A-TIER tool
Free super agent production team?
100k ft — What is this?
Free super agent production team?
50k ft — Why care?
Free super agent production team? — @indibrief; demonstrates AI agents.
10k ft — Key takeaways
(no takeaways yet)
5k ft — Tools & techniques
(no tools tagged)
AI agents, automation
1k ft — Deep-dive / try it
Free super agent production team?
Bytedance, the company behind TikTok, just open-sourced (for free) a full agent system that might outperform tools AI companies are charging a premium for.
It can research, write code, build websites, create decks, even generate video and it runs locally.
Most tools still rely on rigid, sequential pipelines with bottlenecks and heavy communication overhead.
DeerFlow 2.0 changes that:
dynamic task graphs, parallel agents, shared state, and a validation loop that reprocesses or reassigns work.
It doesn’t just generate, it executes (runs code, writes files, uses real outputs).
I tested it. It works.
Comment “code” and I’ll send it.
#AIagents #DeerFlow #AINews #AIResearch #FutureofAI
Transcript excerpt
SpyDance just released, for free, code that does what companies are trying to charge a premium for. Putting pressure on anyone without any real differentiation, they built a complete agent automation system and open-source it. It does research, writes code, builds websites, creates decks, even generates videos, all by itself, and it runs on its own machine. 100% open-source. Agent orchestration is not new. It already existed with auto-gen, land graph, and open-clore. But the problem has always been execution. In practice, most of the systems still behave like pipelines. Predefined steps, fixed order, and limited adaptation during runtime. Even with multiple agents, there is strong sequential dependency, and message passing overhead that creates bottlenecks. Dear Flow 2.0, by SpyDance, changes how execution is handled. Given an objective, the system builds hierarchical task structure. This is not a fixed list. It can expand or reorganize during execution, based on intermediate results. Each part of this structure is assigned to different agents. These agents run in parallel with local context, but share a global state that is continuously updated. Commodination happens through this shared state, not direct message passing. This reduces reordering constraints, and allows independent progress across different parts of the system. There's also an internal validation loop. Outputs are not treated as final. They are evaluated, and if necessary, reprocessed or re-assigned.…