Show HN: Mycodosing.wtf – Research‑Informed Psychedelic Dosage Calculator
I've been frustrated by existing "magic mushroom dosage calculators": most are either visually rough, only handle cubensis, or hard-code one potency value and ignore species, tolerance, and degradation.
So I built Mycodosing.wtf – a research‑informed calculator that tries to do three things better:
Models species‑level potency using psilocybin/psilocin/baeocystin mg/g with separate profiles for truffles (and syntetics!).
Outputs dose ranges (min/median/max) instead of a single magic number, using per‑species uncertainty factors for real‑world variability.
Includes a tolerance model based on 5‑HT2A receptor downregulation (dose‑dependent exponential decay over ~14 days), plus optional weight adjustment and MAOI flag.
The species potencies are pulled and normalized from LC‑MS/MS and classic chemistry papers (e.g., Garcia‑Romeu 2021, Gotvaldová 2021, Gartz/Stríbrný), then wrapped in a neobrutalist UI built with Vite + React. 100% client-side.
I'd love feedback on:
The dose model itself (any flaws in the math or assumptions?) Species data you'd adjust or supplement What you'd want from a v2
Site: https://mycodosing.wtf/ Code: https://github.com/ing-norante/mycodosing.wtf/
Show HN: Open-source UI components for apps that run inside ChatGPT
800M people use ChatGPT and Claude weekly. Right now they get text responses. Soon they'll get real interfaces: product cards, blog posts, booking flows, payment screens rendered directly in the conversation.
We built an open-source component library for this. Install any block with one command and customize it to your brand.
If you're building MCP servers or experimenting with AI-native apps, this might save you time.
Are you building apps for AI assistants? Would love to hear what's missing in your workflow.
Notations of Decimal Numbers
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George Orwell's Six Rules for Writing Clear and Tight Prose
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Teens, Social Media and AI Chatbots 2025
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LangSmith Fetch - CLI for fetching LangSmith data
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ChatGPT's back end: crashes, echoing payloads, and fast fixes
Agent SOPs – Enable AI agents to perform complex, multi-step tasks
Copilot Usage Report 2025
The article discusses the increasing use of AI-powered digital assistants, known as 'copilots', in the workplace. It explores the growth and adoption of these technologies, as well as the implications for productivity, collaboration, and the future of work.
Show HN: SIM – Apache-2.0 n8n alternative
Hey HN, I'm Emir - one of the co-creators of Sim (https://sim.ai/), an open-source [https://github.com/simstudioai/sim/] visual editor to build agentic workflows.
You can run Sim locally using Docker, with no execution limits or other restrictions.
We started building Sim almost a year ago after repeatedly troubleshooting why our agents failed in production. Code-first frameworks felt hard to debug because of implicit control flow, and workflow platforms added more overhead than they removed. We wanted granular control and easy observability without piecing everything together ourselves.
We launched Sim [1][2] as a drag-and-drop canvas around 6 months ago. Since then, we've added:
- 138 blocks: Slack, GitHub, Linear, Notion, Supabase, SSH, TTS, SFTP, MongoDB, S3, Pinecone, ...
- Tool calling with granular control: forced, auto
- Agent memory: conversation memory with sliding window support (by last n messages or tokens)
- Trace spans: detailed logging and observability for nested workflows and tool calling
- Native RAG: upload documents, we chunk, embed with pgvector, and expose vector search to agents
- Workflow deployment versioning with rollbacks
- MCP, Human-in-the-loop blocks
- Copilot to build workflows using natural language (just shipped a new version that also acts as a superagent and can call into any of your connected services directly, not just build workflows)
Under the hood, the workflow is a DAG with concurrent execution by default. Nodes run as soon as their dependencies (upstream blocks) are satisfied. Loops (for, forEach, while, do-while) and parallel fan-out/join are also first-class primitives.
Agent blocks are pass-through to the provider. You pick your model (OpenAI, Anthropic, Gemini, Ollama, vLLM), and and we pass through prompts, tools, and response format directly to the provider API. We normalize response shapes for block interoperability, but we're not adding layers that obscure what's happening.
We're currently working on our own MCP server and the ability to deploy workflows as MCP servers. Would love to hear your thoughts and where we should take it next (:
[1] https://news.ycombinator.com/item?id=43823096
[2] https://news.ycombinator.com/item?id=44052766
Repo: https://github.com/simstudioai/sim
Docs: https://docs.sim.ai
Try it: https://sim.ai
Texas Files Antitrust Suit Against Epic Systems over Health Data
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The Great Al Debate Who Is Right? [video]
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HN time capsule hn comments analyzed by AI with hindsights 10yr later
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Humans Near top of list of monogamous mammals
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Terraform Provider Registry Outage
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Show HN: CIX – deterministic indexing for stable LLM sessions (10-second PoC)
Warehouse worker here – I only come up with ideas and architecture, no coding. The code is a minimal AI-generated PoC. Fork / build / comment if you want to help – I handle design, community handles code.
CIX is a deterministic, human-readable indexing system for LLM sessions. It solves drift, staleness and navigation by anchoring events to a stable temporal index.
Repo → https://github.com/VikingFlow/continuous-index Follow → @VikingFlowAI Support → https://ko-fi.com/vikingflowai
AI as the Great Democratizer
The article discusses how artificial intelligence (AI) has the potential to democratize access to powerful tools and resources, empowering individuals and small organizations to compete with larger entities. It explores the ways in which AI can level the playing field and create new opportunities for innovation and entrepreneurship.
Fedora introduces LLM that suggests using apt to solve the issue
The article discusses the Linux MCP (Microcontroller Platform) Server, a tool that allows users to monitor various system metrics and health indicators on their Fedora systems, providing a comprehensive view of the system's performance and status.