Nav Toor(@heynavtoor):most AI research agents keep the best result and throw away the rest. PRAXIST does the opposite. every useful finding shapes what happens next. competing approaches run in parallel. shared memory connects the evidence. the benchmark results are hard to argue with: → 44% more gold medals than Claude Code + Opus 4.8 → 92% lower token cost → $3,054 vs $38,370 for the same 75-task suite an open-source model outperforming the most expensive setup on earth. at one-twelfth the cost. 100% safe-landing rate in a rocket simulation. industrial SLAM error cut nearly in half. open source starting today. this is how research should work.

2026.08.28 01:53

most AI research agents keep the best result and throw away the rest. PRAXIST does the opposite. every useful finding shapes what happens next. competing approaches run in parallel. shared memory connects the evidence. the benchmark results are hard to argue with: → 44% more gold medals than Claude Code + Opus 4.8 → 92% lower token cost → $3,054 vs $38,370 for the same 75-task suite an open-source model outperforming the most expensive setup on earth. at one-twelfth the cost. 100% safe-landing rate in a rocket simulation. industrial SLAM error cut nearly in half. open source starting today. this is how research should work.

Sapient Intelligence@Sapient_Int

2026.08.28 01:00

Introducing PRAXIST Beta, your autonomous research team🚀 Define the objective, constraints, and what success looks like. PRAXIST discovers the path. From there, PRAXIST takes on the experimental research loop. Multiple Research Peers explore competing approaches in parallel, share useful findings, and build on accumulated evidence across experiments and generations. In partner-provided environments, PRAXIST reached a 100% safe-landing rate in a rocket simulation and reduced accumulated error in an industrial SLAM system from 9.37 centimeters to 5.01 centimeters. From robotics control to quantitative finance, PRAXIST has delivered measurable advances across fundamentally different problems by combining a shared core research architecture with domain-specific tools, knowledge, constraints, and evaluators. More approaches explored. Faster iteration. Greater R&D capacity without proportionally increasing specialist headcount.