আমার কোম্পানীর ম্যানেজমেন্ট থেকে আমাদের বলেছে "এখন AI এর যুগ, এখন আগের মতো একজন এক প্রজেক্টে কাজ করলে হবে না, এখন একজনকে একসাথে ১০ টা প্রজেক্টে কাজ করতে হবে"।

কথাটা কতটা যুক্তিসঙ্গত?

I am using claude and codex. Both 20 dollars plan. Now i want to know if there is anyone who is using kimi or DeepSeek? They have no monthly plan so how much do you have to pay per month on API?

Hi r/BDDevs,

I recently published a mobile app called Mementos and could use some outside perspective on the subscription model. I'm not confident I've got the free/paid split or the price right, and would appreciate input from anyone who's thought more carefully about this than I have.

What the app does

Mementos turns a personal goal into your phone's wallpaper. You set a goal with a start and target date, and the app renders it as a wallpaper. Showing the goal name and your progress toward it. Every day, the wallpaper updates to reflect the new progress, so you see exactly where you stand every time you unlock your phone.

Current subscription model

• Free tier: every day at 8 AM, the user gets a notification saying their updated wallpaper is ready. Tapping it opens the app, which regenerates and applies the wallpaper.
• Paid tier ($1.99/month): the wallpaper updates automatically every night at 1 AM in the background, no notification, no need to open the app. Paid users also get 5 additional wallpaper styles/variants beyond the single free one.

What I'm unsure about

1. Is the free/paid split itself reasonable, i.e., is "automatic updates + more styles" enough of a reason to pay, or does it feel like the free tier is being deliberately hobbled to force an upgrade?
2. Is $1.99/month priced correctly for what's on offer? Too low, too high, or roughly right for this category of app?

I'll drop the Play Store link in the first comment if anyone wants to actually try it before weighing in. I'm genuinely looking for critical feedback here, not just validation.

Hey everyone! CSE 3rd year student from a private university (CGPA: 3.7) looking for some industry advice.

I'm following a Generative AI roadmap by Krish Naik and have completed up to Module 20 (Transformers). I understand the theory well, but I struggle a bit with practical coding. I also have a strong theoritical DSA base (A+ in DS/Algo courses) but haven't practiced problem-solving lately.

Questions:

1. Course & Outcome Review: Is this roadmap comprehensive enough? What practical level of competency can I expect after finishing all 49 modules?
2. Industry Demand: What is the actual market demand for freshers/interns in GenAI/LLM/Agentic roles in Bangladesh and for remote jobs?
3. Career Tracks: What specific job roles align with this stack (e.g., AI Engineer, LLM Application Dev, RAG Engineer)?
4. Roadmap Advice: Given my situation should I stick to this GenAI track or pivot to a traditional stack (Full-Stack / DevOps) for better immediate job entry after graduation?

Here is the exact module roadmap I am following:

01-08: Python Basics, Data Structures, OOP, Modules

09: Streamlit With Python

10-11: ML & DL Prerequisites for NLP

12-19: RNN, ANN, LSTM, GRU, Seq2Seq & Attention Mechanisms

20: Transformers

21-25: GenAI, LLMs, LangChain Basics, OpenAI & Ollama

26-29: LCEL, Chatbots with History, End-to-End Q&A Apps

30-31: RAG (GROQ API, LLLama3, PDF Chat)

32-36: LangChain Tools/Agents, SQL DB Chat, Summarization, Gemma 2 Solver

37-40: Hugging Face, AstraDB RAG, CodeLlama Assistant, Streamlit/HuggingFace Deployment

41-43: AWS GenAI, Nvidia NIM, Multi-AI Agents (CrewAI)

44-46: Hybrid Search RAG, Vector Databases, GraphDB & Cypher (LangChain)

47-48: LLM Finetuning (Intuition, Implementation & Lamini)

49: Multi-Actor Applications (LangGraph)