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Hi cody fellows,
I already solved more than 500 problems -months ago, last july if I remember well- and get this scholar badge, but then it suddenly disappeared a few weeks later. I then solved a few more problems and it reappeared.
Now I observed it disappeared once more a few days ago.
Have you also noticed this erratic behavior of the scholar badge ? Is it normal and / or intentional ? If not, how to explain it ? (deleted problems ?)
Cheers,
Nicolas
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Large Language Models (LLMs) with MATLAB was updated again today to support the newly released OpenAI models GPT-5, GPT-5 mini, GPT-5 nano, GPT-5 chat, o3, and o4-mini. When you create an openAIChat object, set the ModelName name-value argument to "gpt-5", "gpt-5-mini", "gpt-5-nano", "gpt-5-chat-latest", "o4-mini", or "o3".
This is version 4.4.0 of this free MATLAB add-on that lets you interact with LLMs on MATLAB. The release notes are at Release v4.4.0: Support for GPT-5, o3, o4-mini · matlab-deep-learning/llms-with-matlab
Here is a screenshot of a Cody problem that I just created. The math rendering is poor. (I have since edited the problem to remove the math formatting.)
Are there any code restrictions for programming Cody solutions? I could not find anything mentioned at https://www.mathworks.com/matlabcentral/content/cody/about.html, other than toolbox functions not being available.
I'm seeing solution maps shown with low-contrast gray colors instead of the correct symbol colors. I have observed this using both Safari and Chrome. Screenshot:
Hey everyone,
I’m currently working with MATLAB R2025b and using the MQTT blocks from the Industrial Communication Toolbox inside Simulink. I’ve run into an issue that’s driving me a bit crazy, and I’m not sure if it’s a bug or if I’m missing something obvious.
Here’s what’s happening:
  • I open the MQTT Configure block.
  • I fill out all the required fields — Broker address, Port, Client ID, Username, and Password.
  • When I click Test Connection, it says “Connection established successfully.” So far so good.
  • Then I click Apply, close the dialog, set the topic name, and try to run the simulation.
  • At this point, I get the following error:Caused by: Invalid value for 'ClientID', 'Username' or 'Password'.
  • When I reopen the MQTT config block, I notice that the Password field is empty again — even though I definitely entered it before and the connection test worked earlier.
It seems like Simulink is somehow not saving the password after hitting Apply, which leads to the authentication error during simulation.
Has anyone else faced this? Is this a bug in R2025b, or do I need to configure something differently to make the password persist?
Would really appreciate any insights, workarounds, or confirmations from anyone who has used MQTT in Simulink recently.
Thanks in advance!
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David
David
Last activity on 20 Oct 2025 at 21:26

I just learned you can access MATLAB Online from the following shortcut in your web browser: https://matlab.new
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I'm working on training neural networks without backpropagation / automatic differentiation, using locally derived analytic forms of update rules. Given that this allows a direct formula to be derived for the update rule, it removes alot of the overhead that is otherwise required from automatic differentiation.
However, matlab's functionalities for neural networks are currently solely based around backpropagation and automatic differentiation, such as the dlgradient function and requiring everything to be dlarrays during training.
I have two main requests, specifically for functions that perform a single operation within a single layer of a neural network, such as "dlconv", "fullyconnect", "maxpool", "avgpool", "relu", etc:
  • these functions should also allow normal gpuArray data instead of requiring everything to be dlarrays.
  • these functions are currently designed to only perform the forward pass. I request that these also be designed to perform the backward pass if user requests. There can be another input user flag that can be "forward" (default) or "backward", and then the function should have all the necessary inputs to perform that operation (e.g. for "avgpool" forward pass it only needs the avgpool input data and the avgpool parameters, but for the "avgpool" backward pass it needs the deriviative w.r.t. the avgpool output data, the avgpool parameters, and the original data dimensions). I know that there is a maxunpool function that achieves this for maxpool, but it has significant issues when trying to use it this way instead of by backpropagation in a dlgradient type layer, see (https://www.mathworks.com/matlabcentral/answers/2179587-making-a-custom-way-to-train-cnns-and-i-am-noticing-that-avgpool-is-significantly-faster-than-maxpo?s_tid=srchtitle).
I don't know how many people would benefit from this feature, and someone could always spend their time creating these functionalities themselves by matlab scripts, cuDNN mex, etc., but regardless it would be nice for matlab to have this allowable for more customizable neural net training.
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38 votes
Patrick
Patrick
Last activity on 16 Oct 2025 at 17:15

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