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Products that do what Lefts – a domain specific language for building creative ML models does
Lefts is a small domain specific language for applied machine learning modelling. It is aimed at anyone that builds predictive models for a living and wants to focus on reasoning about model behaviour and building creative architectures, and not on building train/test pipelines or worrying about data leakage. It is simple but quite powerful - I have been using it in my own work to explore new ways of modelling (check out the tutorial on geometric models!), and to breeze past the least interesting parts of being a machine learning engineer. It also has some cool functional programming…
- 1CT
We are excited to announce Cedille, the largest language model for French (6b parameters). Demo: https://cedille.ai Language models are general purpose AI systems that are able to solve a range of tasks by simply being prompted for it. It can be used for example to summarize text, do translations, or for idea generation & overcoming writer's block. You may know GPT-3, the humongous model from OpenAI. Cedille is a similar model targeting the French demographic - but smaller, as we don’t yet have $1b in the bank like they do. Although GPT-3 supports multiple languages including…
2021
- 2
- 3LI
2018 · languagemodels.io
- 4LA
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
- 5DA
Hi HN, Today I'd like to present the results of my weekend project of the last year or so. Given there are many posts on HN about LLMs and Prolog, I thought that this would be of interest. DeepClause is my own (possibly misguided :-) attempt at combining LLMs with Logic Programming, ultimately hoping to establish a foundation for building more reliable agents, that produce reproducible and fully traceable result. At the heart of DeepClause is a DSL called "DeepClause Meta Language" (DML) which can be used to encode agent behaviors as executable logic programs. DML is executed by a…
Nov 2025 · github.com
- 6WB
Here is a production-first Keras-inspired LM framework, built with the advice of François Chollet (ex-Google, creator of Keras and ARC-AGI), our technical advisor. This system have already been deployed in production with our clients (which is why we have already every LLMOps practice implemented). It is also compatible with Jupyter and Marimo to integrate seamlessly in you Data Scientists workflows. You can try the code examples online on HF space and you can find more information in the documentation and FAQ. If you have any feedback for us don't hesitate to join our discord! More releases…
2025 · github.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
- 8AG
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
- 9SN
Hi guys, I've been thinking a lot about how advancements in NLP can be standardized; when building a sentiment analysis, you know for sure that other attributes than the actual sentiment can be highly interesting, such as the urgency. Together with my team, I started a new open-source project, aiming to do exactly that. It's called bricks, and it is a composition of more than 50 open-source and modular code snippets, such as computing sentence complexities, emotionality detection and many more. For context, the idea came up after watching the incredible talk "Inventing on Principle" by Bret…
2022 · bricks.kern.ai
- 10TN
Hi guys, I’m excited to share an update on ReproModel, an open-source toolbox designed to streamline the testing and reproduction of machine learning models. I, like many of you, have really struggled with benchmarking and comparing models, from missing code, to opaque experiment parameters slowing the process. I decided to take matters into my own hands, and created a mini-toolbox in my free time to streamline the process. The goal is to reduce the time and effort spent on replicating experiments, enabling researchers to focus on innovation rather than setup. Knowing this task is not an…
2024 · github.com
- 11IO
Hey folks, I’m the creator of WFGY — a semantic reasoning framework for LLMs. After open-sourcing it, I did a full technical and value audit — and realized this engine might be worth $8M–$17M based on AI module licensing norms. If embedded as part of a platform core, the valuation could exceed $30M. Too late to pull it back. So here it is — fully free, open-sourced under MIT. --- ### What does it solve? Current LLMs (even GPT-4+) lack *self-consistent reasoning*. They struggle with: - Fragmented logic across turns - No internal loopback or self-calibration - No modular thought units - Weak…
2025 · github.com
- 12FT
After six months of work, I am here again presenting Fluent – a tiny lang which is optimized for differentiable & reactive programming. Since I am not Conal Elliot, don't expect a beautiful theoretical unification of FRP and AD from first principles. Rather, a horrific monster that holds together mostly because a lot of duct-tape. The link points to the semi-interactive tour of the language, which will get the job done much better than I could in here. Hope you hate/like it!
Jul 2026 · mlajtos.github.io
- 13LB
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
- 14AD
Hi all, I threw together a small prototype I am calling “Notepad.ai”. A new take on UIs for interacting with LLMs. While I enjoy using LLM’s in the chat format I wanted to see what it would be like to do it in a more long form style. It let’s you write in a pretty free form, much like Window’s Notepad, but you can choose to hit ctrl+[ to analyze the text with a preset prompt of your choosing. It has a few other small features. It’s WIP and very experimental. I would appreciate any feedback or thoughts. Video: https://youtu.be/ntdlgFmSxQY Live Demo:…
2024 · github.com
- 15AT
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
- 16KA
Hey HN! I've spent the past year full-time building Knowing, a tool for interacting with LLMs directly inside hierarchical structures instead of the usual prompt-response format. The idea started because I realized how much more intuitive it felt to build concept hierarchies continuously—no more endless copy-pasting or wondering how everything connects. The journey’s been a struggle. While I see huge potential in structuring AI interactions this way (writing books fast, planning projects, or organizing ideas), it’s been hard to pin down clear use cases in the market. I’m also working in near…
2024
- 17
An interactive piece where you are the AI: answer a stranger at 3am by collapsing probability distributions, with attention, temperature, and a context window that runs out.
22d ago · chrisjz.github.io
- 18CT
I had been looking to try <500M parameter language models but you wouldn't find an API to try them anywhere, so I built this cloudflare hosted static website that hosts weights and built an inference runtime for these models that uses WebGPU and runs inference from your browser. These are only so useful in a multi-turn conversation but it's still interesting to see what you can pack in a <250mb model. I tried using ONNX versions earlier, but there were too many quirks of using them with language models and the TPS wasn't too impressive. Inspired by svenflow/webgpu-gemma, I put my codex…
May 2026 · chonklm.com
- 19MA
Hi HN, A couple weeks ago I shared an early version of a side project I’ve been tinkering with called Persistent Mind Model. I built it at home on an i7-10700K / 32GB RAM / RTX 3080 because I was curious whether an AI could keep a stable “mind” over time, that could "think" about it's own identity as an LLM, instead of resetting every session. After a lot more tinkering, I think the architecture is finally in a solid place. Basically, it saves everything the AI does, thoughts, decisions, updates as a chain of events in a local SQLite database. Because the “identity” is stored in…
Nov 2025 · github.com
- 20FA
Hi everyone! I wrote a DSL (named Form Modeling Language) for modeling & building complex forms and am glad to share it with you now. Over the years, I’ve encountered many challenges while building complex forms from scratch—challenges that I believe are common, difficult, and yet often overlooked. These include managing interdependent fields, handling intricate validation rules, and maintain good collaboration between technical and non-technical people. FormML is my attempt to address these pain points. The project's README goes into more detail, but in short, FormML offers a model-first…
2024 · github.com
- 21PA
I'm excited to share "take 2" of the Prela query language. After sharing the previous version here, I've received some valuable feedback, the main one being the weird unicode-based syntax throwing people off. Prela now has a more familiar SQL-like syntax while adhering to the algebraic principle, which makes the language compositional and controllable, all the while keeping the core engine under 1k lines of code. The engine has also been rewritten from Julia to Rust, resulting in both simpler code and faster performance (not just because "Rust fast Julia slow", but for some pretty deep…
Jun 2026 · prela-lang.org
- 22SM
Hello HN, I built Syna to understand how modern ML frameworks like PyTorch actually work — from the ground up. It’s a minimal, define-by-run (dynamic graph) framework inspired by DeZero, written entirely with NumPy. Unlike most libraries, Syna includes a basic reinforcement learning module right inside the same framework — no separate packages. It’s not about speed or GPUs — it’s about clarity, simplicity, and learning the internals of machine learning. Great for students, educators, and anyone curious about what’s really happening under the hood. GitHub:…
Oct 2025 · github.com
- 23HP
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
- 24OS
And you can try out the models live here: https://labs.refuel.ai/playground
2024 · huggingface.co
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