AI · alternatives · 2026

24 alternatives to A surprisingly effective way to predict token importance in LLM prompts

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!

This project presents a method for identifying which tokens matter most in prompts sent to large language models, without requiring access to the model itself. It uses ablation testing combined with embedding cosine… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. A surprisingly effective way to predict token importance in LLM prompts launched in 2023; newer entries below may have overtaken it.

1
Fully client-side GPT2 prediction visualizer▲153
Hi HN! I've found this visualization tool immensely helpful over the years for getting an intuition for how an LLM "sees" some piece of text, and with a bit of elbow grease decided to move all compute to client side so I could make it publicly available. I've found it particularly useful for - Understanding exactly how repetition and patterns affect a small LM's ability to predict correctly - Understanding different tokenization patterns and how it affects model output - Getting a general sense of how "hard" different prediction tasks are for GPT-style models Known problems (that I probably…
2023 · perplexity.vercel.app · its alternatives →
2
PrompTessor▲125
After countless hours of solo coding, designing, and iterating, I’m both excited and nervous to share my first-ever product: PrompTessor — now live. As someone who constantly experiments with AI tools like ChatGPT and Midjourney, I often found myself stuck in a loop of trial-and-error. The issue wasn’t always the AI — it was often the prompt itself. That observation became the starting point for PrompTessor. This tool doesn’t just optimize your prompt — it analyzes it. It gives you: - A prompt score - A breakdown of strengths and weaknesses - A fully optimized version of your prompt - And…
2025 · promptessor.com · its alternatives →
3
Wordllama – Things you can do with the token embeddings of an LLM▲370
After working with LLMs for long enough, I found myself wanting a lightweight utility for doing various small tasks to prepare inputs, locate information and create evaluators. This library is two things: a very simple model and utilities that inference it (eg. fuzzy deduplication). The target platform is CPU, and it’s intended to be light, fast and pip installable — a library that lowers the barrier to working with strings semantically. You don’t need to install pytorch to use it, or any deep learning runtimes. How can this be accomplished? The model is simply token embeddings that are…
2024 · github.com · its alternatives →
4
Open Prompts – dataset of 10M Stable Diffusion generations▲279
Open Prompts is the dataset used to build krea.ai. The data comes from the Stability AI Discord and includes around 10M images from 2M prompts. You can use it for creating semantic search engines of prompts, training LLMs, fine-tuning image-to-text models like BLIP, or extracting insights from the data—like the most common combinations of modifiers.
2022 · github.com · its alternatives →
5
Fine-grained stylistic control of LLMs using model arithmetic▲85
We developed a new framework that enables flexible control of generated text in language models. By combining several models and/or system prompts in one mathematical formula, it lets you tweak your style and combine model outputs with ease. A handy tool for those working with LLMs, looking for more fine-grained control of stylistic output. More details in our paper: https://arxiv.org/abs/2311.14479. Feedback and potential applications are welcome.
2023 · github.com · its alternatives →
6
Tokensift, an open-sourced token-efficiency linter for LLM prompts▲6
Token-efficiency linter for LLM prompts and payloads - ritenv/tokensift
Aug 2026 · github.com · its alternatives →
7
ChainForge, a visual tool for prompt engineering and LLM evaluation▲177
Hi HN! We’re been working hard on this low-code tool for rapid prompt discovery, robustness testing and LLM evaluation. We’ve just released documentation to help new users learn how to use it and what it can already do. Let us know what you think! :)
2023 · chainforge.ai · its alternatives →
8
We fingerprinted 178 AI models' writing styles and similarity clusters▲78
We have a dataset of 3,095 standardized AI responses across 43 prompts. From each response, we extract a 32-dimension stylometric fingerprint (lexical richness, sentence structure, punctuation habits, formatting patterns, discourse markers). Some findings: - 9 clone clusters (>90% cosine similarity on z-normalized feature vectors) - Mistral Large 2 and Large 3 2512 score 84.8% on a composite metric combining 5 independent signals - Gemini 2.5 Flash Lite writes 78% like Claude 3 Opus. Costs 185x less - Meta has the strongest provider "house style" (37.5x distinctiveness ratio) - "Satirical…
Apr 2026 · rival.tips · its alternatives →
9
Prompt Engine – Auto pick LLMs based on your prompts▲93
Nowadays, a common AI tech stack has hundreds of different prompts running across different LLMs. Three key problems: - Choices, picking from 100s of LLMs the best LLM for that 1 prompt is gonna be challenging, you're probably not picking the most optimized LLM for a prompt you wrote. - Scaling/Upgrading, similar to choices but you want to keep consistency of your output even when models depreciate or configurations change. - Prompt management is scary, if something works, you'll never want to touch it but you should be able to without fear of everything breaking. So we launched Prompt…
2024 · jigsawstack.com · its alternatives →
10
2000 Large Language Models (LLM) Prompts▲106
Unlock your knowledge with 2000 LLM prompts
2023 · miguelanticonam.gumroad.com · its alternatives →
11
Faster LLM evaluation with Bayesian optimization▲131
Recently I've been working on making LLM evaluations fast by using bayesian optimization to select a sensible subset. Bayesian optimization is used because it’s good for exploration / exploitation of expensive black box (paraphrase, LLM). I would love to hear your thoughts and suggestions on this!
2024 · github.com · its alternatives →
12
PROMPTMETHEUS▲87
One-shot Prompt Engineering Toolkit
2023 · promptmetheus.com · its alternatives →
13
Promptr for AI▲32
The context manager and skills library for marketing teams
Apr 2026 · promptr.ai · its alternatives →
14
Prompt-Engineering Tool: AI-to-AI Testing for LLM▲37
Spelltest framework simulates conversations between AI ‘synthetic users' in an environment to test and refine LLM-based applications. It ensures your app converse with utmost accuracy and relevance. Post-chat, Spelltest assesses responses, providing qualitative and quantitative feedback on performance. Suitable for both chat and completion modes. When to use: - After modifying your prompt. - When your LLM provider updates. - As a CI step for you repo. All feedback and collaborations appreciated!
2023 · github.com · its alternatives →
15LT
Learning to (Learn at Test Time)▲13
I’m excited to share a project I’ve been working on for over a year, which I believe will fundamentally change our approach to language models. We’ve designed a new architecture, which replaces the hidden state of an RNN with a machine learning model. This model compresses context through actual gradient descent on input tokens. We call our method “Test-Time-Training layers.” TTT layers directly replace attention, and unlock linear complexity architectures with expressive memory, allowing us to train LLMs with millions (someday billions) of tokens in context. Our instantiations, TTT-Linear…
2024 · its alternatives →
16
LLMs can be susceptible to a new kind of malware▲17
New ways of breaking app-integrated LLMs . Contribute to greshake/llm-security development by creating an account on GitHub.
2023 · github.com · its alternatives →
17RA
RL Agent that can auto-optimize your LLM prompts▲14
Hey everyone! Along with my team, I've developed a reinforcement learning system that automatically optimizes LLM prompts, complete with a visualization feature to track both prompt structure and learning progress over time. Take a look here: https://nomadic-ml.github.io/nomadic/cookbooks/Nomadic_Promp... Check out our website too:https://www.nomadicml.com/ In terms of how this visualization works: The RL Prompt Optimizer employs a reinforcement learning framework to iteratively improve prompts used for language model evaluations. At each episode, the…
2024 · nomadic-ml.github.io · its alternatives →
18
`tc` like `wc` but for LLM tokens▲8
Little tool that I made to understand how (un)reasonable my prompts are.
Jan 2026 · github.com · its alternatives →
19
Better Prompt▲5
I got frustrated watching everyone focus on fancy AI frameworks while basic prompts were failing. After spending months writing prompts, I realized that many of them were ineffective—often due to unclear instructions, missing edge cases, and ambiguous language. Built this tool that transforms vague prompts into clearer, more effective prompts, leading to better results from large language models (LLMs). Just input your original prompt, and the tool will give you an improved prompt version.
Jan 2026 · t.me · its alternatives →
20
An all-in-one blog for learning Large Language Models (LLMs)▲5
An all-in-one blog for learning LLM ins and outs: tokenize, attention, PE, and more Project I've been diving deep into the internals of Large Language Models (LLMs) and started documenting my findings. My blog covers topics like: Tokenization techniques (e.g., BBPE) Attention mechanism (e.g. MHA, MQA, MLA) Positional encoding and extrapolation (e.g. RoPE, NTK-aware interpolation, YaRN) Architecture details of models like QWen, LLaMA Training methods including SFT and Reinforcement Learning If you're interested in the nuts and bolts of LLMs, feel free to check it out:…
2025 · comfyai.app · its alternatives →
21
Minimal LLM Post-Training Experiments on an 8GB GPU (SFT, DPO, GRPO)▲21
Minimal, readable LLM post-training experiments on one 8GB GPU. Measures forgetting, seed variance, and RL emergence. - pochenai/nano-llm-posttraining
Aug 2026 · github.com · its alternatives →
22KA
Knowing – an LLM tool built on concept hierarchies, not prompt-response▲5
Hey HN! I've spent the past year full-time building Knowing, a tool for interacting with LLMs directly inside hierarchical structures instead of the usual prompt-response format. The idea started because I realized how much more intuitive it felt to build concept hierarchies continuously—no more endless copy-pasting or wondering how everything connects. The journey’s been a struggle. While I see huge potential in structuring AI interactions this way (writing books fast, planning projects, or organizing ideas), it’s been hard to pin down clear use cases in the market. I’m also working in near…
2024 · its alternatives →
23
Context Compass▲16
I built a small Chrome extension for people who write long conversations in Claude and don’t want to unexpectedly hit the 200,000-token limit. Context Compass automatically counts words and estimates token usage as you write. It updates in real time, gives you a visible percentage bar, and helps power users manage sprawling chats without losing work or flow. I built it because I kept getting the “max context” error and realized there was no way to track usage in Claude. No signups, no tracking, just a simple install. Would love any feedback or ideas for improvements. Thanks!
2025 · chromewebstore.google.com · its alternatives →
24HP
Horizon – Programmatic Prompt Generation and LLM Configurations▲7
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 · its alternatives →

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