I’ve been skeptical of every “game-changing AI release” for the past two years. Every few months, a new model drops and Twitter explodes with claims that AGI is here. Spoiler: it never is. But when Anthropic released Opus 4.5, something actually shifted in how I work. Not because it’s AGI—it’s decidedly not—but because it’s the first model that consistently delivers on complex, multi-step reasoning without falling apart halfway through.

This isn’t a hype piece. This is a practitioner’s field notes from someone who uses these tools daily to ship product at Entropy Labs.

What Makes Opus 4.5 Different

The headline feature is extended thinking—the model’s ability to spend more compute on harder problems before responding. But that undersells what’s actually happening.

Most models optimize for fast, plausible responses. Opus 4.5 optimizes for correct responses, even if it takes longer. The difference becomes obvious when you’re debugging a gnarly race condition or trying to understand why your database query is slow despite having all the “right” indexes.

Here’s what I noticed after a month of daily use:

1. It actually reads the context you give it. Previous models would skim your codebase dump and give generic advice. Opus 4.5 will reference specific lines, notice inconsistencies between files, and ask clarifying questions that show it understood the architecture.

2. It reasons through edge cases unprompted. Ask it to implement a feature, and it’ll often say “but what about X scenario?” before you’ve thought of it yourself. This used to be GPT-4’s strength; Opus does it better.

3. It admits uncertainty differently. Instead of confident hallucinations, it’ll say “I’m not certain about the API behavior here—let me reason through what the docs suggest.” That hedging has saved me hours of debugging bad suggestions.

The Art of Context Control

Here’s the thing most people get wrong: Opus 4.5 isn’t magic. It’s a tool that scales with how well you wield it. The unlock isn’t the model itself—it’s understanding that you need to provide context, not instructions.

What doesn’t work

"Write me a function to handle user authentication"

This gives you generic boilerplate. The model has no idea about your stack, your security requirements, or your existing patterns.

What works

"Here's our current auth setup [paste relevant files]. We're using
Django with DRF, JWT tokens stored in httpOnly cookies, and we
have a custom User model. I need to add support for API key
authentication for programmatic access. Our existing pattern for
middleware is in auth/middleware.py."

The model now understands constraints. It’ll generate code that fits your patterns, not generic Stack Overflow answers.

At Entropy Labs, I’ve started maintaining a CLAUDE.md file in every project—a living document that describes the architecture, conventions, and gotchas. When I start a Claude Code session, that context loads automatically. The quality difference is night and day.

The context hierarchy

  1. Project-level context: Architecture docs, conventions, tech stack
  2. Session-level context: What you’re trying to accomplish, relevant files
  3. Query-level context: Specific question with enough detail to answer precisely

Most people only provide #3 and wonder why responses are generic.

When It Fails (Yes, It Does)

Let’s be real: Opus 4.5 still hallucinates. Here are failure patterns I’ve observed:

Package versions and APIs: It confidently suggested using a deprecated Anthropic API three times in one session. When I gave it the actual docs, it apologized and fixed it. Always verify against current documentation.

Complex async flows: Give it a sufficiently tangled async/await scenario with multiple event loops, and it can lose track. It’s better than previous models, but not foolproof.

Domain-specific knowledge cutoffs: Its training data has a cutoff. Ask about a library released last month, and it’ll make educated guesses that are sometimes wrong.

Cost: Extended thinking uses more tokens. A complex debugging session can easily burn through $10-20 in API credits. At Entropy Labs, we’ve implemented caching for common queries to manage costs.

The pattern I’ve learned: trust but verify. Use Opus for the heavy lifting of understanding codebases, generating first drafts, and reasoning through architecture. But run the code, check the docs, and test the edge cases.

Practical Setup with Claude Code

Here’s my actual workflow:

1. Install Claude Code CLI

brew install claude-code
claude auth login

2. Create your project context

Create a CLAUDE.md in your project root:

# Project: Entropy Dashboard

## Stack
- Backend: Django 5.x, DRF, Celery
- Frontend: Vue 3, Pinia, TailwindCSS
- DB: PostgreSQL 16, Redis
- Infra: AWS ECS, RDS, ElastiCache

## Conventions
- Use class-based views for DRF
- Pinia stores follow the composition API pattern
- All API endpoints versioned under /api/v1/
- Tests use pytest with factory_boy

## Current Pain Points
- N+1 queries in /api/v1/reports/ endpoint
- WebSocket reconnection logic is flaky

3. Use it conversationally

Don’t one-shot complex tasks. Build up context through conversation:

"I'm debugging slow report generation. Can you read the
reports/views.py and reports/serializers.py to understand
the current implementation?"

[Claude reads and summarizes]

"Okay, now let's profile this. What queries should I look
for in django-debug-toolbar?"

[Discussion continues, building shared understanding]

This back-and-forth is where Opus shines. It maintains context across a long session better than any model I’ve used.

The Bottom Line

Opus 4.5 isn’t AGI. It’s not going to replace engineers. But it’s the first AI tool that consistently makes me faster at complex tasks rather than faster at generating code I need to rewrite.

The real unlock is treating it as a collaborator that needs explicit context, not a magic box that reads your mind. Give it the context. Let it reason. Verify the output. Iterate.

If you’re still prompting AI like it’s a search engine, you’re missing the point. These models reward investment in context the same way a good colleague rewards clear communication.

Now if only it could attend my standup meetings for me.