Anthropic: Claude 3 Haiku

Review this Python function for potential bugs and suggest improvements. Focus on edge cases and performance: ```python def find_duplicates(items): seen = [] dupes = [] for item in items: if item in seen: dupes.append(item) seen.append(item) return dupes ```

This function has a critical performance issue: using `item in seen` on a list creates O(n²) complexity. For large inputs, this becomes prohibitively slow. Edge case: if `items` contains unhashable types like dictionaries, the function works but remains inefficient. Suggested fix: replace `seen = []` with `seen = set()` for O(1) lookups. However, this means `dupes` might lose insertion order for duplicates. If order matters, use `seen = {}` and track with `seen.get(item, 0)`. Also consider: what if `items` is empty? The function handles this correctly, returning an empty list.

Haiku excels at practical code review within its 200K token context window, catching both algorithmic issues and edge cases. The response balances technical depth with brevity. At $0.25/Mtok input, this makes it cost-effective for high-volume code analysis tasks, though responses lack the architectural reasoning depth of larger Claude models.

I'm attaching a screenshot of an error message from our deployment pipeline. Can you identify what went wrong and suggest next debugging steps?

The error shows a 'ModuleNotFoundError: No module named redis' during container startup. This indicates the Redis Python client isn't installed in your Docker image. The stack trace points to line 47 in `worker.py`, where the import fails. Next steps: (1) Verify `redis` is listed in your `requirements.txt` or `pyproject.toml`. (2) Check if your Dockerfile actually runs `pip install -r requirements.txt` before the COPY command for application code. (3) Confirm the base image hasn't changed — if you recently switched from an image with pre-installed packages to a minimal base, dependencies may be missing. Try rebuilding with `--no-cache` to rule out layer caching issues.

This example demonstrates Haiku's multimodal capability — analyzing image inputs alongside text. The 200K context window allows including full error logs and multiple screenshots in a single request. The model provides structured debugging steps rather than just identifying the error. Trade-off: vision analysis is less nuanced than GPT-4V for complex diagrams or UI mockups.

Summarize this 8,000-word internal RFC document and extract the three main decision points that need executive sign-off. Format as a bullet list with context for each decision.

**Decision 1: Migration Timeline** — The RFC proposes a 6-month phased migration to the new authentication system, starting Q2. This requires freezing feature development on the legacy auth module for 3 months. Executive approval needed because it impacts two product roadmaps and requires reallocating 4 engineers. **Decision 2: Backwards Compatibility** — Recommendation is to drop support for API clients older than v2.1 (released 2021). This simplifies the migration but affects 12% of current API traffic. Sign-off needed on acceptable customer impact and communication plan. **Decision 3: Infrastructure Spend** — New system requires additional Redis clusters and increased database IOPS, estimated $4,200/month ongoing cost. Approval needed for budget reallocation from the infrastructure reserve.

Haiku's 200K token context window handles lengthy documents without truncation, making it practical for internal knowledge work. The model extracts decision-critical information and provides business context, not just technical summary. At this pricing tier, processing long documents costs significantly less than Opus or Sonnet, though the analysis is more surface-level — it won't catch subtle contradictions across sections.

When Haiku makes sense for 500+ tickets per day

A 12-person SaaS support team routing 600 inbound tickets daily needs fast classification without breaking the budget. Claude 3 Haiku delivers sub-second responses at $0.25/Mtok input and $1.25/Mtok output—roughly 80% cheaper than Opus or Sonnet for the same task. The 200k token context window handles full ticket histories plus knowledge base context in a single call, so you're not stitching together multiple requests. Accuracy on intent classification sits around 92-94% in production (comparable to GPT-4o-mini for structured tasks), which means you'll need a human escalation path for edge cases. If your ticket volume justifies the API integration work and you're comfortable with a model that won't write nuanced long-form responses, Haiku is the right call. For teams under 200 tickets/day, the setup overhead outweighs the cost savings.

Why Haiku wins on invoice processing at scale

A 4-person accounting firm processing 300 invoices per month from PDFs needs structured data extraction without manual keying. Claude 3 Haiku's image input support means you can send invoice scans directly—no OCR preprocessing step—and the 200k context window fits even multi-page vendor statements in one request. At $0.25/Mtok input, you're looking at roughly $0.002 per invoice for extraction (vendor name, line items, totals), which pencils out when you're billing $40/hour for data entry work. Haiku's structured output reliability is solid for tabular data but weaker on handwritten notes or low-contrast scans, so plan for a 5-8% manual review rate. If your invoices are clean and your volume is predictable, Haiku delivers ROI in month one. For firms processing under 100 invoices/month, the integration cost exceeds the labor savings.

When Haiku handles comment filtering for community platforms

A 20k-user forum with 2,000 comments per day needs automated moderation that flags spam, abuse, and off-topic posts before they go live. Claude 3 Haiku's speed (typically 400-600ms for classification tasks) keeps the user experience smooth, and the $0.25/Mtok input cost means you're spending roughly $15/month at that volume. The 200k context window lets you include the last 50 comments from a thread for context-aware moderation, catching subtle trolling that keyword filters miss. Accuracy on clear-cut cases (spam, slurs) is 96%+, but nuanced judgment calls (sarcasm, cultural context) still need human review about 12% of the time. If your moderation queue is under 500 comments/day, a human-only workflow is faster to set up. Above that threshold, Haiku pays for itself in moderator hours within two months.