I have an MSc degree (2019) and have been living in Germany. Although I have no traditional coding experience, I recently built and published EduLens AI — an AI-powered homework solver app — on both the Google Play Store and Apple App Store completely using AI tools and no-code platforms.
The app has:
• Real users
• Google OAuth integration
• Custom domain
• Works in 11 languages
I am currently doing:
• Google Analytics Certification (Skillshop)
• Google Introduction to Generative AI (skills.google)
My questions:
1. As someone with no traditional coding background but real published apps, what job titles should I target?
2. Which certifications would actually help me get hired for remote roles?
3. Is no-code/AI development taken seriously by employers in 2026?
4. Any advice for explaining a career gap since 2019?
Any honest advice appreciated — especially from people in Europe or remote tech roles.
Thankyou!

I work for a US bank and I have observed that my job has shifted more towards creating Agentic workflow (fancy name of using LLM to automate tasks). In the last one year, I haven't touched any ML model. I am curious to know what is the experience of other folks.

Hey everyone,

I’m starting my AI/ML engineering journey from absolute zero. Solo learning is great, but building with a dedicated team is faster. I am putting together a small, high-accountability study group so we can level up together.

💡 Our Goal:

Build a deep, foundational understanding of AI/ML (Data Pipelines, Agent Systems, open-source models).

Move past tutorial hell—we want to actually understand the architecture, not just memorize theory.

Stay ruthlessly consistent and disciplined.

🤝 Who I am looking for:

People who are genuinely serious about engineering their future.

Ready to put in the work daily (even if it's just 1 hour).

A supportive, zero-ego mindset. If someone feels burnt out or stuck on a bug, the team pulls them up.

👥 The Master Plan:

Small & Focused: Capping the group at max 5-10 members so no one gets lost in the crowd.

24/7 Voice Chat: We will have an always-open VC. You can jump in anytime to co-work, share your screen, debug code together, or just hang out while studying.

Building Together: We will practice by building actual small projects and pushing code side-by-side.

📅 We are officially starting on July 11th (but late joiners are completely welcome, we will help you catch up).

If you are ready to lock in, drop a comment or shoot me a DM and I’ll send you the Discord invite. Let's get to work! 🚀

I've been in AI for 4 years, last 2 specifically in production RAG and multi-agent systems. Before that I was in data science, back when this wasn't even called AI engineering yet.

For the last 2 years I was completely heads down in production RAG, building actual RAG applications and multi-agent orchestration. Somewhere in that grind, thanks to doomscrolling eating whatever brain cells I had left, my basics went rusty. To clear that rust I need a serious accountability partner, not another passive group chat.

I've also interviewed fresher AI engineering candidates at my company. So I know what actually gets a resume shortlisted and what makes an interviewer stop listening halfway through an answer.

I've joined learning squads before. Every single one died in two weeks. People show up once, get busy, go quiet, and nobody says anything about it. I'm done doing that.

Here's what's in it for you. If you've been stuck in that pile of scattered ML/DL material, random courses, half-finished playlists, and you can't cleanly explain how something works when someone actually pushes on it, that's where I can help. I've always been good at breaking things down, ask anyone who's crammed with me before an exam, they walked out understanding it better than the professor explained it. I can also tell you exactly what an interviewer is listening for versus what's just noise, because I've sat on that side of the table.

What I need in return: my DSA has gone rusty and needs rebuilding. So does my depth on ML, DL, and transformer internals. I know the one-liner versions, token prediction, self-attention lets tokens attend to each other, produces the KV values, but I can't yet explain the underlying mechanics cleanly when pushed, and it's the same story across a lot of core ML and DL. I want all of that revised properly, not the surface version I already have.

What I'm looking for on your end is honesty, consistency, and actually showing up. If you know you can't hold that, this probably won't work for either of us, and that's fine, better to know now than three days in.

If it sounds like a fit, comment or DM with what you're working on right now and a time window that actually works for you, 1 to 1.5 hours, morning or evening.

I want to understand what the largest ITES firms in the space like Infosys, TCS, TechM, Persistent, Hexaware, Wipro, etc. are thinking about AI. We are currently at an interesting point in history where the stock market valuations of the LLM tech companies are at peak but the actual adoption is seeing mixed reactions everywhere. The next few months will see a 'make or break' situation in this space and the scenario will be more clear. Companies will be forced to adopt a policy of either increasing productivity by embracing AI fully or cutting AI usage due to mounting token and infrastructure costs. There won't be any middle-ground left.

If you work at any of these firms, please share your experience. Which direction is the work environment going? Do you see massive adoption in AI usage? Are you getting incentives or better appraisals based on AI tokens spent on Claude Code, Codex, etc?

I recently made a RAG project which answers me to questions from a pdf file. I want some good and a stand out project. I need some guidance as well regarding my approach

I believe in making projects and then try to understand it but I mostly fail in doing that cause i get to know a very few things

Please help me how can i improve myself

I'm joining college this year and my branch is Aiml. My options are gaming laptops with dedicated gpus. But do I really need it??? Can someone pls help 🙏 in explaining the need for a local gpu if I want to do medium level tasks , and are they possible from cloud gpus??

Which one will be cost effective in the longer run?

Any genuine advice would be highly appreciated

Running through the security tooling options for ai agent traffic specifically, not just llm security. Most comparisons don't distinguish between secures llm calls and secures agent-to-tool and agent-to-agent traffic, which are genuinely different problems.

aws bedrock agentcore converts rest apis and lambda functions into mcp-compatible tools and manages inbound/outbound authentication for agent-to-tool connections. Works well inside the aws boundary. Multi-cloud governance is the hard edge where it stops being useful.

Gravitee covers the full agent traffic stack through an ai gateway that enforces per-agent identity scoping, token-based rate limiting on every mcp tool invocation, audit logging with caller identity and input/output per call, and a2a communication governance alongside traditional api traffic from the same control plane. For deployments where agents are calling both rest endpoints and mcp tools in the same workflow, gravitee manages both under consistent policy enforcement.

Helicone cover llm observability, cost tracking per model, and latency monitoring per request. Neither provides access control at the tool invocation level or any governance over agent-to-agent communication, they're observability tools not governance platforms.

Kong has added token-based rate limiting and basic llm routing as ai gateway features. Agent to agent communication governance was added recently.

Azure apim's ai extensions handle llm proxying and semantic caching. Agent governance is early stage compared to the api management capabilities.

AI security for agent traffic splits into two distinct problems. Access control at the api layer covering what agents can call and with what permissions, and model-level guardrails covering what the model will try to do. Most tools address one category, the gap is in tools that address both from a single enforcement layer.

I’m building a new venture and want to uncover hidden patterns in our user data to refine our product offering.

Claude suggested using K-Means clustering and Hierarchical Dendrograms to isolate our core user archetypes. The math makes sense on paper, but I’m curious about the real-world implementation pitfalls.

I'm looking for my next project, but I don't want to build another portfolio app that no one ends up using.

So, what's something you've always wished existed? It can be a website, app, browser extension, CLI tool, AI tool, or anything else.

What's the problem, and why do existing solutions fall short?

If I find an idea that resonates with enough people, I'll build it and share it with the community. I'm especially interested in solving real problems, even if they're niche.

Drop your ideas below 👇

hi!, i am new to this field, i find claude helpful but the limit it gives seems too low. so i have seen many reels and videos on "free usage" of premium models like opus 4.8 etc...is it real?..i am a student so is there a chance that i can get those models using my student id?...or are there any real methods by which i can use claude for free?

My goals are:

1. Better job security over the next 5–10 years

2.More job opportunities (especially in the US and globally)

3. Less competition for entry-level roles

4. Higher long-term salary and career growth

From what I've seen:

*AI Engineer roles seem to be exploding because of LLMs, RAG, AI agents, MCP, LangGraph, etc., but they also seem extremely competitive since everyone is learning GenAI.

*ML Engineer roles appear to require stronger fundamentals (Python, statistics, scikit-learn, deep learning, MLOps) and seem more common in established companies.

Задался этим вопросом, потому что в нашей компании стали оплачивать нейронки и полностью доверяют проекты некоторые ей, а старший разраб сделал из своего компа просто командую строку с cloude и все делает в ней. В то время, как другие работодатели напрочь отказываются от нейронок в своих проектах. Или те же собеседования, сейчас нет никаких проблем включить нейронку фоном, которая слушает вопрос и дает ответ, а тебе лишь остается прочитать его. В любом исходе - нейронка побеждает, что думаете?

I am fresher I don't know where to start how to start what to start pls guide seniorss

Hi, I am 21M, and almost graduate in Bachelor of Computer Science. I am still considering my career path at the moment though 😢 and I have narrowed down my choice to 2 options: AI engineer or Machine Learning.

From what I know, AI engineer is like Software Engineer, so you mostly build and deploy products using existing models, and you do some techniques like RAG, Fine tune, Prompt Engineer, etc. From that, I assume following AI engineer career path would mean to get more involved in the industry.

While following Machine Learning/Deep Learning, it is mostly researching, and from where I live, I find nearly no jobs that have Machine Learning or Deep Learning in the requirements, but mostly AI engineer.

So I am writing this post to ask about experience in those fields and what I should choose to have a good career. Thank you so much and I appreciate every comment since I may be wrong!

I recently attended the Be 10X AI workshop and have mixed feelings.

Some of the AI tools and prompts were useful, but I'm not sure whether the course offers enough value compared to the free resources available online.

I'd love to hear from others:

• What did you like or dislike?
• Did you purchase any of their advanced programs?
• Did it help you professionally?
• Would you recommend it to others?

Curious to know if my experience was similar to yours.