connectedhomeip VS open-gpu-kernel-modules

They do have example Matter projects for the ESP32-C3/S3. I assume it's over WiFi and not thread.

https://github.com/project-chip/connectedhomeip/tree/master/...

IoT is a big space right now if you look at the companies who commit to https://github.com/project-chip/connectedhomeip. They come together at the CSA Member Meeting.

I followed this doc: https://github.com/project-chip/connectedhomeip/blob/master/docs/guides/esp32/setup_idf_chip.md

"Securing name resolution in the IoT: DNS over CoAP" https://news.ycombinator.com/item?id=32186286

From https://github.com/project-chip/connectedhomeip#architecture... :

> Matter aims to build a universal IPv6-based communication protocol for smart home devices. The protocol defines the application layer that will be deployed on devices and the different link layers to help maintain interoperability. The following diagram illustrates the normal operational mode of the stack:

> [...] It is built with market-proven technologies using Internet Protocol (IP) and is compatible with Thread and Wi-Fi network transports.

> Matter was developed by a Working Group within the Connectivity Standards Alliance (Alliance). This Working Group develops and promotes the adoption of the Matter standard, a royalty-free connectivity standard to increase compatibility among smart home products, with security as a fundamental design tenet. The vision that led major industry players to come together to build Matter is that smart connectivity should be simple, reliable, and interoperable.

> [...] The code examples show simple interactions, and are supported on multiple transports -- Wi-Fi and Thread -- starting with resource-constrained (i.e., memory, processing) silicon platforms to help ensure Matter’s scalability.

Is there already a good (security) comparison of e.g. http basic auth, x10, ZigBee, mqtt, matter?

Bloaty is a nice tool.

When I worked on Matter a couple years ago, we had the problem that its backend http://www.capstone-engine.org/ did not support Xtensa, and produced some Python tools that could take output from bloaty or similar data from readelf or elftools, and produce several kinds of report.

https://github.com/project-chip/connectedhomeip/blob/master/...

My two cents from someone who has dealt with product name changes. Most recently I worked on the Chip repo which is now known as Matter. There are numerous references in the repo to chip despite the name change. See https://github.com/project-chip/connectedhomeip

Not sure if I'm asking in the right place so I apologise ahead. I'm trying to get one of the example projects from Matter onto my esp32 (ESP-WROOM-32) using espresiff. I am doing this on my raspberry pi btw. The problem I'm having now is that it get's stuck when I'm building (idf.py build command). It's always around 800/1135 and it doesn't go past it. The one where it is currently stuck is: [804/1135] Building C object esp-idf/bt/CMakeFiles/__idf_bt.dir/host/nimble/nimble/nimble/host/src/ble_gatts_lcl.c.obj.

I checked out v1.0.0 of the Matter repo and ran

I'm trying to build basic examples (light device for ESP32, Python controller for Raspberry Pi) following the guides on the official repository (https://github.com/project-chip/connectedhomeip). After solving some build errors, I managed to flash the ESP32 and run the controller on the RPi. However, I couldn't manage to commission the ESP32 from the RPi, because I kept getting a weird "mDNS: Timeout" error. Did anyone here have success with it? What am I missing?

I also love that it can be done with just a few code line changes:

https://github.com/NVIDIA/open-gpu-kernel-modules/commit/1f4...

> I do love AMD because its drivers are open source as opposed to nVidia.

AMD's drivers are not really more open that Nvidia's. Similar to Nvidia's Open GPU Kernel Module's[0], AMD's opensource drivers are mostly a shim that wrap firmware blobs[1] in which the functionality you really care about is contained.

[0] https://github.com/NVIDIA/open-gpu-kernel-modules/discussion...

My understanding is that nowadays most of the heavy lifting is done by magic going on in the firmware, so the actual driver is relatively simple and is open source: https://github.com/NVIDIA/open-gpu-kernel-modules

Check this issue

That's only for a small subset of their more recent GPUs, as you can see here: https://github.com/NVIDIA/open-gpu-kernel-modules#compatible...

Update: it is a driver issue, see https://github.com/NVIDIA/open-gpu-kernel-modules/issues/522