Anthropic: Claude Sonnet 5.5 (batch)

Review this Python function for potential race conditions. We're seeing intermittent failures in production when multiple workers access the cache simultaneously.

The function has a classic check-then-act race condition between lines 47-52. Two threads can both pass the `if key not in cache` check before either writes, causing duplicate expensive computations. The `cache[key] = result` assignment itself is atomic in CPython due to the GIL, but that doesn't help here—you need the check and write to be atomic together. Consider using `threading.Lock()` around the entire check-write block, or switch to a `dict.setdefault()` pattern if your compute function is idempotent. The second issue is subtler: your cache eviction logic at line 61 iterates while potentially allowing other threads to modify the dict, which can raise RuntimeError. Wrap that in the same lock.

This example highlights Claude Sonnet 5.5's ability to trace concurrency bugs through code context without needing explicit threading primitives in the prompt. The batch API's $1/$5 pricing makes it viable for async code review workflows where 10-30 second latency is acceptable. The 1M token context window means you can include entire module files for full static analysis.

I need to explain to a non-technical executive why our API response times spiked last Tuesday between 2-4pm. Here's the CloudWatch data and our architecture diagram. Make it clear but don't oversimplify the root cause.

The spike occurred because your payment processor's webhook retry logic created a feedback loop with your rate limiter. Here's what happened: at 2:03pm, a network blip caused 40 webhook deliveries to time out. The processor retried all 40 within the same minute, which hit your per-minute rate limit of 50 requests. Your API returned 429 errors for the excess requests. The processor interpreted 429s as transient failures and retried again—but now with exponential backoff that clustered retries at the top of each minute. This turned your steady 30 req/min baseline into repeated 80 req/min bursts, each triggering more 429s and more retries. The loop broke at 4:12pm when their retry budget exhausted. The fix is to return 429s with a `Retry-After` header so their system spaces out retries, and to whitelist webhook IPs from rate limiting since you control both ends of that integration.

Demonstrates the model's ability to synthesize technical telemetry with business communication needs. The batch processing model suits this use case—incident post-mortems don't need real-time responses, and the lower cost allows teams to run multiple explanation drafts with different audience framings. Note that image input support means you can include architecture diagrams and charts directly rather than describing them in text.

Generate a SQL migration script that adds a `deleted_at` timestamp column to our `users` table for soft deletes, updates all foreign key constraints to respect it, and creates the necessary indexes. Our ORM is Django, Postgres 14.

```sql -- Migration: Add soft delete support to users table BEGIN; ALTER TABLE users ADD COLUMN deleted_at TIMESTAMP WITH TIME ZONE DEFAULT NULL; CREATE INDEX idx_users_deleted_at ON users(deleted_at) WHERE deleted_at IS NULL; CREATE INDEX idx_users_active_created ON users(created_at) WHERE deleted_at IS NULL; -- Update foreign key queries (Django handles this in ORM, but for raw SQL:) CREATE VIEW active_users AS SELECT * FROM users WHERE deleted_at IS NULL; -- Add constraint to prevent deletion of users with active orders ALTER TABLE users ADD CONSTRAINT check_no_active_orders_on_delete CHECK (deleted_at IS NULL OR NOT EXISTS ( SELECT 1 FROM orders WHERE orders.user_id = users.id AND orders.status IN ('pending', 'processing') )); COMMIT; ``` You'll also need to update your Django model to add `deleted_at = models.DateTimeField(null=True, blank=True)` and override the default manager to filter `deleted_at__isnull=True` by default.

Shows the model's grasp of database schema evolution patterns and ORM-specific considerations. The partial index on the WHERE clause is a Postgres-specific optimization that a generic model might miss. Batch API pricing makes this economical for automated schema review pipelines where you generate multiple migration strategies and compare them. The 1M token context means you can include your entire schema definition for constraint validation.

When batch mode cuts document analysis costs by 50% for legal teams

A 12-person legal ops team processes 200-400 contracts nightly—extracting clauses, flagging non-standard terms, routing exceptions to counsel. Claude Sonnet 5.5 in batch mode runs these jobs at $1/Mtok input versus $3 for real-time Sonnet, cutting monthly spend from $1,800 to $900 while the 1M-token window handles even merged deal packets without chunking. Batch jobs complete within 24 hours, which works because contract review isn't time-sensitive and the team triages results each morning. If you need sub-hour turnaround or interactive follow-up during analysis, pay for real-time Sonnet instead. Otherwise, queue the work at 5pm and let batch mode run while you sleep—the cost difference funds an extra seat.

How a 6-engineer startup uses batch Sonnet to maintain living docs

A seed-stage dev team ships 40-60 PRs per week and their internal docs drift within days. They run a Friday batch job that feeds the week's diffs plus existing markdown into Claude Sonnet 5.5, which regenerates architecture overviews, API references, and onboarding guides by Monday morning. The 1M-token context means the model sees the entire codebase structure in one pass, and batch pricing keeps the weekly run under $15 even as the repo grows past 200k lines. The 24-hour SLA is fine because docs are a weekend artifact, not a blocking review step. If your team needs docs updated in CI on every merge, real-time Sonnet is the call—but for weekly or nightly refresh cycles, batch mode makes documentation actually sustainable.

When batch processing turns 10,000 support tickets into product insights

A 30-person SaaS company collects 8,000-12,000 support tickets and NPS comments monthly, and product managers need thematic analysis to guide roadmap planning. They export the corpus as JSON, run it through Claude Sonnet 5.5 batch to extract feature requests, pain points, and sentiment trends, then review the output in a two-hour roadmap session. Batch pricing means the monthly job costs $40-60 instead of $120-180, and the 1M-token window handles the full dataset without sampling or pre-filtering. The 24-hour processing window aligns with their monthly planning cadence—if you're doing this weekly or need real-time escalation tagging, pay for synchronous inference. For end-of-month synthesis where latency doesn't matter, batch mode is the obvious move.