Agent prompt injections: Stop trusting README files
Mitchell Hashimoto seeds AGENTS.md with traps to prove agents blindly trust external text. Learn why 1 corrupted file causes RCE.
Framework reviews, autonomous coders and multi-agent systems — tracked and explained by the AI Agents News desk.
Mitchell Hashimoto seeds AGENTS.md with traps to prove agents blindly trust external text. Learn why 1 corrupted file causes RCE.
Commit 0c522c2 locks the agent server image to version 1.23.1. I break down why this hard stop on drift matters for your Docker sandboxes.
Poor agent harness design drives costs to $2.26 per task. Learn why closed-loop feedback prevents silent failures in autonomous systems.
Silent failures cause 74% of rollbacks. Learn where agent loops diverge from reality in the trace and fix state mismatches before refunds fail.
Running a Llama 3.3 70B model locally now hinges on memory bandwidth rather than raw GPU compute, according to June 2026 performance data.
Short prompts of 250 tokens keep models in peak form while longer inputs cause measurable degradation in output quality and speed.
Testing five small models on an Intel i5 revealed the LFM2.5-350M hits 36 tokens/sec, bypassing memory limits for local CPU inference.
Stop iterative guessing by applying a strict 5-block prompt architecture that shifts probabilistic model outputs toward accurate, stable results.
Maria Perez-Ortiz's Planet-Centered AI paper has one buildable payload: monitorability and trajectory-oriented evaluation. Why greener compute misses the point.
See how parallel routing across 47+ providers delivers 60% cost savings while neutralizing hallucinations in complex agentic loops.
Running queries across 47+ providers in parallel allows the A3M Router to deliver 60%+ cost savings while simultaneously reducing hallucinations.
Analysis shows parallel routing across 47+ providers drives 60% cost savings while eliminating single points of failure in AI systems.