ReasonKit Resources
Technical whitepapers, benchmarks, guides, and insights on structured AI reasoning.
Why AI Needs Structured Reasoning: The Case for Protocols Over Prompts
AI gives you answers fast. But how do you know they're good? Most LLM responses sound confident but skip the hard questions. We built ReasonKit to fix that: five tools that force AI to think systematically, explore all angles, and expose its assumptions before giving you a conclusion.
The Five ThinkTools: A Deep Dive
GigaThink, LaserLogic, BedRock, ProofGuard, and BrutalHonesty. What each tool does and when to use them.
Building Reliable AI Agents
How to create AI agents that don't hallucinate, verify their work, and admit when they don't know.
Choosing the Right Reasoning Profile
Quick, Balanced, Deep, or Paranoid? Match your analysis depth to your decision stakes.
The Science of Structured Reasoning (2025)
Comprehensive 88-page analysis of CoT, ToT, o3/o4-mini, GPT-5.2, DeepSeek R1, and test-time compute scaling. 47+ peer-reviewed papers synthesized with benchmark comparisons and implementation guidance.
The Architecture of Auditable AI
Technical deep-dive into building AI systems with full reasoning traces, confidence metrics, and verifiable outputs.
Building in Public: Week 1
Launch reflections, first users, and why we chose Rust over Node.js for our MCP servers.
Why Tree-of-Thoughts Matters
The cognitive science behind structured reasoning and how ReasonKit implements it at scale.
CB Insights: Top Startup Failure Reasons
Analysis of why 70% of startups fail and how structured decision-making can prevent common pitfalls.
Tree of Thoughts: NeurIPS 2023
Deep dive into Yao et al.'s foundational paper on deliberate problem solving with LLMs.
Self-Refine: Iterative Refinement
How self-feedback loops improve LLM outputs without additional training data.
Building ReasonKit in Public
Weekly updates from Len P. van der Hof, MSc on the journey of building a cognitive engineering platform.