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@KaitoAI yapper & sharing knowledge crypto

[a16z @a16z]

We asked a16z's investors for their takes on the biggest problems builders will tackle in 2026. Here's part 1 of Big Ideas 2026: https://t.co/o1CJ0v5STb

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@stelladaily_ @brevis_zk @vana if $btc proves the authenticity of data by running nodes to validate it, how does brevis validate it?

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@stelladaily_ @brevis_zk gbrev

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@Defiwithguru @billions_ntwk @jgonzalezferrer Gbill

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will there be a big move for $BTC

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@jgonzalezferrer @cysic_xyz amazing ✨ how you did it

[0x Magnum @0xMagnum]

With such a strong @SentientAGI foundation, where is sentiment headed? Bullish or bearish?

Meanwhile, large-cap projects in every sector are always an indicator of where the narrative is headed. With the halving $TAO, will it be bullish for the AI sector?

[0x Magnum @0xMagnum]

The AI ​​narrative is making a comeback.
Meanwhile, @SentientAGI is building AI capable of handling increasingly complex tasks.

Why It Matters

Long-horizon tasks are challenging for AI agents because small errors can accumulate and derail the entire process. Sentient AI aims to address this challenge by designing a systematic meta-agent architecture that allows complex tasks to be managed in a modular and structured manner.

So What is it?

Sentient's “ROMA: The Backbone for Open-Source Meta-Agents” introduces an open-source framework called ROMA (Recursive Open Meta-Agent) for building robust multi-agent systems that handle complex, long-duration tasks.

Key Points

ROMA provides a hierarchical and recursive structure: large tasks are broken down into subtasks by a "parent node," which then passes those subtasks to "child nodes," and the final results are compiled from the bottom up.The workflow includes:

Atomizer — evaluates whether the task is simple enough to run immediately.

Planner — if not, the node breaks the task into subtasks.

Executor — child nodes execute subtasks using appropriate agents or tools.

Aggregator — collects and synthesizes the results from child nodes to produce the final output.

Key advantages of ROMA:

Context flow transparency and traceability, making it easier to debug, develop prompts, and replace agents/tools.

Modularity: You can configure any agent, model, or tool on any node, including human-in-the-loop integration.

Supports parallel execution when subtasks are independent, which means performance can be improved for large tasks.

https://t.co/4YFieBhlkg

[0x Magnum @0xMagnum]

@KoroushAK Yes

[0x Magnum @0xMagnum]

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