Alternatives
Products that do what Lovable for MCPs – No/low-code builder for AI tools does
We built a no/low-code tool that lets you spin up MCPs from a single prompt. MCPs give LLMs access to tools, data, and actions—but they’re hard to build and deploy. Our tool abstracts that: describe what you want, and it auto-generates and hosts the necessary components. No UI flows, no manual chaining—just prompt and go. Examples: • Pull email, parse a DocSend, check Reddit, draft reply • Extract data from a niche site + send a Slack alert • Combine tools without writing glue code Live demo: https://www.youtube.com/watch?v=4uCiaQrgfoE Built over a weekend after getting…
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Hello HN, we're Andrew and Stephen from Keyboard (https://www.keyboard.dev/). After building AI tools for the past year, we recently made a YouTube video on building MCP servers and realized MCP is a total game-changer. It essentially lets AI do anything by connecting to your apps. But the deeper we dove, the clearer it became that security and privacy were complete afterthoughts. Coming from backgrounds at Okta and Stripe, this made us pretty uncomfortable. We kept seeing the same pattern: every app needs its own MCP server, each storing sensitive tokens, with minimal…
2025 · github.com
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Dec 2025 · agentdiff.dev
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Free browser utilities. No account. No server. Ever.
Jul 2026 · sidehustlers113-ux.github.io
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Private developer tools for Mac, built for AI workflows
26d ago · codeswissknife.com
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Hi HN — I'm the creator of FastMCP and wanted to share a new project we've open-sourced called Colin. I obviously love MCP, but I also use skills extremely heavily in my day-to-day work. Being exposed to both has made me very aware of a tension: - Anything with dynamic information, I ship over MCP. This takes work to set up and requires conversational boilerplate to refresh in every conversation. - Anything behavioral, I put in skills. They're lightweight, used automatically, and feel great. But I would never put dynamic information in a skill because keeping it up to date is a pain. And yet…
Jan 2026 · github.com
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Describe any AI app, Get a live, MCP-powered app in minutes
May 2026 · newagentshub.dev
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Hey everyone! we just launched Promptly apps (https://trypromptly.com/), a no-code platform to build generative AI apps and chatbots. We allow users to build web apps and chatbots by chaining LLM APIs (we call processors) from providers like OpenAI, StabilityAI, Cohere etc, without writing any code. We also let users to bring in their own data and store it in a vector database to be used for context augmentation in their apps. Users can import data from a variety of sources including urls, sitemaps, PDFs and other file types Published apps are accessible to app's users via a…
2023 · trypromptly.com
- 19MS
Hey, I'm Nick from Nutrient, I want to share our newly released MCP Server that enables document workflows using natural language — things like redacting, merging, signing, converting formats, or extracting data. While many MCP servers have traditionally been developer-focused, we recognized that the technology could be highly effective in promoting the adoption of tools that are often hidden from end-user interfaces. We’re really interested to see if this side of the protocol could continue to mature. One thing we struggled with was the inability to receive documents from the client (no…
2025 · github.com
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Creating 3D is hard. LLMs seem to be getting better at tool use and spatial understanding. While MCPs have proved to be a good way to use these tools- the current methods have these challenges: - Access to scene graph and core C modules of Blender - Lack of parallelism, only way is to run blender headless - Lack of deterministic and fast verification layer - Inference stack- only way to use inference is to hook another MCP We're building Mixar, think Cursor for 3D. One access point to all generative inference, an agent to build scenes/blockouts, do boring stuff like UVs and export…
Aug 2026 · mixar.app
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We’re building an open-source tool that makes it easy to expose secure, LLM-optimized APIs on top of your structured data—without manually designing endpoints or worrying about compliance. AI agents and LLM-powered applications need structured access to data, but traditional APIs and databases weren’t built with AI workloads in mind. Our tool automatically generates APIs that: - Filter out PII & sensitive data to comply with GDPR, CPRA, SOC 2, and other regulations. - Provide traceability & auditing, so AI apps aren’t black boxes, and security teams stay in control. - Optimize for AI…
2025 · github.com
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Here is a production-first Keras-inspired LM framework, built with the advice of François Chollet (ex-Google, creator of Keras and ARC-AGI), our technical advisor. This system have already been deployed in production with our clients (which is why we have already every LLMOps practice implemented). It is also compatible with Jupyter and Marimo to integrate seamlessly in you Data Scientists workflows. You can try the code examples online on HF space and you can find more information in the documentation and FAQ. If you have any feedback for us don't hesitate to join our discord! More releases…
2025 · github.com
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Hey HN, I built a tool to solve my biggest LLM workflow frustration: context switching. The idea came from trying to meta-prompt in Cursor, I found myself constantly jumping to a browser or dedicated AI app just to improve a prompt. This copy/paste/tweak cycle was a huge productivity killer. I wanted AI to integrate seamlessly into my workflow, not disrupt it. That's why I built Promptive. It's a native macOS app that lets you select any text, in any application, and run a custom LLM prompt on it with a global keyboard shortcut or right-click and select the action in the…
2025 · promptiveai.app
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I built a CLI tool that turns codebases and PRs into diagrams so you can quickly understand how things fit together. Originally made it because I couldn't follow my own AI-generated repos. Just shipped a big update: - Switched from D2 to Mermaid for rendering - Tree-sitter AST parsing + agentic flow instead of raw LLM calls. ~50x faster. - Works on any GitHub repo or PR, not just local - Dropped the web frontend, it's just a CLI now - Published as a pip package Still a ton to improve and I'm building fast. Feedback, issues, PRs all welcome.
Feb 2026 · github.com
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