For years, the language model arms race seemed to belong exclusively to cloud providers and their API keys. But something remarkable has happened in the past eighteen months: open-weight models have matured to the point where sophisticated, capable AI can now run entirely on consumer hardware sitting under your desk. The implications are profound. Your...
Author: Martin Treiber
The AI slop bucket overflow: “Workslop” is the hidden productivity drain no one’s measuring
There's a new term making the rounds in corporate America, and it perfectly captures a frustration that's been building since ChatGPT entered the workplace: workslop. It's the AI-generated equivalent of that colleague who forwards you a 47-slide PowerPoint deck that somehow says nothing at all, except now it's happening at machine speed, in every department,...
The End of Fine-Tuning? Stanford’s ACE Framework Turns Context Into Intelligence
Researchers have long assumed that making models smarter meant touching the weights—fine-tuning, retraining, re-baking billions of parameters until the model finally bends to your task. But what if that entire paradigm—expensive, opaque, and rigid—was becoming obsolete? We've heard "fine-tuning is dead" before, usually from researchers overselling their latest trick. But a new framework from Stanford...
Your AI Chatbot Is Actually a Computer—And You’ve Been Programming It Wrong
Here's something that might rewire how you think about AI: that chatbot you've been treating like a know-it-all friend? It's actually a virtual machine. And every time you type a prompt, you're writing a program. I know, I know. It feels like having a conversation. The model responds in natural language, occasionally tells jokes, sometimes...
AI Can Write Cost Sheets in Minutes. Accurate Ones? That’s Harder.
The promise is clear: Feed an AI your project description, employee data, and supplier catalog, and it will generate a detailed cost sheet in minutes instead of weeks. No more spreadsheet archaeology. No more chasing down procurement for vendor quotes. In theory. Several research teams and startups are already building this future. Early results suggest...
The Art of Building AI Agent Tools: How MCP is Reshaping Software Development
For the past two years, the AI industry has been obsessed with model capabilities—bigger context windows, better reasoning, multimodal understanding. But an uncomfortable truth is emerging: even the most sophisticated models are hamstrung by their isolation from real-world data and tools. The bottleneck isn't intelligence; it's integration. Enter the Model Context Protocol (MCP), Anthropic's answer...
From Alchemy to Architecture: The Evolution of Prompt Engineering
In 1779, the world's first major iron bridge opened over the River Severn in Shropshire, England. Its architect, Thomas Farnolls Pritchard, and builder Abraham Darby III faced a unique challenge: applying an entirely new material—cast iron—to bridge construction at unprecedented scale. While some structural theory existed by the 1770s, Pritchard relied heavily on carpentry methods,...
Vibe coding is broken. Could Controlled Natural Language – CNL – save it?
Six months ago, “vibe coding” was supposed to change everything. Tell the AI what you want, sit back, and watch it generate working software. Andrej Karpathy, ex-Tesla and OpenAI, hyped it as the future: forget syntax, just describe your intent. The demos were intoxicating. Startups bragged about entire codebases written by GPT-like copilots. Prototypes spun...
The Rise of Agentic Vibe Coding: How Replit Agent 3 is Redefining Software Development
When AI researcher Andrej Karpathy floated the phrase “vibe coding” in a 2025 tweet, describing a style of programming where you “give in to the vibes” and focus on intent rather than syntax, he crystallized a cultural shift already underway. His post went viral, resonating with developers willing to let AI assistants take the wheel....
The New Unit Test: How LLM Evals Are Redefining Quality Assurance
Picture this: you've written a function that calculates the square root of a number. Feed it the input 16, and you'll get 4 back every single time—guaranteed. This predictability is the bedrock of traditional software testing, where unit tests verify that each piece of code behaves exactly as expected with surgical precision. Now imagine a...
