My take
Why I use Claude
Claude - especially Opus on long context - handles tasks GPT-4 doesn't reliably. The 1M context window genuinely changes what's possible, and the tone and judgment are calibrated well for production.
Want the broader stack philosophy? Read about how Sri picks tools or browse engineering insights.
Honest assessment
Strengths & tradeoffs
No tool is perfect. Here's what shines and what to watch for.
Strengths
- 1M-token context on Sonnet/Opus tiers
- Strong long-document reasoning
- Excellent code generation and review
- Tool use and computer use
- Three-tier model family for cost/speed tradeoffs
Tradeoffs (honestly)
- Top-tier models are expensive per token
- Streaming and structured output APIs evolve quickly
- Some integrations still OpenAI-first
Fit assessment
When to reach for Claude
Pick the right tool for the job.
Best fits
Reading entire codebases or large doc sets
Coding assistants and Claude Code workflows
High-stakes enterprise reasoning
Long-running agents with tool use
Not ideal for
Image generation
Hyper-low-latency completion at scale (consider Haiku or smaller models)
Common use cases
Resources
Learn more
Curated official docs, tutorials, and writing on Claude.
Services
Where I apply Claude
Engagements where this technology shows up regularly.
Stack
Pairs well with Claude
Tools and platforms I commonly combine with this one.
AI & ML
More in this category
Model providers, frameworks, and stores that power my AI work.
Need help with Claude?
Whether you're starting fresh or optimizing an existing implementation, I can help you get the most out of this technology. Read more in insights or get in touch.