This is a huge moment for AI creators.
Not just another tool update but an actual stage for people making films, characters, worlds, ads, experiments, and everything in between....
The canvas is ready. What will you CRE[AI]TE?
We are excited to launch CapCut’s 1st AI Festival, a global celebration of original work and the imagination AI makes possible.
Submissions are now open through August 10, 2026.
From Films and Series to Creative and Commercial, CRE[AI]TE celebrates storytelling and creativity in all its forms.
Four tracks. One festival. Unlimited possibilities.
$200,000 in total cash awards, led by a Grand Prix, Category Winners, Category Honorees, the Student Vision Award, and Spotlight Selections. Plus film festival premieres, CapCut creator event screenings, industry connections, and more.
Everyone can apply for a free starter pack of CapCut credits and Pro access to kickstart your journey.
Submit your entries and learn more: https://t.co/4S2CTXFMwl
RT + comment in 9hr to get 200 free credits.
🚨 Someone just gave AI agents the brain of a senior cybersecurity analyst
(754 skills, free and open source...)
Here's the details:
AI tools like Claude Code, Cursor, and Copilot can write code and search the web. But they don't think like a real security analyst. This project fixes that.
It gives your AI agent a giant playbook of how a senior analyst actually works.
How it works, step by step:
1️⃣ You ask your AI something like: "Check this memory dump for signs of credential theft."
2️⃣ The agent quickly scans all 754 skills (just a few words each) and picks the ones that fit.
3️⃣ It loads the full skill — a clear, step-by-step workflow written by real practitioners.
4️⃣ It runs the exact steps: which tool to use, what to check first, how to execute it.
5️⃣ Then it verifies the result and maps the finding to known attack patterns.
So instead of guessing, your AI follows the same path an expert would.
What's inside:
- 26 security areas — threat hunting, malware analysis, digital forensics, cloud security, pentesting, incident response & more
- Every skill mapped to 5 major frameworks (MITRE ATT&CK, NIST, D3FEND, ATLAS, AI RMF)
- Works with 26+ AI platforms
Setup is literally one line:
Nobel Winner John Jumper to Leave Google DeepMind for Anthropic..
NOBEL WINNER moves to Anthropic.
John Jumper, who led AlphaFold and won the 2024 Nobel in Chemistry, is leaving Google DeepMind after 9 years to join Anthropic.
- He shared that Nobel with DeepMind's own CEO
- Google had him working on AI coding, not science
- He leaves right after Gemini co-lead Noam Shazeer went to OpenAI
- DeepMind also just lost David Silver, the mind behind AlphaGo
✨️John Jumper (JohnJumperSci on x) said:
"After nearly 9 years, I have decided to leave Google DeepMind and join Anthropic (after taking some time to recharge). I am incredibly grateful for my time at GDM. Demis Hassabis took a real chance letting me lead the AlphaFold team just six months after finishing my PhD, and the entire GDM team taught me so much about how to do great science. GDM is a special place, and I’ll still be excited to hear about what amazing things they discover next."
✨️Anthropic is killing it with the 2026 hiring run:
▪️ Andrej Karpathy (joined in May) — OpenAI co-founder and ex-Tesla AI lead, who came from his own startup Eureka Labs to work on Claude pretraining.
▪️ John Jumper (announced June 19) — Google DeepMind VP and 2024 Nobel laureate in Chemistry for AlphaFold, leaving after nearly nine years.
Old email tools had slow dashboards for 20 years.
Nitrosend kills them!! Just tell your AI what you want.
Full emails ready to send arrive in inboxes in under 90 seconds.
This is the future of email.
yes,yes this tool goes CRAZY 🔥
type ANY topic....about a person or a product or whatever....
and it search through Reddit, X, YouTube, HN, Polymarket all at once
picks what people actually said in the last 30 days, ranks it by real engagement (upvotes, likes, views), drops one clean brief in your lap
no stale blog posts.
just what's real RIGHT NOW
free for Reddit, HN, Polymarket & GitHub. open source too
🚨 ANNOUNCING FUSION AGENT SWARMS - Build Complete SaaS Apps With Top AI Models
Fusion agents combine Kimi 2.7, GLM 5.2 with Opus 4.8 and GPT 5.5
- multi-agent architectures with open-source sub-agents
- build complete SaaS apps
- one click connectors to 100+ products
- create companion iOS and android apps with one prompt
- accept stripe payments
Someone just open-source a tool turns any websites into Android app loacally.
It's called WebToApp.
It converts any website URL into a standalone Android APK in seconds, and it requires absolutely zero Android Studio experience to deploy a full native app.
100% Open Source...
From AGI to ASI...
DeepMind just published a paper with a simple but huge question:
Everyone is racing to build AGI; But what happens *after* we get there?
The paper's answer has a name: ASI. Super-intelligence. Not just smarter than one person, but smarter than whole teams of experts working together for years.
And their main point is this: we probably won't stop at human level. AI is likely to keep going.
Here's why...
AI isn't limited the way we are. It reads in seconds. It thinks faster when you give it more computing power. It never forgets. It can copy itself perfectly. And it can share what it learns with a million copies at once. Every one of these strengths grows as computers get stronger; so the gap between us and AI tends to widen, not shrink.
The paper lays out 4 ways AI could go from "human-smart" to "super-smart":
1. Just scale it up; more chips, more data, bigger models.
2. A brand-new breakthrough replaces today's approach.
3. AI starts building better AI, over and over.
4. Millions of AIs team up and act like one giant brain.
These aren't either/or. All four could happen at the same time.
But it won't necessarily be smooth. The paper is honest about the roadblocks:
→ We may run out of fresh data to train on
→ The cost of chips, energy, and money may not keep up
→ Today's method might hit a wall
→ New ideas get harder to find as the easy ones get used up
The most thought-provoking roadblock is this: today's AI learns *our* ideas from *our* writing. Can it invent ideas humans have never had? An AI trained only on pre-Newton knowledge probably couldn't discover gravity on its own. Real discovery means testing ideas against the real world; and that's slow, no matter how fast the AI thinks.
So what's the big takeaway?
Most people picture AGI as one dramatic moment that changes everything overnight. The paper argues that's probably the wrong picture. More likely, it's a steady wave; breakthrough after breakthrough, across science and tech, for years.
That's a very different future to prepare for. And getting ready for it, the authors say, will take experts from every field, all over the world.
One detail I loved: the paper even includes instructions for AI assistants on how to summarize it. The authors knew people would feed it straight to an AI. They were right.
Worth a read if you think about where AI is heading.