Unabyss
Confluo.in
BaseThread
Supermemory
Claude by Anthropic
mcp skills
Nia
Callio
socketify.py
Set it up once and never re-explain yourself to AI again. Connect the apps you use daily - Unabyss will extract, structure, and update your context automatically. Share it with any AI tool via MCP, with granular control over what each tool can see.
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Unabyss's answer
Unabyss isn't just MCP connectors bolted onto keyword search. It's a full context layer that sits between your tools and your AI: it ingests data from 30+ sources, then cleans, chunks, tags, versions, and connects it into a multi-store context graph (graphs + RAG + semantic search). Your AI tools โ Claude, Cursor, any agent โ pull the right slice of context on demand over MCP, so you never re-explain yourself and never maintain manual .md files again. The structuring and retrieval layer is the moat; raw MCP connectors don't do it.
Unabyss's answer
Most memory tools (Mem0, Letta, Supermemory, Cognee, Personal.ai) or platform-native memory (ChatGPT/Claude/Gemini) lock your context inside one place or treat it as a flat store. Unabyss is MCP-first and portable: your context lives in one user-owned layer and works across every AI tool at once. You get diff-based ingestion so only what changed re-syncs, full version history with newest-version-wins conflict resolution, and iOS-style granular permissions that keep personal and company context cleanly separated โ with an audit trail and one-click revoke. It's the difference between a memory feature and a context infrastructure you control.
Unabyss's answer
Two core personas. First, Builders โ developers, AI consultants, and technical PMs who are MCP-native and already wiring up agents and automations; they activate through MCP naturally. Second, AI Enthusiasts โ founders, operators, marketers, and growth people who use AI every day and are tired of re-explaining their context across tools. We're expanding from this prosumer wedge toward small teams (5โ15 people), where the value shifts to a shared "company brain" and cross-project memory.
Unabyss's answer
Unabyss began with a simple thesis: people should own a portable context layer that any AI tool can use. We started with content creation as the wedge โ an AI ghostwriter with a deep-interview mode that captured how someone actually thinks and works โ and hit $12.5K MRR at $500+ ARPU in seven months. But users kept telling us the magic wasn't the writing; it was that "it knows me." They started asking why their other tools couldn't start from that same context. That pull pushed us to build the full context vault and go all-in on MCP: the real "wow" isn't a vault UI, it's Claude or Cursor instantly having your context with zero copy-paste. We launched on Product Hunt in May 2026 and hit #1 Product of the Day.
Unabyss's answer
Backend: Django 6 + Django REST Framework Web (product + marketing): SvelteKit โ app.unabyss.com and unabyss.com Database: PostgreSQL (including Neon) Distribution: MCP server (primary), plus REST API and OpenAI function-calling adapters Integrations: 30+ native connectors Infrastructure: Docker Compose, VPS deployment behind nginx with SSL
Unabyss's answer
Based on our record, socketify.py seems to be more popular. It has been mentiond 2 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
These "benchmarks" are useless, they're not testing anything real world except the performance of uWebsockets. There are copy errors all over the place. And then an advertisement: https://github.com/cirospaciari/socketify.py#briefcase-comme... Is this a professional framework that produces proper, real-world benchmarks and... - Source: Hacker News / over 3 years ago
After starting the project called socketify.py at https://github.com/cirospaciari/socketify.py, I got pretty good results and reviews, but many people asked if socketify.py could be used to create a WSGI and ASGI server. WSGI and ASGI have a lot of overhead, that's is why I choose not to use them in the first place, but adding an ASGI and WSGI server allows a lot of code already written to run faster! Source: over 3 years ago
Confluo.in - One shared memory for every AI you use. Carry context between Claude, ChatGPT and Gemini โ and stop re-explaining your project every time you switch.
BaseThread - One shared context every AI tool your team uses reads and writes over MCP, so Claude Code, Cursor and ChatGPT stay current together.
Supermemory - ai second brain for all your saved stuff
Claude by Anthropic - A family of foundational AI models
mcp skills - Let AI agents extend themselves with skills
Nia - AI code agent that actually understands your codebase