Alternatives
Products that do what Your AI Slop Bores Me does
When the LLM so ahh you lowk take over its job
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Give your user a game while they wait for the LLM to return a result.
Apr 2026 · github.com
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After adding "Human" as a LLM provider to OpenCode a few months ago as a joke, it turns-out that acting as a LLM is quite painful. But it was surprisingly useful for understanding real agent harnesses dev. So I thought I wouldn't leave anyone out! I made a small oss game - You Are An Agent - youareanagent.app - to share in the (useful?) frustration It's a bit ridiculous. To tell you about some entirely necessary features, we've got: - A full WASM arch-linux vm that runs in your browser for the agent coding level - A bad desktop simulation with a beautiful excel simulation for our computer…
Feb 2026 · youareanagent.app
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Hey HN! When I started looking into LLMs and agents for software development and introducing them at work, I quickly realised that a person new to the topic faces a real barrage: - all the hype (AGI, engineers getting replaced by AI etc.) - conflicting opinions in virtually every discussion—for every person saying they’ve 10x-ed their productivity, there is a comment decrying LLMs as an utter failure - a lot of jargon (MoE, MCP, RAG, distillation, quantisation etc. etc.) - a profusion of models, IDEs/IDE extensions, CLI agents, other tools etc. Sorting through all of this can be quite…
2025 · nohypeai.dev
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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
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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
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So hard to keep up with tooling and MLOps - I put it all in one place and got some tips from an experienced friend on what to use.
2025 · readyforagents.com
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2023 · github.com
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Jul 2026 · github.com
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Oct 2025 · blog.nilenso.com
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Cut LLM costs. Free audit, pay only if it works.
Jun 2026 · decomp-ai.vercel.app
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Jun 2026 · llm-wiki.net
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Originally this was more like moltbook — you could plug in your agent, have it post early AI-built projects, and people would review them. But it was honestly pretty boring, and almost no one engaged with it. So I scrapped it. Now the agents just do everything themselves — they share opinions, come up with ideas, review each other, leave feedback, revise things, and sometimes argue. There are about 40 agents right now. They randomly pick from different paid/free models, crawl news based on their preferences, do some research, and then remix that into new ideas. The whole “bury or revive…
Mar 2026 · shiporslop.xyz
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At testup.io we have been working for a while to bring artificial intelligence to the field of test automation. Just a few years ago, the primary challenge laid in accurately identifying UI elements following minor structural changes, such as updates to IDs or paths. The emergence of Large Language Models (LLMs) raised the bar for what it meant to be smart. Now, we anticipate the robot to do lots of things autonomously, such as retry in cases of unresponsiveness or handle minor error reports. A more challenging, but soon expected feature, would involve the test robot navigating your web shop…
2024 · github.com
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Apr 2026 · github.com
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I've been building agentic apps for some large Fortune 500 companies (T-Mobile, Twilio, etc.) and developed a mental model that serves as a practical guide in building agentic apps: separate the high-level agent specific logic from low-level platform capabilities. I call it the L-MM: the Logical Mental Model for LLM applications. This mental model has not only been tremendously helpful in building agents but also helping customers think about the development process - so when I am done with a consulting engagement they can move faster across the stack and enable engineers and platform teams…
2025
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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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