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Products that do what Agents.blue – learn LLM prompting interactively in <10 minutes does
tl;dr - today i'm launching agents.blue, where you can learn prompting and master working with LLMs, for free, instantly (no sign in required) Last week at Law x LLM Hackathon, I met a lot of amazing engineers, lawyers and more who want to build with LLMs, but they didn’t know how to prompt effectively. The online resources out there are a lot of reading, but not a lot of doing, and in my experience as an engineer and TA, doing is the best way to learn. agents.blue is the free, fast, and interactive tutorial to go from zero to the cutting edge of prompting in under 10 minutes, so you can…
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- 2PE
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
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- 4AP
2023 · promptperfect.jina.ai
- 5SP
I built a system that lets LLMs automatically learn and improve problem-solving strategies over time, inspired by Andrej Karpathy's idea of a "third paradigm" for LLM learning. The basic idea: instead of using static system prompts, the LLM builds up a database of strategies that actually work for different problem types. When you give it a new problem, it selects the most relevant strategies, applies them, then evaluates how well they worked and refines them. For example, after seeing enough word problems, it learned this strategy: 1) Read carefully and identify unknowns, 2) Define…
2025
- 6BA
Hi HN! We launched bloop 10 weeks ago (https://news.ycombinator.com/item?id=35236275) and received a huge amount of feedback (both positive + constructive). We've undertaken a rewrite of the core search framework, which now acts as an LLM agent, significantly improving the number of queries that can be successfully answered. There's a bunch of hype surrounding LLM agents, but we're positive this is one of the first implementations of an agent that can deliver immediate value for engineers working on existing projects, especially larger ones. We'll do a full write up of how the…
2023 · github.com
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Hello! I’m So Tokumaru ( https://tokumaru.work ), high school student from Japan working on ways to leverage large language models (LLMs) for K-12 programming education. Recently, I built a project called "TutoriaLLM," a self-hosted application designed to support educators and students through a multi-modal LLM that offers guided tutorials. TutoriaLLM integrates voice (voice-to-voice) and text-based AI interactions into Blockly, a block editor widely known for its use in Scratch. It enables server-side code execution and allows students to test their code directly in Minecraft…
2024 · github.com
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I've been playing with the idea of an LLM prompt that causes the model to generate and return a new prompt. https://github.com/andyk/recursive_llm The idea I'm starting with is to implement recursion using English as the programming language and GPT as the runtime. It’s kind of like traditional recursion in code, but instead of having a function that calls itself with a different set of arguments, there is a prompt that returns itself with specific parts updated to reflect the new arguments. Here is a prompt for infinitely generating Fibonacci numbers: > You are a…
2023 · github.com
- 12RA
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
- 13PE
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
- 14RA
Hi HN, we are the founders of Relari (https://www.relari.ai). We launched our LLM evaluation stack on HN a few months ago (https://news.ycombinator.com/item?id=39641105), which is now used in production by AI teams at companies like Vanta and PwC. We have since expanded to directly optimizing parts of an LLM pipeline using a data-driven approach. In particular, we see a lot of potential in the Auto Prompt Optimization—which could be an attractive alternative to fine-tuning in many cases—to use data to align LLMs for domain-specific tasks. Here’s a demo video:…
2024
- 15IB
Hey all! I wanted to share this project I've been working on that can maybe help you or your developer friends out. I built a RAG system for our product a while back and didn't realize how easy they were to get started. So I put together my learnings into this online course. It's not quite ready but if you sign up and mention HackerNews, I can get you early access. I'm looking to get feedback on the following: (1) materials — is it engaging & did you learn something? (2) UI/UX of the platform — did you have any issues that prevented you from starting or finishing the tutorial? (3)…
2024 · takehomes.com
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Ask Steve unlocks the power of LLMs like ChatGPT and Gemini in every web page. It's like Github Copilot but for everyday work in the browser. - Create reusable prompts (“Skills”) that can be used on any web page or text selection. Over 100 are included. - Right click on any page or text-selection to run a Skill on it - Chat with any page to quickly get summaries, extract key information or run a Skill on it - Get help writing, rewriting and editing in any text field with Skills for content creation & editing - Add AI buttons to any web page that enable you to trigger a Skill with 1-click.…
2024 · chromewebstore.google.com
- 17OS
Hi HN, We're a small team building AI tutors out of India, and as you might guess, this means we spend a ton of time writing, testing, and refining prompts for LLMs. When we started out, we were using the OpenAI playground but things became tedious when we wanted to compare responses from different models. We tried a bunch of other playgrounds but found them lacking in some features so we built our own. Quick Links: Github: https://github.com/supernova-app/ai-playground Hosted demo: http://playground.getsupernova.ai Demo video:…
2025 · playground.getsupernova.ai
- 18IM
Every time I wanted to use LLMs in my existing pipelines the integration was very bloated, complex, and too slow. This is why I created a lightweight library that works just like scikit-learn, the flow generally follows a pipeline-like structure where you “fit” (learn) a skill from sample data or an instruction set, then “predict” (apply the skill) to new data, returning structured results. High-Level Concept Flow Your Data --> Load Skill / Learn Skill --> Create Tasks --> Run Tasks --> Structured Results --> Downstream Steps And the bast part: Every step can be saved and reused as…
2025 · github.com
- 19IM
Also inspired by this HN submission: https://www.chiark.greenend.org.uk/~sgtatham/quasiblog/findl... The model is gpt-4o-mini-2024-07-18.
2024 · app4.hc11.org
- 20PE
Test your prompt engineering skills by writing prompts and battling them against each other! Your prompt template can access the board state, the move history, and a list of legal moves, and the game engine selects the first legal move in the string response from the LLM you query. My best prompt so far ignores the board state and the move history and just tries to play mates, make captures, and promote pawns. Can you do better?
2023 · github.com
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Duolingo for AI prompting. AI teaching you to be good at AI.
May 2026 · apps.apple.com
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Version-control & govern LLM prompts. Zero redeploys.
Jun 2026 · promptmatrix.github.io
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