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Alternatives

Products that do what Silicon Epoch does

An open-source field guide to modern AI & chip wars

  1. 1
    Zoo234

    A free, open-source playground for AI image models

    2023

  2. 2

    Platform for measuring and training AI agents

    2016

  3. 3

    Designing next-generation Artificial intelligence algorithms

    2016

  4. 4WP

    Anthropic and OpenAI's publicly available models are explicitly guard-railed so that they refuse offensive tasks. And their cyber-focussed models are gated for enterprises. This leaves SMEs and mid market open to major vulnerabilities. AI can be used as both an adversarial and defensive tool in the world of cyber. A worst case outcome is if only the adversaries have access. Meanwhile, most existing AI cyber tools are just wrappers. The problem is that they still have all the guardrails on from the foundation model where they will inherit its refusals. For this project we've post-trained a…

    Jun 2026 · argusred.com

  5. 5AT

    Interactive timeline of every major Large Language Model. Filterable by open/closed source, searchable, 54 organizations tracked.

    Feb 2026 · llm-timeline.com

  6. 6MA

    I've been exploring the (not so=) amazing potential of AI in coding and have compiled a list of tools. From AI-powered IDEs to code generators, this resource is my contribution to the community. I'm still on the fence about including txt2sql projects, as their functionality seems too basic to me. And I'm personally maintaining this, so your feedback is wellcome.

    2025 · aicode.danvoronov.com

  7. 7

    An illustrated guide to understanding Machine Learning

    2016

  8. 8MN

    I also run IRIXNet. I'm here to share my newest SGI-related project.

    2025 · tech-pubs.net

  9. 9

    Why human brains are complicated, superior machines

    2016

  10. 10MC

    Hey HN, I don’t know who else has the same issue, but: Textbooks often bury good ideas in dense notation, skip the intuition, assume you already know half the material, and get outdated in fast-moving fields like AI. Over the past 7 years of my AI/ML experience, I filled notebooks with intuition-first, real-world context, no hand-waving explanations of maths, computing and AI concepts. In 2024, a few friends used these notes to prep for interviews at DeepMind, OpenAI, Nvidia etc. They all got in and currently perform well in their roles. So I'm sharing. This is an open & unconventional…

    Feb 2026 · github.com

  11. 11OS

    2023 · chathub.gg

  12. 12

    Multiple AI models debating & brainstorming together 🤩

    2025

  13. 13

    Your roadmap from Machine Learning to AGI by 2027.

    May 2026 · gumroaddigitale.gumroad.com

  14. 14IP

    To be specific, the content is generated by a GPT-2 based model. https://amzn.to/2TCc0v2 Let me know if you have any questions :-)

    2020

  15. 15TC

    Hello, I wanted to share with you all a interactive map of the economics and physics constraints of the AI buildout. It has macro drivers, industrial chokepoints, and where that shows up in markets. I've added 393 nodes and 562 edges to capture other supply / physics constraints as well. There's no sign up, and no pay wall, it's all free. Please let me know what you think!

    Jun 2026 · atomprophet.io

  16. 16

    Track how AI models feel in everyday use through public community feedback, 7-day experience scores and trends. This is not a capability benchmark.

    23d ago · isaidumber.today

  17. 17PA

    Hi HN, I’m sharing a project I’ve been working on: Panopticon AI, an open-source platform designed for researchers in military modeling and simulations. It’s web-based, integrates with OpenAI Gym, and is intended to support advancements in military AI through realistic simulations, wargaming, and reinforcement learning. The project is still under active development, and I’m looking for contributors to help shape its direction. It’s released under the Apache 2.0 license, so anyone can use, modify, or contribute to it. What Panopticon AI Offers: - A simulation environment that is browser-based…

    2025 · github.com

  18. 18AO

    Hey hackers, the world needs more AI researchers with good taste, and hardcore software folks have some of the best. Many software friends mentioned they learn better from implementations than from papers, but existing open-source examples rarely go beyond basic nanoGPT-level demos. To help bridge that gap, I spent the last two months full-time reimplementing and open-sourcing a self-contained implementation of every major modern deep learning technique from scratch. The result is beyond-nanoGPT, containing 20k+ lines of handcrafted, minimal, and extensively annotated PyTorch code. I'd love…

    2025 · github.com

  19. 19

    A practical playbook for designing production AI systems

    Jun 2026 · topmate.io

  20. 20

    The AI Council. Models debate to verify answers for you.

    Dec 2025 · consilium9.com

  21. 21AG

    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

  22. 22WM

    Hi HN, I’ve spent the last decade building hardware products like humanoid robots, 3D printers, and self-driving tractors. I needed a tool to navigate technical documents faster, so I created one with friends. This tool helps with component search, cross-referencing, comparison, and debugging. We’d love your feedback, whether you find it useful or not. Thank you! Try it here: www.convergelab.ai

    2024 · convergelab.ai

  23. 23

    Learn how AI is actually built, tested, and shipped

    May 2026 · danielhightower.gumroad.com

  24. 24AH

    This paper formally defines where current AGI hits a structural wall — not a technical one. It shows that no amount of scaling, reinforcement learning, or recursive optimization will break through three deep epistemological and formal constraints: 1. Semantic Closure — An AI system cannot generate outputs that require meaning beyond its internal frame. 2. Non-Computability of Frame Innovation — New cognitive structures cannot be computed from within an existing one. 3. Statistical Breakdown in Open Worlds — Probabilistic inference collapses in environments with heavy-tailed uncertainty.…

    2025

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