Alternatives

Products that do what Tokensift, an open-sourced token-efficiency linter for LLM prompts does

Token-efficiency linter for LLM prompts and payloads - ritenv/tokensift

1. 1AS
   A surprisingly effective way to predict token importance in LLM prompts▲14

   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. 2CF
   Clojure for the Brave and True – now free online▲224

   2015 · braveclojure.com

3. 3TL
   `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

4. 4SH
   Simplexhc – Haskell to LLVM compiler (design phase)▲55

   2017 · pixel-druid.com

5. 5OA
   OJ – A Clojure library for talking to your database▲37

   2014 · github.com

6. 6AC
   A Command-Line Sentence Tokenizer Written in Golang▲6

   2015 · github.com

7. 7NM
   Neeilang: Minimalist OOP Language That Compiles to LLVM IR▲44

   2020 · github.com

8. 8LW
   LLM Wiki – Open-Source Implementation of Karpathy's LLM Wiki▲6

   Apr 2026 · llmwiki.app

9. 9PR
   Prompt-refiner – Lightweight optimization for LLM inputs and RAG▲7

   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&#x2F;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

10. 10TT
    TokenPath – token-level citations for LLM output, read from attention▲5

    Jul 2026 · tokenpath.ai

11. 11LT
    LLM Token Visualizer – How big is 128k token input▲6

    2025 · v0-llm-token-visualizer.vercel.app

12. 12IM
    I made a library for LLM prompt injection/exploit/jailbreak detection▲11

    2024 · github.com

13. 13OS
    Open-sourcing our large Clojure(Script) Re-frame/Hasura web app▲21

    2020 · github.com

14. 14AU
    An unmetered LLM API–$6/month, no token tracking, no limits▲12

    Hi HN, I was once given the advice: Don't waste expensive frontier model credits (GPT&#x2F;Claude&#x2F;etc.) on bulk work. Send the boring, repetitive, high-volume jobs to a smaller model, and save the expensive prompts for when you actually need frontier-level reasoning. I complained and told my manager that I shouldnt have to think about using certain models for certain coding tasks, and that one model should handle everything. Well, here we are anyway. If anyone needs a place to absolutely abuse an LLM with high-volume tasks, come beat ours up at https:&#x2F;&#x2F;yolo-auto.com. Here are…

    Jul 2026 · yolo-auto.com

15. 15KL
    Keep large tool output out of LLM context: 3x accuracy 95% fewer tokens▲10

    LLM agents often place raw JSON tool outputs directly in the prompt. After a few tool calls, earlier results get compacted or truncated and answers become incorrect or inconsistent. I built Sift, a drop-in MCP gateway that stores tool outputs as local artifacts (filesystem blobs indexed in SQLite) and returns an `artifact_id` plus compact schema hints when responses are large or paginated. Instead of reasoning over full JSON in the prompt, the model runs a small Python query: def run(data, schema, params): return max(data, key=lambda x: x["magnitude"])["place"] Query code runs in a…

    Mar 2026 · github.com

16. 16IO
    I open-sourced a $1M engine for closing loops in embedding space▲8

    Hey folks, I’m the creator of WFGY — a semantic reasoning framework for LLMs. After open-sourcing it, I did a full technical and value audit — and realized this engine might be worth $8M–$17M based on AI module licensing norms. If embedded as part of a platform core, the valuation could exceed $30M. Too late to pull it back. So here it is — fully free, open-sourced under MIT. --- ### What does it solve? Current LLMs (even GPT-4+) lack *self-consistent reasoning*. They struggle with: - Fragmented logic across turns - No internal loopback or self-calibration - No modular thought units - Weak…

    2025 · github.com

17. 17HP
    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

18. 18PL
    PromptFlow – Low-Code GUI Tool to Chain LLM Prompts with Flowcharts▲7

    2023 · github.com

19. 19LA
    Lightmod, an all-in-one tool for full stack Clojure▲7

    2017 · sekao.net

20. 20IB
    I Built a UI to finetune LLMs x100 faster▲7

    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

21. 21IL
    ICortex: LLM Powered Python Interpreter▲8

    2023 · github.com

22. 22IB
    I built an open-source “unit testing” suite for prompts ⮂ inputs▲5

    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. 23LW
    LLM Wiki Compiler Inspired by Karpathy▲7

    Apr 2026 · github.com

24. 24
    LLMSlim v0.3.0▲4

    Cut LLM token costs 40-70% with offline prompt compression

    Jul 2026 · llmslim.app

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