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Arda Ulusoy🛠️ 1 tool 🙏 9 karmaJan 1, 2026@OpttabHelping brands get more traffic and conversion from AI search in the most effective way. Also including AI presence management features for individuals. -
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Detect AI-generated music before it reaches monetization.Open
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I built a medical tracking App for a friend undergoing cancer treatment. He uses it to record side effects, medications, temperature, blood pressure, and more. The app turns his records into charts, making doctor visits much more efficient. Most importantly, all data stays on his own phone, giving him complete peace of mind!!! We can build apps not only for ourselfs but also as a new type of gift for people we care about. I am excited about this as I have no IT background. NiuNiu keeps its promise that it is easy to build and deploy a real app just by chatting. definately I will try more ideas. -
Building multi-agent AI systems shouldn’t require wrestling with messy Python boilerplate, cloud subscription or complex local setups. I built AgentSwarms (AgentSwarms.fyi) to solve this. It’s an interactive, in-browser visual sandbox for learning, building, testing, and orchestrating multi-agent AI workflows. Instead of relying on a single AI chatbot that easily hallucinates, AgentSwarms lets you drag and drop specialized AI agents—like data extractors, code reviewers, and SQL query nodes—to handle complex tasks reliably. Key features for builders: Visual Edge Tracing: Stop guessing why your AI failed. Click any connection line to see the exact JSON data passing between agents in real-time. In-Browser Execution: Powered by local DuckDB WASM. No backend setup required. Enterprise Templates: Test reverse-engineered Agenitic AI architectures for Cybersecurity, Finance, and E-commerce and 10+ industries right on the canvas. If you are tired of black-box AI debugging and want to master Agentic AI orchestration visually, come try the sandbox for free! Let me know what you think of the new execution history features. 🚀 -
Canopy API v2: Faster Amazon data, lower high-volume pricing, and MCP support Canopy API v2 is live. This release is focused on three things: speed, reliability, and making Amazon product data dead simple to plug into AI agents. - MCP server for AI agents. Canopy now ships a native MCP integration alongside our GraphQL and REST APIs. Connect Claude, Cursor, or any MCP-compatible agent to live Amazon product data, search results, pricing, and seller information with a single config entry. No wrapper code, no glue scripts. - Three ways to query, one data layer. GraphQL when you want precise, nested queries in a single request. REST with a full OpenAPI 3.1 spec when you want simple, predictable endpoints. MCP when the consumer is an agent instead of an app. All three run on the same infrastructure and return the same normalized product data. - Performance improvements. We rearchitected request batching and caching across our edge network. Typical product lookups now return in 200ms at p95, and multi-product queries are dramatically faster thanks to smarter request handling. - Data reliability improvements. New retry and validation layers mean fewer empty fields, fewer stale prices, and consistent product schemas across marketplaces. If we return a field, you can trust it. - Lower prices at volume. High-volume tiers got a real price cut. Requests beyond 20K per month now cost up to 50% less, and usage-based billing means you only pay for what you actually pull. Whether you're building price trackers, market research tools, or AI shopping agents, v2 is the fastest way to get structured Amazon data into your product. Start at canopyapi.co. -
"Finally — an AI cost tool that actually aligns incentives" I've been working in enterprise IT and AI infrastructure for years, and Lexi is one of the most elegantly designed products I've seen in this space. The core insight is simple but powerful: don't charge unless you save money. That alignment alone sets it apart from every other API middleware I've tested. The integration literally took under two minutes — one URL change in our config, and we were live across multiple providers. The cost transparency in the response headers is genuinely useful for us as a team building on top of AI; we can now log, display, and report on exact token costs per request. The O(1) memory compression is the real technical differentiator. Long AI conversations tend to degrade in quality and balloon in cost — Lexi solves both problems simultaneously. For anyone running AI in production at any scale, this is infrastructure you didn't know you were missing. Highly recommended for developers, startups, and enterprise teams alike.
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New: Added multilingual support. Improved: Enhanced the AI agent building experience and refreshed the website visuals for better usability. Fixed: Fixed some known issues to improve overall stability.
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Build AI Mobile & Web Apps - No Code, No Api KeysOpenI built my fitness app and published it to both App Store and Google Play in just 3 days. OnSpace AI handled all the mobile development complexity. I'm now earning from in-app subscriptions. - SponsorFlow - 4x faster than typing🎤 Dictation
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Turn ideas into full-stack apps instantly with GDPR-compliant builder.OpenI tried their Form Builder template to see how it converts descriptions into a user interface. And it produced a functional form almost exactly as I expected. Good for me 👍 -
I love that Shakespeare is totally open source and fully focused on allowing users to build with freedom, rather than just another AI SaaS trying to lock users into a subscription tier. You can use it with any other AI provider, or pay as you go for credits with Shakespeare's default AI. It's really easy to talk to the app, build a first draft, and deploy. I also like how you can edit other open source projects and improve them. Really cool tool for people who want to build with AI but care about open source and freedom.
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