GBrain

GBrain is a team workspace built around a single shared AI memory that stays synced to every AI its members use. Rather than each person keeping private notes and separate account connections for the AI tools they rely on, the workspace holds one memory, one set of connected accounts and one set of skills that everyone on the team draws from. The memory is stored in files the team owns, and the workspace is described as the room a team works in, plus the server underneath it. GBrain is positioned as Garry Tan's AI memory, tools, and skills for any harness, aimed at teams who prompt AI together and want what one person tells an AI to be available to everyone else. Teams today work across several AI tools at once, and each one tends to remember only what was told to it, in its own silo. Notes written while working in one assistant stay invisible to the next one, and account access has to be wired up again and again. GBrain addresses this by putting memory and connected accounts in one place that every AI can reach. The stated example is simple: write a note in Claude Code and ChatGPT knows it. The same principle applies to accounts, where connecting Gmail once means Cursor can search it without a key sitting in a config file. Instead of memory being a feature scattered across separate products, it becomes something the workspace holds and the team shares. Memory is the first of the four parts of GBrain, described as what the workspace knows about the team and the work, held in files the team owns. Everything the workspace has learned is plain markdown in a folder that can be copied, and copying that folder takes the notes with you. Because the memory is made of readable markdown rather than a proprietary store, the team can read it, correct it directly, and carry it out of the product whenever they choose. The open source parts are free to run yourself, which means the team is not locked into the hosted service to keep access to what it has accumulated. This matters because the value in an AI workspace compounds over time: the longer a team uses it, the more context it holds, and the more important it becomes that this context belongs to the team rather than to a vendor. Tools make up the second part: the accounts the workspace reaches, and what each AI may do with them. Email, calendar and the web are connected once, at the workspace level, and then become available to the AIs the team uses. The stated benefit is that connecting Gmail once lets Cursor search it without a key in a config file, removing the repetitive setup work of granting each assistant its own credentials. Some of these tools are metered, such as web search and page crawling, and those are paid for from the usage credit included with the workspace. Keeping connections at the workspace level also means an administrator can express what each AI may do with a connected account, rather than leaving that decision to individual configs scattered across people's machines. Skills are the third part, described as the jobs the workspace knows how to run, on demand or on a schedule. Skills come already installed, so the workspace arrives with work it can already do rather than an empty shell the team has to build from scratch. The scheduled side is what GBrain calls work that runs while you sleep: jobs triggered on a cadence rather than in response to someone sitting at a keyboard. Onboarding and support come direct from the team behind GBrain, which is presented alongside the skills as part of the package. Taken together, memory, tools and skills are the raw material, and the workspace is where the team puts them to use. The workspace is the fourth part, and it is both a shared room for the team and the server underneath it. GBrain is multiplayer, meaning the whole team works in one workspace rather than in separate accounts, and an invitation to the rest of the team costs nothing extra. Members share the same conversation and the same memory, so context from one person's session is available to the others. On the model side, the workspace uses models from Anthropic and OpenAI and they can be switched at any time, and it can run on your own inference or on GBrain's. Getting started is a sign-in rather than a setup project: the workspace is described as running in about two minutes, with nothing to install. The benefits follow from that structure. A team gets one place where what it has learned lives, rather than a set of disconnected memories inside separate AI tools. Access to email, calendar and the web is granted once and reused by every AI, which removes the repeated key-in-a-config-file work. Scheduled skills mean recurring jobs happen without anyone remembering to start them. Because memory is markdown in a folder the team owns, leaving takes the notes with you, and the open source parts are free to run yourself. On cost, one price covers the workspace and everyone invited into it rather than being charged per person, and a monthly usage credit of $100 covers the AI models the workspace thinks with as well as metered tools, with the workspace telling you before you run out rather than after if you reach the limit. Concrete use cases follow from the parts. A developer writes a note while working in Claude Code, and a teammate using ChatGPT can rely on it without being told separately. Someone connects Gmail once in the workspace, and Cursor can search that mailbox with no key in a config file, so a coding assistant can draw on email context. A team member sets up scheduled work that runs while they sleep, so recurring jobs complete on their own cadence. Several people prompt in the same workspace and share one memory and one conversation, so the context of the team accumulates instead of fragmenting per person. And when a team leaves, they copy the markdown folder so the notes come with them and can be carried to whatever they use next. On pricing and plans, the Product Hunt launch offers the whole workspace at $99 for the first month, then $199 a month, billed monthly, and it can be stopped at any time from the workspace's own billing page. The $99 covers the workspace and everyone invited into it, so adding the rest of the team costs nothing extra, and nothing about the workspace changes when the first month ends. Each month includes $100 of usage credit for AI models and metered tools, and more can be bought from the billing page if heavy use exhausts it. The offer ends on September 29, after which the link stops selling that price, though a workspace started before then keeps the price it started on. GBrain's core promise is a single shared AI memory and one set of connected accounts and skills that every AI a team uses can reach, running in a workspace the whole team prompts together in. The memory belongs to the team as plain markdown files, the accounts are connected once instead of per tool, the work can be scheduled, and the whole thing starts with a sign-in rather than an installation.