cool, I'm working on a similar project but using MCP as the flight input layer. Would love to dm and discuss more about how you built it or collaborate.
Is crowd strike like a digital twin / virtual world sandbox for autonomous drones? Do you have any additional information I could check out? Been working on autonomous drone flights but eventually need a digital world to experiment in but have yet to reach that step. Debating working with Unreal Engine or NVIDIA omniverse but unsure what the right direction is.