A more radical vision to try when we have enough funding and people: Record all meetings → generate subtitles/transcripts → generate PRDs → generate TDDs → generate code drafts.
"A more radical vision to try when we have enough funding and people: Record all meetings → generate subtitles/transcripts → generate PRDs → generate TDDs → generate code drafts."
"Use browser-use as an intermediary to forward all internal knowledge (including codebases) to Gemini Web. This improves productivity for all internal work. Combine with new tools like Flowith and Manus. This internal work platform will become the primary interface for every role and every team member."
2. Why This Pipeline Is Particularly Valuable for Small Teams
For new teams and small teams:
Less Historical Debt Not so many "legacy systems" or "historical baggage," can quickly collect all knowledge into this platform
Can 'AI-ize All Links' Large companies might only innovate in 10 links, small teams can AI-ize 100 links, and the final overall efficiency will far exceed large companies
High Knowledge Density Small teams' knowledge is more concentrated, easier for AI to fully understand and process
The core value of this pipeline is:
Transform 'Unstructured Knowledge' into 'Structured Assets' Meeting recordings, discussions, design drafts—knowledge that used to be "scattered everywhere"—can now be automatically converted into part of the codebase
Reduce 'Information Loss' In the past, decisions, discussions, and design ideas from meetings might only be remembered by attendees. Now they can all be recorded, letting AI help with subsequent development and maintenance
Make 'Knowledge Work' Also 'Automate-able' Not just writing code, but even "understanding requirements," "designing systems," "writing documentation"—these tasks can also have AI participate
3. Technical Implementation Path
Core Toolchain:
Meeting Recording and Transcription
Use existing meeting tools' (Zoom, Tencent Meeting, etc.) automatic transcription features
Or use specialized transcription services
Browser Use as Intermediary
Browser Use can let AI access internal systems, codebases, documentation libraries
Forward transcribed text, codebase, and related documentation together to Gemini Web
Gemini Web as 'Knowledge Processing Center'
1 million token context, can load the entire project's knowledge at once
Let AI understand the relationship between "meeting discussion content" and "existing codebase/documentation"
Flowith, Manus, and Other Tools as 'Workflow Orchestrators'
Each link can have human review and confirmation checkpoints
4. Another Use Case: Batch Generation of Operations Content
Besides the "meeting → code" pipeline, there's another practical use case:
"Use generic MCP, integrate tools like Manus and Flowith. For example, operations needs to produce 100 scenario contents. Agent automatically completes this task. After human confirmation passes, it can be imported into our platform. This Agent will become the homepage of everyone's browser—the starting point of all work."
This use case demonstrates:
AI Can Handle 'Repetitive, Batch' Work Operations needs 100 scenario contents. Humans writing would be exhausting, but AI can batch generate
Humans Only Need to 'Confirm and Gatekeep' Generated content, after human review passes, can be directly used
This Agent Can Become a 'Work Entry Point' Not just writing code, all work that needs "batch content generation" can be completed through this Agent
5. Challenges and Limitations of This Pipeline
Main Challenges:
Quality Control
AI-generated PRDs, TDDs, and code might have unstable quality
Need humans to carefully review at each link—cannot be "fully automatic"
Information Accuracy
Meeting recording transcriptions might have errors
Transcription → PRD conversion might lose critical information
Need humans to confirm accuracy at each link
Context Management
If project scale exceeds 10M token, Gemini Web also cannot load it all at once
Need more complex context selection strategies
Practical Implementation Recommendations:
Start with Small-Scale Experiments First experiment with this pipeline in a small project, small team, accumulate experience
Each Link Must Have Human Checkpoints Cannot be "fully automatic." Content generated at each link must be confirmed by humans
Regular Review and Optimization If you find a certain link always has problems, adjust the pipeline design
6. The Future of This Pipeline
Although this pipeline is still in the "radical imagination" stage, as AI capabilities improve, it might become the "standard configuration" for AI-Native teams:
Meeting Recordings Automatically Convert to PRD After meetings end, PRD is automatically generated. Humans only need to review and refine
PRD Automatically Converts to TDD Technical design documents can also be automatically generated. Humans only need to confirm technical solution reasonableness
TDD Automatically Converts to Code Code drafts are automatically generated. Humans only need to review and test
All Knowledge Becomes 'Searchable, Understandable, Reusable' Knowledge that used to be scattered everywhere now becomes part of the codebase, and AI can call it anytime
7. Summary
Meeting recording → PRD → TDD → code—this complete knowledge workflow pipeline, although still in the "radical imagination" stage, demonstrates a possible future for AI-Native teams:
All knowledge work can be participated in and accelerated by AI. Humans no longer need to "manually organize requirements," "manually write documentation," "manually write code." Instead, let AI do it first, and humans only need to review and refine.
For small teams and new teams, this pipeline is particularly valuable because:
Less historical debt, can quickly implement
Can "AI-ize all links," efficiency improvement will be very obvious
High knowledge density, AI can fully understand and process
Although it's still in the stage of "can start experimenting when we have enough funding and people," as AI capabilities improve and toolchains mature, this pipeline might become the "standard configuration" for AI-Native teams.
