Anthropic: Claude Opus 4.7

Review this pull request for security issues. The code adds user authentication but I'm worried about the session handling and password storage approach.

This implementation has three critical security gaps. First, the password hashing uses MD5 without salt — switch to bcrypt with a work factor of 12+. Second, session tokens are predictable (sequential IDs) — use cryptographically random 32-byte values. Third, the logout endpoint doesn't invalidate server-side sessions, only clears the client cookie. An attacker with a stolen token retains access indefinitely. The input validation on line 47 is solid, and the rate limiting logic correctly prevents brute force attempts. Priority fix: replace the password storage before this ships.

Claude Opus 4.7 excels at security review because of its 1M token context window — it can hold entire codebases and cross-reference patterns across files. The response balances specific line-level fixes with architectural concerns. Trade-off: at $25/Mtok output, thorough reviews of large PRs cost more than smaller-context alternatives.

Analyze this 80-page financial report PDF and summarize the key risks the auditors flagged, plus any revenue recognition changes from last quarter.

The auditors raised going-concern doubts on page 63 due to covenant violations in the credit facility — the company has 90 days to renegotiate or face acceleration. Three material weaknesses appear in internal controls (pages 71-74), all related to revenue cut-off procedures. Revenue recognition shifted this quarter: the company now defers 15% of SaaS contracts as unearned (previously 8%) because implementation timelines stretched. This change reduced reported revenue by $4.2M but improves compliance with ASC 606. The footnote on page 52 discloses a $12M contingent liability from a patent dispute, up from $3M last quarter.

File upload support and the 1M token window let Claude Opus handle dense documents that would require chunking in other models. It accurately cross-references page numbers and connects scattered details (the covenant issue ties to the revenue change). Trade-off: processing an 80-page PDF consumes significant input tokens, making per-document costs higher than text-only workflows.

I'm writing a technical whitepaper on distributed consensus algorithms. Explain Raft vs Paxos trade-offs for a systems engineer audience — assume they know networking but not formal verification.

Raft prioritizes understandability: leader election, log replication, and safety properties map cleanly to implementation code. You get strong consistency with a single leader handling writes, and the algorithm's state machine is straightforward to test. Paxos offers more flexibility — multi-Paxos can pipeline proposals without a stable leader — but the proof-to-code gap is wide. Most production systems (etcd, Consul) choose Raft because debugging a split-brain scenario at 3am requires engineers to reason about the protocol, not just read the paper. The performance ceiling is similar for both under normal conditions. Raft's trade-off: leader election pauses writes briefly during failover, while Paxos variants can continue with a quorum. For your whitepaper, frame Raft as the pragmatic default unless you need Paxos's flexibility for exotic topologies.

This example shows Claude Opus's ability to tailor technical depth to audience — it skips formal proofs but includes production system names and operational concerns. The response structure (comparison, then recommendation) fits whitepaper writing. Trade-off: the model's training data has a cutoff, so very recent consensus research (post-2024) won't appear without explicit context.

When 1M-token context makes legal review actually practical

A 12-person law firm needs to cross-reference clauses across 40+ vendor agreements before a merger closes. Claude Opus 4.7's 1M-token window means you can load every contract, every amendment, and every side letter into a single thread—then ask questions that span the entire corpus without re-uploading or chunking. At $5/Mtok input, a 600K-token load costs $3, and follow-up queries stay cheap because context persists. The $25/Mtok output rate stings if you're generating 50-page summaries, but for Q&A workflows where responses stay under 2K tokens, total cost per session runs $4-6. If your team runs fewer than 20 deep-research sessions per week, this beats hiring a paralegal. Above that volume, you're spending $400+/month and should evaluate cheaper long-context alternatives.

Why Opus 4.7 struggles against cheaper models for doc writing

A 5-person DevOps team wants to auto-generate runbooks from Terraform configs and incident logs. Claude Opus 4.7 can ingest the entire codebase and produce coherent docs, but the $25/Mtok output price makes this expensive fast: a 10K-token runbook costs $0.25, and if you're generating 200 docs/month, that's $50 in output tokens alone. Models like GPT-4o or Claude Sonnet 4 deliver comparable quality at $15/Mtok output, cutting your bill by 40%. Opus 4.7 makes sense here only if you need the 1M-token context to reference a massive legacy codebase in every generation—say, 500K+ tokens of context per doc. For typical documentation workflows under 100K tokens of input, you're overpaying for context you don't use.

When Opus 4.7's image+file handling justifies the premium

A 20-person SaaS company receives 300 support tickets daily, many with screenshots, PDFs, and error logs attached. Claude Opus 4.7's native image and file processing means your triage workflow can analyze a ticket, extract text from a PDF invoice, read a screenshot of a broken UI, and route to the right engineer—all in one API call. Competing models either lack multimodal support or require separate OCR pipelines that add latency and failure points. At $5/Mtok input, processing a 5K-token ticket with two images costs roughly $0.03; 300 tickets/day runs $27/day or $810/month. If multimodal triage saves your team 15 hours/week, the ROI clears easily. Below 100 tickets/day, the cost drops under $300/month and becomes a no-brainer for teams already paying for support tools.