Self-improving agents isn’t a single algorithm – it’s a systems engineering problem involving: – eval data curation + maintenance – experiment design to battle overfitting – an update algorithm – human review during the process & especially before prod we share practical learnings + a local research scaffold to autonomously hill-climb harness centered around evals our goal is to give everyone the tooling and infra to measure and iteratively their improve agents. Evals are training data for agents which fuels this loop let's build the future of well-designed, self-improving systems 🚀 Viv (@Vtrivedy10) x.com/i/article/204172946391… — https://nitter.net/Vtrivedy10/status/2041927488918413589#m
SYSTEMS
-
Production Intelligence: Competitive Edge in Industrial Manufacturing
By
–
Production intelligence is becoming a competitive edge. How would you handle this? @IIoT_World @CRudinschi @agentic_factory @IotoneHQ @Softnet_Search @SmartIndustryUS
-
Domain Experts as Essential AI Partners for Actionable Insights
By
–
Their deep, contextual knowledge of physical processes is the ingredient that turns raw data into actionable insight. They're not obstacles, they're essential partners.
-
Hedged Requests Reduce P99.99 DRAM Read Latency
By
–
Hedged requests (apparently inspired by the Tail at Scale paper by myself and Luiz Barroso) applied within a single machine to replicating data across DRAM channels and issuing reads to all channels, using the one that comes back first. ~5-15X reduction in p99.99 read latency.
-
Humachineology: Evolving Technohuman Competencies Framework
By
–
Humachineology and humachinekind provide a framework to study how technohuman competencies have been evolving over time.
-
Building AI Brains: Why Local First Architecture Matters
By
–
Absolutely! If you're building an AI brain, you cannot send the entire data to any API. Local first is a must.
-
Building More Human-Like Artificial Intelligence Systems
By
–
It is about building a more human like AI.
-
LLM Verification Layers in Deterministic Pipeline Systems
By
–
Every LLM output in a deterministic pipeline needs a verification layer. You're essentially building two systems.