Nicheloom visits: 58,069 all-time · 7,637 in the last 7 days

The opportunity tracker for new startups.

Today: 1,953 launches · Niche of the day: specialized calculation tools · See what launched today →

Moltis

AI assistant with memory, tools, and self-extending skills

Get picks like this daily. Today's launch numbers, the top launches, AI and tech news, and a daily opportunity pick, straight to your inbox.

Want to reach these readers? Sponsor an issue.

Hey HN. I'm Fabien, principal engineer, 25 years shipping production systems (Ruby, Swift, now Rust). I built Moltis because I wanted an AI assistant I could run myself, trust end to end, and make extensible in the Rust way using traits and the type system. It shares some ideas with OpenClaw (same memory approach, Pi-inspired self-extension) but is Rust-native from the ground up. The agent can create its own skills at runtime.Moltis is one Rust binary, 150k lines, ~60MB, web UI included. No Node, no Python, no runtime deps. Multi-provider LLM routing (OpenAI, local GGUF/MLX, Hugging Face), sandboxed execution (Docker/Podman/Apple Containers), hybrid vector + full-text memory, MCP tool servers with auto-restart, and multi-channel (web, Telegram, API) with shared context. MIT licensed. No telemetry phoning home, but full observability built in (OpenTelemetry, Prometheus).I've included 1-click deploys on DigitalOcean and Fly.io, but since a Docker image is provided you can easily run it on your own servers as well. I've written before about owning your content (https://pen.so/2020/11/07/own-your-content/) and owning your email (https://pen.so/2020/12/10/own-your-email/). Same logic here: if something touches your files, credentials, and daily workflow, you should be able to inspect it, audit it, and fork it if the project changes direction.It's alpha. I use it daily and I'm shipping because it's useful, not because it's done.Longer architecture deep-dive: https://pen.so/2026/02/12/moltis-a-personal-ai-assistant-bui...Happy to discuss the Rust architecture, security model, or local LLM setup. Would love feedback.

HN · launched Feb 12, 2026 · 131 upvotes (top 8%) · 0 views

Niche: ai personal assistant software · 354 launches, 21 in the last 7 days ▼ -30% · Explore this niche →

Category and tags

  • Horizontal
  • Agent / copilot
  • Prosumer
  • AI-native
  • Commercial product

Visit website ↗ Source listing ↗

Could you build this?

Partial While an agentic orchestration interface can be vibe-coded, writing a high-performance, strictly typed, memory-safe agent server with dynamic skill execution and memory management in Rust requires idiomatic systems programming.

What it would actually take: The architecture involves an async Rust backend (Tokio, Axum) with persistent vector/relational storage (libsql, SQLite, or pgvector) and a plugin/skill system leveraging dynamic trait objects or Wasm runtimes (Wasmtime). The hard engineering involves safe dynamic runtime sandboxing, lifetime management, lock-free concurrency, and secure trait-based extensibility, which AI assistants notoriously struggle to compile cleanly in Rust without human systems expertise.

Discussion

20 comments analyzed.

Competitors mentioned: MistralAI (cheaper alternative), OpenRouter, LiteLLM, VSCode extensions

Concerns raised: Prompt injection and security risks with tool access, Cost is a major problem with agents, Supply chain and attack surface concerns, Scary to execute random binary with API keys, Not all GitHub subscription models available

Feature requests: Native MistralAI support, Complementary mobile app for task backlog and stateful interactions, Ability to delete cron runs from chat list, Show all available models in UI, OpenRouter integration

Competitors

Other products that read as similar to this one : 476 launches clear the similarity bar, closest 8 shown.

Attention rank: #58 of 477 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 106 days after the earliest competitor.

The niche, labels, vibe code rating and competitor comparison on this page are analysis by Nicheloom. If you use them, please credit nicheloom.com. How you may use our data.