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- 😂 GPT-5 'Chart Crime' is Kinda LOL
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Google just built an AI that researches like a human: fixes mistakes, and keeps refining until it nails the answer. We’ve gotta talk about GPT-5’s embarrassing “chart crime.”
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AI INSIGHTS
Google’s research team just dropped Test-Time Diffusion Deep Researcher (TTD-DR)… and it’s making the old “search once and summarize” approach look prehistoric.
Here’s the problem: most AI research tools - even the fancy ones - either do a single pass search and write, or run a bunch of parallel searches and mash them together.
→ That works for simple questions. But we don’t work like that, we draft, see what’s missing, search more, edit, repeat. Until now, AI couldn’t replicate that flow. Here’s how TTD-DR pulls it off:
Creates a detailed outline from the user’s question.
Generates queries, retrieves info, integrates into a draft, repeats.
Produces multiple variants of outlines, search queries, or drafts
→ A separate judge LLM scores each version for completeness
Consolidates into a coherent document.
The results? On long-form research benchmarks, TTD-DR wins 69.1% of head-to-heads against OpenAI Deep Research and 74.5% on DeepConsult.
For multi-hop QA, it’s also ahead - 33.9% vs 29.1% on HLE-Search, and 69.1% vs 67.4% on GAIA.
Of course, it’s not perfect. Right now, it’s locked to search APIs, no live browsing, no code execution. But plug in autonomous web crawling or data-analysis scripts, and you’ve got an AI that can not only think like a researcher… but hustle like one too.
Why it matters: TTD-DR is what “deep research” tools have been promising but not delivering. If OpenAI’s Deep Research was a calculator, this feels like a whole lab assistant. Will we be talking about this as the moment AI research stopped feeling like a gimmick and started feeling like a human?
TODAY IN AI
AI HIGHLIGHTS
👑 It looks like Google & OpenAI worked together to make GPT-5 launch a success. So whatever new model Google is cooking up will probably be coming next week at earliest!?
⚠ Remember how Google indexed 4K chats that were “shared” publicly? Turns out it was actually 96K+ (130K including Grok + Claude chats), at least dozens with false infor.
🚨 As soon as GPT-5 dropped, OpenAI made some interesting data visualizations for the its announcement event (to put it mildly), people are calling them “chart crime”. Here it is.
✨ A writer tried combining NotebookLM, Perplexity & ChatGPT to create a super-efficient AI. And it made the ultimate knowledge power combo. Here’s the full guide.
💀 Former Google exec predicts the world will enter a 15-year AI dystopia in 2027. AI will escalate “the evil that man can do” beyond control. Just like how Grok created 'sexually visuals' of Taylor Swift without prompts.
💰 AI Daily Fundraising: Clay, an AI-driven sales automation company, raised $100 million funding from CapitalG, Sequoia Capital, and Meritech Capital, boosting future revenue expectations.
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AI QUICK HITS
🤖 OpenAI might give a (very) small number of GPT-5 pro queries/mo.
📈 Google tests Google Finance again with AI upgrades, live news feed.
🎬 Former Googlers’ OpenArt now creates ‘brain rot’ videos in just 1 click.
⚖️ xAI’s head of legal, Robert Keele, stepped down after just over a year.
🐾 Google open-sourced an upgraded AI to understand animal sounds.
AI CHART
You wanted to know exactly where a protein hides inside a human cell… without spending months in a lab? (very science fiction). Well, MIT just made it a lot more real.
Researchers from MIT, Harvard, and the Broad Institute have built PUPS - an AI system that predicts the precise location of almost any protein in a single human cell.
Until now, this kind of mapping meant slow, meticulous experiments… and it only worked for proteins we already knew.
Here’s how PUPS works (two-part brain):
A protein language model → learns a protein’s structure from its sequence
An inpainting model → reads the “vibe” of the cell itself, figuring out its type, features, and stress state
It works on proteins and cell types it’s never seen before. It can even flag changes caused by mutations missing from the Human Protein Atlas. In tests, PUPS beat all baseline AI methods:
Shows lower prediction error across all tested proteins
Maintains high accuracy even in difficult or novel cases
If you can instantly see where a protein is in any cell, you can identify disease markers faster, test drug targets without guesswork, and explore parts of cell biology we’ve never mapped before.
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The AI Fire Team
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