u/Odd-Breadfruit-7909

A relative of mine wants to apply to fast for fintech but the minimum eligibility criteria for intermediate is 50 percent. He secured 48 percent. How can he apply then

If a company has already rejected you few months back after extensive interviews and now you’ve joined another company but they reach back again and say if you are still interested despite knowing I’ve joined another one. So they can have a final interaction.

What does that mean and how to deal with this?

We’re currently working on a product focused on helping O Level, A Level, Matric and FSC students get a more practical understanding of what different Computer Science related degrees and career paths actually look like before choosing them.

At this stage, we’re looking to connect with teachers, counselors, educators, and people who can help us reach students going through these decisions and help us test the viability of what we’re building.

Please hit me up or comment if you think you can help with this.

Would be interesting to know if anyone here is working on similar problems in real products because I rarely come across people thinking deeply about this side of LLM behavior. I’d love to join though.

The project itself was completely stateless because I wanted to avoid future context window issues and keep resource usage manageable. Instead of relying on raw conversation history, I extracted important information from each turn, stored conversation summaries separately and used a vector database with metadata for retrieval. For follow up questions, I used a small sliding window buffer combined with weighted reranking based on recency, importance, and semantic similarity under a fixed context budget. The goal was basically to make the tutoring behavior stay stable over longer learning sessions instead of slowly drifting away from the original teaching intent

Would be interesting to know if anyone here is working on similar problems in real products because I rarely come across people thinking deeply about this side of LLM behavior. I’d love to join though.

The project itself was completely stateless because I wanted to avoid future context window issues and keep resource usage manageable. Instead of relying on raw conversation history, I extracted important information from each turn, stored conversation summaries separately and used a vector database with metadata for retrieval. For follow up questions, I used a small sliding window buffer combined with weighted reranking based on recency, importance, and semantic similarity under a fixed context budget. The goal was basically to make the tutoring behavior stay stable over longer learning sessions instead of slowly drifting away from the original teaching intent

I'm a recent graduate but back in my college days, I used LLMs to study since the coursework was too complex for me to grasp so I always gave a particular set of instructions to GPT or Gemini before they start tutoring me. It goes well for first few questions and then collapses. Basically attention mechanism favours recency and my instructions token at the very start of the conversation get buried. I ran all these diagnostic tests to fix this issue for me since i was so done with the overprompting and starting fresh each time.

I built my own bot with gemini's api and used a system prompt. So, it never forgets the teaching instructions. Also, I kept it stateless because of resource constraints and to avoid any future context window issues. I implemented an intelligent memory. Ran a prompt to extract important info from each turn and store in a vector db. Stored conversations summaries too. For followup questions, I had a 2 turn buffer sliding window which only activates if followup. Stored all the info and conv summaries in db with metadata for hybrid search. For reranking, I had a weighted score of recency plus importance plus similarity under a context budget control.

I don’t know if this is considered basic or not for my level but the whole thing started from a real frustration I had while studying. I wasn’t initially trying to build an AI product for the sake of it. I just wanted a tutor that would stay consistent and actually remember how I wanted to learn instead of slowly drifting after a few turns.

Working on this got me really interested in the edtech side of AI, especially around long term learning interactions, memory, personalization and how tutoring systems behave in real usage instead of controlled demos.

I’ve been exploring a few YC startups in the space and it feels like there’s still a lot of room to build around actual learning behavior rather than just content generation.

I'm a recent graduate but back in my college days, I used LLMs to study since the coursework was too complex for me to grasp so I always gave a particular set of instructions to GPT or Gemini before they start tutoring me. It goes well for first few questions and then collapses. Basically attention mechanism favours recency and my instructions token at the very start of the conversation get buried. I ran all these diagnostic tests to fix this issue for me since i was so done with the overprompting and starting fresh each time.

I built my own bot with gemini's api and used a system prompt. So, it never forgets the teaching instructions. Also, I kept it stateless because of resource constraints and to avoid any future context window issues. I implemented an intelligent memory. Ran a prompt to extract important info from each turn and store in a vector db. Stored conversations summaries too. For followup questions, I had a 2 turn buffer sliding window which only activates if followup. Stored all the info and conv summaries in db with metadata for hybrid search. For reranking, I had a weighted score of recency plus importance plus similarity under a context budget control.

I don’t know if this is considered basic or not for my level but the whole thing started from a real frustration I had while studying. I wasn’t initially trying to build an AI product for the sake of it. I just wanted a tutor that would stay consistent and actually remember how I wanted to learn instead of slowly drifting after a few turns.

Working on this got me really interested in the edtech side of AI, especially around long term learning interactions, memory, personalization and how tutoring systems behave in real usage instead of controlled demos.

I’ve been exploring a few YC startups in the space and it feels like there’s still a lot of room to build around actual learning behavior rather than just content generation.

Also I wonder if these skills and this project is enough to join an edtech yc startup or do I need to do more projects before applying

I’ve seen people mention that they landed remote roles at YC startups, so I tried exploring that a bit.

I’m pretty new to this space and have applied to a few roles, but it feels like most of them still expect some level of professional experience.

I don’t have industry experience yet, but I’ve worked on some solid AI/ML projects on my own and feel comfortable in the domain.

Do fresh grads realistically have a chance with YC startups or is prior experience almost always expected? And if people have done it, what made the difference for them?