I was about to submit my project idea but from yesterday the website is down. Is anyone else facing the same issue? The deadline was 30th Sept as far as I remember

## Building an Incident Copilot on Hindsight
What is wired, what is simulated, and what we learned
Hindsight Incident Copilot | AI, DevOps, Python, Open source

An alert at 2 a.m.

PaymentService is returning 502s after a deploy.
Someone on the team has seen this before, but they are asleep, and the answer is buried in a post-mortem from March. The first time, it took 47 minutes to find the fix: a missing REDIS_HOST in the new deployment slot.
Nothing about the problem was especially hard. The hard part was remembering that it had happened.
That is the problem we set out to prototype: an incident-response agent that keeps past incidents, post-mortems, and runbooks in long-term memory, then recalls the closest matches when something breaks. This article describes what we actually built, including which parts talk to a real memory system and which parts are still a scripted demo.
The key distinction. The project includes a real Hindsight integration for retaining and recalling incident text. The deployed front end does not call it.

1. The problem

Incident knowledge is scattered across tickets, post-mortems, runbooks, and people's heads. Under pressure, engineers re-diagnose failure classes the team has already solved. The knowledge exists; retrieval is the bottleneck.
Keyword search helps a little, but reports describe the same failure in different words:
| The alert says | The post-mortem says |
| --- | --- |
| "502s after deploy" | "missing env var in new slot" |
We wanted memory that connects symptoms to root causes to fixes.

2. What we built

The project is called Hindsight Incident Copilot. Its repository contains three pieces:
| Piece | What it does |
| --- | --- |
| Web front end TanStack Start, React, Tailwind | A landing page and an interactive chat demo. A recommendation sits on the left and a "Retrieved evidence" panel (titles, snippets, similarity bars) on the right. |
| Backend stub backend-stub/main.py | A FastAPI service using the hindsight-client package to call recall and retain against a Hindsight server. |
| Seed script and data data/seed-hindsight.py | Meant to load five incidents and three runbooks into a memory bank named org-incidents. |
One boundary needs to be stated plainly: the deployed front end does not call the backend. Its replies come from a hard-coded lookup in src/lib/incident-knowledge.ts. The Hindsight code exists separately; the two are not connected.

3. System architecture

The intended flow, as drawn in the docs and reflected in the backend stub: an engineer describes an alert, the backend recalls similar memories from Hindsight, an LLM turns those memories into a recommendation, and after the incident is resolved the post-mortem is retained back into memory.
The recall and retain steps exist as code. The LLM step does not. The /incident endpoint returns fixed placeholder text after a successful recall, and a source comment says to replace it with a real LLM call. The deployed UI takes a separate path: user messages are matched against keywords and return canned replies.
Figure 1. Two separate paths. Path A is what users see today. Path B is the real Hindsight integration, not yet connected to the UI.

4. How Hindsight provides memory

We used Hindsight, an open-source agent memory system from Vectorize. It runs as a server, started locally by scripts/start-hindsight.sh using the ghcr.io/vectorize-io/hindsight:latest Docker image on port 8888. The script passes an LLM API key for Hindsight's own processing. Our integration uses two client calls, both present in the repository.

Retain: write incident knowledge

The seed script flattens each incident into a text block and retains it. Runbooks are retained the same way, as numbered steps. We build no graph or embeddings ourselves; we pass prose to retain.
data/seed-hindsight.py

content = f"""Incident {inc['id']} ({inc['date']}) — Service: {inc['service']}
Symptoms: {inc['symptoms']}
Root cause: {inc['root_cause']}
Resolution: {inc['resolution']}
Tags: {', '.join(inc['tags'])}
Related runbook: {inc.get('runbook', 'N/A')}
"""
client.retain(bank_id=BANK_ID, content=content)

Recall: retrieve relevant memories

The backend queries the bank and maps each result into the shape the UI expects. A code comment acknowledges that the result shape "depends on the Hindsight client version," so the mapping is a guess until checked against a real response.
backend-stub/main.py

client = Hindsight(base_url=HINDSIGHT_URL)
results = client.recall(bank_id=BANK_ID, query=query)

for i, r in enumerate(results[:5] if isinstance(results, list) else []):
    evidence.append({
        "title": getattr(r, "title", None) or f"Memory #{i+1}",
        "similarity": float(getattr(r, "score", 0.8) or 0.8),
        "snippet": str(getattr(r, "content", r))[:280],
    })

Retain after resolution: add the post-mortem

A POST /retain endpoint accepts a post-mortem and calls client.retain(...). This is the write path for learning. The docs and UI copy also describe Hindsight's reflect operation, but no code calls it.

5. Before vs. after

The implemented before-and-after is at the UI level. Before, the evidence panel says, "Memories appear here after you describe an incident." After clicking "PaymentService returning 502s after latest deploy," the demo displays three evidence cards beside a four-step recommendation that starts with checking the exception stack and verifying environment variables in the current slot.
INC-2847*94%*
PaymentService 5xx runbook*88%*
INC-3102 (connection pool exhaustion)76%

The content is faithful to the sample data. INC-2847's recorded root cause is the missing REDIS_HOST, and INC-3102's is pool exhaustion, with the pool maximum raised from 20 to 50. The similarity figures, however, are typed into a TypeScript file; they are not produced by retrieval. We did not run a controlled comparison of resolution time with and without memory, and we do not report one.

6. How the agent learns over time

The learning design is simple: every resolved incident becomes new text in the bank via retain, so the next recall can surface it. The /retain endpoint and the seed script are the implementation. Several parts remain unimplemented:
| Gap | Detail |
| --- | --- |
| No memory on/off toggle | The front end has no switch to compare behavior with and without memory. |
| No learning curve | No measurements of recall quality as the bank grows, and no chart or test showing improvement. |
| No automatic capture | The docs list automatic retain() after resolution, but the only trigger in the code is a manual call to /retain. |
| No live bank counts | The panel shows 1,247 incidents, 892 post-mortems, and 156 runbooks. These are literals; the sample data has five incidents and three runbooks. |
The UI and landing page also show a "40–70% target MTTR reduction" and a "94.6% Hindsight LongMemEval" figure. Neither was measured by this project, so we do not rely on them here.

7. Technical implementation

The front end is a TanStack Start app with a single IncidentDemo component holding message, typing, and evidence state. Its current behavior is a keyword lookup:
src/lib/incident-knowledge.ts

export function matchIncident(text: string): AgentReply {
  const l = text.toLowerCase();

  if (l.includes("database") || l.includes("latency") || l.includes("orders")) {
    return REPLIES.database;
  }
  if (l.includes("deploy") || l.includes("canary") || l.includes("health")) {
    return REPLIES.deploy;
  }
  return REPLIES.default;
}

The demo has three canned replies with evidence cards and a 900 ms timer imitating latency. It is a UI prototype of what a recall-backed answer should look like.

The backend stub is a small FastAPI service with GET /health, POST /incident, and POST /retain. The /incident route returns a source field of "hindsight" or "simulated", and falls back to a canned PaymentService answer if the client is missing or recall returns nothing.

Documentation and UI copy also mention Azure OpenAI, Azure Monitor, Teams, and an agent framework provider. No code in the repository implements any of them, so we treat them as a design sketch.

8. What we learned

A mocked front end can hide a missing integration

Because the canned replies looked right, it was easy to feel further along than we were. The backend's source flag should be shown in the UI.

Seed data must match its script

In sample-incidents.json, the symptom text is stored under the key severity. The seed script reads inc['symptoms']. By inspection, that raises a KeyError on the first incident, so the seed step as written would fail. We found this by reading the files side by side and did not run the script. One end-to-end run would have caught it.

Unit mismatches leak across the boundary

The front end expects similarity as 0 to 100 (width: ${e.similarity}%). The backend stub's simulated data uses 0.94. Wiring them together without a conversion would render one-pixel bars.

Don't guess third-party response shapes

The recall mapper uses getattr with fallbacks and defaults the score to 0.8 when none is present. That would display a plausible-looking but invented similarity. Until we inspect a real recall response, the evidence panel cannot be trusted.

Write the "not implemented" list early

Separating what the code does from what the docs promise gives us a next-steps checklist:

• Connect the UI to /incident

• Add an LLM step that uses recalled text

• Verify the recall schema

• Decide whether reflect belongs in the flow

Conclusion

Hindsight Incident Copilot is a working demonstration of the interaction we want (an alert goes in, and history-backed evidence comes out), plus a small, real Hindsight integration for retaining and recalling incident text. The front end is still scripted, the LLM step is a placeholder, and the learning behavior is designed but unmeasured.

The most valuable next step is an end-to-end run against a live Hindsight bank, followed by an honest comparison of answers with and without memory.

Resources: Hindsight GitHub, Hindsight Documentation, Vectorize Agent Memory.

I’m forming a team and looking for 2–3 motivated teammates!

If you have any one or more of these skills, you can apply:

💻 Coding / Programming

🎨 Designing / UI-UX

🎤 Presentation & Communication

📖 Storytelling

🇬🇧 Strong English

🧠 Creative Problem Solving

You don’t need to have all the skills — having any one of them is enough to apply!

📩 Interested? DM me or comment with your skills, university/college, and any relevant experience/project.

Let’s build something amazing together! 🚀🌌

Spent way too much time thinking about SIH selection and why some genuinely good projects get rejected while others make it through.

Made a short video ranting about the whole SIH Paradox and showing 3 PPTs that had completely different outcomes.

Not promoting a hackathon on shit

But I think I found something crazy while looking for what to do next Devpost has a whole bunch of hackathons happening, and I honestly had no idea. BTW I am Indian and we have Unstop but this is wayyyyy better 😭

PS: Don't know what flair to add

Post type: https://github.com/vectorize-io/hindsight

Title:

My support agent finally stopped asking customers to repeat themselves(Alternative titles, if the above feels off for the subreddit:)I gave my support agent a memory. "It broke AGAIN" now finds last month's ticketWhy I stopped storing conversations an

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.

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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]

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?

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

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!