Showing 1-11 of 11
Learn a practical pipeline to measure run-to-run agent consistency (R=5), log full trajectories, and inject short ALTK-Evolve guidelines at inference to reduce flip failures.
Public AI leaderboard scores are easy to read but can be gamed and misrepresent real-world performance. Learn how to use them to shortlist models, then verify on your own data.
Practical guide to running Terminal-Bench-Science v0.1: reproduce the 70-task benchmark, compute resolution rates (top model ≈30%), and build a small audit-ready evaluation pipeline.
Guide to reproduce the Neutrality Project Release 01: run 18 models on six political axes, record per-axis means, refusal rates, and 95% confidence intervals.
Short analysis of JamDesk's AI Score Chrome extension: the Web Store listing verifies the tool and UI. Treat its numeric grade as a triage signal, not a replacement for human tests.
Reproducible token-level tests comparing Olmo Hybrid and Olmo 3 show hybrids better on meaning-bearing tokens (nouns, verbs, adjectives, coref), transformers win on verbatim copy.
Build a repeatable harness that records agents' plan steps, API calls, retries, tokens, wall time and cost to reveal friction points in your library and guide rollout decisions.
Use OrgForge to create seeded, reproducible synthetic corporate datasets (JSON/CSV) for testing AI agent workflows — run quick small scenarios or larger stress tests without real PII.
Guides running VAKRA's runnable benchmark—8,000+ local APIs across 62 domains—to record full execution traces, reproduce common multi‑step agent failures, and guide focused fixes.
Build an AI-chat evaluation harness for He Xin’s PEPC formal language to test expressiveness, contradiction handling, and alignment with wargame baselines — includes artifacts and metrics.
Reproducible tutorial to build an APEX-Agents-style test harness measuring AI agents' ability to stitch context across Slack and Google Drive. Includes configs, logs and rollout gates.