Put Your Ego Aside and Start Using AI
The biggest thing stopping engineers from adopting AI isn't the tooling or the risk. It's the refusal to accept that an agent can out-produce them. Here's what that costs.
#agentic-ai
Beyond the Prompt: The Architecture of Loop Engineering
Prompt engineering micro-managed text. Context engineering hydrated it. Loop engineering builds the feedback loop the AI runs inside — and the token economy that makes or breaks it.
Anthroipcs announcement, a Brake Pedal, or a Press Release?
Anthropic wants the option to pause frontier AI development. A platform engineer reads the data behind the 'recursive self-improvement' warning — and the timing.
The AI Headcount Panic, Bad Bets, and Lost Knowledge
Companies across the globe—and Israeli high-tech especially—are mass-laying off in the name of AI. But is AI really the reason, or just the best available excuse?
The Infrastructure Wall: Why Your Agent Demo Died in Production
Everyone prototypes an AI agent in a weekend. Almost nobody ships it cleanly. Here's the wall you're about to hit — and how the platform is evolving to remove it.
ClawJacked, Axios, and the Autonomous Agent Problem
A practitioner's field notes on March 2026: OpenClaw's CVE flood, the Axios npm RAT, and why self-hosted autonomous agents are standing in the blast zone.
I Want a Personal Agent. I'm Not Running One Yet — Here's What Would Change That
Part 2: sandboxing with agent-sandbox, evaluating nanobot and nanoclaw, prompt injection realities, and the pre-flight checklist before I trust an autonomous agent.
Andrej Karpathy Just Made RAG Obsolete — And All You Need Is Three Folders
Andrej Karpathy dropped a paradigm-shifting gist on building personal knowledge bases with LLMs — no vector DB, no embeddings, just raw/wiki/output folders. Here's what it means for the rest of us.
The Agent Cost Wars — Updated: GLM-5, M2.7, and What the Leaderboard Actually Tells Us
A follow-up to my MiniMax M2.5 piece — challenging my own assumptions with fresh Artificial Analysis data, GLM-5, M2.7, and what this means for coders in 2026.
The $1,892 Agent: MiniMax M2.5 and the Dawn of Always-On Intelligence
MiniMax M2.5 achieves near-Opus 4.6 performance at 3% the cost. What this means for always-on agents, the SWE-bench, and the falling cost of intelligence.