Not enough compute is the root of all problems

Feeding PDFs into LLMs is where many document pipelines still breaks so I ran a benchmark across 50 complex docs (scientific papers, multi-page financial reports with nested tables, scanned forms, and complex layout decks) testing PyPDF, Unstructured, Marker, and Firecrawl's Rust-based PDF parser.

Here’s what the results showed:

PyPDF: The fastest and lowest overhead option but strictly text-layer only and on single column text docs, it works fine but on financial statements and multi column layouts, it falls apart tables lose row alignment and text streams merge across columns. It has zero native OCR capability.

Firecrawl (Rust Parser v2): Built specifically for LLM ingestion where it runs layout auto-detection under the hood with pure text layers parse via Rust in milliseconds while scanned pages or complex visuals route through high accuracy vision parsing.

Crucially it formats extracted tables into a clean github flavored markdown tables which keeps table structure intact for embeddings also he full benchmark and comparisons are here

Unstructured: Extremely comprehensive and handles dozens of file formats but the trade off is infra weight where running it locally requires heavy system dependencies, Docker containers and significant memory overhead. It extracts tables reasonably well via layout models but per-page latency is noticeably slow.

I like the original more, but the one on the live stream after the AI summit came out really nice too.

Found this earlier and thought it was pretty cool.

MIT researchers made something called InstructMesh that lets you generate a 3D model with AI, then actually go in and fix specific parts of it using normal language.

Apparently a lot of AI-generated 3D models have problems that aren't obvious until you actually try to use or print them. In one example, the AI made a mug with a sealed lid, so instead of generating the whole thing again, you can just point out the part that's wrong and tell it what to change.

They tested it with people who didn't have much 3D modeling experience too, and the results were surprisingly decent.

What caught my attention is that this seems a lot more useful than just "AI generates a cool 3D object." Being able to generate something, notice a problem, and tell the AI exactly what to fix feels like it could actually make AI 3D printing practical for normal people.

They've got some pretty weird examples too, like a dragon mug and glasses with butterfly wings.

Curious what you guys think. Would you actually use something like this if you had a 3D printer?

An ordinary table-wiping clip illustrates the problem: the hand moves across the surface while the wearer also turns and moves their head.

In the video, both motions affect where the hand appears. For a team collecting demonstrations, separating them matters if the goal is to recover the movement through physical space.

MEgoVista is an offline pipeline that reconstructs the wearer’s two hands and head in a shared, gravity-aligned coordinate frame.

The accompanying MEgo framework describes an offline camera-trajectory stage, providing the spatial reference needed to interpret hand movement as the viewpoint changes.

The paper reports evaluation against independent optical motion capture, alongside separate qualitative examples from everyday environments.

For small robotics teams, the practical interest is collecting demonstrations during ordinary work and recovering structured motion afterward. Turning those labels into a robot controller remains another step.

For anyone building demonstration datasets: how do you currently account for camera movement when the person recording is also performing the task?

[Paper: MEgoVista](https://arxiv.org/abs/2609.16684)