If you've been building with large language models, you've hit this wall: every API call requires re-explaining your entire workflow. Financial reports need 500 tokens of formatting rules. Code generation needs another 300 tokens for style guides. Multiply this across thousands of requests, and you're paying twice—once in API costs, once in context window exhaustion....
Category: Technology
OnPrem.LLM: Running private AI on your own terms—no cloud overlords required
The AI revolution has a dirty little secret: most organizations can't actually use it for their most important work. Sure, ChatGPT is great for brainstorming blog post ideas or debugging code snippets, but ask a hospital administrator if they'll send patient records to OpenAI's servers, or a financial services firm if they'll pipe proprietary trading...
The Probabilistic Revolution: How AI is Making Software Engineering More Like “Real” Engineering
For decades, software engineers have endured a peculiar form of professional imposter syndrome. While their colleagues in mechanical, civil, and chemical engineering designed bridges that probably wouldn't collapse and factories that mostly wouldn't explode, software engineers worked in a deterministic paradise where 2+2 always equaled 4, functions returned predictable outputs, and bugs were logical puzzles...
Agent2Agent Protocol Analysis: The Enterprise AI Interoperability Game Changer
The Agent2Agent (A2A) protocol launched by Google in April 2025 represents the most significant standardization effort in AI agent communication, with backing from over 50 technology partners and a clear path to becoming the "HTTP for AI agents." With production-ready implementations expected by late 2025 and projected market valuations reaching $2.3 billion by 2026, A2A...
The Future of Programming is Writing Better Instructions, Not Better Code
Programming is about to undergo a fundamental shift, and it has nothing to do with learning new frameworks or mastering the latest language features. According to Sean Grove, an alignment researcher at OpenAI, the future belongs to those who can write clear specifications rather than clever code. Speaking at a recent developer conference, Grove made...
Software 3.0 Revolution: The AI-Driven Programming Paradigm Shift
The software development landscape has undergone a seismic transformation, with AI coding assistants reaching 76% developer adoption and $45 billion in generative AI funding in 2024 alone. Andrej Karpathy's prophetic Software 1.0/2.0 framework now extends to Software 3.0, where natural language programming has become reality and "vibe coding" is democratizing software creation for millions. This...
Context Engineering: The Real Challenge Behind Building AI Agents
Remember when we thought building AI applications was just about writing clever prompts? Those days feel quaint now. As enterprise AI deployments scale and agents tackle increasingly complex tasks, a new discipline has emerged from the trenches: context engineering. It's not just about what you tell an AI anymore—it's about orchestrating an entire symphony of...
The Art of Metaprompting: How Top Startups Are Engineering Intelligence
Prompt engineering is no longer just about giving commands—it's about crafting intelligence. As AI startups redefine user experience and operational agility, prompt engineering is emerging as the essential interface between human intention and machine performance. From multi-layered architectures to metaprompting, this new craft is shaping the future of intelligent products. From Commands to Conversations: The...
Claude Code Taking Design Lessons from Dieter Rams
Inspired by Peter Gostev's LinkedIn post about his workflow: Data > Claude Code > Visualization > Deploy, I decided to test Claude Code with my own challenge. Peter had analyzed Epoch AI's dataset of 500 AI supercomputers, creating clean visualizations with a simple prompt. Inspired by his approach, I went on transforming a 340-page PDF...
The Jagged Frontier of AGI: Surprising Superpowers, Baffling Failures
AI researchers are finding that today’s most advanced models are superhuman in some ways and stumbly in others. Take OpenAI’s new “o3” model, for example: it aced challenging business tasks in seconds but tripped over a simple children’s riddle. This paradox – uneven, “jagged” performance across tasks – has led Wharton professor Ethan Mollick to...
