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The Washington Post just threw a curveball at the 5 biggest AI chatbots, and the results might just flip your assumptions about who’s really the smartest bot in the room. One crushed it, one flopped...
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AI INSIGHTS
For nearly 80 years, the Dead Sea Scrolls — fragments of ancient Hebrew and Aramaic writings — have served as windows into Jewish life over 2,000 years ago. But most of these fragments don't include specific dates. Now, a machine learning model named Enoch suggests scrolls are as much as 100 years older than previously believed.
1. Meet “Enoch”: The AI Dating Tool
Trained on: Radiocarbon-14 dating data + Geometric and structural features of handwriting (size, shape, and curvature of letters)
Named after the biblical prophet Enoch, symbolizing ancient wisdom and science
Enoch is not designed to replace scholars but to assist and refine their estimates
2. The AI's Findings: Mostly Agreement, Some Surprises
80% match rate between AI predictions and human estimates.
20% of scrolls dated older than expected, sometimes by up to a century.
Scroll 4Q114 (Book of Daniel):
Humans: ~165 B.C.E.
Enoch: 230–160 B.C.E., consistent but opens room for earlier origins.
Enoch found “Herodian” scripts written before King Herod's time, suggesting earlier script transitions than scholars believed.
Most Dead Sea Scrolls may now contains direct timestamps (like names of kings or specific events).
Why It Matters: I think bringing AI into ancient script analysis is about reshaping our understanding of cultural evolution, literacy, and historical context. If some texts are older than assumed, our understanding of when and how ideas, beliefs, and writing systems spread could change. The past is getting clearer, thanks to the future!
TODAY IN AI
AI HIGHLIGHTS
🎤 OpenAI now lets you record and transcribe audio directly in ChatGPT, auto creates a meeting summary with clickable timestamps. Plus integration with Google Drive. Watch its full livestream here.
💭 The Washington Post ran a real test on 5 major AI chatbots: ChatGPT, Claude, Gemini, Copilot, and Meta AI - taking their tough reading test. Guess the winner below first. Then see full result here.
Which AI bot do you think scored highest?Hint: it wasn’t the one with the biggest hype |
🕵️♂️ French startup, H Company, just launched 3 new AI agents: one hits 92% success and is 5.5x cheaper than OpenAI. One is Manus-style superagent. Try the beta version here.
🎬 AI creator Rory Flynn showed how to make Veo3 video clips longer using only a few text prompts, super easy trick to stretch your footage in seconds. See it here.
🧠 Here’s a wild video breaking down “AI washing” — when companies use “AI” to hype investments or cut jobs. BuilderAI even used humans in India posing as AI (thus the joke AI = Actually Indians).
🎯 Google DeepMind’s CEO believes AI will make us less selfish, AGI will unlock radical abundance, solve root-node problems in 5-10 years. Read his full speech here.
💰 AI Daily Fundraising: Snorkel AI raised $100 million, bringing its valuation to $1.3 billion. Investors include Addition, Prosperity 7 Ventures, and Greylock. Funds will launch new AI-focused products.
AI SOURCES FROM AI FIRE
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🔥 AND after more than 2 years of showing up consistently, going from 0 to nearly 60,000 newsletter subscribers and a 330K+ total audience, it’s time for a fresh look that truly reflects where AI Fire is today… and where the AI space is headed.
But before we roll it all out, we want your thoughts. Seriously, you’ve been here since the early days, and you’re basically on the AI Fire advisory board at this point 😄
Which logo feels more “AI Fire” to you? |
AI CHEAT SHEET
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AI QUICK HITS
🚀 HubSpot now connects its CRM data to ChatGPT to use data easily
📓 NotebookLM finally gives us highly-requested feature: shared notebooks
🌐 OpenAI opened internet access for its Codex software engineering agent
💻 Mistral's new vibe coding client takes direct aim at GitHub Copilot
⚖️ Reddit sued Anthropic for allegedly not paying for using AI training data
AI CHART
Researchers published BioReason, a new AI architecture that combines a DNA model with LLM reasoning, showcasing a 15% performance gain on biological benchmarks.
BioReason is the first model to deeply integrate a DNA foundation model with a large language model (LLM). This allows the LLM to reason over genomic data, not just text.
It was created and curated three major benchmarks:
KEGG-based biological reasoning dataset (1,449 entries): connects genetic variants to disease pathways.
Variant Effect Prediction (VEP) for coding variants (50,083 entries).
VEP for non-SNVs like insertions or deletions (36,088 entries).
Significantly outperforms LLM-only or DNA-only models, proving the power of tight multimodal integration.
Query: What’s the impact of a PFN1 variant on chromosome 17? BioReason predicted Amyotrophic Lateral Sclerosis (ALS). It truly narrates the biology & generates a 10-step mechanistic explanation.
I think this level of reasoning is essential for clinical trust, drug discovery, and early-stage diagnostics. It could soon enable automated hypothesis generation, speeding up genetic research dramatically.
AI JOBS
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