Creating an AI native solution to manage workflows of my live streaming business (https://www.cheerarena.com)
Most workflow softwares are complex to extend & customize. Building an AI native, structured workflow orchestrator from scratch for agentic era.
As a starting point, have designed and implemented an AI native data store to store semantic linked structured input & output data of workflow steps/tasks. These structured input/output act as spec and guard rails for the workflow tasks.
From the blog " Gemini CLI spawns a new process within a pseudo-terminal in the background, leveraging the node-pty library...So how does this virtual terminal running in the background show up on your screen? Think of it like a video stream. Our new serializer takes a snapshot of the pseudo terminal at every moment—capturing every piece of text, every color, and even the cursor's position. These snapshots are then streamed to you, allowing you to see and interact with the terminal application in real-time. It's not just a stream of text; it's a live feed."
An MCP server exposes tools that a model can call during a conversation and returns results according to the tool contracts. Those results can include extra metadata—such as inline HTML—that the Apps SDK uses to render rich UI components (widgets) alongside assistant messages.
Total PRs between Codex vs Cursor is 208K vs 705, this is an enormous difference in absolute PRs. Since cursor is very popular, how does their PRs is not even 1% of codex PRs?.
From the PDF - "One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are "search" and "learning".
The second general point to be learned from the bitter lesson is that the actual contents of minds are tremendously, irredeemably complex; we should stop trying to find simple ways to think about the contents of minds, such as simple ways to think about space, objects, multiple agents, or symmetries. All these are part of the arbitrary, intrinsically-complex, outside world. They are not what should be built in, as their complexity is endless; instead we should build in only the meta-methods that can find and capture this arbitrary complexity. Essential to these methods is that they can find good approximations, but the search for them should be by our methods, not by us. We want AI agents that can discover like we can, not which contain what we have discovered. Building in our discoveries only makes it harder to see how the discovering process can be done."
Working on - "real-time conversations in rich video streaming". Have created rich video composition, mixing, streaming studio (http://www.thecheerlabs.com), working on to bring real-time conversations that can be mixed in real-time for streaming/recording.
VS Code Editor which is based on Electron, is really fast, even with large codebase & many open tabs. Their monaco engine (https://microsoft.github.io/monaco-editor/) uses custom, virtual code processor that is optimized for surgically updating underlying DOM. It also uses WebGL + canvas rendering to show minimap of the file.
Similar approach (custom virtual processor) is leveraged by Google docs/sheets.
Canvas rendering may be the last resort when nothing worked.