OpenAI: GPT-5.4 Nano (batch)

Review this Python function for edge cases. It's supposed to merge overlapping time intervals but users report silent failures on certain inputs.

The function assumes intervals arrive pre-sorted and fails when they don't—no explicit sort before the merge loop. It also doesn't handle empty input (returns uninitialized variable) or single-element lists (skips the loop entirely, returns nothing). The overlap check `current[1] >= next[0]` misses exact boundary cases where intervals touch but don't overlap. Add input validation, sort at entry, and decide whether touching intervals should merge based on your domain requirements.

Batch processing suits this kind of code review well—submit 50 functions overnight, get detailed analyses by morning. The 400k token context means you can include the entire module for better edge-case detection. Trade-off: no real-time feedback loop, so you can't immediately clarify ambiguous requirements during the review.

Summarize the key regulatory changes in these six quarterly compliance reports (attached PDFs, ~180 pages total). Focus on data retention requirements that affect our EU operations.

Three major shifts: GDPR Article 17 interpretation now requires explicit user consent for retention beyond 90 days in marketing contexts (Q2 guidance). The Digital Services Act added mandatory transparency reporting for content moderation decisions—7-day window, affects your user-generated content flows (Q3). New adequacy decision for UK transfers means you can drop the additional safeguards you implemented post-Brexit, but only for personal data, not special category data (Q4). Two minor updates on breach notification timelines and one clarification that doesn't change your current setup.

The extended context window handles all six reports in one pass without chunking—critical for catching cross-quarter policy evolution. Batch mode's cost advantage ($0.10 input vs. real-time models at $2-5/Mtok) makes this viable for regular compliance monitoring. Limitation: you're reading yesterday's reports, not today's breaking regulatory news.

Generate 20 variations of this product description for A/B testing. Keep the core facts identical but vary tone from technical to aspirational. Original: 'Wireless charging stand, 15W output, aluminum body, works with MagSafe.'

Variation 1 (technical): 'Delivers 15W Qi-certified fast charging through precision-aligned coils. CNC-machined 6061 aluminum dissipates heat efficiently. MagSafe-compatible geometry ensures optimal coupling.' Variation 8 (aspirational): 'Your phone floats into place. Fifteen watts of wireless power flow through sculpted aluminum. MagSafe's invisible pull meets a stand that looks like it belongs on your desk.' Variation 15 (practical): 'Charges fast. Stays cool. The aluminum build means it won't slide around, and MagSafe means you don't fumble with alignment.' [17 more variations spanning the technical-aspirational spectrum, each preserving the factual claims while shifting voice and emphasis.]

Batch processing excels at bulk content generation where you need consistent quality across many outputs but don't need them instantly. The model maintains factual consistency across all 20 versions—no hallucinated specs. The cost structure makes this 50x cheaper than generating variations on-demand during a live campaign. You're trading immediacy for economics and can't iterate interactively on tone.

GPT-5.4 Nano batch for 200-page board decks on a schedule

A 5-person finance team consolidates 30 subsidiary reports (averaging 180 pages each) into executive summaries by the 5th of every month. GPT-5.4 Nano batch accepts file uploads directly and the 400k token context fits an entire 180-page PDF in one pass, eliminating the chunking errors that plague smaller models. At $0.10/Mtok input and $0.63/Mtok output, processing 30 reports (roughly 45M tokens input, 600k tokens output) costs about $8.25 per month versus $90+ for real-time alternatives. The batch queue runs overnight so summaries land in inboxes by 6 AM. This works because board meetings happen on a fixed calendar; if stakeholders need ad-hoc deep-dives during the month, you need synchronous access and should budget accordingly.