This paper presents a multi-agent AI architecture that achieves 92.1% reliability by organizing specialized AI agents into teams with opposing roles and hierarchical oversight, similar to corporate organizational …
GEPA introduces a novel prompt optimization approach that uses natural language reflection and Pareto-based evolutionary search to optimize compound AI systems, achieving superior performance compared to reinforcement …
A randomized controlled study examining how AI assistance affects skill formation in software development, finding that while AI can improve productivity, it significantly impairs conceptual understanding, code reading, …
Solving Complex Optimization Challenges: Lessons from the Listen Labs Berghain Competition A competitive programming challenge reveals the delicate balance between algorithmic strategy and random chance in real-world …
Replacing a $3000/mo Heroku Bill with a $55/mo Server Idealist.org migrated their staging environments from Heroku to a single Hetzner server, cutting costs by 98% while enabling unlimited development environments. The …
How Staff Engineers Navigate Corporate Politics Through Strategic Alignment Corporate politics frustrates many software engineers, but staff engineers who master strategic alignment can drive meaningful technical change. …
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 …
