Alternatives
Products that do what SpikeCore AI does
Turn any RFP into a citation-backed bid response
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AI-native technical questionnaire filling for sales teams
2025
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- 20OB
Today, we're launching the Open Benchmarks Grants: a $3M commitment to fund open-source and academic teams building benchmarks for AI agents. In partnership with HuggingFace, PrimeIntellect, FactoryHQ, Together, Harbor, and PyTorch, the grants provide funding, data development support, and research collaboration. Our ability to measure AI has been outpaced by our ability to develop it, and we believe this evaluation gap is one of the most important problems in AI. Open benchmarks are one of the most important levers for advancing AI safely and responsibly—but the academic and open-source…
Feb 2026 · benchmarks.snorkel.ai
- 21IA
We built InsForge because we wanted to use pure prompts to build a production-grade app faster. In practice, AI isn’t reliable enough when generating code or configuring backend pieces like schema, RLS, auth, or functions. Postgres MCP and Supabase MCP are a good starting point, but they felt more like API wrappers for agents than something designed to guide AI safely through backend tasks. So we built our own MCP server on top of Postgres and added a set of context-engineering tools to make prompt-driven workflows more predictable. It gradually turned into a Postgres-based BaaS. Key…
Nov 2025 · insforge.dev
- 22TA
Hey everyone! We've built Traycer, a tool that transforms your GitHub issues—everything from descriptions and attached images to ongoing conversations—into clear, actionable implementation plans. You can easily import these plans into your IDE with our extension or use them with any other coding assistant you prefer. We'd love to hear your thoughts and feedback. Traycer is totally free for open-source projects, and we've got a 2-week free trial if you're working with private repos. Give it a try and let us know what you think!
2025 · traycer.ai
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I’m one of the co-founders of Doctly AI. I wanted to share our story. We didn’t originally set out to build a PDF-to-Markdown parser. It all started when we were building a RAG solution for a company that deals with regulatory agencies. All of their data was in PDFs, and as it is apparently with lawyers, they like to print and scan documents to make it hard on their counterparts. These documents contained complex tables that barely make sense, are rotated, and handwriting is mixed in between. Many pages are number ruled and potentially rotated. We spent a lot of time trying to get clean data…
2024
- 24MD
We’re excited to share ML-Dev-Bench, a new open-source benchmark that tests AI agents on real-world ML development tasks. Unlike typical coding challenges or Kaggle-style competitions, our benchmark simulates end-to-end ML workflows including: - Dataset handling and preprocessing - Debugging model and code failures - Implementing new model architectures - Fine-tuning and improving existing models With 30 diverse tasks, ML-Dev-Bench evaluates agents across critical stages of ML development. To complement this, we built Calipers, a framework that provides systematic performance evaluation and…
2025 · github.com
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