Alternatives
Products that do what Axiom Flow does
Learn by teaching an AI that gets it wrong
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I think graph is a wrong abstraction for building AI agents. Just look at how incredibly hard it is to make routing using LangGraph - conditional edges are a mess. I built Laminar Flow to solve a common frustration with traditional workflow engines - the rigid need to predefine all node connections. Instead of static DAGs, Flow uses a dynamic task queue system that lets workflows evolve at runtime. Flow is built on 3 core principles: * Concurrent Execution - Tasks run in parallel automatically * Dynamic Scheduling - Tasks can schedule new tasks at runtime * Smart Dependencies - Tasks can…
2024 · github.com
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Hi HN, I'm a 19-year-old aerospace student. I spent the last 13 months building a custom Linux distro from scratch because I wanted to see if we could treat the OS kernel as a mathematical engine rather than a deterministic administrator. The Stack: Flux (The Shell): A custom math-native shell where x² and ∑ are valid syntax. It parses mathematical notation directly into optimized SIMD instructions (no Python wrapper). Tenet (The Scheduler): Written in Tenet (my custom DSL for game theory). The scheduler is a Nash Equilibrium solver compiled to native code. In my benchmarks (Ryzen 7 5800HS),…
Feb 2026 · fawazishola.ca
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Education APIs for courses, tests, videos, and AI agents
May 2026 · tutorflow.io
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Hi, I built Axiomeer, an open-source marketplace protocol for AI agents. The idea: instead of hardcoding tool integrations into every agent, agents shop a catalog at runtime, and the marketplace ranks, executes, validates, and audits everything. How it works: - Providers publish products (APIs, datasets, model endpoints) via 10-line JSON manifests - Agents describe what they need in natural language or structured tags - The router scores all options by capability match (70%), latency (20%), cost (10%) with hard constraint filters - The top pick is executed, output is validated (citations…
Feb 2026 · github.com
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Hey HN! We just released a new library for building LLM-powered applications: @axflow/models. It is part of a larger suite of libraries we're developing for TypeScript developers working with generative AI. This library provides the simplest APIs for 1) invoking the most popular LLM and embedding models (openai, anthropic, cohere, huggingface, etc.) 2) streaming LLM responses to clients, including augmenting the streams with additional arbitrary data and 3) building client-side applications with React hooks. @axflow/models has zero dependencies and is built using only the…
2023 · docs.axflow.dev
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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
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Hi HN, I'm a solo developer learning to code, and I'd love to share my second real project: MapMyLearn, an AI-powered app that automatically generates personalized learning paths based on any topic you input. What it does: Takes a topic (e.g. "history of capitalism", "learn Rust", or "data storytelling") Uses AI to break it down into a structured course with modules and submodules Each submodule includes: - Detailed, pedagogical content (developed based on online sources to mitigate hallucinations) - A quiz of 10 questions - Recommended resources - An AI chatbot for Q&A - Optional audio…
2025
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