Clear, practical breakdowns of the AI papers and ideas that matter: agents, reasoning, safety, multi-agent systems. Written for practitioners, not academics.
SWE-Bench scores tick up every quarter, but production failure rates aren't dropping. A METR study found half of test-passing PRs wouldn't be merged. The more capable we make agents, the less reliably they behave.
Your agent framework doesn't matter if the model underneath it can't call tools reliably. We tested and ranked eight open-weight models specifically for agent use cases: tool calling accuracy, multi-step reasoning, context retention, hosting economics, and licensing terms.
Compare single-agent and multi-agent architectures on complexity, cost, debugging, and when orchestration helps.
Compare EU AI Act, US, and UK AI regulation on compliance, penalties, timelines, and impact on developers.
Compare RAG, long-context windows, and fine-tuning on accuracy, cost, latency, and production readiness.
Compare Llama 4, Qwen 3, and DeepSeek V4 open-weight models on benchmarks, context windows, licensing, and deployment.
Compare Model Context Protocol, Agent-to-Agent Protocol, and Agent Communication Protocol on transport, authentication, tool discovery, and real-world adoption.
The three orchestration patterns proven in production: sequential pipelines, parallel fan-out, and evaluator-optimizer loops. Trade-offs and kill-switch design.
Build reliable agent workflows with OpenAI Agents SDK: traces, tool-call guardrails, handoffs, retries, and deployment checks.
Mixture of Experts models are cheaper per token. That's the headline every vendor leads with. But 'cheaper per token' and 'better for your workload' aren't the same thing.
From the team behind Swarm Signal
Budget trackers, business planners, and productivity templates — built by the same team. No subscriptions, no fluff.
Queue is empty. Click "+ Queue" on any article to add it.