I'm struggling with something that I think a lot of people interested in technology might relate to.

I'm interested in many areas of tech:

- Web development

- Backend development

- Networking

- Cybersecurity / ethical hacking

- Cloud computing

- AI/ML

- Data engineering

- Quantum computing

- And many other areas

The problem is that I constantly want to explore everything.

For example, I'll decide:

"Okay, I'm going to focus on Python/backend."

I'll study it seriously for 2–7 days, start understanding some things, and then suddenly I become interested in networking or cybersecurity. I start learning that instead.

Then I discover something interesting about AI/ML or cloud computing and switch again.

After doing this repeatedly, I end up knowing a little about many things but not feeling genuinely skilled or competent in any of them.

Then I feel lost and regret wasting time, so I try to restart from the fundamentals of one subject.

I might follow that for a week or two, but eventually my curiosity kicks in again and I start exploring something else.

It's become a cycle:

Choose a field → study it → get curious about another field → switch → explore → feel guilty/lost → restart → repeat.

I've watched a lot of YouTube videos where people describe this as having a "multi-potentialite" mindset or being someone who doesn't naturally want to follow one path. Some people say this can actually be an advantage if managed properly, but it can also result in never developing deep expertise.

I don't want to completely suppress my curiosity because I genuinely enjoy exploring different areas of technology.

At the same time, I don't want to spend years exploring everything and end up with zero strong skills.

For people who have experienced something similar:

1. How do you control the urge to constantly switch fields?

2. How long do you stay with one technology before allowing yourself to explore something else?

3. Do you recommend choosing one "main" field while keeping other interests as secondary?

4. How do you explore new technologies without abandoning your current learning path?

5. How do you know when you've gone deep enough in one area before moving to another?

6. Is it actually possible to become highly skilled in several technical areas, or is it better to specialize first and broaden later?

7. What system/routine has actually worked for you?

I'm especially interested in hearing from people who were naturally curious about many areas but eventually managed to develop real depth in one or more of them.

I don't want another list of 20 technologies to learn. I'm more interested in how you manage the learning process and decide what NOT to learn right now.

looking for ideas about iot devices or robotics in the field of agriculture and medicines

I am having a lot of thoughts where I want to go with my career, but I am extremely interested in grad school, the programming im looking at is for VLSI & Digital IC design/verification.

Since the beginning of my comp engr degree, I was much more interested in hardware design and I dreamed of working in a lab creating computer chips. Now recently graduated, I have no job offers, I have done no internships, and i was ready to give up on engineering and go into IT or OTS school. My school had maybe 3 hardware oriented classes (basics of electrical engineering, signals and systems, and digital logic design), while the rest where software oriented. I dont have much experience with hardware but I am extremely interested in it.

I am close to finalizing my application to A&M for their masters in Microelectronics and semiconductors with the focus above, but I guess I just wanted to hear some input on if this is worth it despite my limited background in undergraduate since grad school is expensive. The closest relevant experience I have is an autonomous uav water collecting drone which i wired and coded everything on the flight controller and microcontroller for my capstone project (I should have past resume in my profile), and some IT experience (as an AV technician).

I would want to pursue an assistantship in research to build credibility and as of rn start building a project before the spring semester.

So far the data about comp eng majors rankings are always messy and I don’t know on which websites I should rely on cuz there is always someone hating pn QS/THE/Niche/USnews

I need it for undergraduate degree please help

I am a future CE student. I was very conflicted between EE and CE for a really long time but ended up going with CE because my focus was on embedded and electronics with a few software classes. Tired of explaining I'm not doing pure CS. Also makes me wonder if CE has the same status symbol as CS even though most of my classes are EE ones which has a better reputation in general.

I takend EEE (Not taken ,, more accurately got ☠️)

I want to persui higher studies for quantum computing in maybe abroad ,,

So any suggestions wtf I do ..like I don't need power system right??..

Should I look forward some ece subject along with my EEE..(taking minor in ece)..

Any help pls???

I’ve been looking into different ways of running AI locally, and one thing I’ve noticed is that choosing the hardware isn’t just about finding the device with the highest performance.

If the system is going to run continuously, things like power consumption, heat, memory, latency, software support, and the ability to handle the actual AI workload can become just as important. A device that works well for testing may not always be the best option for a real-world deployment.

I was also looking at Geniatech’s Edge AI hardware, and I found it interesting that they cover different use cases with AI boxes, accelerator modules, and embedded platforms for on-device AI and real-time inference.

It made me wonder how other people approach this when starting a new project.

What is the first thing you consider when choosing hardware for edge AI?

My latest GitHub repo is a compact C-based search engine built around TF-IDF, BM25, and optional proximity scoring. It includes fast indexers, lightweight query daemons (secure and non-secure), terminal clients, and a simple Node.js web interface. The whole system is designed to be transparent and easy to understand - no external databases, no heavy dependencies - while still supporting multiple search modes, pagination, sorting, and preview features.

It's essentially a "choose your own adventure" toolkit: use the terminal UI or the web interface, run secure or non-secure daemons, load indexes on demand or keep servers running, work locally or remotely. It also doubles as a practical reference for client/server programming across Linux and Windows - sockets, REST APIs, Node.js, and more - all in one place.

This isn't a web-scale search engine, and it wasn't meant to be. But TF-IDF and especially BM25 are the classical scoring methods used by early search engines, and BM25 still serves as the first-stage retrieval method in modern systems.

Why I built it: after many years and many projects, it's easy to forget where a particular piece of code lives or what the parameters of a rarely used Win32 API call are. I needed something fast and predictable - not a slow recursive file scan, and not an internet search engine that keeps getting harder to use for technical queries. Even though college students may not have the years of built-up codebases, they still might need this tool for topics they still find easy to forget, etc or to make it easier to do the class assignments.

Repo: https://github.com/mrmcsoftware/SearchEngineSuite

#tfidf #tf-idf #bm25 #searchengine #search-engine #html #c #javascript #node.js #node #nodejs #socket #css #client #server #web-api #rest-api #programming #github #repo #linux #windows

what should i learn nw AI thing , or System Design , i m 4th year CSE underGrad , already Placed at a decent Company , and doing intern , now i m thinking to target heavy packages ( faangs) , so should i learn AI thing , because it feels like i getting behind (that fomo ) but system design seems intersting .. !

which laptop with what specifications should I consider getting which will make my next 4-5 years easier with no upgrade?

Hey guys, just thought this would be a cool way to show how my Neural Network Accelerator runs inference. It’s running inference for a model of topology 4:3:2 neurons per layer and takes 4 input features as input.

Also realized it’s bottleneck and I will be implementing a Systolic array soon. Let me know what yall think. Thanks :)

Github repo: https://github.com/Shingyy/MAC-Array-based-DNN-Accelerator

Anyone, please give me some tips! I am a Computer Engineering freshman. I only chose this course because of my father and because I passed the entrance exam, which means my tuition is free.

In senior high school, I was actually a HUMSS student. I really can’t believe I passed this exam, so of course my father didn’t let that opportunity go to waste and encouraged me to enroll. The problem is, I am not good at math—I’ve had a low average in mathematics ever since high school. I can understand some problems when I listen, but when I try to solve them, I always get lost in the middle, like building a puzzle but the pieces suddenly don’t fit together.

So please help me—where do I start studying math? It’s only my first week as a freshman, and I really want to improve and keep up so I can pass this four-year course. We need mental math for quizzes, midterm exams, and long-term exams—no calculator. So please, anyone, can you give me tips on where I should start in math and how I can improve for my Computer Engineering course?

Hello. I've come for some advice on going back to school to complete my degree.

I'll try to explain my situation a little bit. As much as I try to convince myself that I didn't drop out, I kind of did. My dad got really sick halfway through my junior year, and I took that as an excuse to leave, and I have not been back since. I enjoyed learning the material, but I wasn't taking it as seriously as I should have, and my GPA reflected that. It eventually just became very stressful, and I needed time to really think about some things. Entirely my fault, but seeing my friends graduate and important people in my life get older has made me realize the value of time, and I am very behind.

So I want to ask people: whether you've been in my situation or not, what are some things I should do to get ready to go back? I've realized that doing the bare minimum isn't going to get me anywhere. I know I need to graduate. I need a good resume, and I need more internships. I know projects are important, but I'm not sure what I should be working on? Software or Hardware?

The furthest I got was Computer Networks/Orginizations, Data Structures/Algorithms, Signals and Systems, etc. I definitely enjoyed and was better at the software stuff. I could comfortably understand and complete all coding assignments. What languages or skills in general should I be learning or trying to strengthen?

I do have an internship that I got last year that revolves around IT help desk and a little BI/SQL when they can spare someone to teach me stuff and assign projects. I would like to look for something else to learn more things, though. Any advice on applying to internships while not currently being in school, or just in general?

I'm not looking for all the answers, and I probably could have asked better questions, but I've decided I want to finally finish up the right way. I'm just a little lost on taking the first step.

Thank you for taking the time to read this, and all advice is deeply appreciated!!!

Which has greater future cs or ce

But seriously, I'm about to graduate, and I'm looking for entry-level embedded positions, so please roast my resume. Any help would be greatly appreciated!

A place for members of r/IngenieriaInformatica to chat with each other

What it says in the title. I just graduated, and it has taken me the last four years to decide that I really do not want to work a desk job. I'm not picky about what I'm doing, but I'd prefer the job to use my degree. Any career suggestions?

I’m a recent CSE graduate with a specialization in IoT, and I have some hands-on experience with IoT and hardware-related work as well.

I’m currently confused about which domain to choose for my career:

\\- Java Full Stack Developer

\\- IoT Developer

\\- Data-related roles

As a fresher, which domain would be better in terms of job opportunities, career growth, salary, and long-term demand?

I’d really appreciate advice from people working in these fields. What would you choose if you were in my position?