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Products that do what Context Engineering in AI does

The Next Evolution After Prompt Engineering

  1. 1

    Reusable AI Memory for Smarter Prompts Anywhere

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  2. 2

    Build, test & deploy AI prompts across 1600+ models at scale

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  4. 4PE

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  11. 11IB
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    Brief228

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  13. 13IL
  14. 14

    The context manager and skills library for marketing teams

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  15. 15

    Helps you write better prompts

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  16. 16

    Become the Context Layer for modern AI systems.

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  17. 17PE

    2023 · prompt-engineering-jobs.com

  18. 18

    "Context Engineering: Two AI Operating Systems"

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  19. 19

    AI prompt finder

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  20. 20PE

    Hey HN, We've been hard at work on a tool that we believe will change the game for developers, data scientists, and anyone working with models that rely on textual prompts. I'm excited to introduce our new tool: Automated Prompt Engineering (APE). Problem: As many of you know, how you phrase a prompt can significantly impact the results you get from models, especially with sophisticated language models. It often requires numerous iterations to hone in on the right prompt to obtain the desired response. Solution: APE is designed to tackle this exact problem. With APE, you can: - Iterative…

    2023

  21. 21AW

    I've been presenting at local meetups about Context Engineering, RAG, Skills, etc.. I even have a vbrownbag coming up on LinkedIn about this topic so I figured I would make a basic example that uses bedrock so I can use it in my talks or vbrownbags. Hopefully it's useful.

    Apr 2026 · github.com

  22. 22

    artificial intelligence

    Feb 2026

  23. 23UA

    Hey HN! I'm Fabio and I built UltraContext, a simple context API for AI agents with automatic versioning. After two years building AI agents in production, I experienced firsthand how frustrating it is to manage context at scale. Storing messages, iterating system prompts, debugging behavior and multi-agent patterns—all while keeping track of everything without breaking anything. It was driving me insane. So I built UltraContext. The mental model is git for context: - Updates and deletes automatically create versions (history is never lost) - Replay state at any point The API is 5 methods:…

    Jan 2026 · ultracontext.ai

  24. 24OS

    We implemented Stanford's Agentic Context Engineering paper which shows agents can improve their performance just by evolving their own context. How it works: Agents execute tasks, reflect on what worked/failed, and curate a "playbook" of strategies. All from execution feedback - no training data needed. Happy to answer questions about the implementation or the research!

    Oct 2025 · github.com

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