Alternatives
Products that do what `tc` like `wc` but for LLM tokens does
Little tool that I made to understand how (un)reasonable my prompts are.
- 1AS
We explored a novel method to gauge the significance of tokens in prompts given to large language models, without needing direct model access. Essentially, we just did an ablation study on the prompt using cosine similarity of the embeddings as the measure. We got surprisingly promising results when comparing this really simple approach to integrated gradients. Curious to hear thoughts from the community!
2023 · heatmap.demos.watchful.io
- 2
Token-efficiency linter for LLM prompts and payloads - ritenv/tokensift
9d ago · github.com
- 3LT
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
- 4PA
Hey HN! We just launched PromptL: a templating language built to simplify writing complex prompts for LLMs like GPT-4 and Claude. Why PromptL? Creating dynamic prompts for LLMs can get tricky, even with standardized APIs that use lists of messages and settings. While these formats are consistent, building complex interactions with custom logic or branching paths can quickly become repetitive and hard to manage as prompts grow. PromptL steps in to make this simple. It allows you to define and manage LLM conversations in a readable, single-file format, with support for control flow and…
2024 · promptl.ai
- 5IB
After fine-tuning GPT for a personal project, I realized how tedious it is to write plain text in a massive JSON file. That's why I built this app for my own use, and I want to see if others could benefit from a tool like this as well ;)
2024 · finetuna-ui.com
- 6IM
It’s written in Python and I call it GoalChain. It lets you build a conversation flow graph that the user traverses. When there’s enough input it spits out a dictionary with the defined fields. Otherwise it will jump state to state as led by the user. It was fun to write, and it’s surprisingly effective if you keep in mind you’re prompt-engineering every string and field name. README.md has a mini-tutorial. Would be cool to get some ideas for how to build it further and what improvements I could make.
2024 · github.com
- 7AT
Oct 2025 · blog.nilenso.com
- 8PR
Hi HN, While building RAG agents, I noticed a lot of token budget was wasted on formatting overhead (HTML tags, JSON structure, whitespace). Existing solutions felt too heavy (often requiring torch/transformers), so I wrote this lightweight, zero-dependency library to solve it. It includes strategies for context packing, PII redaction, and tool output compression. Benchmarks show it can save ~15% of tokens with negligible latency overhead (<0.5ms). Happy to answer any questions!
Dec 2025 · github.com
- 9AB
All LLM user interfaces I've seen so far are somewhat frustrating: * ChatGPT web requires a lot of copy-paste, it rewrites whole document even if you need to update a part of it, etc. * Github Copilot completions are rather unreliable and do not leave an option to specify what you want; Copilot's chat sidebar is little more than ChatGPT integrated into the IDE * Google Docs have right UI for non-code text, but they use really dumb model (not Gemini 1.5 Pro). Also won't work for code. So... I wrote a Emacs Lisp function which calls LLM with contents of the buffer to generate text according to…
2024 · x.com
- 10HP
Hi HN. I heard you like dev tools and AI, so we wanted to share our project that we’ve been working on. We’re working on Horizon [1] - a higher level abstraction for LLMs so that developers can spend less time trying to grapple with LLMs to make them work and more time with users. This is the starting feature set which takes an auto-ML approach to identify the optimal LLM model, hyperparameters, and prompt - instead of just giving you the tooling to figure it out yourself. You can read more about it in our documentations. Our view is that as LLMs become increasingly commoditized and prompts…
2023 · gethorizon.ai
- 11CL
I tried to port LLMLingua-2's official Python implementation into TypeScript. For best performance, open the URL with a WebGPU enabled web browser. Learn More: https://github.com/atjsh/llmlingua-2-js
2025 · atjsh.github.io
- 12
- 13PL
2023 · github.com
- 14LB
For the past few months I've been building a lot of things with LLMs (GPT-3, Codex, etc.) as I've been trying to push them to their limits (especially towards applying them to the tabular data domain) When working on this, I've found there are some common patterns for solving problems (templating, chaining, functional-programming style operations, etc.) As I've iterated, I've come to believe that a functional style interface is likely going to power a new wave of systems I'm calling "prompt-machines"(systems where the core new unit of work is a "named" LLM prompt, extending the "function"…
2022 · github.com
- 15LP
A CLI tool for managing and semantically diffing LLM prompts. Goes beyond text diff by detecting meaning-level changes using embeddings (OpenAI or local). Useful for versioning, testing, and CI/CD workflows.
2025 · github.com
- 16AL
May 2026 · github.com
- 17LW
Apr 2026 · llmwiki.app
- 18LC
2024 · colab.research.google.com
- 19ET
This is a simple text editor, made using gtkmm 3 and llama.cpp, that allows you to explore the possible continuations (ranked by descending probability) that an LLM would output after each token. I was quite surprised that there didn't seem to be a tool like that out there yet, so I decided to make my own. Source is on Github (https://github.com/blackhole89/autopen), though the code is still in a very rough shape.
2024 · youtube.com
- 20TF
i built TXTOS because my models kept forgetting and bluffing. i wanted a portable fix that works across providers without code or setup. TXTOS is a single .txt you paste into any LLM chat. it boots a small reasoning OS that gives you two things by default: a semantic tree memory that survives long threads, and a knowledge boundary guard that pushes back when the model is out of scope. what it is plain text. no scripts, no trackers, no api calls. MIT. the file encodes a protocol for reasoning, memory, and safety. you can diff it and fork it. it is not “a clever prompt”. it behaves like a tiny…
2025 · github.com
- 21
- 22IB
Hi HN, I'm pleased to share Promptspot, an open-source (Apache License 2.0) project that helps automate testing of large language model (LLM) prompts against an array of input data. Modern LLMs offer an enormous amount of leverage if you "teach the bot to fish" — i.e. simply prompt it with both a "system prompt" (which typically doesn't change often) and a dynamic input, which is often application state, search results, recent activity, user profile data, etc. Existing playgrounds and prompt management systems often lack the rigor and flexibility required for this dynamic approach — and as…
2023 · github.com
- 23
- 24
Ranked by how close each launch is in meaning, then by votes. Refine with a description →