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Results for
The Cody Contest 2025 has officially wrapped up! Over the past 4 weeks, more than 700 players submitted over 20,000 solutions. In addition, participants shared 20+ high-quality Tips & Tricksarticles—resources that will benefit Cody users for years to come.
Now it’s time to announce the winners.
🎉 Week 4 winners:
Weekly Prizes for Contest Problem Group Finishers:
@JKMSMKJ, @Yu Zhang, @Oliver Jaros, @Pauli Huusari, @Karl, @Marcos Silveira, @goc3, @Ildeberto de los Santos Ruiz, @Norberto, @Eric
Weekly Prizes for Contest Problem Group Solvers:
Weekly Prizes for Tips & Tricks Articles:
This week’s prize goes to @WANG Zi-Xiang. See the comments from our judge and problem group author @Matt Tearle:
‘We had a lot of great tips for solving Cody problems in general and the contest problems specifically. But we all know there are those among us who, having solved the problem, still want to tinker and make their code better. There are different definitions of "better", but code size remains the base metric in Cody. Enter Wang Zi-Xiang who compiled a list of many tips for reducing Cody size. This post also generated some great discussion (even prompting our insane autocrat, Lord Ned himself, to chime in). I particularly like the way that, while reducing Cody size often requires some arcane tricks that would normally be considered bad coding practice, the intellectual activity of trying to "game the system" makes you consider different programming approaches, and sometimes leads you to learn corners of MATLAB that you didn't know.’
🏆 Grand Prizes for the Main Round
Team Relentless Coders:
2nd Place: @Roberto
Team Creative Coders:
Team Cool Coders
Congratulations to all! Securing a top position on the leaderboard requires not only advanced MATLAB skills but also determination and consistency throughout the four-week contest. You will receive Amazon gift cards.
🥇 Winning Team
The competition was incredibly tight—we even had to use the tie-breaker rule.
Both Team Cool Coders and Team Relentless Coders achieved 16 contest group finishers. However, the last finisher on Cool Coders completed the problem group at 1:02 PM on Dec 7, while the last finisher on Relentless Coders finished at 9:47 PM the same day.
Such a close finish! Congratulations to Team Cool Coders, who have earned the Winning Team Finishers badge.
🎬 Bonus Round
Invitations have been sent to the 6 players who qualified for the Bonus Round. Stay tuned for updates—including the Big Watch Party afterward!
Congratulations again to all winners! We’ll be reaching out after the contest ends. It has been an exciting, rewarding, and knowledge-packed journey.
See you next year!
Over the past three weeks, players have been having great fun solving problems, sharing knowledge, and connecting with each other. Did you know over 15,000 solutions have already been submitted?
This is the final week to solve Cody problems and climb the leaderboard in the main round. Remember: solving just one problem in the contest problem group gives you a chance to win MathWorks T-shirts or socks.
🎉 Week 3 Winners:
Weekly Prizes for Contest Problem Group Finishers:
@Umar, @David Hill, @Takumi, @Nicolas, @WANG Zi-Xiang, @Rajvir Singh Gangar, @Roberto, @Boldizsar, @Abi, @Antonio
Weekly Prizes for Contest Problem Group Solvers:
Weekly Prizes for Tips & Tricks Articles:
This week’s prize goes to @Cephas. See the comments from our judge and problem group author @Matt Tearle:
'Some folks have posted deep dives into how to tackle specific problems in the contest set. But others have shared multiple smaller, generally useful tips. This week, I want to congratulate the cumulative contribution of Cool Coder Cephas, who has shared several of my favorite MATLAB techniques, including logical indexing, preallocation, modular arithmetic, and more. Cephas has also given some tips applying these MATLAB techniques to specific contest problems, such as using a convenient MATLAB function to vectorize the Leaderboard problem. Tip for Problem 61059 – Leaderboard for the Nedball World Cup:'
Congratulations to all Week 3 winners! Let’s carry this momentum into the final week!
In just two weeks, the competition has become both intense and friendly as participants race to climb the team leaderboard, especially in Team Creative, where @Mehdi Dehghan currently leads with 1400+ points, followed by @Vasilis Bellos with 900+ points.
There’s still plenty of time to participate before the contest's main round ends on December 7. Solving just one problem in the contest problem group gives you a chance to win MathWorks T-shirts or socks. Completing the entire problem group not only boosts your odds but also helps your team win.
🎉 Week 2 Winners:
Weekly Prizes for Contest Problem Group Finishers:
@Cephas, @Athi, @Bin Jiang, @Armando Longobardi, @Simone, @Maxi, @Pietro, @Hong Son, @Salvatore, @KARUPPASAMYPANDIYAN M
Weekly Prizes for Contest Problem Group Solvers:
Weekly Prizes for Tips & Tricks Articles:
This week’s prize goes to @Athi for the highly detailed post Solving Systematically The Clueless - Lord Ned in the Game Room.
Comment from the judge:
Shortly after the problem set dropped, several folks recognized that the final problem, "Clueless", was a step above the rest in difficulty. So, not surprisingly, there were a few posts in the discussion boards related to how to tackle this problem. Athi, of the Cool Coders, really dug deep into how the rules and strategies could be turned into an algorithm. There's always more than one way to tackle any difficult programming problem, so it was nice to see some discussion in the comments on different ways you can structure the array that represents your knowledge of who has which cards.
Congratulations to all Week 2 winners! Let’s keep the momentum going!
In just one week, we have hit an amazing milestone: 500+ players registered and 5000+ solutions submitted! We’ve also seen fantastic Tips & Tricks articles rolling in, making this contest a true community learning experience.
And here’s the best part: you don’t need to be a top-ranked player to win. To encourage more casual and first-time players to jump in, we’re introducing new weekly prizes starting Week 2!
New Casual Player Prizes:
- 5 extra MathWorks T-shirts or socks will be awarded every week.
- All you need to qualify is to register and solve one problem in the Contest Problem Group.
Jump in, try a few problems, and don’t be shy to ask questions in your team’s channel. You might walk away with a prize!
Week 1 Winners:
Weekly Prizes for Contest Problem Group Finishers:
@Mazhar, @Julien, @Mohammad Aryayi, @Pawel, @Mehdi Dehghan, @Christian Schröder, @Yolanda, @Dev Gupta, @Tomoaki Takagi, @Stefan Abendroth
Weekly Prizes for Tips & Tricks Articles:
We had a lot of people share useful tips (including some personal favorite MATLAB tricks). But Vasilis Bellos went *deep* into the Bridges of Nedsburg problem. Fittingly for a Creative Coder, his post was innovative and entertaining, while also cleverly sneaking in some hints on a neat solution method that wasn't advertised in the problem description.
Congratulations to all Week 1 winners! Prizes will be awarded after the contest ends. Let’s keep the momentum going!
What a fantastic start to Cody Contest 2025! In just 2 days, over 300 players joined the fun, and we already have our first contest group finishers. A big shoutout to the first finisher from each team:
- Team Creative Coders: @Mehdi Dehghan
- Team Cool Coders: @Pawel
- Team Relentless Coders: @David Hill
- 🏆 First finisher overall: Mehdi Dehghan
Other group finishers: @Bin Jiang (Relentless), @Mazhar (Creative), @Vasilis Bellos (Creative), @Stefan Abendroth (Creative), @Armando Longobardi (Cool), @Cephas (Cool)
Kudos to all group finishers! 🎉
Reminder to finishers: The goal of Cody Contest is learning together. Share hints (not full solutions) to help your teammates complete the problem group. The winning team will be the one with the most group finishers — teamwork matters!
To all players: Don’t be shy about asking for help! When you do, show your work — include your code, error messages, and any details needed for others to reproduce your results.
Keep solving, keep sharing, and most importantly — have fun!
The main round of Cody Contest 2025 kicks off today! Whether you’re a beginner or a seasoned solver, now’s your time to shine.
Here’s how to join the fun:
- Pick your team — choose one that matches your coding personality.
- Solve Cody problems — gain points and climb the leaderboard.
- Finish the Contest Problem Group — help your team win and unlock chances for weekly prizes by finishing the Cody Contest 2025 problem group.
- Share Tips & Tricks — post your insights to win a coveted MathWorks Yeti Bottle.
- Bonus Round — 2 players from each team will be invited to a fun live code-along event!
- Watch Party – join the big watch event to see how top players tackle Cody problems
Contest Timeline:
- Main Round: Nov 10 – Dec 7, 2025
- Bonus Round: Dec 8 – Dec 19, 2025
Big prizes await — MathWorks swag, Amazon gift cards, and shiny virtual badges!
We look forward to seeing you in the contest — learn, compete, and have fun!
From my experience, MATLAB's Deep Learning Toolbox is quite user-friendly, but it still falls short of libraries like PyTorch in many respects. Most users tend to choose PyTorch because of its flexibility, efficiency, and rich support for many mathematical operators. In recent years, the number of dlarray-compatible mathematical functions added to the toolbox has been very limited, which makes it difficult to experiment with many custom networks. For example, svd is currently not supported for dlarray inputs.
This link (List of Functions with dlarray Support - MATLAB & Simulink) lists all functions that support dlarray as of R2026a — only around 200 functions (including toolbox-specific ones). I would like to see support for many more fundamental mathematical functions so that users have greater freedom when building and researching custom models. For context, the core MATLAB mathematics module contains roughly 600 functions, and many application domains build on that foundation.
I hope MathWorks will prioritize and accelerate expanding dlarray support for basic math functions. Doing so would significantly increase the Deep Learning Toolbox's utility and appeal for researchers and practitioners.
Thank you.
Pick a team, solve Cody problems, and share your best tips and tricks. Whether you’re a beginner or a seasoned MATLAB user, you’ll have fun learning, connecting with others, and competing for amazing prizes, including MathWorks swags, Amazon gift cards, and virtual badges.
How to Participate
- Join a team that matches your coding personality
- Solve Cody problems, complete the contest problem group, or share Tips & Tricks articles
- Bonus Round: Two top players from each team will be invited to a fun code-along event
Contest Timeline
- Main Round: Nov 10 – Dec 7, 2025
- Bonus Round: Dec 8 – Dec 19, 2025
Prizes (updated 11/19)
- (New prize) Solving just one problem in the contest problem group gives you a chance to win MathWorks T-shirts or socks each week.
- Finishing the entire problem group will greatly increase your chances—while helping your team win.
- Share high-quality Tips & Tricks articles to earn you a coveted MathWorks Yeti Bottle.
- Become a top finisher in your team to win Amazon gift cards and an invitation to the bonus round.
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.
Edit 15 Oct 2025: Removed incorrect code. Replaced symmatrix2sym and symfunmatrix2symfun with sym and symfun respectively (latter supported as of 2024b).
The Symbolic Math Toolbox does not have its own dot and and cross functions. That's o.k. (maybe) for garden variety vectors of sym objects where those operations get shipped off to the base Matlab functions
x = sym('x',[3,1]); y = sym('y',[3,1]);
which dot(x,y)
dot(x,y)
which cross(x,y)
cross(x,y)
clearvars
x = symmatrix('x',[3,1]); y = symmatrix('y',[3,1]);
z = symmatrix('z',[1,1]);
sympref('AbbreviateOutput',false);
dot() expands the result, which isn't really desirable for exposition.
eqn = z == dot(x,y)
Also, dot() returns the the result in terms of the conjugate of x, which can't be simplifed away at the symmatrix level
assumeAlso(sym(x),'real')
class(eqn)
try
eqn = z == simplify(dot(x,y))
catch ME
ME.message
end
To get rid of the conjugate, we have to resort to sym
eqn = simplify(sym(eqn))
but again we are in expanded form, which defeats the purpose of symmatrix (et. al.)
But at least we can do this to get a nice equation
eqn = z == x.'*y
dot errors with symfunmatrix inputs
clearvars
syms t real
x = symfunmatrix('x(t)',t,[3,1]); y = symfunmatrix('y(t)',t,[3,1]);
try
dot(x,y)
catch ME
ME.message
end
Cross works (accidentally IMO) with symmatrix, but expands the result, which isn't really desirable for exposition
clearvars
x = symmatrix('x',[3,1]); y = symmatrix('y',[3,1]);
z = symmatrix('z',[3,1]);
eqn = z == cross(x,y)
And it doesn't work at all if an input is a symfunmatrix
syms t
w = symfunmatrix('w(t)',t,[3,1]);
try
eqn = z == cross(x,w);
catch ME
ME.message
end
In the latter case we can expand with
eqn = z == cross(sym(x),symfun(w)) % x has to be converted
But we can't do the same with dot (as shown above, dot doesn't like symfun inputs)
try
eqn = z == dot(sym(x),symfun(w))
catch ME
ME.message
end
Looks like the only choice for dot with symfunmatrix is to write it by hand at the matrix level
x.'*w
or at the sym/symfun level
sym(x).'*symfun(w) % assuming x is real
Ideally, I'd like to see dot and cross implemented for symmatrix and symfunmatrix types where neither function would evaluate, i.e., expand, until both arguments are subs-ed with sym or symfun objects of appropriate dimension.
Also, it would be nice if symmatrix could be assumed to be real. Is there a reason why being able to do so wouldn't make sense?
try
assume(x,'real')
catch ME
ME.message
end
Something that I periodically wonder about is whether an integration with the Rubi integration rules package would improve symbolic integration in Matlab's Symbolic Toolbox. The project is open-source with an MIT-licensed, has a Mathematica implementation, and supposedly SymPy is working on an implementation. Much of my intrigue comes from this 2022 report that compared the previous version of Rubi (4.16.1) against various CAS systems, including Matlab 2021a (Mupad):
While not really an official metric for Rubi, this does "feel" similar to my experience computing symbolic integrals in Matlab Symbolic Toolbox vs Maple/Mathematica. What do y'all think?
Have you ever been enrolled in a course that uses an LMS and there is an assignment that invovles posting a question to, or answering a question in, a discussion group? This discussion group is meant to simulate that experience.
The functionality would allow report generation straight from live scripts that could be shared without exposing the code. This could be useful for cases where the recipient of the report only cares about the results and not the code details, or when the methodology is part of a company know how, e.g. Engineering services companies.
In order for it to be practical for use it would also require that variable values could be inserted into the text blocks, e.g. #var_name# would insert the value of the variable "var_name" and possibly selecting which code blocks to be hidden.
Modern engineering requires both robust hardware and powerful simulation tools. MATLAB and Simulink are widely used for data analysis, control design, and embedded system development. At the same time, Kasuo offers a wide range of components—from sensors and connectors to circuit protection devices—that engineers rely on to build real-world systems.
By combining these tools, developers can bridge the gap between simulation and implementation, ensuring their designs are reliable and ready for deployment.
Example Use Case: Sensor Data Acquisition and Processing
- Kasuo Hardware Setup
- Select a Kasuo sensor (e.g., temperature, microphone, or motion sensor).
- Connect it to a DAQ or microcontroller board for data collection.
- Data Acquisition in MATLAB
- Use MATLAB’s Data Acquisition Toolbox to stream sensor data directly.
- Example snippet:
s = daq("ni");
addinput(s,
"Dev1", "ai0", "Voltage");
data = read(s, seconds(
5), "OutputFormat", "Matrix");
plot(data);
- Signal Processing with Simulink
- Build a Simulink model to filter noise, detect anomalies, or design control logic.
- Simulink enables real-time visualization and iterative tuning.
- Validation & Protection Simulation
- Add Kasuo’s circuit protection components (e.g., TVS diodes, surge suppressors) in the physical design.
- Use Simulink to simulate stress conditions, validating system robustness before hardware testing.
Benefits of the Workflow
- Faster prototyping with MATLAB & Simulink.
- Greater reliability by incorporating Kasuo protection devices.
- Seamless transition from model to hardware implementation.
Conclusion
Kasuo’s electronic components provide the hardware foundation for many embedded and signal processing applications. When combined with MATLAB and Simulink, engineers can design, simulate, and validate systems more efficiently—reducing risks and development time.
With AI agents dev coding on other languages has become so easy.
Im waiting for matlab to build something like warp but for matlab.
I know they have the current ai but with all respect it's rubbish compared to vibe coding tools in others sectors.
Matlab leads AI so it really should be leading this space.
When you compare MATLAB Plot Gallery with matplotlib gallery, you can see that matplotlib gallery contains a lot of nice graphs which are easy to create in MATLAB but not listed in MATLAB Plot Gallery.
For example, "Data Distribution Plots" section in the MATLAB Plot Gallery includes example for pie function instead of examples for piechart and donutchart functions, etc.
mlapp being a binary is a pain point for source control. It means that you either have to:
- have hooks in your source control system to zip/unzip a mlapp. However, The Mathworks have informed users not to rely on this as the mlapp format may change.
- do all your source control in MATLAB. This is non standard behaviour. Source code and source control should be independent of each other. Web front-ends to source control systems, 3rd party source control apps, CI/CD systems and much more are extremely limited in what they can do with mlapps.
I wish an mlapp could just be a directory full of the required text/other files.
Requested to post this here from reddit.
There is no call to rescan audio devices in audioPlayerRecorder, even though PortAudio has such a call. I have a measurement environment that takes a long time to initialise. If I forget to plug in my audio device, I have to do it all over again...
Share your ideas, suggestions, and wishlists for improving MathWorks products. What would make the software absolutely perfect for you? Discuss your idea(s) with other community users.
Guidelines & Tips
We encourage all ideas, big or small! To help everyone understand and discuss your suggestion, please include as much detail as possible in your post:
- Product or Feature: Clearly state which product (e.g., MATLAB, Simulink, a toolbox, etc.) or specific feature your idea relates to.
- The Problem or Opportunity: Briefly describe what challenge you’re facing or what opportunity you see for improvement.
- Your Idea: Explain your suggestion in detail. What would you like to see added, changed, or improved? How would it help you and other users?
- Examples or Use Cases (optional): If possible, include an example, scenario, or workflow to illustrate your idea.
- Related Posts (optional): If you’ve seen similar ideas or discussions, feel free to link to them for context.
Ready to share your idea?
Click here and then "Start a Discussion”, and let the community know how MATLAB could be even better for you!
Thank you for your contributions and for helping make MATLAB Central a vibrant place for sharing and improving ideas.
作ったコードは公開して使ってもらいましょう！ということでその方法をブログで紹介します。
GitHub や File Exchange で公開しているコードがあれば、ぜひこのスレで教えてください！
ブログで紹介している大まかな３ステップをここにまとめます。
1. GitHub でコードを公開・開発する
- GitHub 上でのリポジトリ公開はコミュニティ形成にもつながります。
- R2025a 以降は MATLAB の Markdown サポートも強化されており、README.md を充実させると理解や導入が促進されます。
2. File Exchange に展開（GitHub と連携して自動同期）
- File Exchangeで公開することで MATLAB 内から検索・インストールが可能になります。
- GitHub と File Exchange の連携設定により、GitHub の更新を自動的に File Exchange に反映させることも可能です。
3. 「Open in MATLAB Online」ボタンやリンクを追加
- GitHub リポジトリに「Open in MATLAB Online」リンクやボタンを埋め込むことで、ブラウザ上でコードを試せます。
