AI tool comparison
LamBench vs last30days-skill
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Research & Benchmarks
LamBench
120 λ-calculus challenges that cut through AI benchmark gaming
50%
Panel ship
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Community
Free
Entry
LamBench is a benchmark of 120 fresh lambda calculus programming questions designed by Victor Taelin (creator of the HVM runtime) to test genuine AI reasoning capabilities rather than pattern-matched performance on contaminated datasets. Questions range from implementing basic operations like addition for λ-encoded natural numbers to deriving generic folds for arbitrary data types. The benchmark measures both accuracy (percentage of 120 tasks solved correctly) and speed (average solution time). Current top performers include GPT-5.4 at 91.7% accuracy, Anthropic's Opus 4.6 at 90.0%, and GPT-5.3-Codex at 89.2%. Lower-tier models bottom out at 28-58% accuracy — revealing significant gaps in symbolic reasoning capability that other benchmarks obscure. Taelin released LamBench in direct response to community requests for a benchmark resistant to training data contamination. Lambda calculus is a clean, closed formal system — ideal for testing reasoning because memorizing examples provides minimal advantage over actually understanding the abstractions.
Research Tools
last30days-skill
Research any topic across 10+ platforms from the last 30 days
75%
Panel ship
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Community
Free
Entry
last30days-skill is an AI agent skill that aggregates, deduplicates, and synthesizes recent discussions about any topic from Reddit, X/Twitter, YouTube, Hacker News, Polymarket, Bluesky, TikTok, and Instagram simultaneously. The core value proposition: instead of manually searching eight platforms and stitching together what people are actually saying, you ask once and get a grounded summary with citations ranked by engagement and cross-platform convergence. The ranking system is unusually sophisticated for a community project—it combines text similarity, engagement velocity, source authority, and cross-platform convergence detection (penalizing topics that only appear on one platform). For prediction markets, it evaluates topics as outcomes within broader events rather than naive title matching. A handle resolution feature identifies X/Twitter accounts from natural language names alone. Zero configuration is needed for Reddit, HN, and Polymarket; unlocking other sources requires API keys from ScrapeCreators and Exa. The project reached 18k stars in its first week, largely driven by prompt researchers discovering it surfaces "what actually works" for tools like ChatGPT or Midjourney. Results auto-save to ~/Documents/Last30Days/ by default, and a watchlist mode supports scheduled topic monitoring with an external cron scheduler.
Reviewer scorecard
“Lambda calculus is a great choice for a hard-to-contaminate benchmark — you can't just memorize your way to success on symbolic reasoning. The gap between top models (90%+) and mid-tier (50-60%) is much larger than most leaderboards show, which gives it real signal.”
“The cross-platform convergence scoring is clever—topics that only trend on one platform get penalized, which filters out astroturfing and PR-driven hype. The handle resolution for X accounts is a nice touch for competitive intelligence workflows where you know a person's name but not their handle.”
“120 questions is a very small sample size for a benchmark claiming to measure fundamental reasoning — statistical noise could easily explain a 5-10% difference between models. And lambda calculus is a narrow domain; strong performance here doesn't generalize to most real tasks.”
“Most of the headline platforms require paid API keys from ScrapeCreators to actually work, so the 'zero-config' claim is misleading—you get Reddit and HN out of the box, which is not exactly a revelation. The 18k stars look suspiciously like another viral GitHub moment that won't translate to sustained usage.”
“As LLMs saturate mainstream benchmarks, we'll rely increasingly on formal, symbolic tasks to measure genuine reasoning progress. LamBench points toward a class of evaluation that correlates with the kind of compositional thinking needed for real AGI-level capabilities.”
“The watchlist mode with scheduled monitoring is the feature that turns this from a one-off research tool into genuine trend intelligence infrastructure. As public discourse increasingly happens in fragmented, platform-specific bubbles, multi-source aggregation with convergence detection becomes essential signal.”
“Lambda calculus reasoning benchmarks are fascinating from a research perspective but have zero direct connection to creative workflows. The leaderboard is worth bookmarking to track which models are actually getting smarter vs. just getting better at gaming evals.”
“For content creators trying to find what's actually resonating versus what's being pushed, the engagement velocity scoring is invaluable. Knowing that a prompt technique has 1000 upvotes spread over a week versus 1000 upvotes in 2 hours tells you completely different things about audience authenticity.”
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