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AI · alternatives · 2026

24 alternatives to Trained an LLM to predict "What will Trump do?"

Hey HN! I RL-tuned an open-source LLM (gpt-oss-120b — 120B MoE, but only 5.1B active params) to predict "What will Trump do?" in any situation, trained on nothing but public news collected automatically from search queries. The trained model beats GPT-5, and both dataset and trained model are open sourced. Data generation: Generated 2,108 binary forecasting questions from just a search query and a date range using the Lightning Rod SDK (https://github.com/lightning-rod-labs/lightningrod-python-sd...). Questions are generated from historic news articles — like "Will Trump…

This project fine-tunes an open-source 120-billion-parameter language model to forecast political outcomes, specifically predicting Trump's actions in various scenarios. The model was trained on 2,108 automatically… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

  1. 1

    Built a ~9M param LLM from scratch to understand how they actually work. Vanilla transformer, 60K synthetic conversations, ~130 lines of PyTorch. Trains in 5 min on a free Colab T4. The fish thinks the meaning of life is food. Fork it and swap the personality for your own character.

    Apr 2026 · github.com · its alternatives →

  2. 2WS

    We’ve trained a generative AI model to browse the web and answer questions/retrieve code snippets directly. Unlike ChatGPT, it has access to primary sources and is able to cite them when you hover over an answer (click on the text to go to the source being cited). We also show regular Bing results side-by-side with our AI answer. The model is an 11-billion parameter T5-derivative that has been fine-tuned on feedback given on hundreds of thousands of searches done (anonymously) on our platform. Giving the model web access lessens its burden to need to store a snapshot of human knowledge…

    2022 · beta.sayhello.so · its alternatives →

  3. 3AL

    Hi HN! We partnered with the Atlas team to build a tool called AI Predict [0] that allows anyone to ask any question about the future and get a thoroughly researched, AI-generated prediction on how likely it is to be true. How it works: Atlas replicated a Berkeley paper [1] that showed LLMs could make predictions as accurate as the crowd. We’re using a mix of models from OpenAI and Anthropic, with information retrieval powered by NewsCatcher [2]. The system is live and fully functional, though it might struggle with hyper-local questions outside of the public domain (e.g., “Will I have…

    2024 · aipredict.fun · its alternatives →

  4. 4
    Dolly▲113

    Democratizing the magic of ChatGPT with open models

    2023 · github.com · its alternatives →

  5. 5AL

    Lately I've felt exhausted due to the deluge of AI/GPT posts on hacker news, and have seen similar grumblings. I threw together this frontend that filters out anything with the phrases AI, LLM, GPT, or LLaMa for use until the hype dies down a bit. Before anyone asks, yes I did try to use ChatGPT to help, and while the code it provided was helpful, it needed some heavy bug-fixing. Edit: One other note I forgot to mention. The favicon is generated by Stable Diffusion, I asked it to generate an "Aritificial Intelligence Favicon", and then I added the red circle with line through it.

    2023 · save-buffer.github.io · its alternatives →

  6. 6

    Bloomberg’s 50B parameter LLM, built for finance tasks

    2023 · bloomberg.com · its alternatives →

  7. 7

    Discussing prediction markets for the GOP nomination

    2015 · cultivatelabs.com · its alternatives →

  8. 8

    Hey HN, I built TrumpPosted to track the potential financial market impact of Donald Trump's social media posts in near real-time. It works by: Scraping his posts almost immediately. Sending the post content + metadata to an AI (Llama 3.3 70b via Groq) for analysis against specific financial subjects (stocks, bonds, crypto, or user-defined like 'Tesla stock'). The AI generates a headline, market sentiment score (0-100), summary, attitude analysis, and detailed commentary. Subscribers get email alerts within ~30 seconds containing this analysis tailored to their chosen subject. All analyses…

    2025 · trumpposted.com · its alternatives →

  9. 9

    Twitter bot trained to speak like Donald Trump

    2016 · x.com · its alternatives →

  10. 10GD
  11. 11

    Do you know that Small Language Models (SLM) can outperform LLMs if trained (fine-tuned) on domain specific datasets, like your companies knowledge base? The issue: fine-tuning a SLM usually requires a dataset of 10k - 100k records, which is huge. I created a platform for applying "human in the loop" augmentation techniques on your small dataset, so that you can start with maybe 100 records and build-up quickly huge datasets and launch a training without prior knowledge. I implemented 2 techniques, based on LLM distillation, however: more to come. HN, what Do you think? Do you see value in…

    2025 · tunetrain.ai · its alternatives →

  12. 12

    hi friends! i'm pretty tired of engagement bait and all the political nonsense on my x/twitter feed. i was curious if i could use an llm to filter out these type of content, so i prototyped a quick chrome extension. it uses LLama 3.3 to analyze the tweet through https://groq.com/ (because they are super-super fast). the extension is available in the chrome store, also there is a link to the repo. - you can tweak the system prompt for the filtering - but you need your own API key from Groq (you can get one for free)

    2025 · unbaited.danielpetho.com · its alternatives →

  13. 13TT

    Simulate anything on a map from a text prompt -- and conduct risk analysis against LiveUA map's global realtime data points from social media and news sources. I trained a GPT-2-size model on historical incident data used to predict things that will go wrong. As historian Benjamin Breen mentions, the leading language models are good historians, so the application will simulate historical events pretty well also. I include a Multi-Agent RL Urban Mobility model in progress displayed on the map as small white cubes representing traffic and pedestrians. Around SF, it uses real census data and…

    2025 · mused.com · its alternatives →

  14. 14

    A Neural Network trained on Donald Trump transcripts

    2016 · twitter.com · its alternatives →

  15. 15

    My puny version of ChatGPT. This was based on the excellent LLM lecture series by Andrej Karpathy: https://www.youtube.com/watch?v=kCc8FmEb1nY The main points of differentiation are that my version is token-based (tiktoken) with code to load up multiple text files as a trining set. Plus, it has a minimal server which is a drop-in replacement for the OpenAI REST API. So you can train the default tiny 15M parameter model, and use that in your projects instead of ChatGPT. I trained it on 20Mb of Project Gutenberg encyclopaedias, then fine-tuned it on 120 dad jokes, to get a Q: A:…

    2023 · github.com · its alternatives →

  16. 16

    We asked 1,000 people to evaluate 20 paragraphs of political topics. Ten were written by RoboTrump, and 10 were text from real Trump speeches.

    2019 · lawsuit.org · its alternatives →

  17. 17NA
  18. 18

    Minimal, readable LLM post-training experiments on one 8GB GPU. Measures forgetting, seed variance, and RL emergence. - pochenai/nano-llm-posttraining

    Aug 2026 · github.com · its alternatives →

  19. 19

    I've been tinkering with getting Llama-8B to bootstrap its own research skills through self-play. The model generates questions about documents, searches for answers, and then learns from its own successes/failures through RL (hacked up Unsloth's GRPO code). Started with just 23% accuracy on Apollo 13 mission report questions and hit 53% after less than an hour of training. Everything runs locally using open-source models. It's cool to see the model go from completely botching search queries to iteratively researching to get the right answer.

    2025 · github.com · its alternatives →

  20. 20PG

    I’m Andrew, co-founder of Recall. Over the past few days I’ve been building Predict, a playground where anyone can: - propose skills we should measure in language models—live examples include difficult math, memory-manipulation resistance, code generation, and empathy under bad news - write evals (graded prompts) for those skills - forecast which models will score highest once GPT-5 is released Why this exists Benchmarks leak into training data quickly; scores are unreliable and labs still declare progress. The prediction tool aims keeps the target moving by letting the crowd define both the…

    2025 · its alternatives →

  21. 21IB

    I’ve been playing around with local LLMs for the past couple of months and decided to build something that can run on an iPhone. It’s a universal app built with SwiftUI and the excellent ggml library. The model is an SFT fine tuned and 4 bit quantised version of the RedPajama-INCITE-Chat-3B-v1 OSS LLM. It works reasonably well on recent-ish (~3 year old) iPhones, iPads and Macs. It was launched on the App Store yesterday[1] and Product Hunt today[2]. It seems to be reasonably ok at natural language interactions, but given its size, does pretty badly at coding and reasoning. Also, it…

    2023 · its alternatives →

  22. 22

    AI Trump is here. And he’s not holding back.

    2025 · its alternatives →

  23. 23TT

    For the last few weeks, I have been working on training an LLM from scratch and deploying it in production on Google Cloud Platform. Finally, I trained a 30 million parameter model on 1 billion tokens and deployed it as a web service. You can access the LLM using this site - https://kunalmishra.info The following steps were taken to build Tiny Tales GPT 1. Downloaded and preprocessed 8GB of dataset using multiprocessing library. 2. Tokenized the data using byte pair encoding to create 1 billion tokens sharded in different bin files. 3. Defined a training setup and trained the model…

    2024 · kunalmishra.info · its alternatives →

  24. 24

    Hey HN, I fine-tuned a small open-source model on golf forecasting and it beats GPT-5 at predicting golf outcomes. The same approach can be used to build a specialized model in any domain, you just need to update a few search queries. We fine-tuned gpt-oss-120b with LoRA on 3,178 golf forecasting questions, using GRPO with Brier score as the reward. Our model outperformed GPT-5 on Brier Skill (17% vs 12.8%) and ECE (6% vs 10.6%) on 855 held-out questions. How to try it: the model and dataset are open-source, with code, on Hugging Face. How to build your own specialized model: Update the…

    Feb 2026 · huggingface.co · its alternatives →

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