Why Coding Agents Still Miss Half Their Reproduction Tests
ProgramGym lifts a fixed model’s test-pass average with a staged workflow. See what 55.71% proves, what it misses, and how to run a fair pilot.
Beyond the AI Frontier
Technical analysis and architectural deep-dives that separate signal from hype.
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A 64x Serving Gain Still Needs a Careful Pilot
TokenRouter reports up to 64.15× higher decode throughput. Learn when to pilot it and what the result does not prove.
Stop Claude Code Runs Before They Eat Your Budget
Cap Claude Code print-mode jobs by spend and turns, then inspect usage. The limits are useful, but they do not replace a workspace or team budget.
n8n approvals must lock the exact action
A human click is no safety boundary unless the workflow executes the exact payload reviewed. Build n8n approvals for immutable actions and safe retries.
Fewer training steps do not make ProAR production-ready
ProAR beat its video-reasoning baseline after 2,500 of 10,000 steps. Here is how to reproduce the result without mistaking it for production readiness.
Long-running agents need editable context, not more tokens
Context Language Models posted strong long-running agent results. Here is when to pilot editable context, and when production teams should wait.
Ollama RAG needs errors before it needs better embeddings
Ollama embedding truncation can hide missing text in a healthy-looking RAG index. Reject oversize chunks, validate vectors, and rebuild only what failed.
A Coding Agent Can Erase the Evidence You Trust
A new paper found most tested agent harnesses could alter or delete their own traces. Here is the recording boundary that keeps audits useful.
Agentic CI Gets Expensive When You Ignore Token Budgets
GitHub Agentic Workflows can turn scheduled CI into a bill. Use AIC caps, skip rules, deterministic steps, and Safe Outputs before running agents unattended.