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Clone the ai-native-boilerplate and add a compact, versioned ruleset (160+ rules across six layers). Use pre-prompts or post-validators to make agent outputs predictable and auditable.
Step-by-step pilot to evaluate WeatherNext 3: run 0–24h forecasts for 1–3 locations, compare to truth data, check latency/null-rate, and publish validated forecasts.
I ran 11 Claude subagents to run 11 PPTX skills on the same 5-slide brief. Many skills rasterize tables/charts; experiment shows which ones produce editable, fast decks.
GrowthSpree rewired its agency around AI - AEO demand capture, cohort-led ABM, and a retrieval server - then broke a multi-year plateau to reach $1.5M ARR in six months.
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.
Step-by-step guide to set up TreasuryBench, a repeatable benchmark that tests personal-finance AI assistants with synthetic personas, saves run metadata, and makes comparisons auditable.
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.
An AWS Strands Agents design moved extraction, summarization and caching out of prompts into deterministic tools, cutting measured LLM token use by ~96% and lowering costs, privacy risk.
Step-by-step prototype to run multiple LLMs in parallel, use token-level confidence (logprobs/entropy) to weight and stitch outputs, and reproduce Sup AI's HLE gain (52.15% vs 44.74%).
Run Hive Memory locally to give AI coding agents persistent, cross-project context and session history (JSON/Markdown on disk at ~/.cortex). Use MCP clients like Claude Code or Codex.
Teams are moving from hand-coding to composing AI agents. This post explains practical impacts - new failure modes, hiring shifts, testing thresholds - and a 1-2 sprint checklist.
Moruk OS is an open-source, local Linux platform for autonomous AI agents. It breaks goals into subtasks, runs code, supports multiple LLMs, plugins, and local memory.