Daniel Marin | nexus.xyz's banner

Daniel Marin | nexus.xyz

@danielmarinq • 102,107 subscribers

Founder & CEO @NexusLabs // prev. @Stanford

Shorts

OK the Nexus CLI has some fixes deployed: - Given a Node ID, it runs end-to-end Node ID generation on the Nexus OS is still getting fixed.

OK the Nexus CLI has some fixes deployed: - Given a Node ID, it runs end-to-end Node ID generation on the Nexus OS is still getting fixed.

108,633 Aufrufe

Videos

Anya Rossi

sweetdream.ai

SweetDream.ai•Sponsored•Livecam

Watch Anya Live

Anya is streaming live right now! Join her private show and enjoy exclusive content.

Exclusive private shows

1.2k viewers online

Private Show

Join now for exclusive access

Free preview available • Premium content

1) Full end-to-end AI software automation is near. At Nexus, we have the goal of achieving full end-to-end automation of our software production system by end of year. We are experimenting with this via a fast iteration closed-loop self-improving system. I think it's quite interesting, so I want to share publicly as I haven't seen too many engineering teams talk publicly about findings like this yet. In our tests, each autonomous improvement cycle has the following structure: - 1. Pre-planning: High-level architectural goals - 2. Planning: Medium-level planning and spec authoring, including specs for all tests, benchmarks, regression tests, etc. - 3. Execution: Full end-to-end agentic implementation cycle spanning 1-2hrs of autonomous work. This is the bulk of the productive session where the actual code is written, tested, and progressed. - 4. Evaluation: Complete running of tests, benchmarks, telemetry and profiling of the post-execution state system - 5. Retrospective: Thorough retrospective of the end-to-end run, including telemetry of the agent itself, such as cost, tokens used, times context limits were hit, etc. - 6. Self-improvement: Reasoning based on execution metrics and meta-level metrics about the agentic system itself The view below shows a 3D visualization of 29 generations of a self-improving codebase authored by our E2E automation system

1) Full end-to-end AI software automation is near. At Nexus, we have the goal of achieving full end-to-end automation of our software production system by end of year. We are experimenting with this via a fast iteration closed-loop self-improving system. I think it's quite interesting, so I want to share publicly as I haven't seen too many engineering teams talk publicly about findings like this yet. In our tests, each autonomous improvement cycle has the following structure: - 1. Pre-planning: High-level architectural goals - 2. Planning: Medium-level planning and spec authoring, including specs for all tests, benchmarks, regression tests, etc. - 3. Execution: Full end-to-end agentic implementation cycle spanning 1-2hrs of autonomous work. This is the bulk of the productive session where the actual code is written, tested, and progressed. - 4. Evaluation: Complete running of tests, benchmarks, telemetry and profiling of the post-execution state system - 5. Retrospective: Thorough retrospective of the end-to-end run, including telemetry of the agent itself, such as cost, tokens used, times context limits were hit, etc. - 6. Self-improvement: Reasoning based on execution metrics and meta-level metrics about the agentic system itself The view below shows a 3D visualization of 29 generations of a self-improving codebase authored by our E2E automation system

Daniel Marin | nexus.xyz

40,838 Aufrufe • vor 27 Tagen

The Nexus blockchain, live map In preparation for Testnet III

The Nexus blockchain, live map In preparation for Testnet III

Daniel Marin | nexus.xyz

51,168 Aufrufe • vor 1 Jahr

Keine weiteren Inhalte verfügbar