Startup Sphere
Visualized Startup Sphere
What it does
I scraped every startup on TrustMRR, generated high dimensional embedding vectors for each one, then performed a 3D Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction, then normalized into unit vectors centered around a sphere, performed clustering, and finally re-scaled the height of each point based on the MRR of each startup.
Does the same job
all alternatives →- MOMap of YC Startups2024 · yc-map.vercel.app · ▲104
Hey Everybody! Hope you had a merry christmas Today I had a bit of fun with Claude. Started by scraping YC's startups list, then ran them through OpenAI's embedding service, then UMAP'd the embedding to reduce the dimension to just two coordinates and then just forced Claude to write React that would compile to visualize that. I had fun and I think it's interesting, so take a look! Also note that you won't be able to zoom on mobile (found about this Plotly limitation way too late). If there's interest I can fix this issue by changing plotting libs tomorrow :) Merry christmas
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Ever since I remember I had a lot of curiosity regarding hyper dimensional spaces. Picturing higher dimensions, such an impossible yet exciting idea... So years ago I came across a small GIF of a tesseract. Since then it left me wondering how cubes from even higher dimensions would look like... Years passed and I became a software developer, decided to tackle the problem myself and ncube was the result. ncube allows you to visualize rotating hypercubes of arbitrary dimensions. It works by rotating the hyperdimensional vertices and applying a chain of perspective projections to them until the…
- GRGitHub Repo Visualizer Using D32024 · see-my-repo.netlify.app · ▲76
I built this as part of my quest to properly learn data visualization. The code is the easy part! Some lessons learned: - personal verification of the the general truth that pie charts are tough! and the returns are not great for the effort due to people's difficulties perceiving angles - may not use "vanilla" d3 with no React. was difficult to adapt for mobile - the GitHub API provides fairly standardized responses so building dynamic charts wasn't too bad. But when working with streaming data (say Kafka) I can see this getting interesting... schema registry should help but creating a view…
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More growth this month
the category →
AstraPixels▲267A pixel-art solar system at its real current positions.
Growth · 29d ago · astrapixels.com
Launched alongside, May 2026
the whole month →
Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com