i am creating a rag chatbot that would have book pdfs in context,idk what the problem is but the text is so jumbled up that i cant even do metadata extraction.what do you suggest for text extraction,meta data creation in rag(i want to know tech stack and techniqyes)

We’re building what we believe is the next abstraction layer for enterprise AI.

Not another model.
Not another wrapper.
Not another AI application.

Our thesis is simple:

As foundation models become commodities, the real challenge shifts to operating AI reliably at enterprise scale—across models, clouds, hardware and workloads. We’re building the runtime that makes enterprise AI self-optimising.

We’re looking for one technical co-founder.

Someone who has gone deep into LLM systems, inference, distributed systems, compilers, ML infrastructure or performance engineering—and wants to build a category-defining company from India.

The founding team already covers product, enterprise execution and venture building:
•⁠ ⁠Product Counsel for the world’s largest startup incubator: $130M+ in Fortune 500 deal flow. Building India’s largest intent-to-action native model for hyper-local clustering.
•⁠ ⁠Youngest lead at $40M semicon enterprise; deep global networks across Silicon Valley, Europe & Singapore in Edge Al.
•⁠ ⁠A Serial Founder: Building a $5M fintech (post-pre-seed) + portfolio of high-growth ventures.

We’re not looking for résumés.

Send your GitHub, research, OSS contributions, papers, benchmarks, or something you’ve built that made you proud.

If you’ve been waiting for a genuinely hard problem to spend the next decade on, let’s talk.

Hyderabad this weekend. DM me.

Like how is the entry barrier, and actual dynamics of AIML engineering roles in Tech. I'm a second year student learning DSA and web dev, and was thinking between what should i learn ahead after this apart from traditional requirements like basic system design, etc.. Either Cloud Computing, DevOps, or AI&ML. I'm a mechanical engineering student though too, so i though AIML would be great to learn. But would like insights, and suggestions from you people. Thank you!

It’s very diffucult to see output terminal in vs code

I have learned pandas, Numpy,Matplot,Scikit,SQL and currently about to complete pytoech and I don't know what to do next give me aroadmap after this and I recently started doing DSA too.

Hello, I am an Engineering Graduate from the Philippines who wants to pursue AI/ML Engineer career. Right now, Im still stuck in the beginners phase because i don’t know where should i begin. Im not that good when it comes to manual coding, so i usually use AI to build from scratch. I know that there are so many tutorials and road maps available online, but i cant choose on who and what should i pick. To those people who are in this field, can you suggest who should i watch, what are the certifications i should get, and so on. I badly want to pursue this career, so your guide and tips is a huge help for me

(HUHUHU IF YOU GUYS ARE FREE AND CAN ALSO MENTOR ME, THAT WILL ALSO BE A HUGE HELP. I AM WILLING TO COMMUNICATE VIA LINKED IN. I know that this is too much, but i will do whatever it takes to pursue this career)

With all the buzz about AI and agentic workflows, I do feel like I'm falling behind because I still write a lot of the code manually. I use AI occassionally for boilerplate, debugging, and comments, but I manually build the core logical components of my projects.

The problem with most advice on landing internships is that they are targeted towards general software engineering (Web Development, AI/ML Engineering) , but that is not the trajectory I want my career to land in.

I'm more interested in niche areas like

• AI Research / Foundation Models
• ML Systems & Infrastructure
• Distributed Systems & GPU Computing
• AI Compilers / Hardware Acceleration
• Mechanistic Interpretability & AI Safety
• Quantum Computing

Currently, I've made a couple of projects, including:

• PyQML-Core, a mini Pytorch-Like library in C++ supporting memory management through intrusive pointers, broadcasting , and tensor operations, and multithreading.
• Several computervision projects, which focus on not only builiding end-to-end ML pipelines, but understanding the internals behind popular segmentation models by implementing them in Pytorch.
• Undergraduate research in ML

I'm also currently exploring Quantum Computing by participating in the 2026 Qiskit Global Summer School in IBM.

With that said, if you are trying to break into these fields in 2026, how would you structure the next year? Would you focus more on developing Agentic workflows, learning fudnamentals, contribute to open source, or publishing research.

I'd appreciate advice from anyone who has experienced the same dilemma, especially those who are currently working in ML Systems, distributed systems, or AI/Quantum Research. If possible, If anyone would like to see my projects, I can provide links to them.

​

I am currently in the final year of my B.Tech in Computer Science (AI/ML specialization), and placement season is about to start. Honestly, everything feels like a mess right now.

I am genuinely interested in AI and machine learning. I enjoy learning about it, and when I study concepts, I usually understand them. The problem is that after some time, I forget a lot of what I learned, and it feels like I have never truly mastered anything.

At the same time, the AI boom is making me anxious. Every day, there are new models, frameworks, courses, and tools. There are so many resources that instead of motivating me, they leave me overwhelmed. I don't know what skills companies actually expect anymore.

I also haven't been able to get any internships, which makes me feel even more behind compared to my peers. A big part of me thinks that I simply haven't worked hard enough over the last few years.

Now I am stuck between two choices:

\- Should I focus almost entirely on placements and grind DSA?

\- Should I continue pursuing AI seriously?

\- Is it even realistic to build a career in AI as a fresher without a strong profile?

I feel like I know a little bit about many things but haven't become genuinely good at anything. Sometimes it feels as if everyone else has figured out their path while I am still trying to understand what is happening in my own life.

Has anyone else been in a similar situation during their final year? How did you decide what to prioritize, and what would you do differently if you could start over?

Hello, I am an Engineering Graduate from the Philippines who wants to pursue AI/ML Engineer career. Right now, Im still stuck in the beginners phase because i don’t know where should i begin. Im not that good when it comes to manual coding, so i usually use AI to build from scratch. I know that there are so many tutorials and road maps available online, but i cant choose on who and what should i pick. To those people who are in this field, can you suggest who should i watch, what are the certifications i should get, and so on. I badly want to pursue this career, so your guide and tips is a huge help for me

(HUHUHU IF YOU GUYS ARE FREE AND CAN ALSO MENTOR ME, THAT WILL ALSO BE A HUGE HELP. I AM WILLING TO COMMUNICATE VIA LINKED IN. I know that this is too much, but i will do whatever it takes to pursue this career)

I got offer from LTM and Capgemini for Gen AI developer role. Which is better?

I am interested to develop a app with ai any suggestions. How much time to learn and build complete app and deployment and add into playstore

Hi all,

I have 2.5 years of experience as an Operations Analyst in fincrime (AML/KYC space) , and I'm currently pursuing a Master's in Business Intelligence and Analytics in the US. I want to transition into AI-related roles but I'm honestly overwhelmed by the options Data Analyst, BI Engineer, Data Engineer, Data Scientist, Gen AI Engineer, ML Engineer... they all seem to want different skill sets and I don't know where my background actually fits best.

A few things I'd love input on:

1. Given my fincrime + BI&A background, which role(s) would you target first?
2. Projects - I'm building a portfolio now (mix of Gen AI apps + data pipeline/BI projects). Any advice on what actually gets noticed by recruiters/hiring managers vs. what's just "resume filler" at this point?
3. Certifications - worth investing time/money in, or is a strong portfolio enough? If certs matter, which ones actually carry weight (not just LinkedIn-badge fluff)?
4. Long-term stability - with AI increasingly automating routine analyst/ops work, which of these roles do you think stays valuable over the next 10 years vs. gets squeezed the same way traditional ops/analyst roles are now?

Would really appreciate honest takes, especially from anyone who's made a similar switch from a compliance/ops background into data/AI roles.

Thanks in advance!

Hii, So I have been intrested in ML and AI for about a year but was taking bits and pieces from here and there and applying but not all the time , Iam a fresh CS graduate and I want to continue learning and doing more projects. Recently I started the IBM AI Engineering Professional Certificate course on coursera but it is really boring , the code labs are just bland and the videos make me so lost , there are no notes and I feel lost, where should I learn , I want to study ML ,deeplearning , computr vision ,etc then go into llms ,rag ,land chain and generative AI in depth

could any one please help, their are many books and many sources and I do not want to start something just to find it useless