Alternatives
Products that do what Swiftgum (Open Source) – Turn Data into LLM-Ready Markdown does
Hey HN, We’re the co-founders of Swiftgum (swiftgum.com), an open-source platform that ingests and normalizes documents from virtually any platform, ready for your favorite LLM. Swiftgum lets you connect various data sources like Google Drive and Notion, transform the data into markdown, and load it into your vector database of choice, such as Postgres, Supabase, Milvus, or Weaviate. Why We Built It Building a basic RAG system is easy—just embed your documents and query them with an LLM. But for enterprise or multi-user scenarios, you quickly run into challenges: Data sources are messy.…
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The project is called QueryStorm. It uses Roslyn to offer C# (and VB.NET) support in Excel, as an alternative to VBA. I've posted about it before, but a lot has changed since then so figured I'd share an update. The current version includes a host of new features, namely a C# debugger, support for NuGet packages, and the ability to publish Excel extensions to an "AppStore" (which is essentially a NuGet repository). The AppStore can be used by anyone with the (free) runtime component. Another great addition is the community license, which is a free license for individuals and small companies…
2023 · querystorm.com
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Long story short: We (Dataherald) just open-sourced our entire codebase, including the core engine, the clients that interact with it and the backend application layer for authentication and RBAC. You can now use the full solution to build text-to-SQL into your product. The Problem: modern LLMs write syntactically correct SQL, but they struggle with real-world relational data. This is because real world data and schema is messy, natural language can often be ambiguous and LLMs are not trained on your specific dataset. Solution: The core NL-to-SQL engine in Dataherald is an LLM based agent…
2024 · github.com
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I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...
2024 · embedding.io
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I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet. It runs locally in ~/.wuphf/wiki/ and you can git clone it out if you want to take your knowledge with you. The shape is the one Karpathy has been circling for a while: an LLM-native knowledge substrate that agents both read from and write into, so context compounds across sessions rather than getting re-pasted every morning. Most implementations of that idea land on Postgres, pgvector, Neo4j, Kafka, and a dashboard. I…
Apr 2026 · github.com
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I got tired of Claude hallucinating Swift APIs. It does a good job at Python and TypeScript, but ask it about SwiftUI and it's basically guessing. The problem? Apple's docs are JavaScript-rendered, so when you paste URLs into AI tools, they just see a blank page. Copy-pasting works but... c'mon. So I built something that converts Apple Developer docs to clean markdown. Just swap developer.apple.com with sosumi.ai in any Apple docs URL and you get AI-readable content. For example: - Before: https://developer.apple.com/documentation/swift/double - After:…
2025 · sosumi.ai
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We built SwiftAI, an open-source Swift library that lets you use Apple’s on-device LLMs when available (Apple opened access in June), and fall back to a cloud model when they aren’t available — all without duplicating code. SwiftAI gives you: - A single, model-agnostic API - An agent/tool loop - Strongly-typed structured outputs - Optional chat state Backstory: We started experimenting with Apple’s local models because they’re free (no API calls), private, and work offline. The problem: not all devices support them (older iPhones, Apple Intelligence disabled, low battery, etc.). That…
2025 · github.com
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This lets you talk to local LLMs in Apple Notes. I saw Obsidian Ollama (https://github.com/hinterdupfinger/obsidian-ollama) and thought it was handy, but I'm too lazy to migrate away from the Apple ecosystem, so I quickly hacked this together. I tend to use Notes as a scratchpad for prompts, so it's nice to do some quick inference without leaving the app. Notes doesn't really support plugins so I'm using the macOS accessibility API for reading selections and then stream responses using the clipboard (not ideal but it works).
2024 · smallest.app
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A free, open-source Markdown editor for macOS with customizable appearance profiles, optional Vim keys, and local writing corrections.
26d ago · writemd.app
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Hi HN, Since 2019, I’ve been working on a writing platform designed for creating complex documents (e.g., theses). I personally use it for everything as it also allows to classify documents in categories so you can organize them efficiently. As of a few months ago, the app is also available in the browser, and you can now invite coworkers to collaborate on a document in real time. The app is somewhat inspired by LyX. It offers an intuitive, modern editor, but users don’t need to know any LaTeX. When it’s time to export, they can choose from a range of templates (IEEE paper, thesis, etc.). A…
2025
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RAG-ready web scraping that cuts your LLM token costs
Apr 2026 · geekflare.com
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Hey HN! We're excited to announce the launch of Tonic Textual, the secure data lakehouse for LLMs. Simply stated, Tonic Textual allows you to build generative AI systems on your own unstructured data without having to spend time extracting and standardizing your data. In minutes you can build automated, scalable unstructured data pipelines that extract, centralize, standardize, and enrich data from your documents into an AI-optimized format ready for embedding, fine-tuning, and ingesting into a vector database. While in-flight, we also scan for sensitive information and protect it via…
2024 · tonic.ai
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G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…
2023 · github.com
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Hey HN, I am the founder of Tensorlake. Prototyping LLM applications have become a lot easier, building decision making LLM applications that work on constantly updating data is still very challenging in production settings. The systems engineering problems that we have seen people face are - 1. Reliably process ingested content in real time if the application is sensitive to freshness of information. 2. Being able to bring in any kind of model, and run different parts of the pipeline on GPUs and CPUs. 3. Fault Tolerance to ingestion spike, compute infrastructure failure. 4. Scaling compute,…
2024 · getindexify.ai
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2024 · github.com
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Hey everyone! I made a Mac app for exploring large language models. It’s fast, small, has a tiny memory footprint. It’s immutable by design with both immediate time travel and automatic versioning as foundational elements. The app is written in Swift and a bit of Rust for the tokenizer. I used SwiftUI for structure and animations and Cocoa for advanced behavior. All storage is SQLite and local-only. You can go through the database as needed and backup it as well. The app has support for variants, which is the `n` parameter in the OpenAI chat completion API—equivalent to the drafts feature in…
2023 · thellm.app
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Hey HN, I’m the author. I built Misata because existing tools (Faker, Mimesis) are great for random rows but terrible for relational or temporal integrity. I needed to generate data for a dashboard where "Timesheets" must happen after "Project Start Date," and I wanted to define these rules via natural language. How it works: LLM Layer: Uses Groq/Llama-3.3 to parse a "story" into a JSON schema constraint config. Simulation Layer: Uses Vectorized NumPy (no loops) to generate data. It builds a DAG of tables to ensure parent rows exist before child rows (referential integrity).…
Dec 2025 · github.com
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