Carta connects founders, investors, and limited partners through world-class software, purpose-built for everyone in venture capital, private equity and private credit.
Frontend Platform: You will own the core infrastructure and monorepo that underpins our entire frontend. Our mantra: "Create a development experience that people will miss when they leave."
Design Systems (Ink): You will build the foundational React components, tooling, and accessibility standards used by hundreds of Carta engineers to ship consistent UI. Check out our docs site at https://ink.carta.com
Building a set of experiments that explores LLMs visual understanding of your photos to learn about you, especially given the recent learnings from deepseek-OCR. Part of the experiments delve into storing the memories with GraphRAG so they can be effectively retrieved without losing too information.
After reading this post and the readme, I'm not convinced that this is solving a real, observed problem. You outline an example with the long-term coaching mentorship, but why or how is your solution preferable to telling Claude to maintain a set of notes and observations about you, similar to https://github.com/heyitsnoah/claudesidian?
the jazz metaphors do not help provide additional context.
Fair feedback. Claudesidian is a productivity system where you organize knowledge and Claude assists. StoryKeeper is relational infrastructure that maintains emotional continuity across AI sessions and agent handoffs. Different layers of the stack, both valuable. I'll update the docs to make this clearer — appreciate the push for concreteness.
In case you need conversational data for the experiment you want to try, I developed an open-source cli tool [1] that create transcripts from voice chats on discord. Feel free to try it out!
Agree with the first half of the article, but every example the author pointed out predates AI. What are examples of companies that have been founded in the past 3 years and prove the authors point that the data model is the definitive edge?
Just had a chat with AI to see how we could address the issues mentioned in the article. You can create models that cater to multiple use cases. You can split the domain model into facts (tables) and perspectives (views). This gives you a lot of flexibility in addressing the different perspectives presented in the artcile from a shared domain model.
Yes, but by a negligible margin. My program is designed for multi-track audio, which means I run this in parallel on multiple 3 hour recordings, and get results in 12 minutes.
You haven’t shared any architectural details. What model? What size? How can anyone be sure that what you’re building is truly offline?
What does that even mean? Why would OSS make it slower? Why would it be an overkill?
This is not Producthunt, you have to give at least some kind of explanation for your claims.
I wanted to build my own speech-to-text transcription program [1] for Discord, similar to how zoom or google hangouts works. I built it so that I can record my group's DND sessions and build applications / tools for VTTs (Virtual TableTop gaming).
It can process a set of 3-hour audio files in ~20 mins.
I personally love senko since it can run in seconds, whereas py-annote took hours, but there is a 10% WER (word error rate) that is tough to get around.
Apply Here:
- https://job-boards.greenhouse.io/carta/jobs/7544237003
- https://job-boards.greenhouse.io/carta/jobs/7504452003
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Carta connects founders, investors, and limited partners through world-class software, purpose-built for everyone in venture capital, private equity and private credit.
Frontend Platform: You will own the core infrastructure and monorepo that underpins our entire frontend. Our mantra: "Create a development experience that people will miss when they leave."
Design Systems (Ink): You will build the foundational React components, tooling, and accessibility standards used by hundreds of Carta engineers to ship consistent UI. Check out our docs site at https://ink.carta.com
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Tech: React, TypeScript, Rspack/Vite/Rush, Node.js.
We are looking for engineers who care deeply about developer experience, accessibility, and enabling great design at scale.
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