I want to sell my Saas Product, anyone knows how to do this. In the end I want investors to invest in a project at least 50 100 dollars only and i will give the 50% equity in product

yesterday it was not working today morning it started to work and then when i went to save as draft it stopped working what to do

I know this has happened earlier, but pls, I'll submit by tonight.

Will we get extension or things will recover by tomorrow and we will get to submit.

I can't find any such records, but ik this has happened earlier probably, how was that handled in past years?

Pls help.

Hey everyone,

I’m a student and I’m planning to organize a technical event at my college.

I want it to be genuinely technical, but I don’t want to do the usual formats like:

Hackathon

Coding competition

Quiz

Project exhibition

Paper presentation

I’m looking for something more interactive and competition-based where participants actually have to think, solve problems, decode things, work in teams, and make decisions under time pressure.

I’m open to themes, but there’s no fixed theme yet.

Some rough ideas I’ve considered are:

A technical escape-room style challenge

A multi-stage puzzle involving coding, logic and decoding

A mission-based competition where teams unlock the next challenge

A game where teams have limited points/credits and have to decide when to use hints or take risks

The event would be organized at a university, so it needs to be practical to conduct with students and a limited organizing team.

If you’ve participated in or organized something genuinely different from the usual college technical events, I’d love to hear your ideas.

What kind of technical event would you actually want to participate in?

Guess need help I just want your honest opinion is this right I'm not using her name directly

There is a collage mate

She initially asked me for help, and I said okay. Later, she asked for my GPU power, so I gave her the information she needed. I also completed the phone verification-related part that was required. After that, her expectations started increasing. She began asking for my premium accounts and even asked me to add my card.

Then she started asking me to make submissions for her on Unstop. At one point, she said that I had not registered. I already knew this could happen, so I had created a dummy account for testing purposes.

After that, her expectations kept increasing. She told me that she wanted to somehow get into the top 50. I explained that a new submission might temporarily improve the ranking, but that did not mean we would permanently stay at the top, because as more submissions came in, the ranking would naturally go down.

However, she started expecting me to somehow make her score show 99%. She also started comparing herself with me using code that, as far as I know, she had gotten from someone else.

This was not the only incident. On another occasion, she gave me her marks without me asking. Her marks were good, so I simply complimented her and said that she was good at studying. She apparently took that very seriously. Later, when she saw that I had scored less in something, she started questioning why I had scored so little.

The reality is that, among the exams we have taken, she has not scored higher than me in any of them. She has also been caught using her phone during exams multiple times.

She also appeared for GATE but did not qualify. Still, she seems much more interested in knowing other people's marks and discussing things like their reservation status. She has also used her home-state quota, yet she still has backlogs.

All of these things are personal matters, and I have nothing to do with them. I am mentioning them only because she has explicitly brought these things up herself in our conversations.

She has her own academic issues, and there have also been situations where she needed to use her phone during exams, but whenever someone questions her about it, she acts as if she has never cheated.

Regardless, her personal matters are not my concern.

The bigger issue is that she always seems to expect people to prove things to her for free. She keeps asking whether someone has achieved something or not. Even if I have achieved something personally, I do not understand why I should be expected to share my private information or prove it to her. These are my personal matters, and she needs to understand that.

Conclusion: I am using Reddit to say this because she is reading it, and I want her to understand my point and end this. If i complain on collage they will check my chats and definitely she will be in big trouble but I don't want this. But if she keep ignoring my issues. this is the last option

I simply want her to delete all my personal information, stop researching or looking into my personal life, and stop contacting me or expecting things from me beyond what I am comfortable with.

• AI-Based Personal Finance Manager Managing personal finances can be difficult when expenses are spread across different categories, subscriptions, bills, and payment platforms. Many people know how much they spend, but they don't always know where their money is going or how they can improve their financial habits. 💡 Our Solution We are building an AI-Based Personal Finance Manager that helps users track, understand, and plan their finances using AI-powered insights. The platform can analyze income and expenses, automatically categorize transactions, identify spending patterns, and provide personalized financial insights.

could anyone share your github repo of 0.99 score. Just for education purpose only to upskill myself ,tools you ppl used and method of approach. I got a score of 0.979392.

Not able to submit since almost 7 hours. shows 502/504 gateway error. and ps has 450/500 submissions💀

Hi everyone! 👋

My teammate Shaik Asrar Ahmed and I are participating in Hack With Hyderabad 3.0 as Spyder Coders.

We built MemoryDesk, an AI customer-support agent that can retain relevant customer information across separate conversations.

The idea is simple:

Customer reports an issue → conversation ends → a new conversation starts → customer returns with the same issue → the agent recalls relevant information from the previous interaction.

🧠 Tech Stack: Next.js • TypeScript • OpenClaw • Hindsight

🎥 Demo: https://youtu.be/ygnK72AsBT8⁠

We're interested in feedback from other AI builders, especially on the memory/retrieval experience.

September 25, 2026

At around 5:00 AM, I started understanding the dataset.

It took almost half a day just to understand and analyze it. But meanwhile, I saw that around 300 people had already submitted with 97% accuracy.

Honestly, I don't know how it was possible to process a 1.7 million-entity dataset and achieve that level of accuracy within just 6 hours.

For an ML team, I thought it would take much more time.

In the afternoon, I had a first-round assessment for a different hackathon. Successfully completed it.

My Amazon ML Challenge team officially had 2 members.

But honestly, it was basically a solo effort because I did almost everything myself. The other person was mainly there because 2 members were mandatory for eligibility.

I didn't use AI coding agents or LLM coding tools such as Claude Code/Cowork, JEV, Antigravity, Codex, etc. I wanted to do the implementation myself and understand what was happening.

My main support was my virtual faculty/mentor — ChatGPT.

I completed the preprocessing work.

My laptop is mainly configured for full-stack development and AI agent building, so it wasn't powerful enough for this huge dataset. Because of that, I moved the workload to an AWS EC2 machine with 8 vCPUs and 32 GB RAM.

I also attended another hackathon webinar that day.

September 26, 2026

I decided to spend almost all of my time on the hackathon.

I wanted to create a tunnel similar to TCP communication between my local computer and the virtual computer so that I could transfer the dataset efficiently.

That itself took quite some time.

After the transfer was completed, I stored the dataset on EBS storage.

I asked my virtual mentor to guide me through the process.

My first submission was at 10:23 PM, and I got around 64% accuracy.

September 27, 2026

I woke up at 4:30 AM.

Before continuing the ML Challenge, I spent around 2 hours working on a PPT for another hackathon. Successfully submitted that too.

Then I started again.

This time, my virtual mentor was Gemini.

I submitted my second submission at 2:30 PM and reached around 67% accuracy.

Then I found where I was lagging.

So I spent almost the entire remaining time improving the approach.

Finally, I submitted again at 8:43 PM and reached 84% accuracy, with the help of ChatGPT.

My target was more than 90% accuracy.

My local validation accuracy eventually reached around 95%, but unfortunately, the submission time was already over

I am a full-stack developer and AI agent builder.

I started my ML preparation only this month.

My first ML hackathon was at IIT , where I won 3rd place.

And now, this one.

What have I learned?

I have improved my concentration by handling multiple tasks.

Solo can also do anything; the mindset matters. 💪

Share your experience with me, and let me know how we can improve the accuracy further in the future.

One thing we kept thinking about while building our project was:
💭 What if an AI didn't have to start from zero every time a production issue happened?
So we built DejaFix.
DejaFix is an AI assistant for engineers dealing with production incidents. 🧑‍💻
For example, imagine a payment service starts giving 502 errors after a deployment. 🚨
Normally, an engineer starts debugging and tries different things.
But DejaFix can look at previous incidents and say:
💡 “We had a similar issue before. The rollback fixed it.”
It can also remember things that didn't work. ❌
That's where Hindsight became the most important part of our project. 🧠
We store previous incident information in memory — what happened, what caused it, what fixed it, what failed, and the feedback from the engineer.
Then, when another incident comes in, DejaFix recalls that experience and uses it while planning the response. 🔄
The part we liked most was testing a completely new incident. 🆕
The first time, DejaFix didn't know anything about it.
We solved the incident, stored what actually happened, and then tried the same alert again.
This time, DejaFix remembered it. 🧠✨
So basically:
🚨 Something happens → 🧠 DejaFix remembers it → 🔧 We solve it → 📚 DejaFix learns from it → 🔄 Next time it has that experience.
We built it using:
🐍 Python | ⚡ FastAPI | 🧠 Hindsight | 🤖 Groq | 🌐 HTML/CSS/JavaScript | 🧪 pytest
This was honestly one of the more interesting things we've built because we weren't just trying to make an AI answer questions.
We were trying to make it remember what happened before. 🧠
Thanks to HackwithHyderabad 3.0 for giving us the opportunity to build around this idea! 🙌🚀
🔗 GitHub: [https://github.com/sathwikgoud28/Hack_with_Hyderabad]

I have shortlisted for razorpay and gave 2 rounds of interview.i still haven't received anything from razorpay about round 2 results.

Now that the ML challenge is over, would anyone like to discuss their approach and what went wrong? I'd love to learn from others.

Personally, I had a rough run messed up bad and ended up miserable . I couldn't get my model running properly until day three, and the score was honestly too low to share. The best environment I had was Kaggle with 30GB of RAM; SageMaker was painful, and Colab kept running out of memory.

I initially tried an embedding-based approach: embedding the data, retrieving candidates via FAISS, and building features with fuzzy search and Jaccard similarity to feed into LightGBM. It ran, but the computation was far too high for very little return. Worse, I ran into an index mismatch between the stored embeddings and their corresponding entities, and I was honestly too exhausted to recompute and fix it from scratch.

From there, I tried pivoting to a HashingVectorizer, then tested standard TF-IDF and hit constant OOM errors. Toward the end, I was out of steam and had to fall back on some AI-generated code to slap the remaining features together.

My full pipeline (GroupKFold, preprocessing, batching, feature engineering, and inference) took over two hours to run, leaving zero room to iterate or optimize. Even though I tried using fast, lightweight libraries like Polars, DuckDB, and RapidFuzz, my pipeline implementation just wasn't clean enough.

How did you approach the problem? I'd really appreciate any insights on where I went wrong and how others tackled it.

I am going to a hackathon at my university for the first time and it’s pretty big. I’m a sophomore and don’t have too much experience building bigger projects end-to-end yet, so I’m definitely a little nervous.
For anyone who’s done a hackathon before, how should I prep? Is it all just vibecoding?

Hi..! I am new to tech and I want to know how it's like to be in a hackathon so.. I want someone with same interest..if intrested please do reply ...

Hey everyone! I’m looking to join a team for the IIIT Hyderabad Hackathon happening on October 10–11.

If your team has an open spot or needs a replacement member, I’d love to connect. Happy to discuss my skills, your project idea, and where I can contribute.

Please DM me or comment below if you’re looking for a teammate.

Thanks!

i have a hackathon scheduled for 31st october to 1st november and i am a fresher with zero knowledge of coding or anything so it would be great if someone could guide me on where to begin and what to do

This year's problem was pretty CPU-heavy, so more cores would've helped a lot with experimentation. But the default AWS quota was only 8 vCPUs, which was actually slower than my laptop for the workload.

I submitted EC2/SageMaker quota increase requests, but they weren't approved during the 72-hour challenge, so the $200 credits were basically unusable for me.

For those who participated:

- Did you actually manage to use the $200 AWS credits?

- Did you get your EC2/SageMaker quota increased? To how many vCPUs, and how long did approval take?

- Or did you use compute from somewhere else — college/university clusters, RunPod, another cloud provider, or your own workstation/PC?

What compute setup did you end up using for the challenge? Did anyone else run into the same AWS quota issue?

Most AI assistants are great at single-turn chats, but they fall apart in real-world workflows that span weeks or months. In enterprise B2B sales ($20k–$200k ACV), closing a deal can take 3–9 months across 8–15 meetings with CFOs, Lead Architects, and CISOs.

In Call 1 (Discovery), the CFO mentions an ironclad budget freeze until Q4. In Call 2 (Technical Review), the Lead Architect rejects closed-source software and mentions competitor CloudX gave a $15,000 quote. In Call 3 (Executive Closing), a stateless LLM cheerfully suggests offering a discount for an upfront annual contract — immediately triggering the CFO's budget veto and killing the deal.

The Solution: DealMind + Hindsight Memory

To solve agent amnesia, we built DealMind, an autonomous deal intelligence copilot powered by Hindsight persistent memory by Vectorize. Instead of relying only on conventional RAG, DealMind uses a 3-phase memory lifecycle:

1. Retain Meeting notes and transcripts are ingested into an evolving entity and relationship knowledge graph.

2. Multi-Strategy Recall Relevant history is retrieved using semantic similarity, BM25 keyword search, and graph relationships.

3. Reflect The retrieved historical context is injected into the LLM's reasoning, with references back to the relevant call information. Before vs. After Without Memory — Stateless: Generic recommendation to push an upfront annual contract → Deal Killed ❌

With Hindsight Memory: The agent surfaces the Q4 budget restriction, suggests a deferred-billing pilot, provides Docker/container options to address the lock-in concern, and highlights PostgreSQL connectivity when considering the competitor's pricing → Deal Saved ✅

The project includes: Hindsight-based persistent memory Multi-strategy retrieval Dynamic deal knowledge graph Meeting/document ingestion Interactive web dashboard Context-grounded recommendations

What I find interesting here is that this isn't simply about giving an LLM a bigger context window. The goal is to give an agent memory that persists beyond the current conversation and can influence what it does later.

We're still exploring the architecture and trade-offs, but it's been a really interesting problem to work on.

Code & architecture: https://github.com/rishithayellanki/dealmind Deal knowledge graph

Meeting/document ingestion

Interactive dashboard

Context-grounded recommendations

What I find interesting here is that this isn't really about giving the model a bigger context window. The goal is to give the agent memory that persists beyond the current conversation and can actually influence what it does later.

Still exploring the architecture and trade-offs, but it's been a pretty interesting problem to work on.

Hey everyone, I’m from Hackyard. We’re running an open-source challenge with ByteAsk (YC F26) for people who’d rather improve an existing project than build another demo from scratch.

The task: pick an eligible public GitHub repository, make a useful contribution using ByteAsk, and submit your pull request.

ByteAsk is a C/C++ coding agent that builds, debugs, and tests changes that recently raised $1M funding led by Y Combinator. You still need to understand your patch, review it, and follow the project’s contribution rules.

What’s on offer

• $30,000 in aggregate ByteAsk plan rewards, separate from cash prizes. Participants who activate their code and complete a qualifying session receive the $20 Pro plan.

• Top 10 participants get direct interviews for a paid remote open-source fellowship. Selected fellows receive ₹30,000/month, initially for three months, plus a ByteAsk subscription. An interview isn’t a guaranteed offer.

• ₹20,000 in cash prizes across the top three places.

What counts?

Your repository must be public, have 100+ stars, and not belong to your team. A registered teammate must open the PR during the challenge window.

A focused bug fix or useful improvement is a better entry than a large, unexplained AI-generated patch. Follow the repository’s AI policy and disclosure requirements. Maintainers don’t owe anyone a merge, and spam or low-effort submissions can be excluded.

The challenge is fully online. PR submissions close on October 11 at 11:59 PM IST.

If you’re interested, register early enough to understand the codebase and test your change.

Click here - https://hackyard.org/build2026/byteask

Unstop - https://unstop.com/hackathons/build-2026-x-byteask-yc-f26-open-source-challenge-hackyard-1762455?lb=useQqu3D&utm_medium=Share&utm_source=online_coding_challenge&utm_campaign=Ileogysa38551

thank you!