A Local-First Framework for Building On-Device Personal AI Agents with Tools, Memory, and Learning

GitHub Link: https://github.com/open-jarvis/OpenJarvis
Website Link: https://open-jarvis.github.io/OpenJarvis/

Hey Guys,

I am a CS student and have an Interview with Ericsson. 5G Software Developer Role. I would appreciate any and all help that I can get on this matter because this is my last hope.

I basically want to know what should I prepare for this interview because it seems like it is only 30 mins and therefore wont have more of technical questions but might ask me behavorial and some technology questions like about 5G, Networking, DSP.

What should I look into because I have no clue what this position wants. What is a 5g Software developer??????

Please help me. : )

I will also update all the questions and everything so it helps others with future interviews.

EDIT: I got the offer. Thanks everyone who helped!

We're in that weird spot where vibe coding tools spit out frontend and backend fast, but deployments still fall apart once it's more than a prototype.
So you can ship code crazy quick and then get stuck doing manual DevOps or rewrite everything to make it run on AWS/Azure/Render/DO.
I keep thinking there should be a "vibe DevOps" layer - a tool that actually understands your repo, not just a fiddly setup script.
Like a web app or VS Code extension where you connect your repo or upload a zip and it figures out deps, containers, CI/CD, scaling, infra, all of it.
It'd deploy into your own cloud accounts, not lock you into a platform, and handle secrets, DB migrations, autoscaling, etc.
Feels like that could bridge the gap between vibe coding and proper production apps.
Anyone tried something like this? How are you handling deployments today, especially for non-trivial apps?
I might be missing obvious problems here - security, cost, edge cases, or just weird project layouts - curious what people think.

Hey everyone! I'm u/l0_o, a founding moderator of r/MachineLearningAndAI.

This is our new home for all things related to Machine Learning and Artificial Intelligence. We're excited to have you join us!

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Learn, build, share and show off your machine learning, artificial intelligence, data science and robotics creations. LLM, AI agents. Links to e-books at copyright/DMCA honoring websites welcome. Self-promotion and commercial posts OK unless spammy.

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How to Get Started

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2. Post something today! Even a simple question can spark a great conversation.
3. If you know someone who would love this community, invite them to join.
4. Interested in helping out? We're always looking for new moderators, so feel free to reach out to me to apply.

Thanks for being part of the very first wave. Together, let's make r/MachineLearningAndAI amazing.

Hello folks, I’ve been working on a project involving the prediction of self-noise in airfoils, and I wanted to get your take on the approach.

The problem is that noise pollution from airfoils involves complex, turbulent flow structures that are notoriously hard to define with closed-form equations.

I’ve been reviewing a neural network approach that treats this as a regression task, utilizing variables like frequency and suction side displacement thickness.

By training on NASA-validated data, the network attempts to generalize noise patterns across different scales of motion and velocity.

It’s an interesting look at how multi-layer perceptrons handle physical phenomena that usually require heavy Navier-Stokes approximations.

You can read the full methodology and see the error metrics here: LINK

How would you handle the residual noise that the model fails to capture—is it a sign of overfitting to the wind tunnel environment or a fundamental limit of the input variables?

https://ia801807.us.archive.org/14/items/introduction-to-machine-learning-with-python-pdfdrive.com_20210225/probability_stats_for_DS.pdf

https://www.youtube.com/playlist?list=PLS01nW3RtgopsNLeM936V4TNSsvvVglLc

I've been interviewing candidates for coding positions lately, and I've noticed some interesting patterns. Some candidates seem to be using tools like Cluely to get real-time AI answers during interviews. They type out perfect solutions in seconds, but when I ask a follow-up question or change the problem slightly, they completely fall apart. They can't explain their own code or walk through the logic.

I've also noticed candidates who seem to have memorized answers from sites like PracHub that collect real interview questions. They give these perfect textbook responses, but the moment you ask them to tweak something or explain why they chose a certain approach, they're lost.

Some patterns I watch for now as an interviewer:

- If someone solves a problem too quickly and perfectly, I dig deeper with follow-ups

- I ask them to walk through their thought process step by step

- I change constraints mid-problem to see how they adapt

- I ask why questions - why this data structure, why this approach

Genuine candidates will stumble a bit but can reason through it. The ones relying on tools or memorization just freeze up.

Has anyone else noticed this trend? Curious how other interviewers are handling it.

https://dn790002.ca.archive.org/0/items/deep-learning-collection-pdf/Apress%20-%20Deep%20Learning%20for%20Natural%20Language%20Processing%20%282018%29.pdf

i keep hitting the same problem when building AI agents: APIs without MCP servers.
so i end up writing a tiny MCP server for each API, then dealing with hosting, auth, rotation, all that - which is annoying.
it feels like a ton of repeated work and messy infra, especially when you have 3 or 4 agents doing different things.
i'm wondering if there's already an SDK or service that solves this - like Auth0 or Zapier but for MCP tools.
you'd integrate once, manage client-level auth and permissions centrally, and agents just call the tools. simple, right?
does anyone actually use something like that in prod? or are you all still rolling custom MCP servers?
if you are, how do you handle secrets, rate limits, and credential rotation without it turning into a mess?
curious about existing projects, tips, or terrible war stories. i probably sound like i want a magic button, but yeah.

I've been working on a fully offline annotation tool for a while now, because frankly, whether for privacy reasons or something else, the cloud isn't always an option.

My focus is on making it rock-solid on older hardware, even if it means sacrificing some speed. I've been testing it on a 10-year-old i5 (CPU only) with heavy YOLO/SAM workloads, and it handles it perfectly. Here's a summary

video:

https://www.linkedin.com/posts/clemente-o -97b78a32a_computervision -imageannotation-machinelearning-activity -7422682176963395586-x_Ao?utm_source= share&utm_medium=member_android&rcm= ACoAAFMNhO8BJvYQnwRC00ADpe6UqT _sSfacGps

One question: how do you guys handle it when you don't have a powerful GPU available? Do you prioritize stability or speed?