Unabyss
Claude by Anthropic
mcp skills
Nia
Supermemory
OnBrand by SlideSpeak
Callio
AgentKey
GitHub Copilot
Cursor
Windsurf Editor
Claude Code
Codeium
replit
Tabnine
Amazon CodeWhisperer
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.
Trained on billions of lines of public code, GitHub Copilot puts the knowledge you need at your fingertips, saving you time and helping you stay focused.
Unabyss GitHub CopilotUnabyss'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
It definitely increases my productivity.
Based on our record, GitHub Copilot seems to be more popular. It has been mentiond 387 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.
Where llms.txt genuinely gets read is a different layer: coding and agent tooling โ Cursor, Claude Code, GitHub Copilot, Windsurf โ pulling a documentation site's pages with less token waste, plus emerging agent protocols like OpenAI's Agents SDK. That's real, and it's growing fast. - Source: dev.to / about 1 month ago
You need an active GitHub Copilot subscription. Plans are available at individual, business, and enterprise tiers at github.com/features/copilot. Once active, all tools use your GitHub account credentials. - Source: dev.to / about 2 months ago
For over a decade PhpStorm (starting in my WordPress era) and later WebStorm have been my main IDEs for web development. So when GitHub Copilot launched, it was a natural choice to try it out in WebStorm. It was one of the first AI coding tools I used, and it had a big impact on how I thought about AI-assisted coding. - Source: dev.to / about 2 months ago
Before we get into it, there are some things about AI usage worth addressing. I've had my fair share of scepticism in the past, but recent model releases have made it increasingly difficult to argue that AI isn't a viable tool for the majority of workstreams, including building user interfaces. Most large language models are trained on public data scraped from the internet, which means your internal design system... - Source: dev.to / about 2 months ago
Most developers still treat GitHub Copilot like a very good autocomplete engine. That's useful, but it's not the real unlock. - Source: dev.to / 2 months ago
Claude by Anthropic - A family of foundational AI models
Cursor - The AI-first Code Editor. Build software faster in an editor designed for pair-programming with AI.
mcp skills - Let AI agents extend themselves with skills
Windsurf Editor - Tomorrow's editor, today. Windsurf Editor is the first AI agent-powered IDE that keeps developers in the flow. Available today on Mac, Windows, and Linux.
Nia - AI code agent that actually understands your codebase
Claude Code - Transform hours of debugging into seconds with a single command. Experience coding at thought-speed with Claude's AI that understands your entire codebaseโno more context switching, just breakthrough results.