Agent Swarms Gather 1500 Data Rows in Hours via Specs
Kimmy agent swarms parallelize data collection (1500 US data centers or 300+ model releases since 2020) from 6-8 hours per agent to minutes of oversight, using 2-3 page markdown specs, then K2.6 builds websites from Excel.
Parallelize Massive Data Collection with Agent Swarms
Collecting 1500 rows on US AI data centers or 300+ model releases (name, API cost, context window) since 2020 takes a single agent 6-8 hours of web searches, validation, and repetition. Agent swarms cut this by launching waves of sub-agents, each assigned a research domain; they report structured data back to the main agent until complete. Spend 5-10 minutes upfront defining a 2-3 page markdown spec (AI-assisted) outlining task parameters, data fields, and validation rules—then let swarms handle the rest while you multitask. Output: clean Excel files ready for analysis or visualization, reducing human effort from days to near-zero oversight.
Spec-Driven Development Trumps Vague Prompts
Vague prompts like "gather all US data centers into Excel" waste tokens and fail; instead, craft detailed markdown specs (2-3 pages) specifying exact columns (e.g., location, size, AI focus), sources, validation steps, and output format. This mirrors spec-driven development: architect first in documents, then execute. For larger scopes or longer horizons, specs ensure reliability over iterative chatting. Same for website generation—don't say "build a site from this Excel"; detail tech stack (e.g., HTML/CSS/JS), page structure, UI components, and architecture in markdown. Result: polished sites with breakdowns, charts, and filters from raw data.
Leverage K2.6 for Long-Horizon Coding and Optimization
Kimmy's K2.6 excels at extended tasks, scoring 58.6% on Swebench Pro (top-tier) and outperforming K2.5 in UI/UX for data viz sites from identical prompts/Excel. Use Kimmy CLI for raw coding: prompt K2.6 to ingest Excel and output full sites. For inference boosts, K2.6 optimized Qwen 3.5 0.8B on M3 Max from 15 to 193 tokens/second (20% above LM Studio baseline) over 12 hours. Trade-off: upfront spec time pays off for complex projects but skip for quick iterations; scales agent collaboration as AI handles more end-to-end work.