#Coding

AIBlock's AI capabilities lead describes using Claude Fable to plan large code migrations and direct smaller models like Opus and Sonnet on individual tasks. He says Block routes frontier and smaller models by task and keeps merges and production deploys behind human dual approval.

Why it matters: Block's engineering lead describes how frontier models orchestrate large migrations and how access, effort levels, and safeguards are managed across an organization.

AICognition's 2025 performance review says Devin works best on clear, verifiable tasks such as migrations, vulnerability fixes, and unit tests. The company reports a 67% PR merge rate, up from 34% last year, and cites a bank that cut migration time per file from 30-40 hours to 3-4 hours. It also says Devin struggles with ambiguous requirements, mid-task scope changes, and soft-skill work that still needs human engineers.

Why it matters: The report pairs concrete migration, vulnerability, and test-coverage figures with named weaknesses, letting engineering leaders judge where an agent fits in their own workflow.

AICognition tested OpenAI's o1-mini and o1-preview in a simplified Devin-Base agent, comparing them with GPT-4o on its internal cognition-golden benchmark. The chart reports Devin-Base scores of 25.9% with GPT-4o, 34.6% with o1-mini, and 51.8% with o1-preview, versus 74.2% for the production Devin. The post also describes the benchmark's realistic environments, simulated users, and agent-based evaluation.

Why it matters: The post explains how Cognition evaluates coding agents with autonomous, environment-based tests, which shows how base-model swaps are measured in practice.