A landing page built around a real-time WebGL centrepiece: a rotating glass cube that samples an environment cubemap through its own back faces, over a mirrored floor.
#WebGL#GLSL#CreativeCoding#JavaScript#Frontend#Threejs
Just shared some changes we’re making to the teams at @GoogleDeepMind.
@DemisHassabis is stepping up to become Chair of @GoogleDeepMind & Chief Scientist of Alphabet, in addition to leading @IsomorphicLabs. He’ll be able to dedicate his time and focus on shaping the future of AGI and scientific discovery. It’s work that is vitally important to Alphabet and humanity, and I can’t imagine a better person than Demis to do it. He’ll stay closely connected to Koray and the GDM teams.
@Koraykv will become the SVP, @GoogleDeepMind, responsible for all aspects of model development, GDM research, and @Geminiapp & dev teams. Koray has been at GDM for 13 years and is a world-renowned expert in the field, starting our deep learning team and driving breakthroughs like WaveNet & DQN. GDM is in great hands!
Excited for this next chapter. You can read my note along with the message Demis sent to @GoogleDeepMind here: https://t.co/mvsvrDUai7
She built 100+ agents for Anthropic and made $1.3M - and in 60 minutes leaked everything she knows at Stanford:
02:07 - her first agent for Anthropic brought her $1.3M
08:34 - agents replace a team of 50 engineers worth $200k a month
19:47 - one agent did overnight what the company planned for 5 years
after watching I launched my first agent - $7k in the first week and zero employees needed.
Save & watch - the article below is step by step how to build your first agent like hers.
Attention isn't magic, it's a weighted lookup, and this whiteboard walkthrough makes that click.
Q/K/V, softmax scores, multi-head, encoder blocks, all in 17 minutes.
GPT-Live can listen while it speaks.
To make that feel natural at ChatGPT scale, we rebuilt the voice stack from client to model.
This new architecture keeps audio flowing continuously, so deeper reasoning and tool use don't interrupt the conversation.
After planning your idea, it’s time to build. ����️
This video shows you how you can use the Google ecosystem to construct and deploy AI-native apps seamlessly for the Build with Gemini @XPrize Hackathon, live through August 17.
Anthropic Engineer:
"The biggest mistake in AI right now - people are building graphs and loops without self-improving eval agent
We made that mistake at Anthropic - It cost us 2 years"
here's how he build it, step by step:
step 1 → take 50 real user prompts - run your agent - if it passes 80%+, your eval is too easy - sweet spot is 50%
step 2 → every failed run is a transcript - feed it to Haiku: "what went wrong?" - your eval set builds itself for free
step 3 → score two things: right answer AND right path - right answer, wrong path = breaks next week
step 4 → new model drops, run the same eval - his +9% was fake - 6% was the model dodging a bug - the transcript caught it, the dashboard didn't
step 5 → plug evals into CI - no green evals = no deploy - this is the loop that fixes itself
99% people never did this - but it pays for itself on day one
save this and build it today - full graph engineering guide in the article below ↓
Introducing the Science One Framework, an experimental autonomous research prototype that builds verifiable evidence chains. We demonstrate how natively maintaining these chains eliminates hallucinated citations and enables reproducible AI science. Read the blog to learn more: https://t.co/sRchtF605B
Still the best hour on graph engineering ever recorded, Andrew Ng breaking down how to build agentic knowledge graphs from scratch:
00:00 - what agentic knowledge graphs actually are
03:05 - building a graph from scratch
13:58 - the architecture behind multi-agent systems
22:57 - building a real one with Google ADK
01:06:02 - why graphs are the future of AI agents
I've seen $500 courses that teach less.
Watch it, then take it further with my step-by-step guide on graph engineering below.
We’re giving scientists, mathematicians, and engineers free access to our frontier models—starting with 10,000 researchers and expanding to 100,000 through 2027.
ChatGPT for Academic Researchers is built to accelerate discovery across disciplines.
The coding agent leaderboard is not a model race. LangChain moved from 30th to 5th on Terminal-Bench 2.0 without touching the model. The gap was harness.
La biología en PDF acaba de morir otra vez.
Un tío hizo una app donde rotas células, aíslas orgánulos y comparas estructuras 3D como un videojuego.
UI: GPT Images 2. Código: Gemini 3.1 Pro.
Los libros de texto ya no mandan.
TechCrunch is joining forces with @Stripe to spotlight Australia's most ambitious early-stage startups.
Together, we're looking for founders to pitch live at Stripe Tour Sydney on August 19, 2026. The grand winner receives Stripe credits and automatic entry into TechCrunch Disrupt 2026 in San Francisco.
Applications close July 6, so apply here now: https://t.co/34FfFXL7dk
The biggest story in the Codex paper isn't that engineers code faster. It's buried in a table, 31% of Finance's agent work is engineering and coding. 25% for Product and Marketing. None of these are engineers. They're doing it anyway. Without headcount.
You can now run any Dockerfile on Vercel.
# 𝙳𝚘𝚌𝚔𝚎𝚛𝚏𝚒𝚕𝚎.𝚟𝚎𝚛𝚌𝚎𝚕
𝙵𝚁𝙾𝙼 𝚐𝚘𝚕𝚊𝚗𝚐:𝟷.𝟸𝟺
𝙲𝙾𝙿𝚈 . .
𝚁𝚄𝙽 𝚐𝚘 𝚋𝚞𝚒𝚕𝚍 -𝚘 /𝚜𝚎𝚛𝚟𝚎𝚛 .
𝙲𝙼𝙳 ["/𝚜𝚎𝚛𝚟𝚎𝚛"]
https://t.co/xOUMi4zxpD