As someone who often backpacks off grid, the features listed are pretty interesting. Anyone have any alternative products this competes with? The "adventure" watch is a new thing for me
I have heard of Garmin, but neither of the others so they must have a more narrow marketing reach. I will review their product catalog and see how the value compares. I could have an agent go scour the web for product recommendations and substitute products, but I enjoy the discourse and perspective on HN so I like to ask here.
As a user of both Garmin (and Suunto, Coros, Polar would be in the same category) and Apple Watch Ultra, you'll find one main difference again and again: battery life. The not-Apple-watches will last a week or more before charging, AW will make it a day or two.
OTOH, as far as a smart watch goes, AW will be head and shoulders better. For me, it's a lot of little cuts with Garmin. For instance, setting a timer for my tea is like six button presses on the Garmin, and that's with a shortcut keypress to get to the timer. AW: there's a complication on the watch face, one press. Or, "Siri, five minutes". Three presses otherwise. I haven't owned anything from the other brands in a long time, so can't comment on the likes of Suunto or what have you.
Some will tell you that AW is "okay", but "serious athletes" use a Garmin. Meh, whatever. I've got a running resume as long as my arm, and I just preordered the AW Ultra 4 to replace my Garmin (and my AWU 1). AW gives me the data I need, and it will last long enough for me to get through a 100 mile race, so I won't miss much from Garmin. And good $DEITY, Garmin's prices are getting ridiculous. $1200 for a titanium Fenix? I don't care how long the battery lasts, that thing better wax my fucking car.
If you want to save a little money, I've heard nothing but good things about Coros.
No wonder the price of Jetson's is so high. Would love to buy one for hobby purposes but prices just keep rising. Very little way to police the second hand market of chips, as we have seen with China and data center GPU's.
Flying drones with natural language input instead of remote controllers. Instead of having a human on the control sticks the entire time, what if they could describe the goal they are trying to achieve, and then the drone goes and flies according to the agreed intent? I know traditional drones already have autopilots, but they seem to be static pre-planned routes and could benefit from advancing the capability to be more dynamic and flexible. Eventually I want to combine a live camera stream to run a local vision model that would identify and notify images of interest the drone sees while flying. From there the operator can decide if they want to re-task the mission or adjust as needed. That is the general technology direction, but I hope to expand this into the wildland firefighting vertical. Instead of putting humans up in helicopters and fixed wing aircraft, I hope we can leverage drones instead. I understand not every use case will be able to replace with a drone, but enough of them could be to make it worthwhile in my opinion. Still early days of research and development, but super excited about the tech and combining AI + robots for the common good. Currently a huge fan of the startup Seneca, and hope to help expand the industry or join them.
1) I recently published my latest milestone here: jakedecamp.com
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.
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.
I was looking into the MCP route too, and found some libraries abstracting mavlink for this use case (there’s at least one white paper documenting failure modes of LLMs trying to issue mavlink commands without an abstraction), but realized that autopilot like PX4 exists. My use case was more about autonomous flight, and it seemed better to just set waypoints and put some guards on other inputs. When paired with QGroundControl plans, all I needed to do for most flight paths was generate or update a .plan file using an LLM and other methodologies. I wasn’t super happy with the QGroundControl -> Gazebo rendering (no tie into real world terrain out of the box), but it did sort of work out of the box without too much effort!
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.
The video/image perception is why I started looking into this domain in the first place, but I haven't gotten there yet. I hadn't seen Cosys-Airsim! I bet it could work in tandem w/ QGroundControl just like Gazebo does. Might be looking into that :)
Yes, one of the axis on FPV controllers usually is not springing back to neutral position, for throttle. This may seem like a small difference it is important.
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.
