SourceVane · Practical AI
Choose an AI workflow by cost, data handling or local deployment. Start with a practical decision and follow the source-reviewed guide.
Put the news to work
Measure workload, retries and accepted outputs before comparing models, batching or reusable prompts.
Work through account policies, retention, local records and access boundaries before sharing team data with an AI workflow.
Check model routing, server access, external traffic and release changes before depending on a local AI workflow.
Claude changes, cost choices and data checks · Ollama changes and local deployment checks · DeepSeek models, API prices and operating conditions
See what happened next · Compare verified API prices · Estimate a workload · Read the weekly index · Get future updates
Source-reviewed guide
Ollama 0.33.3 adds cached-prompt token reporting and Gemma 4 image/audio support on MLX. For local-AI teams, those changes warrant an upgrade check—not a claim that every model runs faster.
Source-reviewed guide
Claude batches trade immediacy for lower request costs; prompt caching targets repeated prefixes. For small API teams, the choice starts with waiting time and input reuse—not the discount headline.
Source-reviewed guide
Ollama can route cloud-model requests through a local endpoint. Teams handling sensitive inputs should verify model routing and cloud settings—not assume localhost alone defines the data boundary.
Source-reviewed guide
Claude Code’s data rules depend on the account and deployment. For teams sharing repository context, “not used for training” is only one check—not a complete retention or access policy.
Measurement guide
Count failed attempts, final successful tasks and token use separately so retries do not hide the real cost of useful API work.
What happened next · API prices · Decision guides · Latest AI coverage · Follow updates
