u/Thin_Ad_7459

so I am new to federated learning I have grasp of basic concepts of it. Recently I selected Federated learning project for my final year and to my surprise I get allotted to one of the most notoriously strict guide. So I am now being over cautious about how to implement it. Long story short my project data is raw csv and I am now panicking how will they react if in demo I will show them to drop raw csv as input from client side doubting it would be counter intuitive and wont show end to end pipeline if any of you guys know how to prepare and present projects around federated learning I would love to get some advice.

Hi! I am final year BE student recently I took a project based in our my contribution is system and application of system in dyslexia. For that I though the most used dyslexia dataset of handwriting would be suitable. I downloaded dataset and then realised it is single letter dataset which is giving mnist kinda vibe! Also apparently large portion of it is synthetic. I searched but I didn't find clinically approved dataset of handwriting for dyslexia. In nutshell:

dataset is mnist looking so I am at worry if examiners will state why you are using such looking dataset for final year project!!

dataset is used for at least 9 papers already so it is being used

But has its limitations (vastly synthetic, mnist looking)

Our clg is forcing for at least two papers to publish (not for our degree requirement btw) and I am worried if the dataset use itself will cause problems for paper

though one of main novelty is mechanism but other one is integration(incremental) and I am worried that people will call out why I used that dataset

sorry I carried away in my emotions here is the dataset I am talking about: https://www.kaggle.com/datasets/drizasazanitaisa/dyslexia-handwriting-dataset

->can simplicity of it justified as proof of concept for presentation or report?

->will using this dataset can cause problems at time of publication?

I am sorry for dragging clg thing into this I though it would be better to get some context about scope for project

I am sorry I cant give full context as I wanted to publish research on it (though I will hardly try for mid tiers only)

also sorry in advance if I did spelling or grammatical error where should I post this

Hi! I am final year BE student recently I took a project based in our my contribution is system and application of system in dyslexia. For that I though the most used dyslexia dataset of handwriting would be suitable. I downloaded dataset and then realised it is single letter dataset which is giving mnist kinda vibe! Also apparently large portion of it is synthetic. I searched but I didn't find clinically approved dataset of handwriting for dyslexia. In nutshell:

1. dataset is mnist looking so I am at worry if examiners will state why you are using such looking dataset for final year project!!
2. dataset is used for at least 9 papers already so it is being used
3. But has its limitations (vastly synthetic, mnist looking)
4. Our clg is forcing for at least two papers to publish (not for our degree requirement btw) and I am worried if the dataset use itself will cause problems for paper
5. though one of main novelty is mechanism but other one is integration(incremental) and I am worried that people will call out why I used that dataset

sorry I carried away in my emotions here is the dataset I am talking about: https://www.kaggle.com/datasets/drizasazanitaisa/dyslexia-handwriting-dataset

->can simplicity of it justified as proof of concept for presentation or report?

->will using this dataset can cause problems at time of publication?

I am sorry for dragging clg thing into this I though it would be better to get some context about scope for project

I am sorry I cant give full context as I wanted to publish research on it (though I will hardly try for mid tiers only)

also sorry in advance if I did spelling or grammatical error

Hi! I am final year BE student recently I took a project based in our my contribution is system and application of system in dyslexia. For that I though the most used dyslexia dataset of handwriting would be suitable. I downloaded dataset and then realised it is single letter dataset which is giving mnist kinda vibe! Also apparently large portion of it is synthetic. I searched but I didn't find clinically approved dataset of handwriting for dyslexia. In nutshell:

1. dataset is mnist looking so I am at worry if examiners will state why you are using such looking dataset for final year project!!

2. dataset is used for at least 9 papers already so it is being used

3. But has its limitations (vastly synthetic, mnist looking)

4. Our clg is forcing for at least two papers to publish (not for our degree requirement btw) and I am worried if the dataset use itself will cause problems for paper

5. though one of main novelty is mechanism but other one is integration(incremental) and I am worried that people will call out why I used that dataset

sorry I carried away in my emotions here is the dataset I am talking about: https://www.kaggle.com/datasets/drizasazanitaisa/dyslexia-handwriting-dataset

->can simplicity of it justified as proof of concept for presentation or report?

->will using this dataset can cause problems at time of publication?

I am sorry for dragging clg thing into this I though it would be better to get some context about scope for project

I am sorry I cant give full context as I wanted to publish research on it (though I will hardly try for mid tiers only)

also sorry in advance if I did spelling or grammatical error

I've been thinking about something and wanted a reality check from people who use Cursor, Claude Code, Copilot, etc. regularly.

Most agent workflows seem to follow the same pattern: make a change, write it to the codebase, run tests, and roll back if something breaks. That works for obvious failures, but it feels very reactive.

What I'm wondering is whether anyone has run into issues where the change technically "works" but quietly damages the structure of the codebase.

For example:

• An agent moves a file or changes imports and accidentally introduces a circular dependency. Nothing breaks immediately, tests pass, and the problem only shows up much later when someone hits a weird import error.
• It refactors or simplifies a file that happens to be a dependency hotspot used across dozens of places, without realizing how central that file is.
• It moves logic across architectural boundaries because the local change looks reasonable, but it slowly erodes the separation between layers.

These aren't necessarily test failures. They're the kinds of issues that make a codebase harder to work with over time.

In your experience, does this happen often enough to matter?

Or do code reviews, CI checks, and existing tooling catch most of these problems before they become real issues?

I'm trying to understand whether there's an actual gap here: validating the impact of a change on the codebase structure before the agent writes it, instead of relying on tests and reviews afterward.

Curious whether this is a real pain point or if I'm overestimating the problem.

I know for current year, placement was down the hill, I just finished my TE exam and I am scared af are there any BE students for guidance here. what was your overall experience of on campus placements. Any tips for us for upcoming campus. Is the placement condition so bad that some of are not even guaranteed to have a job regardless of ctc?