Proof of Thought: Neurosymbolic Program Synthesis for Robust AI Reasoning Large language models excel at generating human-like text but struggle with consistent logical reasoning, especially in novel domains. Proof of …
Top Programming Languages 2025: IEEE’s Annual Rankings and the AI Impact IEEE Spectrum’s 2025 programming language rankings reveal Python maintaining its dominance while sparking debate about whether AI …
Getting AI to Work in Complex Codebases Large language models promise to revolutionize software development, but most developers struggle to apply them effectively to real production codebases. The challenge isn’t …
Markov Chains: The Original Language Models Markov chains represent the foundational approach to language modeling that predates modern LLMs by decades. Understanding their mechanics reveals both the evolution of text …
Sampling and Structured Outputs in LLMs Large language models generate text token by token, but production applications need structured data. Modern constraint systems solve this by masking invalid tokens during …
Scaling AI Application Development at BlackRock: Building Custom Knowledge Apps for Investment Operations BlackRock’s engineering team compressed AI application development from 8 months to days by building a …
Engineering AI Systems That Endure: Beyond the Bitter Lesson Building reliable AI systems while navigating weekly model releases and evolving techniques requires rethinking how we apply engineering principles to …
Stripe Launches Tempo: A New L1 Blockchain for Stablecoin Infrastructure Stripe has entered the blockchain space with Tempo, a Layer 1 blockchain designed specifically for stablecoin payments and real-world business …
Engineering Effective AI Evaluations: Lessons from Production LLM Deployments Building robust AI applications requires more than good prompts—it demands systematic evaluation frameworks that enable rapid iteration and …
The Bitter Lesson: Why Computational Scale Beats Human Knowledge in AI Seventy years of AI research reveals a fundamental truth: general computational methods ultimately outperform approaches based on human knowledge. …
