prats226

11 ms·

31.
by prats226 1y ago
One of the major miss right now seems to be in tool calling specs, you specify function names, description, inputs but not outputs. I believe with reasoning models planning things, it would be important to understand output format, descript
32.
by prats226 1y ago
The advice anthropic gives in building agents is to ditch the abstractions like agent frameworks etc and just code it yourself. I believe its also applicable to MCP to same degree?
33.
by prats226 1y ago
Unfortunately my reducto account was disabled rigth after this launch. But would be uploading benchmarks for rest at https://idp-leaderboard.org/
34.
by prats226 1y ago
Sorry I meant instead of having one big piece of metal which would have restriction in terms of length, having 2 pieces of lenses seperated by just vaccum? You would have lesser material, easier launch and higher magnification because of la
35.
by prats226 1y ago
Why aren't telescopes built this way?
36.
by prats226 1y ago
An issue would be as soon as you make questions public, even by letting hosted LLMs predict on them, they are tainted. You can't use them anymore. So would it be a one time test dataset?
37.
by prats226 1y ago
For LLMs, programming languages are basically additional languages that we speak. So how it handles low-resource programming languages is same as how it handles speaking languages with less contribution in training data? DSL's would be
38.
by prats226 1y ago
I think the choice mainly stems from how you want to use the output. If the output is going to get fed to another LLM, then you want to select markup language where 1) the grammer would not cause too many issues with tokenization 2) which L
39.
by prats226 1y ago
Given that input is image and not raw pdf, its not completely unexpected
40.
by prats226 1y ago
I think author has taken a very long term view of what's happened so far without getting too politically specific. I found it very informative to get a sense of how many abstractions are there in the system. So I atleast know where to
41.
A Primer on US Healthcare(generativevalue.com)
7 points by prats226 1y ago|5 comments
42.
OCR with Semantic Understanding(nanonets.com)
2 points by prats226 1y ago|0 comments
43.
by prats226 1y ago
Reminded my of this article: https://ampcode.com/how-to-build-an-agent Some of the things are just more natural in python being a dynamic language. Eg decorator to quickly convert methods into tool calls, iterating over too
44.
by prats226 1y ago
So far bigger bottleneck I have found in writing agents is in scaling integrations and not the for loop for agent. Lack of libraries for go is a really big challenge.
45.
by prats226 1y ago
What feels missing is cheap wrt what. You have to analyze value created for a service and value captured by company. Eg if you use LLM's to do multiplication of two numbers, its doing billions of computations to get one computation inc
46.
by prats226 1y ago
I don't think model sizes increased suddenly, there might not be emergent properties for certain tasks at smaller scales but there was improvement at slower rate for sure. Competition to improve that metric albeit at lower pace led to
47.
by prats226 1y ago
If we use some metric as proxy for intelligence, emergence simply means a non-linear sudden change in that metric?
48.
Intelligent Document Processing Leaderboard(idp-leaderboard.org)
3 points by prats226 1y ago|0 comments
49.
by prats226 2y ago
Is there a technical paper released about the model architecture? Great resolution points to diffusion style generation rather than just token based?
50.
by prats226 2y ago
Content creation might shift from web to conversations with LLM? Eg if you are not completely satisfied with the output? Also so far we haven't run out of things to train LLM on, eg images etc are still underutilized, lot of high value
51.
SmolDocling: An ultra-compact VLM for end-to-end multi-modal document conversion(arxiv.org)
66 points by prats226 2y ago|12 comments
52.
by prats226 2y ago
This seems like intended usage? The server actually executes the moves and interacts with the environment, the core orchestration or reasoning is offloaded to claude?
53.
by prats226 2y ago
SEO and UX feedback: All the headers and H1/H2 tags are black in color with dark blue background making text unreadable
54.
by prats226 2y ago
This is CLIP. Here if you would see, the model is pretrained to ingest both images and text. However if you would see the prediction mode, its basically text generation only. None of the multi-modal architectures think of both image and tex
55.
by prats226 2y ago
I feel like success of LLM's have been combination of multiple factors coming together favourably: 1) Hardware becoming cheap enough to train models beyond a size where we could see emergent properties. Which is going to become cheape
56.
by prats226 2y ago
That's an interesting point. The bias might or might not be intentional. From the benchmarks I have seen, lot of tools solve slightly different problems altogether and also target slightly different data distribution and in the end hav
57.
by prats226 2y ago
Can share link? Maybe some kind of mistake.
58.
by prats226 2y ago
Bias wrt ordering is a great point. What we consider structured information in this benchmark is irrespective of how its presentation (Order, format etc), it should be directly comparable. So the benchmark does that it into account. Example
59.
by prats226 2y ago
Don't think you can. And also there is big difference in plain old OCR, which is just getting all text out from image and document processing which is can you only get the relevant information in a good structure that can be directly p
60.
by prats226 2y ago
Automation is combination of both, accuracy and accuracy of confidence scores. Good way to think about automation is recall at high precision which is what you need for true automation where you don't worry about documents that are ver

More ›