Anthropic Claude Haiku Latest

Review this pull request diff and flag any potential bugs or edge cases I should test before merging. Focus on the authentication middleware changes.

Three concerns stand out in this middleware update: 1. Line 47: The token expiry check uses `>` instead of `>=`, so tokens expiring at exactly the current second will pass validation when they shouldn't. 2. Lines 89-92: The error handler doesn't distinguish between malformed tokens and expired tokens, returning generic 401s for both. This makes client-side retry logic harder to implement correctly. 3. Line 103: The new caching layer doesn't invalidate on password reset events, so users who reset passwords could still authenticate with old tokens until cache TTL expires (currently 5 minutes). The refactor itself is clean and the performance improvement from caching is solid.

Haiku excels at quick code review with its 200k context window handling full PR diffs easily. The $1/Mtok input pricing makes it economical for high-volume review workflows. Response quality is practical rather than exhaustive—catches obvious issues but may miss subtle architectural concerns that slower, deeper models would flag.

Extract all invoice line items from this PDF and output as JSON with fields: description, quantity, unit_price, total. The document is 8 pages.

Here's the structured data from your invoice: ```json [ {"description": "Cloud Infrastructure - Production", "quantity": 1, "unit_price": 2847.50, "total": 2847.50}, {"description": "API Gateway - Standard Tier", "quantity": 3, "unit_price": 125.00, "total": 375.00}, {"description": "Database Backup Storage (TB)", "quantity": 4.7, "unit_price": 23.00, "total": 108.10}, {"description": "Support Plan - Enterprise", "quantity": 1, "unit_price": 500.00, "total": 500.00} ] ``` Total extracted: 47 line items across 8 pages. Two entries on page 6 had partially obscured unit prices—I've marked those with null values for manual review.

Haiku's multimodal support handles document extraction at speed, processing 8-page PDFs in seconds rather than minutes. The file input modality means no pre-processing pipeline needed. Accuracy on clean invoices is high, though complex table layouts or poor scan quality will produce gaps requiring human verification—typical for fast inference models.

Summarise this 40-page technical specification document. Prioritise: system requirements, breaking changes from v2, and new API endpoints. Keep it under 200 words.

**System Requirements:** Minimum Node 18.x (up from 16.x), PostgreSQL 14+ now required, Redis 7.0+ for session management. Docker deployments need 4GB RAM baseline. **Breaking Changes from v2:** - Authentication endpoints moved from `/auth/*` to `/v3/auth/*` - Webhook payload structure changed: `event_type` renamed to `event_name` - Rate limiting now per-organisation instead of per-user (affects multi-tenant setups) - Removed deprecated `/legacy/reports` endpoints entirely **New API Endpoints:** - `POST /v3/analytics/export` - bulk data export with filtering - `GET /v3/audit/logs` - compliance audit trail access - `PATCH /v3/settings/notifications` - granular notification preferences - `DELETE /v3/cache/invalidate` - manual cache control for admins Migration guide starts page 34. Backward compatibility layer available until Q3 2025.

The 200k token context window lets Haiku ingest entire specification documents in one pass, avoiding the chunking strategies smaller models require. Summarisation is efficient and hits key points, though nuance in edge-case behaviour gets compressed. At $1 input per million tokens, processing lengthy docs costs pennies—viable for routine documentation workflows.

When Haiku wins on support ticket routing at 500+ tickets/day

A 12-person SaaS company handling 600 support tickets daily needs instant classification without burning budget on overkill models. Claude Haiku Latest hits the sweet spot here: $1/Mtok input means you can process every ticket through a 2,000-token prompt (ticket + routing rules + examples) for under $1.20 per thousand tickets. The 200K context window lets you load your entire help center and past resolution patterns into a single prompt, so routing accuracy stays high without fine-tuning. Output cost at $5/Mtok is irrelevant when you're generating 50-token classification responses. If your tickets average under 1,000 tokens and you need sub-second latency, Haiku delivers the throughput without the GPT-4 price tag. Switch to a reasoning model only when tickets escalate to complex technical diagnosis.

Why Haiku handles contract summaries for small legal teams

A 4-person contract review shop needs to summarize 40 NDAs and service agreements weekly, each running 8,000-15,000 tokens. Claude Haiku Latest processes these at $1/Mtok input, so a 12,000-token contract costs $0.012 to read and $0.0025 to output a 500-token summary—under two cents per document. The 200K context window means you can feed in full contracts plus your firm's standard clause library for reference without chunking. Image and file modalities let you handle scanned PDFs directly if you're not pre-processing with OCR. Total weekly cost for 40 summaries: under $1. The trade-off is nuance—if 10% of your contracts have ambiguous indemnification clauses that need deep reasoning, you'll want a second-pass review with Opus or GPT-4. For straightforward extraction and summarization at volume, Haiku keeps the workflow fast and the invoice low.

When Haiku adds structure to live transcripts without lag

A 20-person product team runs daily standups and wants automated action-item extraction from live transcripts streaming in at 150 words/minute. Claude Haiku Latest processes each 5-minute transcript chunk (roughly 750 tokens) in under a second at $0.00075 per chunk, so a 30-minute meeting costs $0.0045 to enrich in real time. The 200K context lets you maintain the full meeting history in-prompt, so action items reference earlier discussion without losing coherence. Output pricing at $5/Mtok is negligible when you're generating 100-token summaries per chunk. If your meetings involve dense technical architecture debates where subtle distinctions matter, you'll want a smarter model for final review. But for extracting who-owns-what and surfacing decisions as they happen, Haiku delivers speed and cost efficiency that keeps the feature viable at team scale.