Why Do AI Data Centres Use So Much Water ? Is it creating a water crisis? https://
youtu.be/DpfffbzEcno?si
=6KZ-h4-T9VaTYgVo
… via @YouTube #AI #artificialintelligence #datacentre @lexfridman @KirkDBorne @Ronald_vanLoon @erikbryn @antgrasso @sallyeaves @Nicochan33 @HaroldSinnott @mvollmer1 @marcusborba
AI
-
Why AI Data Centres Use So Much Water and Its Water Crisis Impact
By
–
-
India’s AI Vision Is Different: Here’s Why
By
–
India's AI Vision Is Different. Here's Why https://
youtu.be/mLkm3TeZSTA?si
=B8Oal0SWTPc4s6ta
… via @YouTube #India #AI #artificialintelligence @sonu_monika @enilev @Jagersbergknut @TysonLester @chidambara09 @labordeolivier @BetaMoroney @tlloydjones @Nicochan33 @jeancayeux @RLDI_Lamy @pierrepinna -
Surprise: GLM-5.2 third behind GPT-5.5 and Opus 4.8
By
–
When I read all the posts about the general surprise at the actual performance of GLM-5.2, which matches the claims, and that many benchmarks confirm it (generally just behind GPT-5.5 and Opus 4.8 in third place), I can even imagine that the founder
-
Turn any paper into running code with autoarxiv
By
–
Turn any paper into running code.
— Akshay 🚀 (@akshay_pachaar) 21 juin 2026
Just swap arxiv → autoarxiv in the paper url.
That hands the paper to an AI agent from alphaXiv. It reads the abstract, the claims, and the linked GitHub repo, then clones the codebase and works through the usual setup pain like dependencies,… pic.twitter.com/UOPJnWdfLJTurn any paper into running code. Just swap arxiv → autoarxiv in the paper url. That hands the paper to an AI agent from alphaXiv. It reads the abstract, the claims, and the linked GitHub repo, then clones the codebase and works through the usual setup pain like dependencies,
-
Reference to GPT-5.6’s superiority in front-end
By
–
This is a reference to the fact that GPT-5.6 is significantly better in front-end, isn't it?
-
From RAG retrieval to knowledge compilation: the LLM Wiki
By
–
RAG is already becoming the “old way” The future of AI memory is not retrieval.
It’s compilation. Here’s the shift in one sentence: From searching information To structuring knowledge The new model? LLM Wiki Instead of: Chunking documents Running similarity -
Three ways to run sub-agents: Fork, Teammate, Worktree
By
–
Nowadays everyone think sub-agents are magic.
They're not. There are basically 3 ways people run them right now. 𝟭. 𝗙𝗼𝗿𝗸
The agent spins off a task, gets the result, and continues. 𝟮. 𝗧𝗲𝗮𝗺𝗺𝗮𝘁𝗲
Multiple agents work together and share context. 𝟯. 𝗪𝗼𝗿𝗸𝘁𝗿𝗲𝗲 -
The gap between pilot and scaling blocks AI strategies
By
–
The gap between "we launched a pilot" and "we know how to scale this" is where most corporate AI strategies are quietly blocked.
-
End of programming, machines generate their own code
By
–
Fin de la programmation.
— Stephane Mallard (@StephaneMallard) 21 juin 2026
Les machines qui génèrent leur propre code.
Nous y sommes. https://t.co/sO1JWjn1gNEnd of programming.
Machines that generate their own code.
We are there. -
Meta AI unveils Artifacts tab to store presentations and documents
By
–
Meta AI gets a new Artifacts tab on the web. All presentations, documents, web pages and other creations would be stored there. Bridging the gap.