Truth, Beauty, Justice, and the Data Scientist’s Dilemma
A practical reading of why AI agents can automate much of analytics execution without replacing the human judgment that makes data science useful.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Beyond Stack Overflow: CodeAssistBench Exposes the Real Gaps in LLM Coding Help
CodeAssistBench shows why coding assistants that shine on Q&A benchmarks still struggle inside real, recent, multi-turn software support workflows.
Game of Prompts: How Game Theory and Agentic LLMs Are Rewriting Cybersecurity
A practical reading of how game theory and agentic LLMs can reshape cybersecurity architecture, from strategic threat modeling to multi-agent SOC workflows.
Homo Silicus Goes to Wall Street
A comparison-based reading of how leading LLMs answer financial preference questions, and why their synthetic rationality creates a suitability problem for AI finance.
Inside Out: How LLMs Are Learning to Feel (and Misfeel) Like Us
A logits-based method for mapping emotion hierarchies in LLMs turns affective AI evaluation from a label-accuracy contest into a structural audit problem.
Thoughts, Exposed: Why Chain-of-Thought Monitoring Might Be AI Safety’s Best Fragile Hope
A mechanism-first reading of why visible reasoning traces may offer a rare but fragile safety signal for agentic AI oversight.
Causality Pays: A Smarter Take on Volatility-Based Trading
A mechanism-first reading of Vol-TS, a volatility-and-causal-inference trading framework that turns noisy stock movement into directional lead-lag signals.
Memory Games: The Data Contamination Crisis in Reinforcement Learning
A forensic reading of why random rewards can appear to improve LLM reasoning when public benchmarks have already leaked into model memory.
Personas with Purpose: How TinyTroupe Reimagines Multiagent Simulation
TinyTroupe shows why synthetic personas need simulation machinery, not just chatty agents with demographic labels.
Reasoning at Scale: How DeepSeek Redefines the LLM Playbook
DeepSeek-R1 shows that frontier reasoning is less about one brilliant model trick and more about aligning reinforcement learning, verifiable rewards, efficient architecture, and distillation into one disciplined system.