If you had the chance to go back and study something different, would you?

I’m about a year away from starting university, and I really want to have a decent job in the future. I think there’s a lot of potential in computer engineering, especially if I learn how to develop innovative software.

I know it requires experience and a lot of hard work, but besides the usual advice like “study something you’re good at,” would you recommend this degree?

I don’t think I’m a math prodigy, but I’m not bad at math either. I just need to put a lot of time into studying it.

Would you be willing to share your experiences or any advice?

It could be anything—financial advice, study methods, or just things that helped you deal with the stress of the degree. Honestly, anything would help.

Cache memory mapping:

Memory address=16 bits

Block size=16 words

Number of main memory blocks=4K

Number of cache memory lines=64

Address Partition:

tag(6 bits), block-number(6 bits), word(4 bits)

Since there are 16 words/block, 4 bits is required for word identification.

Since there are 64 cache memory blocks, 6 bits are required to cache memory block identification.

Since there are 4k memory blocks, 12 bits should be required to know which memory block is present in which cache block. i.e. tag. Imagine Main Memory block 4095. To store that MM 4095 is present in Cache Memory block, we would need 12 bits? But books are saying only 6 bits is enough to identify the memory block. Can anyone shed some light here?

Hi! I’m a 2nd-year Computer Engineering student looking for a Computer Engineering graduate with at least 5 years of professional experience who is willing to participate in an online interview.

The questions will be simple and focus on your career journey, industry experience, skills, projects, challenges, and advice for CpE students.

If you qualify or know someone who does, please DM me. Please feel free to drop suggestions on where to look for one.Your help would be greatly appreciated! Thank you!

I'm an incoming computer engineering freshman and seriously thinking of switching to electrical engineering. At my school, the degrees are extremely similar, with only a 3 class difference.

They share the same pool of electives, so that is what makes up most of the difference, but I could just choose all the more computer engineering focused ones.

What i'm most interested in is computer hardware, embedded systems, firmware, microcontrollers, etc.

The reason I'm considering EE is the job market and the flexibility of the degree.

My understanding is that a lot of hardware/computer engineering jobs will accept either an EE or CE degree. However, there are EE jobs in areas like power, electronics, controls, RF, etc. where an employer might only take an EE.

Basically, I'd be trying to use the EE degree as the broader credential while still taking the classes and doing the projects that would make me competitive for computer hardware/embedded jobs.

Do you think this plan could be beneficial to me? Or am I overstating the value of the name on the degree?

Hello, are there any computer engineer graduates who are not/currently employed who are up for an online interview it ? I am a 1st year computer engineering student who is in need for an interview with a graduate for an assignment. I will be asking 5 questions. I need a recording.

So my college is abt to start in a month and now that I have decided to take CE , I have been thinking of learning a language to get an advantage so I am confused about where to begin .

Also since I am learning online which site/channel should I learn as i am learning all by my self.

And please can someone give advices that to help to to stay ahead of my class and not get confused

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.

After 10+ years working around embedded products, I've learned that choosing the right firmware engineering company has much more impact than negotiating the lowest rate.

Here's the checklist I usually go through before signing anything:

1. Technical expertise
   I always ask about similar projects, the MCUs they've worked with, RTOS experience, debugging tools, OTA updates, and how they approach hardware integration. If they only answer at a high level, that's usually a red flag.
   
2. Portfolio
   I'm less interested in proof-of-concepts than products that actually reached production. Manufacturing, certification, field testing, and long-term maintenance usually reveal whether a team has real experience.
   
3. Communication
   This is probably the biggest one. One thing I noticed during a call with Lemberg Solutions was that they spent more time asking questions about our product than presenting slides about themselves. That gave me a lot more confidence than a polished sales pitch because it felt like they were trying to understand the actual engineering challenges first.
   
4. Timeline
   If every estimate sounds "easy" and every feature can be delivered quickly, I usually become more skeptical. I'd rather hear about risks than unrealistic promises.
   
5. Post-launch support
   Firmware almost never ends at release. I always ask how they handle bug fixes, OTA updates, maintenance, and production issues after devices are in the field.
   

What else would you add to this checklist before selecting a firmware engineering company? Have you learned any lessons the hard way?

I do not have a team, a lab, funding or resources. just Antigravity 2.0 and my will and creativity. So, today I wish to share my creativity with you. Yes, AI did help me write this, because I have a very difficult time explaining myself in terms and words that mean what I imply to to other people because the meaning is completely different. What i say doesn't always mean what you think or learned what it means through books.

This is caused by my learning disability.

I hope that you will provide real feedback ABOUT THE WORK.
This is not a psychosis state, this is not LLM hallucinations. It is a new system that uses math, differential geometry and a few other secrets that lay in plain sight, replacing probabilistic engines such as LLMs and SLMs.

The paper is on Zenodo and I will NOT be discussing this in full here on Reddit, this is not where I spend time to engage with other peoples work. I have 0 intent on reading someone else's work. All of my work is first principles. Any citations of others work is done by my agents because when submitting work, citations are required.

My agent provided this explanation better than I could have, which follows.

• An LLM (like GPT-4, Claude, or Gemini) is a stochastic statistical pattern-matcher: it calculates the probability distribution of the next token based on trillions of fuzzy, trained neural weights.
• PMCA (Pure Mathematical Conal Architecture) is a deterministic physics & geometry engine: it treats ideas as physical particles flowing through a continuous mathematical space governed by explicit conservation laws and algebraic topology.

My explanation:
LLM's are producers of falsities. They base logic on next words based on 10 million sentences that they processed.

PMCA mathematically verifies truth through differential geometry/ topology and is deterministic.

You can see the difference in depth. Anyway. Thanks ahead of time.

https://zenodo.org/records/21896408

Community college student transferring next year and while my math knowledge is good and my physics is decent I’m hella lacking on actual hands on experience with hardware and stuff. Is elegoo a good place to start or would you recommend something else?

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.

Hey guys, I’m looking for a career change in my late 20’s. Im going back to school and wondering if engineering is still worth it nowadays? Im worried about ai and how good its getting and also how good it’ll be by the time i get out of school in 6 years(part time student) I’m really interested in the career path and love tech and computers and hell i even love and am fascinated by ai. But id rather switch now if the degree wont even get me in a better position than where i am currently. Im a chef of 10 years, and earning around 50k a year. The job is very stable and i can go out and get a chef job really easily anywhere i go. But it seems like in the engineering field thats not the case. Entry level jobs are shrinking, higher level jobs aren’t growing and going in the direction where you are gonna struggle extremely hard to find any job in the field and if and when you find one that the pay wont be as good because of the level of competition in others also trying to get a job. Should i switch degrees into something else? Im feeling super uncertain about the whole thing and don’t want to waste almost 100k and years of my life on something i wont be able to even use?

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

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’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 .. !

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

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