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Products that do what 2000 Large Language Models (LLM) Prompts does
Unlock your knowledge with 2000 LLM prompts
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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
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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
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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
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Hello everyone. I built an AI-based toolset to help me with language learning. I wanted to be able to easily generate very specific study content and get rapid feedback on my writing. Unlike most language apps, it doesn’t actually try to teach you a language. Instead, it’s a collection of tools for people at an intermediate level who already have a learning process It’s particularly great for Anki users. There a demo video on the login page, and I set up anonymous auth for people who want to test it without creating an account. Feedback and bug reports welcome.
2025 · drillapp.xyz
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2018 · languagemodels.io
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I’ve been building LLM tooling for a small VC fund and found myself explaining the same mental model over and over to non-technical people around me: how a stateless LLM becomes a chatbot, how tool use works, what an agent is mechanically, and why context windows shape all of it. I never found a guide that covered that full chain at the level I wanted, so I wrote one. It’s nine short chapters, each building on the last. Deliberately simplified: the goal is a useful mental model, not a textbook. Feedback, corrections, and contributions welcome: github.com/ymyke/aiaiai
Apr 2026 · aiaiai.guide
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2024 · promptql.hasura.io
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Little tool that I made to understand how (un)reasonable my prompts are.
Jan 2026 · github.com
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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
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As I was playing with the Outlines library (https://outlines-dev.github.io/outlines/), I discussed with my friend Maxime how funny it would be if we set up a way to pair LLMs in chess matches till one wins. The first time I tried it, it required substantial prompt engineering to get some of those LLMs to propose valid moves. Large language models can mostly stay focused and even play rather well; see https://news.ycombinator.com/item?id=37616170 for example. However small language models aren't as easy to convince. Some of those LLMs have seen very little…
2024 · huggingface.co
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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
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