AYayush2390·Apr 3, 2025Submission
How to write good prompts for generating code from LLMs
11 points · Open discussionpotpie-ai
potpie
Python2024–2025
potpie-ai / potpie
potpie
Context Graph for AI Native SDLC
3Distinct people
6Mentions
0Explicit recommenders
5.8KGitHub stars
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by week6 dated mentions
Observed mentions: 2024-11-04 13:32:51 UTC to 2025-04-03 12:39:05 UTC. Weeks start Monday, UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Nov 4 | 2024-11-04 00:00:00 UTC | 2024-11-11 00:00:00 UTC | 2 | 2 | 0 |
| Nov 11 | 2024-11-11 00:00:00 UTC | 2024-11-18 00:00:00 UTC | 0 | 0 | 0 |
| Nov 18 | 2024-11-18 00:00:00 UTC | 2024-11-25 00:00:00 UTC | 0 | 0 | 0 |
| Nov 25 | 2024-11-25 00:00:00 UTC | 2024-12-02 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2 | 2024-12-02 00:00:00 UTC | 2024-12-09 00:00:00 UTC | 0 | 0 | 0 |
| Dec 9 | 2024-12-09 00:00:00 UTC | 2024-12-16 00:00:00 UTC | 0 | 0 | 0 |
| Dec 16 | 2024-12-16 00:00:00 UTC | 2024-12-23 00:00:00 UTC | 0 | 0 | 0 |
| Dec 23 | 2024-12-23 00:00:00 UTC | 2024-12-30 00:00:00 UTC | 0 | 0 | 0 |
| Dec 30 | 2024-12-30 00:00:00 UTC | 2025-01-06 00:00:00 UTC | 0 | 0 | 0 |
| Jan 6 | 2025-01-06 00:00:00 UTC | 2025-01-13 00:00:00 UTC | 0 | 0 | 0 |
| Jan 13 | 2025-01-13 00:00:00 UTC | 2025-01-20 00:00:00 UTC | 0 | 0 | 0 |
| Jan 20 | 2025-01-20 00:00:00 UTC | 2025-01-27 00:00:00 UTC | 3 | 0 | 3 |
| Jan 27 | 2025-01-27 00:00:00 UTC | 2025-02-03 00:00:00 UTC | 0 | 0 | 0 |
| Feb 3 | 2025-02-03 00:00:00 UTC | 2025-02-10 00:00:00 UTC | 0 | 0 | 0 |
| Feb 10 | 2025-02-10 00:00:00 UTC | 2025-02-17 00:00:00 UTC | 0 | 0 | 0 |
| Feb 17 | 2025-02-17 00:00:00 UTC | 2025-02-24 00:00:00 UTC | 0 | 0 | 0 |
| Feb 24 | 2025-02-24 00:00:00 UTC | 2025-03-03 00:00:00 UTC | 0 | 0 | 0 |
| Mar 3 | 2025-03-03 00:00:00 UTC | 2025-03-10 00:00:00 UTC | 0 | 0 | 0 |
| Mar 10 | 2025-03-10 00:00:00 UTC | 2025-03-17 00:00:00 UTC | 0 | 0 | 0 |
| Mar 17 | 2025-03-17 00:00:00 UTC | 2025-03-24 00:00:00 UTC | 0 | 0 | 0 |
| Mar 24 | 2025-03-24 00:00:00 UTC | 2025-03-31 00:00:00 UTC | 0 | 0 | 0 |
| Mar 31 | 2025-03-31 00:00:00 UTC | 2025-04-07 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
6 sourcesIn my opinion, 2025 is going to be the year of AI agents. In just the first three weeks of 2025, GitHub announced its Copilot integration in VS Code, LlamaIndex introduced Agent Workflow, and AutoGen v4 was launched. And this is just the beginning.
The fact that AI agents leverage popular LLM models like OpenAI's GPT, Google's Gemini, Claude, and many others opens the door for their application in virtually every domain—hospitality, security, computer science, healthcare, education, and beyond.
We are working on a project Potpie (https://github.com/potpie-ai/potpie). It's basically an open-source AI agentic platform that helps the developers to build AI Agents that truly understand your complex codebases and performed desired actions. Unlike majority of the AI Agent platforms and GenAI models, Potpie's AI Agents can understand the overall
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context of your codebase thoroughly
We are working on a project Potpie (https://github.com/potpie-ai/potpie). It's basically an open-source AI agentic platform that helps the developers to build AI Agents that truly understand your complex codebases and performed desired actions. Unlike majority of the AI Agent platforms and GenAI models, Potpie's AI Agents can understand the overall context of your codebase thoroughly
In Ask HN: Are there any real examples of AI agents doing work?Evolution of AI is truly revolutionizing how developers code. With AI tools like ChatGPT, Copilot, Gemini, etc more and more developers are becoming dependent on these tools to generate code. But a majority of the popular AI code generators are incapable of understanding the context of complex codes and this leads to incorrect code snippets.
We are working on Potpie (https://github.com/potpie-ai/potpie), which targets this very problem and helps the developers to build AI agents that truly understand your complex codebase and perform desired actions
In Ask HN: What does programming with AI feel like?Show HN: I built an agent to make open source contributions easier
Over the last few months I’ve built a tool that breaks down your codebase into a knowledge graph, generates and stores inferences about the code with each node and provides tooling for agents to fetch graph structure, perform similarity search, fetch code etc in order to do engineering tasks.
My favourite thing about this is how easy it makes getting started with a new codebase that I have no context about. As a young engineer when I was trying to get into open source, it always seemed out of reach because the large codebases always scared me and documentation was not fun to read.
Use cases I’ve tried so far:
- Generating “Getting started” documentation for a project: I could not find any steps on how to build and test the AgentOps SDK locally. I’m more of a microservices guy, had never heard of tox or built SDKs. The tool was able to
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read the configuration files and tell me exactly how to build and run the tests [1]
- Generating a plan for changes to be made for a Github issue description - Tried this with the Portkey AI gateway and Signoz codebase where I picked up an open issue from the repo and tried to get the agent to tell me what needs to be done. [2][3]
- Understanding behaviour of a repo that is not documented - I was debugging a latency issue and noticed an evaluation task prompt in our LLM observability, our agents are built with CrewAI, but I could not find any reference to this on their documentation. Using the tool I was able to pinpoint where it was coming from and its purpose. [4]
I’m experimenting with new use cases everyday and trying to see what else is possible. How would you use these agents?
[1] Video: https://youtu.be/KonguA9PD80
Chat: https://app.potpie.ai/chat/0192dd3f-078e-753f-b5f8-00b724d9085e
[2] Video: https://youtu.be/XSZMtIawy5s
Chat: https://app.potpie.ai/chat/0192c3ea-3dfa-7090-b5fb-3f3d50bbc301
[3] Video: https://youtu.be/N9wUI8h0f5w
Chat: https://app.potpie.ai/chat/0193013e-a1bb-723c-805c-7031b25a21c5
[4] Video: https://youtu.be/FeTQhSBguf4
Chat: https://app.potpie.ai/chat/0193074b-5fec-7308-9158-67dbbf4cd516
Show HN: AI Agents for engineering use cases like debugging, LLD,testing etc.
Hi I am Aditi and I co-founded Potpie AI with my college mate Dhiren. We are building an open-source infrastructure to create custom agents for engineering use-cases like debugging, system design, integration testing, PR review etc. The agents are powered by a knowledge graph built on your code base to provide better context and memory, leading to better planning and execution. Currently we offer 6 ready-to-use agents but you can also build your custom agents. You can tune agent parameters like purpose, goals, background etc. and they are also empowered by pre-built tooling like code retrieval, keyword similarity search, node specific code retrieval, branch diff comparison, fetching code file structure etc.
To get started try Potpie Agents on popular open source repos like caldotcom, agentops, portkey, mem0, CrewAI etc. Don’t forget to st
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ar our repo! We both are super open to chat with folks who try our tool and help them make their software development process agentic.