Launching today
Mnemcore
Turns hours of team video and notes into searchable memory
35 followers
Turns hours of team video and notes into searchable memory
35 followers
Mnemcore turns your organization’s video into searchable, shared knowledge. Capture timestamped notes, search across every recording, and ask questions to get grounded answers linked directly to the original moments. Built for teams that rely on video but need a better way to remember, find, and act on what they see.
Products used by Mnemcore
Explore the tech stack and tools that power Mnemcore. See what products Mnemcore uses for development, design, marketing, analytics, and more.
Engineering & Development 2
Engineering & Development 2
bunny.netThe simple way to deliver, secure, and build on the web
5.0 (6 reviews)
Also considered:
CloudflareVideo is core to Mnemcore, so we needed infrastructure that was fast, reliable and affordable without requiring us to build an entire video-processing pipeline ourselves.
We chose bunny.net because Bunny Stream gave us a straightforward way to upload, encode, secure and deliver long-form video while keeping costs predictable. Its API was easy to integrate into our FastAPI backend, and the playback experience has been reliable across our Vue frontend.
For an early-stage product handling full matches and other long recordings, the combination of developer experience, performance and cost made bunny.net the practical choice over larger, more complicated cloud-video platforms.
SupabaseThe open source Firebase alternative
5.0 (847 reviews)
Also considered:
NeonMnemcore depends on organizations, permissions, videos, timestamped notes, semantic search and background processing, so we needed more than a basic database.
Supabase gave us PostgreSQL, authentication, storage integrations, row-level security and pgvector in one platform. Using real Postgres was especially important because Mnemcore combines relational data, full-text search and vector similarity rather than treating everything as an isolated AI workflow.
It allowed us to move quickly without giving up control of our schema or locking our data into a proprietary database model. The local development experience, SQL tooling and integration with Codex also made it possible for a solo founder to build and iterate across the entire data layer quickly.
General 1
General 1
OpenAI Codex CLIFrontier reasoning in the terminal
5.0 (26 reviews)
Also considered:
CursorI chose Codex CLI because I wanted an AI development tool that could work directly inside the real codebase rather than only generate isolated snippets in a chat window.
Mnemcore spans a Vue 3 frontend, FastAPI backend, Supabase/PostgreSQL database, pgvector retrieval, video infrastructure and OpenAI integrations. Codex CLI helped me reason across that entire system, inspect existing code, make coordinated changes and validate the results without constantly copying context between tools.
I used it to build Mnemcore end to end—from database migrations and API endpoints to frontend workflows, retrieval logic and evaluation cases. It was especially useful when debugging failures that crossed multiple layers of the application or when simplifying architecture that had become overly complex.
What stood out was not just code generation, but the ability to collaborate iteratively inside the repository. I could explain the product goal, review the implementation, test it, identify weaknesses and refine the approach in the same workflow.
Codex CLI meaningfully increased what I could build as a solo founder and made it possible to move quickly without losing visibility into how the system actually worked.