While doing a lot of vibe coding( not straight vibe coding, but spec driven-architecturally designed) and building full fledged application hosted on AWS and publishing to playstore, I generated an interest in agent memory and then built something around the memory, but still I wasn't convinced with the way coding agents explores the codebase by ripgrep or get-content.

So, I went deep into data science - basically graph database, gremlin, GraphSAge, A*, and many other. The thing I noticed was that " A model doesn't know what it doesn't know".

Which means, a function might have been changed or updated for 100 times, and each change carries some info, coupling, architectural descions(few times), and many such. But the agents at the time of exploring it sees the function as a static image without any layers beneath it. Sometime it might do - git diff but, a function being designed in that way carries a huge coupling with other functions over different commits. The agent barely figures out these relationships and concludes its descions merely on calls, imports, indexing of the other functions, docs, git log... But a developer trying to fix or update to accomodate a newer update, it first understands what and why of class, file, functions and related then updates.

This is why, Agents prefer on building a completely new functions, methods rather than just updating a small change in the same function.( the reason behind is that, an agent doesn't know that it doesn't know). Yes, I read github repo - ponytail which tells similar but it is mostly a smart prompting to compell the agents to grill before writing.

But, what if we provide the exact info that it misses, what if we provide, which functions are structurally similar, what if we traverse from commit -follow the edge(might be descions, update, fix, or such) land to some function states of those functions, understand them, traverse to that function, understand the clusters of those functions, read which functions are co-changed with that particular on which specific problem - then traverse the vector aided traversal with the edges leading to other functions... and so on.

I mean yes I am talking about embeddings of nodes, edges on some graph database and providing tools to the agent to explore the codebase, not only exploring, grill the codebase, understand the each bit of things before concluding any descions and recursively traverse between interconnected nodes with the help of multi-weighted edge between nodes.

I know 30% of the graph data should be populated by LLMs, but these can be done by any lightweight models or even self hosted ollama models on 16GB VRAM Mac or so.

If it sounds any good, then I have been building and had been testing for past 2 months, and results are way more efficient and effective. Token consumption literally reduced by 5x and effectiveness is still not as good as it should be because models are trained to use the cli commands, but working on that as it needs more depth knowledge of data science.

I reallly need you guys to put me on right boat if you have any things to say, grill, the comment section is all yours. - Pardon for my English

Hi everyone,

I recently appeared for the Cisco Technical Intern 2027 on-campus online assessment through my university recruiting process in the US. Sharing my experience for anyone preparing.

MCQs and Subjective Section

The theoretical section was more technical than I expected.

Topics included:

• Operating Systems
• Computer Networks
• Object-Oriented Programming
• Computer Architecture
• C/C++ fundamentals

The subjective section was not just short factual answers. Most questions required proper technical explanations, so they took considerably more time than typical MCQs.

Coding Round

There were 2 implementation-focused coding problems. The coding section was challenging mainly because of the time pressure. With 42 total questions, you have to move quickly and avoid getting stuck too long on any one section.

Both problems were solid DSA/data-structure questions, and the expected style was closer to clean C++ implementation using structs/classes rather than just plain functions and arrays.

Coding Question 1: Implement an LFU Cache

Related Cisco practice problem: Implement an LFU Cache

The task was to design a cache that supports get and put operations while evicting the least frequently used key when capacity is full.

If multiple keys have the same frequency, the least recently used key among them should be evicted.

Core idea:

• Use a hashmap for key to node/value lookup
• Track frequencies separately
• Maintain ordering within each frequency bucket
• Keep track of the current minimum frequency
• Make both get and put efficient

This was not too bad conceptually, but the implementation details can get messy under time pressure.

Coding Question 2: Solve max-subarray, min-unique, and grid-jump

Related Cisco practice problem: Solve max-subarray, min-unique, and grid-jump

This was a multi-part implementation-style problem. It tested whether you could quickly recognize common patterns and code them cleanly.

The parts were around:

• Finding a maximum subarray with indices
• Handling uniqueness/minimization logic
• Solving a grid-jump style traversal problem

Core idea:

• Kadane-style logic for the max subarray part
• Hashing/frequency tracking for uniqueness constraints
• BFS/graph traversal thinking for the grid component

The tricky part was not that each subproblem was impossible, but that the combined format required speed, clean structuring, and careful edge-case handling.

Difficulty

MCQs: Medium
Subjective: Medium-Hard, mainly due to length and technical depth
Coding: Medium-Hard

The biggest challenge was time management. The coding questions were manageable if you recognized the patterns quickly, but the subjective section consumed more time than expected.

Preparation Tips

• Revise OS, CN, OOP, Computer Architecture, and C/C++ fundamentals
• Practice cache/data-structure design problems like LRU/LFU
• Practice Kadane’s Algorithm and array implementation problems
• Practice BFS/grid traversal problems with edge cases
• Be comfortable writing clean C++ code using structs/classes
• Take mock OAs under strict time limits

Overall, the OA felt very implementation-heavy and CS-fundamentals-heavy. I would recommend balancing DSA practice with theory revision instead of preparing only LeetCode-style coding problems.

Hope this helps anyone preparing for Cisco Technical Intern 2027.

This is a piece of code I found while refactoring our insanely complex drag-and-drop component canvas that already went through several refactors for performance optimization in the past:

function refreshShape(shapeId: number) {
return shapeId;
};

I'm not even kidding. My teammate and I laughed so hard. It was probably written by someone at some point to do something and either got distracted or was multitasking. This was before LLMs were being integrated in our workflows.

Hi everyone, I'm currently a sophomore Computer Science student, and lately I've been feeling like I've fallen far behind. I passed my introductory programming courses Python, C, C++, Java/OOP but over time I feel like I've forgotten almost everything because I rarely used those concepts consistently. One thing I've realized about myself is that I learn best with routine and structure. I don't do well jumping between random YouTube videos or constantly changing resources. I'd much rather have one high quality source for a topic, study it consistently every day, solve exercises, and gradually build my understanding.

So I have a few questions:

If you could start over as a sophomore, how would you rebuild your CS fundamentals?

What would your roadmap look like over the next 1 year of dense study?

What resources books, courses, websites would you choose for DSA, Operating Systems Computer Networks Computer Architecture Databases Linux Cybersecurity (just enough to understand the field before specializing).

How do you deal with forgetting? I constantly feel like I learn something, then months later I can't remember enough to use it confidently. Is this normal? How do experienced engineers retain knowledge without trying to memorize everything?

Is it realistic. or even useful, to aim for a broad understanding of all the major CS areas before specializing? Or should I focus deeply on one area much earlier?

I see NeetCode recommended everywhere, but it seems heavily focused on coding interviews and LeetCode. Is that actually a good place to build CS fundamentals, or is it mainly interview preparation? Should I first study algorithms from a textbook/course before using NeetCode?

If you've ever felt behind and successfully caught up, I'd really appreciate hearing what worked for you. I'm especially interested in advice from people who felt "late" compared to their peers but eventually became competent engineers.

Thanks in advance.

Hi everyone,

Over the past few months, we've been building TadreebLMS, an open-source Learning Management System focused on enterprise and corporate training.

We've recently completed a major restructuring of the project

How can we use AI in our product and process to speedup the development, process and customer support ?

The project is built with:

• PHP / Laravel
• MySQL
• Bootstrap / JavaScript
• Docker

GitHub:
https://github.com/Tadreeb-LMS/tadreeblms

Issues:
https://github.com/Tadreeb-LMS/tadreeblms/issues

We have a huge roadmap like SCRUM Integration, UI upgrade as per FIGMA, Gap Analysis Module Integration, Integrations with HR Systems etc...

Please anyone experience or architect in PHP can give recommendation on best practices, gaps in the system etc..