AI tool comparison
last30days-skill vs Talkie
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
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.
Research
Talkie
A 13B LLM trained exclusively on texts from before 1931
75%
Panel ship
—
Community
Free
Entry
Talkie is a 13-billion parameter language model trained exclusively on English-language texts published before 1931 — the largest vintage language model built to date. Created by researchers Nick Levine, David Duvenaud (University of Toronto), and Alec Radford (of GPT and DALL-E fame), it represents a novel approach to understanding what training data really does to a model. The research insight is elegant: modern LLMs are so thoroughly contaminated by modern internet data (directly or through distillation) that it's nearly impossible to isolate what the model "knows" from what it absorbed during training. Talkie solves this by hard-cutting the training corpus at 1931 — predating digital computers entirely. This lets the team run controlled experiments impossible with contemporary models, such as teaching the model to write Python from examples alone and measuring how quickly it generalizes. Talkie was trained on ~260 billion tokens of historical text and fine-tuned using direct preference optimization with Claude as judge on structured historical documents (etiquette manuals, letter-writing guides). It's openly available on Hugging Face for research use. It also happens to produce wonderfully formal, slightly anachronistic prose.
Reviewer scorecard
“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.”
“The ability to test code-learning from scratch on a model that's never seen a modern codebase is genuinely useful for ML research. The methodology here is cleaner than anything I've seen for studying data contamination.”
“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.”
“Fascinating as a research artifact, but this isn't a production model. The limited vocabulary and cultural frame mean it's not useful for most practical tasks. It's a museum piece, not a tool.”
“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.”
“This is exactly the kind of fundamental research the field needs. Understanding what training data does to language models — not just benchmark scores — is critical as we scale to more powerful systems. Radford's involvement adds serious credibility.”
“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.”
“The prose it generates has a formal, unhurried quality that modern LLMs can't replicate. For period-accurate creative writing, historical fiction, or vintage-voice content, Talkie is the only model worth using.”
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