AI tool comparison
Claude Opus 4.7 vs Nothing Ever Happens
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Foundation Models
Claude Opus 4.7
Anthropic's new flagship — 87.6% SWE-bench, 1M context
75%
Panel ship
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Community
Paid
Entry
Claude Opus 4.7 is Anthropic's latest flagship model, released April 16. It scores 87.6% on SWE-bench Verified — a 13-point improvement over Claude Opus 4.6 — and 94.2% on GPQA, making it competitive with the top frontier models on coding and scientific reasoning benchmarks. The context window extends to 1 million tokens with substantially improved retrieval accuracy at the far end of the window. The release introduces "Routines" — a first-party feature for defining persistent agentic workflows that Claude can execute autonomously across multiple sessions. Routines are defined in structured YAML and can include tool calls, conditional logic, and human-in-the-loop checkpoints. Anthropic positions this as a more reliable alternative to custom agent frameworks for common use cases. Pricing remains unchanged from Opus 4.6: $5/M input tokens, $25/M output tokens. The vision input resolution has been increased by 3.3x, which meaningfully improves performance on documents, diagrams, and UI screenshots. Available via API immediately and rolling out to Claude.ai Pro and Team plans over the next week.
AI Experiments
Nothing Ever Happens
An autonomous bot that always bets 'No' on Polymarket doom predictions—and profits
75%
Panel ship
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Community
Free
Entry
Nothing Ever Happens is a deliberately simple autonomous trading bot that buys "No" contracts on Polymarket prediction markets—specifically targeting non-sports questions about dramatic or catastrophic events. The thesis: humans systematically overestimate the probability that scary predicted events will actually happen. The bot filters markets using LLM-based criteria to exclude sports (where outcomes are more unpredictable) and focuses on the long tail of geopolitical, tech, and social predictions that tend toward "nothing happens." Built by Sterling Crispin (an artist and technologist known for his work on Apple Vision Pro), the project is equal parts satirical commentary and functional trading system. It logs all positions, P&L, and reasoning chains so you can audit its decisions. The name references an internet phrase mocking catastrophist news cycles—"nothing ever happens" is the skeptic's rebuttal to perpetual crisis framing. The HN post hit 370 points and 180+ comments in a few hours, sparking genuine debate about whether this is a sound strategy, a fun toy, or a comment on prediction market epistemology. Real-world results aren't yet published, but the idea of using an LLM as a "doom filter" for prediction markets is novel enough to be worth watching.
Reviewer scorecard
“87.6% on SWE-bench isn't a small improvement — that's a meaningful jump for real-world coding tasks. The Routines feature addresses the biggest pain point with Claude in production: reliable multi-step agent behavior without building a custom framework.”
“Clean architecture, good logging, and a legitimately interesting hypothesis about prediction market psychology. The LLM filtering layer for 'doom vs. non-doom' questions is a smart abstraction. Even if the strategy underperforms, the codebase is a solid template for automated Polymarket bots.”
“Benchmarks look great but the 1M context window performance hasn't been independently validated at the limits. Routines sound powerful but the YAML spec is still in beta with known edge cases. If you're running stable Opus 4.6 workflows, wait a week for the community to stress-test this before migrating.”
“The strategy looks good in backtests but Polymarket's liquidity is thin and arbitrageurs will price this edge away quickly once it's well-known. Also: 'nothing ever happens' is survivorship bias dressed as strategy—the times something DOES happen, you're wiped out. Don't put meaningful capital here.”
“Anthropic is quietly winning the enterprise coding agent race. The combination of top SWE-bench scores with the Routines feature is a moat — developers don't switch orchestration frameworks easily once workflows are deployed. This release deepens that lock-in strategically.”
“Autonomous agents that trade prediction markets based on LLM-assessed epistemic calibration is a genuinely new thing. If this works at scale, it could actually make prediction markets more accurate by algorithmically correcting for human doom-bias. That's a more interesting outcome than any individual P&L.”
“The 3.3x vision resolution upgrade is underrated for design work. Document analysis, layout review, and iterating on visual mockups are all dramatically better. I can finally paste a full Figma export and get coherent feedback on the entire design rather than just the top half.”
“Sterling Crispin making a 'nothing ever happens' bot is peak art-meets-tech. It's a functional piece of commentary on the anxiety economy—we're so primed for crisis that prediction markets misprice normalcy. The aesthetic of it is as interesting as the trading logic.”
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