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addy999

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1 ポイント·投稿者 addy999·8 か月前·0 コメント

Show HN: NativeBlend – Text to fully editable 3D Models that don't suck

native-blend-app.vercel.app
2 ポイント·投稿者 addy999·9 か月前·1 コメント

Building Effective Text-to-3D AI Agents: A Hybrid Architecture Approach

addy.rocks
20 ポイント·投稿者 addy999·9 か月前·3 コメント

I built a Context7 alternative that costs 40% less with similar code quality

1 ポイント·投稿者 addy999·10 か月前·0 コメント

LLMs can now generate 3D models (example)

i.imgur.com
3 ポイント·投稿者 addy999·10 か月前·1 コメント

Web agents can simplify web browsing

onequery.app
2 ポイント·投稿者 addy999·2 年前·0 コメント

I liked Microsoft's OmniParser model so wrapped it in an API

github.com
1 ポイント·投稿者 addy999·2 年前·0 コメント

Ask HN: How do you handle repetitive research tasks in your workflow?

1 ポイント·投稿者 addy999·2 年前·2 コメント

コメント

addy999
·9 か月前·議論
In my testing I tried Gemini 2.5 Pro, Qwen 3 Coder 480B and Opus
addy999
·10 か月前·議論
Thanks to tools like Blender and MCP servers, models from Gemini to Qwen can be used to create fully usable models.

Having LLMs generate code to build a model by using tools like an artist would drastically increases quality over models produced directly by text-to-3d models like Hunyuan3d

More examples I was able to generate: https://imgur.com/a/3d-models-generated-by-ai-agent-qzOMpqr
addy999
·2 年前·議論
[dead]
addy999
·2 年前·議論
I'm building an AI web agent to fetch detailed information. Looks up and browses sites just like you.

Have just over 500 developers signed up and releasing our client library soon.

https://www.onequery.app
addy999
·2 年前·議論
I find v0.dev is remarkably good at this. But deploying straight from there is not frictionless atm.
addy999
·2 年前·議論
Your approach makes a lot of sense. Especially for "old school" websites with simple Server Side rendered pages, tables are a treasure of data. Wikipedia tables would be a great use of your tool too.

I was thinking more on the lines of programmatic access that `enforces` structured output. LLMs are really good at this step. Define a schema and get an output that is guaranteed to fit.

You can see some examples of what I mean here: https://query-rho.vercel.app/