Alternatives
Products that do what ADHD for LLMs does
5.2x better reasoning for LLMs
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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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I really struggle to keep my notes organised, and often can't make any sense of hand written notes after meetings. I did some experiments with Claude and ChatGPT where I blast random thoughts at them during a meeting or brainstorming session and then have them output a nice document that pulls it all together at the end. It worked incredibly well but it was a bit cumbersome to keep sending messages to the chat and telling the ai each time what to do. So I took the idea and built a tool, it makes it easy to throw random notes and voice recordings (images and document support on the way) and…
2025 · scatternote.com
- 13FM
Hi HN, We've been frustrated with how confidently LLMs hallucinate—a dangerous flaw in high-stakes domains like health and medicine. The standard "I am not an expert" disclaimer feels insufficient since we all ignore those statements. Our approach is a RAG/agentic system built to solve this. It runs on ~40M+ scientific papers, but goes beyond simple retrieval. A multi-agent workflow decomposes queries, cross-references claims against multiple sources, and synthesizes answers, ensuring every key statement is cited directly from the literature. Beyond the literature, our agent system has…
2025 · my-openhealth.com
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I built a local-first UI that adds two reasoning architectures on top of small models like Qwen, Llama and Mistral: a sequential Thinking Pipeline (Plan → Execute → Critique) and a parallel Agent Council where multiple expert models debate in parallel and a Judge synthesizes the best answer. No API keys, zero .env setup — just pip install multimind. Benchmark on GSM8K shows measurable accuracy gains vs. single-model inference.
Mar 2026 · github.com
- 16AG
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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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
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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, 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
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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
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I had really bad health anxiety the last 2 years that severely impacted my quality of life. I started using Claude for support and was surprised by how much it helped. I realised how much potential there is for LLMs to provide an objective perspective on stressful thoughts - so I built an app that helps you form a habit around this to reduce long term anxiety. You vent what's on your mind and an LLM will evaluate your thoughts objectively (heavily leaning on CBT techniques), to help you maintain a more balanced perspective. Technical stack: - Flutter (to release cross-platform) - OpenAI API…
2025 · resetapp.co.uk
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Utilizing Llama3, I developed an ADHD assessment tool that synthesizes findings from the Adult ADHD Self-Report Scale along with 23 other tests to create comprehensive diagnostic reports.
2024 · adhdtest.ai
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A coding environment designed to be "recursively self improving".... but a whole lot more. Uses web based chatbots to save tons of money while being within terms of service and still being efficient. Runs right off the web (with no signup) and saves to your browser and/or straight to disk. Open source. Lots of cool sample/template apps.
Jun 2026 · recursi.dev
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I'm Guy, the founder behind Snyk — now building Tessl, a package manager for agent skills. We’ve recently witnessed that most teams still treat skills as static artifacts: markdown files, created or copied from repo to repo. This approach offers a strong initial boost, but quickly creates debt: - Skills are duplicated, and updates never roll out. - Poor quality skills go unseen, misguiding agents instead of helping. - Skill knowledge grows stale, and don’t keep up with the systems and practices they describe. Without a way to evaluate skills, teams have no clear way to understand how good a…
Feb 2026 · tessl.io
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