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Folks who work for large tech companies: How are you using Cursor?

Hacker News - Sat, 03/22/2025 - 10:58pm

I am an employee of a large tech firm. One of those Silicon Valley staples, but probably not the one you are thinking of. Recently we have gotten mass licenses for Cursor and my team and I have been exploring the possibilities.

We are all already well aware of the autocomplete potential and are generally utilizing it individually for such workflows. But I am interested in what we can accomplish beyond this basic usage.

We have already assembled a working group which has created company MCP servers for corporate resources in JIRA, Wiki, etc. And we are actively exploring the potential there.

My question for all of you bright people in this community: Have you found any compelling use cases for Cursor tooling beyond the typical coding co-pilot behavior?

I have struggled a bit to get base Cursor w/ Sonnet to complete entire multi-file feature changes alone, even when they are relatively simple(vibe coding).

It is just not as consistent as I would have expected in those scenarios. Although context providing techniques like building cursor rules, based on example former commits, seem to improve things significantly.

I would love to share some ideas with you folks since we can be a bit isolated in our individual corporate tech bubbles, and I get the feeling many of you are doing some amazing things I would love to try out as well.

Comments URL: https://news.ycombinator.com/item?id=43450576

Points: 2

# Comments: 0

Categories: Hacker News

Show HN: NailGenie – Edit nail designs conversationally with AI

Hacker News - Sat, 03/22/2025 - 10:53pm

Show HN: NailGenie - Edit nail designs conversationally with AI

I built NailGenie (https://nailgenie.org) to solve the "that's not what I meant" problem in nail design. It's an AI platform that lets you iteratively edit nail art through simple conversation rather than static generation.

THE TECHNICAL CHALLENGE

The core challenge was building a system that could understand contextual, incremental editing commands for a specific visual domain. Most generative AI solutions focus on one-shot generation, not a continuing dialogue about the same image.

We solved this by:

1. Fine-tuning Gemini on a dataset of nail designs with paired editing instructions

2. Building a stateful context management system to track editing history

3. Creating a visual diffing algorithm that preserves nail boundaries during edits

4. Implementing an instruction parser that handles ambiguous editing requests

The backend reaches ~98% instruction comprehension on our test set and produces edits in ~2.7 seconds on average.

TECH STACK

- Frontend: Next.js App Router with TypeScript and React Server Components

- UI: Shadcn/UI + TailwindCSS (we chose these for rapid iteration)

- Backend: Supabase for authentication, storing edit history, and managing user credits

- Deployment: Vercel edge functions for low-latency API responses

- AI: Custom-tuned Gemini models with a multi-stage processing pipeline

DEVELOPMENT CHALLENGES AND LEARNINGS

The biggest challenges were:

1. Instruction ambiguity: "Make it more pink" means different things to different users. We implemented a clarification system that refines ambiguous requests.

2. Edge detection: Early versions struggled with nail boundaries. We built a specialized segmentation model to ensure edits only affected the nail area.

3. Performance: Initial processing was ~8s per edit. We optimized by parallelizing our pipeline and caching intermediate representations, cutting time by ~65%.

4. Cold starts: Edge function cold starts were killing the experience. We implemented background warmers and optimized model loading.

THE WHY AND WHAT'S NEXT

I'm not a nail expert, but I noticed my girlfriend spending hours browsing examples before salon visits, then being frustrated when the result didn't match her vision. The challenge of creating a system that bridges this communication gap became technically fascinating.

Current metrics: - ~450 users in closed beta

- Average session: 8.3 edits per design

- 82% completion rate (users reaching a final saved design)

FUTURE PLANS

- Open source our instruction parsing logic next month

- Add API access for nail salons to integrate directly

- Implement real-time collaborative editing

TRY IT YOURSELF

NailGenie is live with free starter credits. I'd appreciate any feedback, especially on:

- Instruction parsing accuracy

- Performance bottlenecks you experience

- UI/UX pain points

https://nailgenie.org

Comments URL: https://news.ycombinator.com/item?id=43450557

Points: 1

# Comments: 0

Categories: Hacker News

Stop using the elbow criterion for k-means

Hacker News - Sat, 03/22/2025 - 10:51pm

Article URL: https://arxiv.org/abs/2212.12189

Comments URL: https://news.ycombinator.com/item?id=43450550

Points: 1

# Comments: 0

Categories: Hacker News

Can AI Compress Like a Genius?

Hacker News - Sat, 03/22/2025 - 10:40pm

Article URL: https://arxiv.org/abs/2503.13992

Comments URL: https://news.ycombinator.com/item?id=43450505

Points: 1

# Comments: 0

Categories: Hacker News

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