diptanu

7 ms·

1.
Building Scalable GitHub Runner Infrastructure on Sandboxes(tensorlake.ai)
1 points by diptanu 1mo ago|0 comments
2.
Firecracker disk snapshots in O(changed bytes), not O(disk size)(tensorlake.ai)
3 points by diptanu 2mo ago|0 comments
3.
Zero-copy TLS ingress with kTLS and splice(2) for sandboxes(tensorlake.ai)
1 points by diptanu 2mo ago|0 comments
4.
Filter and Rank: Robust Multi-Cloud Sandbox Orchestration at Scale(tensorlake.ai)
1 points by diptanu 4mo ago|0 comments
5.
Sandbox plumbing infrastructure for computer-use agents(tensorlake.ai)
3 points by diptanu 5mo ago|0 comments
6.
by diptanu 6mo ago
The tricky part of doing this in production is cloning sandboxes across nodes. You would have to snapshot the resident memory, file system (or a CoW layer on top of the rootfs), move the data across nodes, etc.
7.
Kosong: Kimi AI's Agent SDK(github.com)
1 points by diptanu 10mo ago|0 comments
8.
by diptanu 10mo ago
There was an unusual traffic spike around that time, if you try now it should be a lot faster. We were calling up but there was not enough GPU capacity at that time.
9.
by diptanu 10mo ago
We haven’t tested Chandra yet, because it’s very new. Under the hood Tensorlake is very similar to Marker - it’s a pipeline based OCR API, we do layout detection, Text Recognition and Detection, Table Structure Understanding, etc. We then u
10.
by diptanu 10mo ago
It does, we have users in Europe and Asia using it with non English languages. Can you please send me a message at diptanu at tensorlake dot ai, would love to see why it didn’t work.
11.
by diptanu 10mo ago
OP mentioned Gemini and not Google’s Vertex OCR API which has very different performance and accuracy characteristics than Gemini
12.
by diptanu 10mo ago
Hey! I am the founder of Tensorlake. We benchmarked the models that our customers consider using in enterprises or regulated industries where there is a big need for processing documents for various automation. Benchmarking takes a lot of t
13.
Roles and Intelligence for Individual Contributors(raees.me)
1 points by diptanu 11mo ago|0 comments
14.
RAG isn't dead, the bar has gone up(tensorlake.ai)
2 points by diptanu 1y ago|0 comments
15.
by diptanu 1y ago
We parse PDFs to convert them to text in a linearized fashion. The use case for this would be to use the content for downstream use cases - search engine, structured extraction, etc.
16.
by diptanu 1y ago
Yeah we don't handle this yet.
17.
by diptanu 1y ago
Yes this! We training it on a ton of diverse document images to learn reading order and layouts of documents :)
18.
by diptanu 1y ago
There are many cases images are exported as PDFs. Think invoices or financial statements that people send to financial services companies. Using layout understanding and OCR based techniques leads to way better results than writing a parser
19.
by diptanu 1y ago
Disclaimer - Founder of Tensorlake, we built a Document Parsing API for developers. This is exactly the reason why Computer Vision approaches for parsing PDFs works so well in the real world. Relying on metadata in files just doesn't s
20.
Show HN: Tensorlake-Ingest, Parse, and Orchestrate Documents for AI Workflows(tensorlake.ai)
4 points by diptanu 1y ago|1 comments
21.
by diptanu 2y ago
We started with using LLMs for parsing at Tensorlake ( https://docs.tensorlake.ai ), tried Qwen, Gemini, OpenAI, pretty much everything under the sun. My thought was we could skip 5-6 years of development IDP companies have done o
22.
by diptanu 2y ago
I don’t think what you are describing as heavy is that big of a deal if an external orchestration system is required only for deployment, while the workflow can be developed and tested without a server on a laptop or notebook. Bringing in o
23.
by diptanu 2y ago
Great points. Besides performance, centralized coordination and distributed dataplane is better for operability of schedulers as well. Some examples - Being able to roll out new features in the scheduler, tracing scheduling behavior and dec
24.
Show HN: rerank-ts – TypeScript Library for Re-Ranking Search Results with LLMs(github.com)
1 points by diptanu 2y ago|0 comments
25.
by diptanu 2y ago
We use grid search to figure out what's the best chunking strategy to use. Create a bunch of different strategies such as recursive chunking, semantic, etc, and parameterize them and see which one works best. The "best" chunk
26.
by diptanu 2y ago
Rust is really good for building solid services. Indexfiy has never crashed for us in unexpected ways. The other option was to build this in C++ but the state of package management in C++ is really bad so I chose to use Rust.
27.
by diptanu 2y ago
Sorry just seeing this! There are various aspects of how it handles ingestion spikes - 1. The ingestion api writes to blob stores, which are horizontally scalable. Only when an ingestion finishes, we write the metadata to the replicated st
28.
by diptanu 2y ago
Thanks! It's pretty general purpose. It has a retrieval API for RAG use cases. It can be used for building agents too, which might only care about data from certain data sources and get invoked on any changes. Some of our users use it
29.
Show HN: Open-source real time data framework for LLM applications(getindexify.ai)
92 points by diptanu 2y ago|6 comments
30.
by diptanu 2y ago
This approach was studied in a paper called Evaporate+ - https://www.vldb.org/pvldb/vol17/p92-arora.pdf They used active learning to pick the best function among candidate functions generated by the LLM on a sampl

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