خليفة

[Google Cloud Tech @GoogleCloudTech]

Introducing the Open Knowledge Format (OKF), an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format.

AI is only as smart as the context we give it. As we build more advanced, agentic AI systems, they need accurate metadata and context to be useful. But in most organizations, that context is locked inside fragmented data catalogs, isolated wikis, scattered code comments, or the minds of senior engineers. Every time a new AI agent is built, teams are forced to solve the exact same context-assembly problem from scratch.

To solve this, we've announced OKF, a vendor-neutral, open specification that formalizes the "LLM-wiki pattern" into a portable, interoperable format. It provides a standardized way to represent the enterprise knowledge that modern AI systems rely on.

— Just markdown: readable in any editor, renderable on GitHub, indexable by any search tool
— Just files: shippable as a tarball, hostable in any git repo, mountable on any filesystem
— Just YAML frontmatter: for the small set of structured fields that need to be queryable: type, title, description, resource, tags, and timestamp

We’ve also shipped reference implementations to help you hit the ground running, including an enrichment agent for BigQuery, a static HTML visualizer, and live sample bundles on @github → https://t.co/ilhAMCrcTc

➕ Knowledge Catalog can now natively ingest OKF!

Stop reinventing data models and building bespoke integrations for every new AI tool. Here's more about how OKF works → https://t.co/FR4kJRsgEH

[Claude @claudeai]

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use.

Its capabilities exceed those of any model we’ve ever made generally available.

[🇪🇸 ⁸ ⁷⁷ #CAMPEONES ⭐️⭐️ @P8BCN]

THE SAN ANTONIO SPURS HAVE ELIMINATED THE OKLAHOMA CITY THUNDER

[VISION IA @vision_ia]

Première mondiale ! Un robot a travaillé 200h non-stop, et trié plus de 249 000 colis à lui seul. Pas une seule panne, pas une seule pause et tout a été diffusé en live pour le prouver.

À la base c'était un défi de 8h. Le robot a tellement bien tourné qu'ils ne l'ont jamais coupé. 200 heures plus tard il tournait encore.

Le truc de fou, c'est qu'il y a quelques jours un stagiaire a fait un duel contre le robot sur un shift de 10h. Le gars a gagné. De justesse, 2.79 secondes par colis contre 2.83 pour la machine. Sauf que le stagiaire a fini avec l'avant-bras en vrac. Le robot lui il a continué 190 heures de plus sans broncher.

Et c'est là que je comprends pas. On a littéralement un robot humanoïde qui fait un boulot d'entrepôt en continu, sans supervision, tout est géré par son IA embarquée. Si le robot bug, il se reset tout seul et reprend. Si il a un souci hardware, il sort de la ligne et un autre prend le relais automatiquement.

Malgré tout ça, la majorité des gens ne voient pas ce qui arrive. On scrolle, on passe, on se dit "c'est cool" et on oublie. Mais c'est pas "cool". C'est un changement de civilisation. Les tâches physiques répétitives vont être automatisées.

La robotique humanoïde c'est le sujet dont personne ne parle assez. On commence à peine à parler d'IA avec bien du retard, sauf qu'il faut comprendre que l'étape d'après c'est l'IA incarnée, cad, les robots.

[Google @Google]

We just released Gemma 4 — our most intelligent open models to date.

Built from the same world-class research as Gemini 3, Gemma 4 brings breakthrough intelligence directly to your own hardware for advanced reasoning and agentic workflows.

Released under a commercially permissive Apache 2.0 license so anyone can build powerful AI tools. 🧵↓

[QuranVersesDaily @QuranVersesNow]

Yusuf (12:73)

"They said, "By Allah, you have certainly known that we did not come to cause corruption in the land, and we have not been thieves.""

Watch the Short:
https://t.co/7nS7bsZVTk

#Quran #IslamicReminder

[Trustmebro @trustmetobro]

TurboQuant in plain English:

Think of a smart AI chatbot like ChatGPT or Gemini. When you chat with it for a while or give it a long document to read, it has to remember everything you said earlier. It stores that memory in a special notebook inside the computer called the key-value cache.

That notebook gets huge really fast. It eats up tons of memory (RAM) and slows everything down. On a phone, laptop, or even a normal computer, this means:

• The AI can only handle short chats before it chokes.
• It needs expensive powerful hardware.
• Responses get slow.

Google Research just released TurboQuant, a new compression trick that:

• Shrinks that memory notebook by at least 6× (sometimes way more).
• Makes the AI up to 8× faster.
• Does it with zero loss in accuracy (the AI is just as smart as before).

It’s like taking a giant photo file, compressing it to 1/6th the size with a perfect zip tool, and the picture still looks identical when you open it. No blurry edges, no missing details.

What this actually means for regular people:

• AI chatbots can now handle much longer conversations without slowing down or running out of memory.
• It works better on phones, laptops, and cheaper computers—no need for giant data-center GPUs.
• Future AI (including Google’s own models) will feel snappier and cheaper to run.
• The little animation in the tweet shows colorful bars (representing AI memory) getting neatly packed into a tiny grid. That’s exactly what TurboQuant is doing behind the scenes.

Bottom line: Google figured out a smarter way to make AI’s memory tiny and lightning-fast without sacrificing quality. This is the kind of behind-the-scenes breakthrough that will make AI feel way more practical in everyday apps soon. No magic, just really clever math that finally works perfectly.

[خليفة @babttz]

Well well. Here we go 🚀

[Animesh Koratana @akoratana]

Introducing: PlayerZero

The world's first Engineering World Model that puts debugging, fixing, and testing your code on autopilot.

We've raised $20M from Foundation Capital, @matei_zaharia (Databricks), @pbailis (Workday), @rauchg (Vercel), @zoink (Figma), @drewhouston (Dropbox), and more

PlayerZero frees up 30% of your engineering bandwidth by:
1.⁠ ⁠Finding the root cause for bugs & incidents in minutes that engineering teams take days to identify.
2.⁠ ⁠Predicting in minutes, edge case issues that a 300-person QA team would take weeks to find.

------

Here's why this matters:

No one in your org has a complete picture of how your production software actually behaves.

Support sees tickets. SRE sees infra. Dev sees code. Each team builds their own fragmented view - and none of these systems talk to each other. When something breaks, everyone scrambles to stitch the picture together by hand.

PlayerZero connects all of it into a single context graph -

→ The Slack thread where your lead said "we went with X because Y fell apart in prod last time"
→ The PR review where an engineer explained the tradeoff
→ The lifetime history of your CI/CD pipeline, observability stack, incidents, and support tickets

So you can trace any problem to its root cause across every silo.

And it compounds. Every incident diagnosed teaches the model something new. The longer it runs, the deeper it understands - which code paths are high-risk, which configurations are fragile, which changes tend to break which customer flows.

So when you sit down to debug a live issue, you have your entire org's collective reasoning and production memory behind you - instantly.

------

Zuora, Georgia-Pacific, and Nylas have reduced resolution time by 90% and caught 95% of breaking changes and freeing an average of $30M in engineering bandwidth.

------

Our guarantee:

If we can't increase your engineering bandwidth by at least 20% within one week, we'll donate $10,000 to an open-source project of your choice.

Book a demo - https://t.co/dH1dulIwSS

[Google AI @GoogleAI]

We’re launching a brand new, full-stack vibe coding experience in @GoogleAIStudio, made possible by integrations with the @Antigravity coding agent and @Firebase backends.

This unlocks:

— Full-stack multiplayer experiences: Create complex, multiplayer apps with fully-featured UIs and backends directly within AI Studio

— Connection to real-world services: Build applications that connect to live data sources, databases, or payment processors and the Antigravity agent will securely store your API credentials for you

— A smarter agent that works even when you don't: By maintaining a deeper understanding of your project structure and chat history, the agent can execute multi-step code edits from simpler prompts. It also remembers where you left off and completes your tasks while you’re away, so you can seamlessly resume your builds from anywhere

— Configuration of database connections and authentication flows: Add Firebase integration to provision Cloud Firestore for databases and Firebase authentication for secure sign-in

This demo displays what can be built in the new vibe coding experience in AI Studio. Geoseeker is a full-stack application that manages real-time multiplayer states, compass-based logic, and an external API integration with @GoogleMaps 🕹️

[NIK @ns123abc]

🚨 MICROSOFT ABOUT TO SUE OPENAI & AMAZON

>be microsoft
>invest $1B in openai
>gets exclusive azure cloud deal
>invest another $10B+
>gets rights to 49% of profits +IP
>Azure goes brrrrrr

>Altman lies to board, quietly launches ChatGPT
>board fires him for being a lying manipulative snake
>Satya goes to war for Altman. saves his entire career
>Altman retvrns in 5 days
>immediately purges everyone who purged him
>full control. no oversight. thanks Satya!

>fast forward to 2025
>OpenAI restructures from non-profit to PBC
>MSFT $13.8B is now worth $135B. 10x return
>plus 27% of OpenAI
>but gives up cloud exclusivity + profit share
>KEEPS API clause
>all API calls contractually MUST route through Azure
>Satya thinks life is good lol

>5 months later
>Sam Altman becomes strong enough to betray you
>"raises $110B round"
>doesn't need satya daddy's money anymore
>announces $50B deal with AMAZON
>$138B in AWS cloud commitments

>amazon and openai claim they built some cope called a "Stateful Runtime Environment"
>Microsoft lawyers hmmm
>Altman: it's not what it looks like. i can totally explain

>so it's technically not an API call because it's "stateful"
>and it's a... "Runtime Experience"
>totally di!erent thing
>pls ignore the TCP packets lol

>Microsoft engineers look at the SRE architecture
>"THIS IS NOT TECHNICALLY POSSIBLE without violating the contract."

*Satya finds out he's been cucked*

Microsoft exec literally tells FT: "We know our contract. We will sue them if they breach it."

>AWS quietly gives employees a memo on which words are legally safe lmao
>can say: "powered by" or "enabled by" or "integrates with" OpenAI
>cannot say: "enables access to" or "calls on" ChatGPT
>also cannot suggest frontier models are "available on AWS"

Microsoft: "If Amazon and OpenAI want to take a bet on the creativity of their contractual lawyers, I would back us, not them."

Scam Altman strikes AGAIN.

[Defend Intelligence (Anis Ayari) @DFintelligence]

C’est le futur dont nous avons besoin, mais la plupart des gens qui ne comprennent pas comment fonctionne l’IA ne comprendront pas pourquoi.

Donc laissez-moi vous expliquer rapidement.

L’IA générative a pris une telle puissance parce qu’on a trouvé un moyen de mieux “condenser” l’information et surtout d’apprendre cette condensation avec des architectures comme les transformers.

Cette information condensée est ensuite représentée sous forme de petits points qu’on appelle « embeddings ».

Ça permet de comprendre rapidement les relations entre les points.

Les embeddings les plus simples à calculer et à exploiter au début, c’était surtout le texte. C’est aussi pour ça qu’on a commencé par là.

Puis petit à petit, les autres types d’informations qui existent, comme le son, l’image ou la vidéo, se sont adaptés à ces méthodes.

Chaque type d’information est ce qu’on appelle une modalité.

Depuis plusieurs années, on fait des systèmes d’IA multimodaux, c’est-à-dire des modèles qui comprennent plusieurs types d’informations, mais souvent encore de manière séparée ou partiellement unifiée.

Par exemple, pour lire un texte dans une image, on peut utiliser un OCR, récupérer ce texte, puis le passer comme du texte classique pour le transformer en embedding.
Ou encore pour analyser une image on avait des modèle d’embeddings spécifique qui décrivait en texte l’image puis on donnait cette description au LLM.

Ce n’est pas de la triche, c’est juste une méthode.

Le futur, c’est ce que vous voyez là : une information omnimodale.

C’est-à-dire que tous les types d’informations pourraient être condensés dans une forme unique.

Ça va permettre de construire des systèmes beaucoup plus rapides, beaucoup plus fluides, et beaucoup plus intelligents.

Un des derniers grand mode qui manque vraiment par exemple, c’est le toucher, notamment pour avoir des robots capables de réagir correctement dans notre vie quotidienne.

Certains chercheurs essayent même d’intégrer des signaux cérébraux.

Bref, au fond, tout ça est surtout une histoire de condensation de l’information.

Et c’est super qu’on aille dans cette direction.

[خليفة @babttz]

Another one

[Thariq @trq212]

Voice mode is rolling out now in Claude Code. It’s live for ~5% of users today, and will be ramping through the coming weeks.

You'll see a note on the welcome screen once you have access. /voice to toggle it on!

[jeremy @jrmys]

@trq212 the team’s shipping speed is crazy

[VISION IA @vision_ia]

Le plus grand débat de l'histoire du Seigneur des Anneaux vient d'être résolu.

Par une IA.

"Pourquoi ils prennent pas les Aigles directement pour aller à la montagne du destin ? "

Quelqu'un a demandé à Seedance 2.0 de générer cette scène. Sam pose la question à Frodon. Frodon réalise que ça a du sens. Cut direct : ils chevauchent les Aigles, balancent l'anneau dans le volcan, la tour de Sauron explose.

THE END. 15 secondes. Film terminé.

Peter Jackson a mis 3 films et 11h pour raconter cette histoire.

L'IA l'a fait en un prompt. 😂

2.7M de vues en quelques heures. Le post est devenu viral parce que tout le monde s'est dit la même chose :
On peut maintenant générer la version du film qu'on a toujours voulu voir.

Seedance 2.0 ne fait pas que de la vidéo. Il fait du fan service à l'échelle industrielle.

[Typical African @Joe__Bassey]

This is Senegal 🇸🇳, one of the best African countries to visit in 2026.

[Jarrod Watts @jarrodwatts]

https://t.co/kYK1WYefiK

[خليفة @babttz]

🤣🤣💀 great read

[Peter Girnus 🦅 @gothburz]

Last quarter I rolled out Microsoft Copilot to 4,000 employees.

$30 per seat per month.

$1.4 million annually.

I called it "digital transformation."

The board loved that phrase.

They approved it in eleven minutes.

No one asked what it would actually do.

Including me.

I told everyone it would "10x productivity."

That's not a real number.

But it sounds like one.

HR asked how we'd measure the 10x.

I said we'd "leverage analytics dashboards."

They stopped asking.

Three months later I checked the usage reports.

47 people had opened it.

12 had used it more than once.

One of them was me.

I used it to summarize an email I could have read in 30 seconds.

It took 45 seconds.

Plus the time it took to fix the hallucinations.

But I called it a "pilot success."

Success means the pilot didn't visibly fail.

The CFO asked about ROI.

I showed him a graph.

The graph went up and to the right.

It measured "AI enablement."

I made that metric up.

He nodded approvingly.

We're "AI-enabled" now.

I don't know what that means.

But it's in our investor deck.

A senior developer asked why we didn't use Claude or ChatGPT.

I said we needed "enterprise-grade security."

He asked what that meant.

I said "compliance."

He asked which compliance.

I said "all of them."

He looked skeptical.

I scheduled him for a "career development conversation."

He stopped asking questions.

Microsoft sent a case study team.

They wanted to feature us as a success story.

I told them we "saved 40,000 hours."

I calculated that number by multiplying employees by a number I made up.

They didn't verify it.

They never do.

Now we're on Microsoft's website.

"Global enterprise achieves 40,000 hours of productivity gains with Copilot."

The CEO shared it on LinkedIn.

He got 3,000 likes.

He's never used Copilot.

None of the executives have.

We have an exemption.

"Strategic focus requires minimal digital distraction."

I wrote that policy.

The licenses renew next month.

I'm requesting an expansion.

5,000 more seats.

We haven't used the first 4,000.

But this time we'll "drive adoption."

Adoption means mandatory training.

Training means a 45-minute webinar no one watches.

But completion will be tracked.

Completion is a metric.

Metrics go in dashboards.

Dashboards go in board presentations.

Board presentations get me promoted.

I'll be SVP by Q3.

I still don't know what Copilot does.

But I know what it's for.

It's for showing we're "investing in AI."

Investment means spending.

Spending means commitment.

Commitment means we're serious about the future.

The future is whatever I say it is.

As long as the graph goes up and to the right.

[Global Index @TheGlobal_Index]

@TheFigen_ A father reviews a 4-month old baby and has an interesting perspective

[Anthropic @AnthropicAI]

We disrupted a highly sophisticated AI-led espionage campaign.

The attack targeted large tech companies, financial institutions, chemical manufacturing companies, and government agencies. We assess with high confidence that the threat actor was a Chinese state-sponsored group.

[خليفة @babttz]

@dashboardlim BUILD