Yes, many servers don’t expose Date to browsers (Access-Control-Expose-Headers), so you can’t read it cross-origin. Also, CDNs/proxies can cache or rewrite headers, my goal was a boring, local /time on the same gateway the tech is testing. Another thing was that Date is seconds-resolution, I want ms and a stable JSON shape. I have found Cloudflare trace to be handy in past, but it’s not my box, it may rate-limit, and adds an extra network hop. For the core question "Is it me or the edge box" I wanted the box itself to answer.
I wanted to see how far WebGPU could go without build steps or frameworks. I simulated 10k robots with collision avoidance entirely on the GPU via HTML hitting ~120 FPS on an M1 Mac.
Note: This is a sanity probe, not a time discipline tool. rtt/2 and browser timers can be noisy, so treat green/red as a hint, not truth, especially on congested factory networks.
The feedback is loud and clear and honestly it's fair, though I feel sad.
My intent was to use Apple as a metaphor for interoperability, but it came across as a fantasy post totally disconnected from the real world hell of industrial automation. I skipped the why (legacy hardware, safety, vendor lock-in) and it made the whole post feel naive. That's on me.
As for the AI feel everyone picked up on, You're not wrong. I wrote the original draft and all the core ideas, but I did use an LLM to help review and polish the language.
I see now that was a huge mistake. It polished away all the personal voice and in the trenches, grit that made my previous posts work. It turned a real idea into that slick, empty keynote feel you all hated.
It's a painful but really valuable lesson in authenticity. Thanks for keeping me honest. I'll stick to the gritty, unpolished engineering problems from now on.
I chose "Debugging Humidity" as a metaphor for all the invisible, pervasive environmental factors that you have to deal with in the physical world. Latency, power flicker, interference, etc. It's the "stuff in the air" that messes up clean logic.
The title was actually inspired by a real incident where a device kept failing every afternoon. We eventually realized that condensation from the facility's massive air conditioning unit was dripping onto the enclosure right above the SoC. We were, quite literally, debugging the effects of humidity. I should have included that story in the post itself.
There is absolutely no substitute for an engineer seeing, smelling, and hearing the environment where their code will actually run.
The number of times a mysterious software glitch on a remote device has been traced back to insect nests, rodent chewed cables, or just a thick layer of industrial grime is not zero. It gives a whole new meaning to flushing the cache :)
Wow, that's a fantastic and terrifying example. "Retrieving the device starts at $10K" is about as high-stakes as it gets for software reliability. It perfectly crystallizes the difference between rebooting a cloud instance for pennies and the extreme costs of failure in the physical world.
Your suggestion of actuator.rotateTo(Degrees(90)) is precisely the right way to engineer a robust, idempotent command for the physical world.
I used the simpler, non-idempotent rotate(90) example intentionally to illustrate the default trap. How a pure software mindset can dangerously oversimplify a physical action.
How we validate crash, pause, and disk‑full on a single node in CI using Docker, tmpfs, SSE watches, and Prometheus—no server code changes, in our code changes
A developer‑focused guide on implementing the Model Context Protocol (MCP) as a cloud‑hosted tooling layer for LLM apps. Grounded in a working implementation we built and deployed; includes tuning notes, metrics, and pitfalls.
This is a great visual guide! I’ve also been working on a similar concept focused on deep understanding - a visual + audio + quiz-driven lesson on LLM embeddings, hosted on app.vidyaarthi.ai.
Our goal is to make abstract concepts more intuitive and interactive — kind of like a "learning-by-doing" approach. Would love feedback from folks here.
(Not trying to self-promote — just sharing a related learning tool we’ve put a lot of thought into.)
Thanks for the feedback, that's a really good feedback, we will add more interactive controls in next try of this for learners to control the flow rather than us controlling it.
I built this lesson using a tool I'm developing to help explain complex systems (like robotic motion control) visually and interactively. Would love any feedback — especially from folks working in robotics, control theory, or simulation.