Alternatives
Products that do what toon-parse does
Fit 40% more context into your LLM prompts for free
- 1
- 2
- 3ML
2025 · simonwillison.net
- 4
Cut LLM token costs by converting JSON to TOON format
May 2026 · jsondevtools.org
- 5LC
Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming. Recently we came up with a fast way to generate text that matches a regex (https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We…
2023 · github.com
- 6
- 7WW
I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
Nov 2025 · github.com
- 8RL
We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…
Mar 2026 · github.com
- 9AJ
Hey HN, we’re building an open specification that lets agents discover and invoke APIs with natural language, built on the OpenAPI standard. agents.json clearly defines the contract between LLMs and API as a standard that's open, observable, and replicable. Here’s a walkthrough of how it works: https://youtu.be/kby2Wdt2Dtk?si=59xGCDy48Zzwr7ND. There’s 2 parts to this: 1. An agents.json file describes how to link API calls together into outcome-based tools for LLMs. This file sits alongside an OpenAPI file. 2. The agents.json SDK loads agents.json files as tools for an LLM that…
2025 · github.com
- 10AT
I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com
- 11LT
Current AI-assisted CLI tools are often part of larger systems and work better on Linux. I built llm-term to address these. It's a Rust-based tool that compiles into a single binary file. You only need to download the binary, add it to your PATH, and configure your OpenAI key to get started. While llm-term offers an option for gpt-4o, it works great with gpt-4o-mini. So it's not costly. I appreciate any feedback or suggestions.
2024 · github.com
- 12IW
Hey HN, I made Browser-Use, an open-source tool that lets (all Langchain supported) LLMs execute tasks directly in the browser just with function calling. It allows you to build agents that interact with web elements using natural language prompts. We created a layer that simplifies website interaction for LLMs by extracting xPaths and interactive elements like buttons and input fields (and other fancy things). This enables you to design custom web automation and scraping functions without manual inspection through DevTools. Hasn't this been done a lot of times? Good question, as a general…
2024 · github.com
- 13
- 14LA
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
- 15
RAG-ready web scraping that cuts your LLM token costs
Apr 2026 · geekflare.com
- 16
- 17LT
2023 · github.com
- 18
JSON to TOON Converter Online - Reduce LLM Token Costs
Feb 2026 · jsontoon.org
- 19LT
This is my take on the common "use llms to generate shell commands" utility. Emphasis is placed on good CLI UX, simplicity, and flexibility. `llm2sh` supports multiple LLM providers and lets LLMs generate multi-command sequences to handle complex tasks. There is also limited support for commands requiring `sudo` and other basic input. I recommend using Groq llama3-70b for day-to-day use. The ultra-low latency is a game-changer - its near-instant responses helps `llm2sh` integrate seamlessly into day-to-day tasks without breaking you out of the 'zone'. For more advanced tasks, swapping to…
2024 · github.com
- 20
- 21TC
Hi HN, I built a CLI for uploading documents and querying them with an LLM agent that uses search tools rather than stuffing everything into the context window. I recorded a demo using the CrossFit 2025 rulebook that shows how this approach compares to traditional RAG and direct context injection[1]. The core insight is that LLMs running in loops with tool access are unreasonably effective at this kind of knowledge retrieval task[2]. Instead of hoping the right chunks make it into your context, the agent can iteratively search, refine queries, and reason about what it finds. The CLI handles…
2025 · github.com
- 22CJ
Nov 2025 · github.com
- 23ID
ISON (Interchange Simple Object Notation) - a data format optimized for LLMs and Agentic AI. The problem: JSON wastes tokens. Curly braces, quotes, colons, commas - all eat into your context window. ISON uses tabular patterns that LLMs already understand from training data: JSON (87 tokens): { "users": [ {"id": 1, "name": "Alice", "email": "[email protected]"}, {"id": 2, "name": "Bob", "email": "[email protected]"} ] } ISON (34 tokens): table.users id:int name:string email 1 Alice [email protected] 2 Bob [email protected] Features: - 30-70% token reduction - Type annotations - References between…
Dec 2025 · github.com
- 24CL
2018 · github.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →