Alternatives
Products that do what Chess-LLM, using constrained-generation to force LLMs to battle it out does
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…
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- 2LA
The initial idea for the game came during the final day of Game AI school in Cambridge. There, we had a Jam where we explored the idea of using LLMs as a game engine for fights. We then built a full web version in just a week. There is no need to register or pay to play. Test it out!
2023 · llmarena.com
- 3PE
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
- 4PP
I've been working on applying LLMs to long-context, verifiable problems over the past year, and today I'm releasing a benchmark of 62,000 pencil puzzles across 94 types (sudoku, nonori, slitherlink, etc.). The benchmark also allows for intermediate checks /rule breaks for all varieties at any step. I tested 51 models against a subset (300 puzzles) in two modes: single-shot (output the full solution) and agentic (iterate with verifier feedback). Some results: - Best model (GPT 5.2@xhigh) solves 56%. (~ half the puzzles are unsolved by any model) - Agentic solves average 29 turns. The…
Mar 2026 · ppbench.com
- 5AD
Ever wish you could get the best arguments for both sides of a debate? I built an AI-powered debate platform that pits language models against each other on controversial topics. Each AI is randomly assigned a side (pro/con). You vote before and after to see if you were persuaded. Most content today presents lopsided arguments. They provide strong points for one side, weak ones for the other. This project aims to surface the strongest arguments from both sides, using LLMs to simulate a fair debate. With enough usage, I want to use it to benchmark LLMs. My hypothesis is that randomly…
2025 · bot-bicker.vercel.app
- 6CA
Two years ago I sat down to build a chess engine for my kids' Christmas present. Wanted it to play badly enough to be beatable but not randomly bad: different styles so they could choose what to face. While building those styles I noticed the choices started looking like actual historical players' tendencies. Went looking. Turns out: given enough games from one person, you can extract enough of their decision pattern to reproduce it. Two years later, 41 players, ~3,000 games each. Tal sacrifices unsoundly. Morphy attacks like it's 1850. Capablanca grinds endgames. Same engine underneath,…
Apr 2026 · playchessgate.com
- 7AA
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
- 8PA
Hi, this is my first HN submission so please forgive the rough edges. For this project gathered chess positions from some well-known chess players with a lot of games. (Usually ~25k games and 1M+ positions. The most for any player was GM Aman Hambleton with 1.8M positions indexed.) With each board position I create a probability distribution of moves the player has made from that position. Then, I simply draw from that distribution (with sampling temperature) to choose a move. As long as there's at least one game with a given position, you can keep playing. You'll see that it's pretty easy…
2022 · d2frm2q2a7fq8v.cloudfront.net
- 9HP
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
- 10LA
Hey Hacker News! I've been working on an open-source project called LLM Alignment Template, a comprehensive toolkit designed to help researchers, developers, and data scientists align large language models (LLMs) with human values using Reinforcement Learning from Human Feedback (RLHF). What the project does: Interactive Web Interface: Easily train models, visualize alignment metrics, and manage alignment with an accessible UI. Training with RLHF: Align models effectively to human preferences using feedback loops. Explainability: Built-in dashboards to help understand model behavior using…
2024 · github.com
- 11WM
We wanted to test if a smaller model like GPT-4.1-mini could beat its bigger brother 4.1 at the game Tic-Tac-Toe using only context engineering. We put them in a 100-game tournament. For the smaller model, we gave it a few examples of winning moves from past games right before it made its own move. The results were clear. Without the examples, the smaller model struggled against GPT-4.1. With the examples, its effectiveness increased by nearly 200%, and it consistently won. It's a simple demonstration, but it shows that a smaller, faster model with good, timely examples can outperform a more…
2025 · github.com
- 12LB
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
- 13AO
Hi, We are building an open-source framework for loading and structuring LLM context to create accurate and explainable LLM answers using knowledge graphs and vector stores. We built the tool with four main concepts in mind: 1. Loader -> uses dlt in the backend to load and structure the data 2. Cognify step -> creates a graph with summaries, labels and factoids that are interconnected across the documents and stored as a representation in the vector store 3. Optimizer -> Uses DSPy to optimize LLM queries, and we plan to extend it to most of the knobs we can turn, like chunking etc. 4. Search…
2024 · github.com
- 14LG
Hi there, I've decided to jump on the AI train and put something together with low effort & high reward, to see if it can get any traction. What do you think? Is it a promising area? Do you guys have ideas for me? There is obviously going to be sea of LLM generated content out there and one project adding up to it might not necessarily be what world needs. In the same time there is something intriguing about the area. Well, please play with it and let me know what y'all think. Much appreciated.
2023 · canonica.ai
- 15AC
Hello, This is something I've wanted to build for awhile (first bought the domain in 2020 (then lost it for a year+)). It's a tactics solver on each new tab page. There are 5 modes of play: Easy (<1200 elo) Medium (1200-1800) Hard (1800-2400) Expert (2400+) Speedrun (solve as many puzzles as possible in 60 seconds). I'm having a ton of fun with it, and definitely noticing improvements with actual games (definitely solved the most puzzles in my life while developing this extension). If you check it out, please let me know what you think. Any feedback is good!
Apr 2026 · leetchess.com
- 16LF
Hey HN, I built SWE-Kit, LLM toolkit (Function callable tools) which makes building agents specialised in coding like Devin very easy. I noticed a typical pattern while building local agents: creating & perfecting LLM tools to interact with system or codebase was the repeated and time-consuming. We created a layer that simplifies building agents that can interact with code, file system, git, shell and allows you to quickly solve for a wide variety of coding agent use cases. Aren’t there open coding agents already? Well, yes, but most folks would want to solve their specific use case like a…
2024 · swekit.dev
- 17A1
I've seen a lot of comments about how complex frameworks like LangChain can be. Over the holidays, I wanted to see how minimal an LLM framework could get if we stripped away everything non-essential. The result is an LLM framework in just 100 lines of code. These 100 lines capture what I see as the core abstraction of most LLM frameworks: a nested directed graph that breaks down tasks into multiple LLM steps, with branching and recursion to enable agent-like decision-making. From there, you can layer on more advanced features like agents, RAG, task decomposition, and more. I’ve intentionally…
2025 · github.com
- 18GA
I wanted to learn more about RAG implementations, so I built something to solve the constant digging through manuals whenever we play a game. It's fairly simplistic, but actually has worked pretty well for some of these conflicts. Everythings Open Source on GitHub if you're curious (or have ideas), and I'd love to hear feedback from fellow boardgamers!
2024 · gamegame.ai
- 19CR
hi everyone. how does moving llm call prompts and output structure definitions away from code into configuration land sound? would you use something like this if it was stable and well documented enough? please don't hold back the criticism. i appreciate all feedback (constructive & otherwise).
2024 · github.com
- 20AG
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
- 21ET
This is a simple text editor, made using gtkmm 3 and llama.cpp, that allows you to explore the possible continuations (ranked by descending probability) that an LLM would output after each token. I was quite surprised that there didn't seem to be a tool like that out there yet, so I decided to make my own. Source is on Github (https://github.com/blackhole89/autopen), though the code is still in a very rough shape.
2024 · youtube.com
- 22AT
We kept shipping “simple” LLM features that were fluent-but-wrong. After too many postmortems we wrote down the failure patterns and added a small reasoning layer in front of the model. It’s model-agnostic, sits beside your existing stack, and you can implement it from a single PDF (MIT). What’s inside the PDF A problem map of 16 failure modes we kept hitting in real systems (OCR/layout drift, table-to-question mismatches, embedding≠meaning, pre-deploy collapse, etc.). Four lightweight gates you can add today: Knowledge-boundary canaries (empty/adversarial/known-fact probes).…
2025 · github.com
- 23AG
Since ChatGPT became popular, I've been wondering: what would an LLM-powered app that's not chat-centric look like ? Would an encyclopedia that's almost entirely generated on-the-fly be any good? Can we use AI hyper links to replace most of the typing? Since I haven't found anything close to what I had in mind, I decided to give it a try and see for myself. WikiGen.ai is a website that's almost entirely generated by AI, with a few contextual tools to assist users with readability levels, explanations, and fact checking. (Demo: https://www.youtube.com/watch?v=MG0CpSE0cFI) I…
2025 · wikigen.ai
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An arena where LLMs write robot controllers and their code competes. - nigrosimone/llms-robot-arena
just now · github.com
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