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Products that do what Multilspy – Cross platform framework to develop Language Server Clients does
multilspy is a cross-platform library designed to simplify the process of creating language server clients to query and obtain results of various static analyses from a wide variety of language servers that communicate over the Language Server Protocol. It is easily extensible to support any language that has a Language Server and currently supports Java, Rust, C# and Python. We aim to continuously add support for more language servers and languages. Language servers are tools that perform a variety of static analyses on code repositories and provide useful information such as type-directed…
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Multilspy is a library that simplifies the use of language servers for static analysis of code. Language servers are tools that provide useful information about source code, such as code completion, symbol definition, references, etc. Multilspy abstracts the setup and communication with language servers, and exposes a common interface for different languages. Multilspy currently supports Java, Rust, C#, and Python, and can be easily extended to other languages. Multilspy is part of the monitors4codegen project, which introduces Monitor-Guided Decoding (MGD) for code generation using Language…
2023 · github.com
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2015 · spacy.io
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2018 · github.com
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2020 · crates.io
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2015 · github.com
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2014 · skepsi.me
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Hi HN, I’ve been working on an experimental programming language called XXML. The project started from a frustration I kept running into across systems languages: Languages with strong ownership tend to avoid runtime reflection. Languages with rich reflection usually rely on GC or give up memory guarantees. Compile-time code generation often requires a separate macro language. I wanted to explore whether those tradeoffs are truly necessary. What XXML is trying to do XXML is a statically-typed, native language that: Uses explicit ownership and borrowing (no garbage collector) Supports runtime…
Dec 2025 · xxml-language.com
- 9A1
I've seen a lot of comments about how complex frameworks like LangChain can be. Over the holidays, I wanted to see how minimal an LLM framework could get if we stripped away everything non-essential. The result is an LLM framework in just 100 lines of code. These 100 lines capture what I see as the core abstraction of most LLM frameworks: a nested directed graph that breaks down tasks into multiple LLM steps, with branching and recursion to enable agent-like decision-making. From there, you can layer on more advanced features like agents, RAG, task decomposition, and more. I’ve intentionally…
2025 · github.com
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2018 · github.com
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2020 · github.com
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2021 · github.com
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I built Hermes, an open-source Python framework for multi-agent financial research. Most AI “equity research” demos stop at generating text. In practice, real workflows require pulling structured XBRL financials from SEC filings, extracting labeled sections like MD&A and Risk Factors, merging macro and market data, building actual Excel models with formulas, and generating investment memos in Word or PDF. Hermes is designed to handle that full pipeline end to end. It includes 35 financial data tools covering SEC EDGAR (via edgartools), FRED, Yahoo Finance market data, and RSS-based financial…
Feb 2026 · github.com
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Three.js recently introduced TSL (Three.js Shading Language), a way to write shaders in pure JavaScript/TypeScript that compiles to both GLSL and WGSL. I built this editor to provide a visual interface for the tsl ecosystem. It allows developers to prototype shaders for WebGPU/WebGL and see the results in real-time. This is a beta release and I'm looking for feedback.
Jan 2026 · tsl-graph.xyz
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I wanted to share a project I've been working on for the past few weeks: llgtrt. It's a Rust implementation of a HTTP REST server for hosting Large Language Models using llguidance library for constrained output with NVIDIA TensorRT-LLM. The server is compatible with the OpenAI REST API and supports structured JSON schema enforcement as well as full context-free grammars (via Guidance). It's similar in spirit to the Python-based TensorRT-LLM OpenAI server example but written entirely in Rust and built with constraints in mind. No Triton Inference Server involved. This also serves as a demo…
2024 · github.com
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I'm excited to announce Kreuzberg v3.0, which was released yesterday. Kreuzberg is an MIT licensed Python library that extracts text from a wide range of documents (PDFs, images, office files etc.) without depending on external APIs dependencies. Its different from other libraries and commercial offerings in this space by being designed to be (1) lightweight, (2) CPU orientated, (3) simple to user and (4) have async support as a first class citizen. The v3.0 release completely reworks the architecture for extensibility. Kreuzberg now now supports: - Multiple OCR backends (Tesseract,…
2025
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2020 · quicklang.net
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Mix is a multimodal agents SDK. It comes with a GUI playground for testing and debugging SDK workflows. • Built for multimodal workflows instead of code based workflows • The GUI playground is built from the typescript SDK • All project data is stored plain text and native media files - absolutely no lock-in. • The backend is an HTTP server, check out our python and typescript SDK's
Sep 2025 · github.com
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I had been looking to try <500M parameter language models but you wouldn't find an API to try them anywhere, so I built this cloudflare hosted static website that hosts weights and built an inference runtime for these models that uses WebGPU and runs inference from your browser. These are only so useful in a multi-turn conversation but it's still interesting to see what you can pack in a <250mb model. I tried using ONNX versions earlier, but there were too many quirks of using them with language models and the TPS wasn't too impressive. Inspired by svenflow/webgpu-gemma, I put my codex…
May 2026 · chonklm.com
- 20MC
Hi HN, I'm excited to introduce Mixlayer, a platform I've been working on over the past 6 months that allows you to code and deploy prompts using simple JavaScript functions. Mixlayer recreates the developer experience of using LLMs locally without having to do all of the local setup yourself. I originally came up with this idea when using LLMs on my MacBook and thought it’d be cool to build a product that makes it easy for everyone. It compiles your code to a WASM binary and runs it alongside a custom inference stack I wrote in Rust. When you integrate LLMs in this way, your code and the…
2024 · mixlayer.com
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LeNLP is a toolbox dedicated to NLP, made with Rust, dedicated to Python
2024 · github.com
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I’ve been working on a multi-agent academic research lab, and I wanted to share it here today primarily to give a massive shoutout to the developers behind ZeroClaw. When designing the architecture for this, I needed an autonomous agent runtime that was lightweight, entirely agnostic, and highly secure for local execution. ZeroClaw’s pure Rust implementation provided exactly the zero-overhead foundation the project required. Because they solved the core runtime execution so elegantly, I was able to spend my time building the higher-level orchestration on top of it—like the retrieval graph…
Mar 2026 · rainlabteam.vercel.app
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Hi everyone, wanted to share about gline-rs, an inference engine for GLiNER models written in Rust. This family of lightweight language models proved to be efficient at zero-shot Named Entity Recognition (NER) and other tasks such as Relation Extraction, while consuming less resources than large generative models (LLMs). This implementation has been written from the ground up in Rust, and supports both span- and token-oriented variants (for inference only). The goal is to provide a production-grade and user-friendly API in a modern and safe programming language, including a clean and…
2025 · github.com
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2019 · languagetool.org
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