341 karma · joined May 5, 2020
I really underestimated the cost
Maybe because I am a vim user instead of eMacs?
Shapes are converted into trees of paths, and those trees are compressed using a symbolic lookup table
A LLM can then use those symbols to reason about shapes, and a tree edit distance can be used to group them in categories
If they are of one of the composite colors, they should appear in their natural hue
Else they will just appear darker
I knew a good friend who lived in Okinawa, and never locked his front door even when going abroad
Location: Paris, France
Remote: Flexible
Willing to relocate: Case-by-case
Technologies: Product Management, AI/ML (LLMs, NLP, Pytorch), Python, Javascript, React, FastAPI, SQL, MongoDB, AWS, Google Cloud.
Résumé/CV: https://drive.google.com/file/d/1PjAo-PtmdDnmPqd5esLQx54sK01InWDC/view?usp=share_link
LinkedIn: https://linkedin.com/in/emmanuel-federbusch
Github: https://github.com/nesco
Email: emmanuel at federbusch dot fr
Hi HN, I am a Technical Product Manager / Engineer looking for new opportunities.Most recently, I served as CTO for a startup organizing pharmacological data for LLMs, selected for CES 2025.
Previously, as a Product Manager at Allphins, I helped grow ARR from $125K to $2M by extending platform capabilities for reinsurers. And before that, as a ML Engineer @ IBM, I helped BNP Paribas deploy OCR internally to automatize operational processes using OCR, saving significant manual effort.
Feel free to contact me, even if it's just to connect!
There were qualitatively leaps in my day-to-day usage:
Claude Sonnet 3.5 and ChatGPT O1 were good for writing slop and debugging simple bugs
Grok Thinking and Sonnet 3.7 were good to catch mildly complicated bugs and write functions with basic logic. They still made mistake
But recently, Gemini 2.5 pro has been scary good. I liked to made fun of the feel-the-AGI crowd but for the first time a model made me raise an eyebrow
It can one shot unusual function with complicated logic and subtle edge cases
Even when there is an objective ordering of the children of every node, you still have four traversal options: {preorder, postorder} × {BF, DF}.
Note: For children lacking an objective ordering, you might apply generic rules to define a traversal order, but you’d end up with as many depth-first traversals as there are possible orders—essentially a crude heuristic. If you want the evaluation order to be dynamic at each step (e.g., using RL), the complexity grows geometrically worse. That’s been my experience tinkering with a custom AST DSL for ARC-AGI.
Now, I wonder what surprises are to be found in the full dataset.
The focus on solving cost efficiently discrete tasks might actually lead us towards deep learning systems that could be used reliably in production, and not just give a whoa effect or need to be constantly supervised
Claude Electron app vs Claude Safari PWA
Pros:
* Can be opened with a customizable shortcut
* Takes less RAM (400 MiB < 600 MiB)
Cons:
* Needs to be downloaded
* Weight 150 MiB
* Slower to start
* Loose language localisation
* Loose Safari's capabilities:
- Printing the content of the current page
- Speech2Text
- Accessibility
- ...Time doesn’t seem to end in the universe at large…
In nerdspeak, the geometries are not the same, one is isotropic the other anisotropic