lurk-rs
BentoML
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lurk-rs | BentoML | |
---|---|---|
6 | 16 | |
396 | 6,537 | |
7.3% | 3.0% | |
9.6 | 9.8 | |
7 days ago | 5 days ago | |
Rust | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
lurk-rs
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Ask HN: Who is hiring? (December 2022)
Lurk Lab @ Protocol Labs | Multiple Positions | REMOTE | Full-time
Lurk Lab is building Lurk (https://github.com/lurk-lang), a Turing-complete programming language for recursive zk-SNARKs. Lurk implements a minimal Lisp whose program executions can be proved in zero-knowledge, yielding succinct proofs that are concretely small and fast to verify. Lurk uses a Rust implementation (https://github.com/lurk-lang/lurk-rs) for expression evaluation, proving, and verification, with Nova (https://github.com/microsoft/Nova/) as its proving backend. Because Lurk is Turing-complete, it can be used to make and prove arbitrary computational claims (within resource limits).
We are looking for strong cryptography engineers, researchers, functional programming language specialists, applications developers, and start-up leaders/web3 entrepreneurs who want to build next-generation SNARK technology.
Ideal candidates will be knowledgeable about the state of the art in zero-knowledge proofs and (if looking for an engineering position) strong in Rust.
We are specifically hiring for:
- Rust Cryptography Engineers
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Ask HN: Who is hiring? (November 2022)
Lurk Lab is building Lurk (https://github.com/lurk-lang), a Turing-complete programming language for recursive zk-SNARKs. Lurk implements a minimal Lisp whose program executions can be proved in zero-knowledge, yielding succinct proofs that are concretely small and fast to verify. Lurk uses a Rust implementation (https://github.com/lurk-lang/lurk-rs) for expression evaluation, proving, and verification, with Nova (https://github.com/microsoft/Nova/) as its proving backend. Because Lurk is Turing-complete, it can be used to make and prove arbitrary computational claims (within resource limits).
We are looking for strong cryptography engineers, researchers, documentation specialists, applications developers, and start-up leaders/web3 entrepreneurs who want to build next-generation SNARK technology. Relevant programming languages include Rust, Lisp, and (less significantly) WASM.
We are hiring for:
- Rust Cryptography Engineers, https://grnh.se/d94e94ec4us
- Software Engineers for Lurk Application Development, https://grnh.se/de7e82424us
- Documentation Engineer, https://grnh.se/10e2ca4d4us
- Start-up operator / business lead (currently unlisted, email [email protected] with CV and a brief cover letter describing your experience driving the business end of deep technical projects in the web3 space)
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How to Prove You Know a Secret Without Giving It Away
I recently published a fairly detailed blog post about how to formulate expressive provable programs in Lurk (https://github.com/lurk-lang/lurk-rs). Although this post goes into no details about the underlying proving mechanism, it does build to some pretty powerful ideas. If you haven't thought about the implications of being able to prove correctness of a computation without revealing some or all details of what the computation actually was, you might enjoy it. https://blog.lurk-lang.org/posts/prog-intro/
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Ask HN: Who is hiring? (October 2022)
Lurk Lab @ Protocol Labs | Multiple Positions | REMOTE | Full-time contract-to-hire
Lurk Lab is building Lurk (https://github.com/lurk-lang), a Turing-complete programming language for recursive zk-SNARKs. Lurk implements a minimal Lisp whose program executions can be proved in zero-knowledge, yielding succinct proofs that are concretely small and fast to verify. Lurk uses a Rust implementation (https://github.com/lurk-lang/lurk-rs) for expression evaluation, proving, and verification, with Nova (https://github.com/microsoft/Nova/) as its proving backend. Because Lurk is Turing-complete, it can be used to make and prove arbitrary computational claims (within resource limits).
We are looking for strong cryptography engineers, researchers, documentation specialists, applications developers, and start-up leaders/web3 entrepreneurs who want to build next-generation SNARK technology. Relevant programming languages include Rust, Lisp, and (less significantly) WASM.
We are hiring for:
- Rust Cryptography Engineers, https://grnh.se/d94e94ec4us
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Lurk – Language for Recursive ZK-SNARKs Inspired by Common Lisp and Scheme
Nova (for example) doesn't require a trusted setup. The circuit is just a schematic description of the underlying computation. In the case of the Lurk core language, this computation is 'one reduction step of a Lurk evaluation' (https://github.com/lurk-lang/lurk-rs/blob/master/spec/reduct...). Coming up with a 'fixed computation' that yields general computation is part of the design problem for Lurk (or any other Lurk-like language). Even if we did need a per-circuit trusted setup (which we don't), we could perform such a setup for our core circuit and use it to prove arbitrary programs. For example, although we have not actually performed the trusted setup, we do have an example using Groth16 (which does require a trusted setup to be secure) and aggregates the potentially many discrete reduction steps to produce a succinct proof.
BentoML
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Who's hiring developer advocates? (December 2023)
Link to GitHub -->
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project ideas/advice for entry-level grad jobs?
there are a few tools you can use as "cheat mode" shortcuts to give you a leg up as you're getting started. here's one: https://github.com/bentoml/BentoML
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Two high schoolers trying to use Azure/GCP/AWS- need help!
Then you can look into bentoml https://github.com/bentoml/BentoML which is used to deploy ml stuff with many more benifits.
- Ask HN: Who is hiring? (November 2022)
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[D] How to get the fastest PyTorch inference and what is the "best" model serving framework?
For 2), I am aware of a few options. Triton inference server is an obvious one as is the ‘transformer-deploy’ version from LDS. My only reservation here is that they require the model compilation or are architecture specific. I am aware of others like Bento, Ray serving and TorchServe. Ideally I would have something that allows any (PyTorch model) to be used without the extra compilation effort (or at least optionally) and has some convenience things like ease of use, easy to deploy, easy to host multiple models and can perform some dynamic batching. Anyway, I am really interested to hear people's experience here as I know there are now quite a few options! Any help is appreciated! Disclaimer - I have no affiliation or are connected in any way with the libraries or companies listed here. These are just the ones I know of. Thanks in advance.
- PostgresML is 8-40x faster than Python HTTP microservices
- Congratulations on v1.0, BentoML 🍱 ! You are r/mlops OSS of the month!
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Show HN: Truss – serve any ML model, anywhere, without boilerplate code
In this category I’m a big fan of https://github.com/bentoml/BentoML
What I like about it is their idiomatic developer experience. It reminds me of other Pythonic frameworks like Flask and Django in a good way.
I have no affiliation with them whatsoever, just an admirer.
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[P] Introducing BentoML 1.0 - A faster way to ship your models to production
Github Page: https://github.com/bentoml/BentoML
- Show HN: BentoML goes 1.0 – A faster way to ship your models to production
What are some alternatives?
sprig - 🍃 Learn to code by making games in a JavaScript web-based game editor.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
Nova - Nova: High-speed recursive arguments from folding schemes
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twenty-first - Collection of mathematics routines and cryptography for the twenty-first century
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
HANDSONTABLE - JavaScript data grid with a spreadsheet look & feel. Works with React, Angular, and Vue. Supported by the Handsontable team ⚡
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
taiga - A framework for generalized shielded state transitions
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
o1js - TypeScript framework for zk-SNARKs and zkApps
kubeflow - Machine Learning Toolkit for Kubernetes