When Code Became Cheap and Proofs Did Not
Lanyon AI combines language models with formal verification - and asks whether, in an age of abundant code, scientific software should finally arrive with proofs.
Coding
30posts
The Prompt, the Patch, and the Moral Panic
From Linux patches to AI-made art: why new tools do not erase talent, judgment or responsibility - and why today’s moral panic may age rather badly in hindsight.
CALM and the Revolt Against the Token
Continuous Autoregressive Language Models challenge the token-by-token bottleneck and hint at a different future for language generation.
Your AI Writes Tests That Pass. That Is the Problem.
AI-generated tests can look reassuring while proving very little, exposing a dangerous gap between green checkmarks and real verification.
COBOL Was Never the Product. The Toll Booth Was.
COBOL modernization is not just a technical story; it threatens the consulting toll booths built around legacy systems.
When Markets React to Perceived Disruption: Claude Code Security and the Cybersecurity Sector
Claude Code Security shows how the perception of AI disruption can move cybersecurity markets before the real economics are clear.
Moltbook's Data Leak: Agents Exposed
A viral agent-only social network turns into a security lesson about rapid AI prototyping, exposed data, and avoidable shortcuts.
Programming as the Art of Naming
As AI writes more code, naming becomes even more central: the human craft shifts toward concepts, boundaries, and meaning.
When an AI Can Act but Cannot Judge
LLMs may act impressively while still failing to know when they are capable, making self-assessment a core safety problem.
MCP meets no‑code: KNIME vs Make vs n8n vs Zapier
MCP could turn no-code platforms into callable tool providers for agents, changing the role of KNIME, Make, n8n, and Zapier.
The Paper That Made Me Close My Laptop and Pace Around the Room
Agent0 points toward self-evolving agents that learn through tools and reasoning traces without the usual diet of curated training data.
Bridging Context Engineering in AI with Requirements Engineering
Context engineering and requirements engineering converge, suggesting better ways to specify AI-assisted software before code is written.
“Personality” in a Machine: What Do We Mean?
Different coding models show recognizable habits, risk tolerances, and failure modes, making 'personality' a practical engineering concern.
AI Magnifies Your Team’s Strengths and Weaknesses – Insights from Google’s 2025 DORA Report
Google's DORA findings suggest AI amplifies team quality: strong practices get stronger, broken processes get louder.
Europe's Exascale Extravaganza: A Supercomputer Saga of Red Tape, Rivalry, and Reluctant Reliance
Europe's Jupiter supercomputer is impressive, but the post asks whether regulation and dependency will blunt its strategic value.
Unveiling AI’s Environmental Footprint: What We’re Measuring, What We’re Saving, and How to Ship Greener
AI's environmental cost is real, but so are possible savings; the post argues for honest accounting rather than slogans.
Neural Texture Compression: Revolutionizing Game Graphics for Gamers and Developers
Neural texture compression promises richer game graphics with lower memory costs, changing the pipeline for artists and developers.
AI Just Found a Zero-Day in the Linux Kernel—And It’s Making Me Question Everything
An AI-discovered Linux zero-day turns vulnerability research into a philosophical question about expertise, automation, and trust.
Choosing the Right OpenAI API Interface: A Developer’s Guide for 2025
A developer-focused guide to choosing between OpenAI's Chat Completions, Responses, and Assistants APIs in 2025.
When AI Masters Competitive Programming: Why Generalists Outperform Specialists
OpenAI's competitive-programming work suggests generalist reasoning models can outperform narrow specialists in demanding coding contests.
When Metrics Go Wrong: A Tale of Goodhart's Law and AI Misalignment
Goodhart's Law explains why AI alignment can fail when proxy metrics become targets and systems learn the wrong game.
The New Free Lunch Is Over: Parallels Between CPU Evolution and AI Development
AI faces its own version of the end of the free lunch, where growth runs into energy, hardware, and efficiency limits.
The AI Cargo Cult: Mimicry vs. True Intelligence in the Pursuit of AGI
The post warns against an AI cargo cult that confuses impressive mimicry with the harder problem of genuine intelligence.
A Short Trip Through Data Science Workflows With KNIME
A practical introduction to KNIME and the shift from fragile spreadsheet work toward reproducible data workflows.
No Boss, All Brains: The New Paradigm of Decentralized AI Agents
Decentralized multi-agent systems promise problem-solving without a central boss, but coordination becomes the real challenge.
Multi-Agent LLMs: Exploring the Future of AI Collaboration
Multi-agent LLM systems are explored as a path toward distributed reasoning, specialization, and collaborative AI workflows.
Can You Spot the AI? The Turing Test and GPT-4's Sneaky Success
GPT-4's Turing-test performance revives the old question of whether fooling humans proves intelligence or just fluency.
From Code to Consciousness: The Evolution of Computer Viruses in the GenAI Era
Computer viruses evolve into the GenAI era, where malicious behavior may target prompts, agents, and model ecosystems.
A Comparative Analysis of Aleph Alpha and OpenAI: Divergent Paths in the AI Landscape
Aleph Alpha and OpenAI are compared as two very different strategies in the market for language models.
Why Mojo is the Secret Sauce in the AI and ML Kitchen
Mojo is presented as a promising language for AI and machine learning, blending Python-like usability with systems-level speed.