
upload | 跨 AI 的长期记忆
A shared memory layer that persists your decisions, preferences, and projects across different AI tools and devices.
upload — 跨 AI 的长期记忆层,让 ChatGPT、Claude、Codex、Hermes、OpenClaw、WorkBuddy 等共用同一份记忆,换工具换设备都接着上次继续,注册即用免装插件
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A shared memory layer that persists your decisions, preferences, and projects across different AI tools and devices.
upload — 跨 AI 的长期记忆层,让 ChatGPT、Claude、Codex、Hermes、OpenClaw、WorkBuddy 等共用同一份记忆,换工具换设备都接着上次继续,注册即用免装插件

Mask personal data and reduce AI token costs by processing data locally before sending to Claude, ChatGPT, Gemini, or other APIs.
@AI_303030 · X

Backend platform providing auth, storage, database, and LLM APIs (Claude, OpenAI, Gemini) via a single JavaScript library.
ent101 · HN
The Safe, Production-Ready Backend for AI-Generated Apps

Sync data across devices in real-time with automatic security inspection via an API.
@mygridsyncdev · X
Just launched GridSync ⚡️ Sub-150ms zero-trust payload inspection & real-time sync for modern APIs. Try the free tier on RapidAPI or check out the docs: #buildinpublic #API #FastAPI

Write, organize, and run prompts across multiple AI platforms in one interface.
@promptboxxx · X
Have all your AIs in one place and never repeat a prompt

Build, verify, and ship software with teams and AI agents in one connected workspace.
u/OGMYT · Reddit
Validating a micro-SaaS for checking AI-built software before it ships I am building **Flows**, a system that turns an AI software goal into ordered implementation steps, checks, repair prompts, and a build report. The business thesis is not “better prompts.” It is reducing the cost of getting from an AI-generated first draft to a checked, shippable result. Metrics I want to measure: - prompts per completed project - tokens or credits per checked step - first-pass check rate - repair atte

Create branded developer portals from API specs and documentation with AI-assisted writing.
@jitendraballa · X

API platform providing company and people data for AI agents and business platforms.
@zeynepavann · X
Thank you to everyone who joined our "map your TAM from the terminal" session with last week. We built live in the terminal using Claude Code and the MCP. I wrote up the whole thing afterwards. The seven stages, why each one exists, what to do with the list once you have it, and the outreach half we covered at the end. The recording is linked inside. The skill is public too, so you can run it on your closed-won book. You bring your own accounts; the engine does the expansion, scoring, and contact-finding. Comment "TAM" and I'll send you both skill and guide.

@rdbuilds7 Memory Layer for AI Agents Product - https://t.co/mbRK424Ws4 Guide - https://t.co/W9xosDWLzQ
@Pankaj41521 · X
Memory Layer for AI Agents Product - Guide -

An AI agent that reads your GitHub repository, proposes changes, and opens pull requests.
@MetriQAI · X
— Stop re-explaining yourself to every AI. One workspace. Every AI. Shared memory.

Hire AI agents to automate business tasks like customer support, sales, and operations.
@ishaqjan619 · X
Agentic Infrastucture Platform, launch and build scalable AI agents in one go.

Coordinate multiple AI coding agents and projects in one unified terminal interface.
@soracstv · X
I run my AI coding agents with AgentsRoom, a visual command center for multi-agent development. @AgentsRoomDev #VibeCoding #AI