Johnny B. =)

[Anthropic @AnthropicAI]

New on the Engineering Blog: The access and permissions we grant agents should evolve with their capabilities. In our own products, we set these parameters through sandboxing, which limits the scope of any potentially destructive actions.

Read more: https://t.co/KfBKW8O9kP

[NotebookLM @NotebookLM]

Mo sources mo problems? Not anymore:

Rolling out now, NotebookLM can auto-label & categorize sources (when you have 5+), so you can spend less time scrolling and more time thinking/learning/philosophizing, etc.

Rename, reorganize, & personalize (emojis!) to your ❤️'s content.

[Andrew Ng @AndrewYNg]

How we prompt AI is very different in 2026 than 2022 when ChatGPT came out.

I'm teaching a new course, AI Prompting for Everyone, to help you become an AI power user — whatever your current skill level.

It covers skills that apply across ChatGPT, Gemini, Claude, and other AI tools. How to use deep research mode for well-researched reports on complex questions. How to give AI the right context, including more documents and images than most people realize you can provide. When to ask AI to think hard for several minutes on important decisions like what car to buy, what to study, or what job to take. And how to use AI to generate images, analyze data, and build simple games and websites.

I also cover intuitions about how these models work under the hood, so you know when to trust an answer and when not to.

Along the way, you'll see flying squirrels, a creativity test, some of my old family photos, and fireworks.

Join me at https://t.co/tcQc4iJAJG

[Lenny Rachitsky @lennysan]

Claude Code's Head of Product: "The PM role is changing a lot. And it's changing really quickly.

The most important thing for building AI-native products is iterating quickly and finding a way to launch features every single week.

Putting less emphasis on making sure that you are aligning multi-quarter roadmaps with your partner teams, and more emphasis on, okay, how can we figure out the fastest way to get something out the door."

[Claude @claudeai]

We've redesigned Claude Code on desktop.

You can now run multiple Claude sessions side by side from one window, with a new sidebar to manage them all.

[Claude @claudeai]

Claude for Word is now in beta.

Draft, edit, and revise documents directly from the sidebar. Claude preserves your formatting, and edits appear as tracked changes.

Available on Team and Enterprise plans.

[Chief Nerd @TheChiefNerd]

🚨 Anthropic CEO Dario Amodei: “We are so close to these models reaching the level of human intelligence, and yet there doesn't seem to be a wider recognition in society of what's about to happen … There hasn't been a public awareness of the risks.”

[The Startup Ideas Podcast (SIP) 🧃 @startupideaspod]

Boris Cherny, the creator of Claude Code, shared his entire setup.

He runs 5-10 Claudes in parallel. Half his coding happens from his phone.

Here's his 3-part formula for better results:

Use the smartest model available
— Counterintuitive: it's actually cheaper
— Smarter model = fewer tokens = lower total cost
— "Once the plan is good, the code is good"

Invest in your Claude MD
— Plain text file. No special format.
— Whole team contributes multiple times a week
— Every mistake Claude makes gets added so it never happens again

Give Claude a way to verify its own output
— Let it run the code. Let it see the browser.
— "Imagine you're a painter wearing a blindfold"
— Same thing for an AI that can never check its work

His morning routine: wake up, kick off 3 sessions from his phone, check in later.

His workflow: start in plan mode
→ lock the plan
→ auto-accept edits
→ done.

No fancy setup.

No complex tooling.

Just multiple Claudes, a good plan, and a shared knowledge base.

[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 🕹️

[Google AI Studio @GoogleAIStudio]

we’ve just launched project spend caps for the Gemini API in AI Studio.

visit the new dashboard to set a dollar amount for your maximum spend to keep your costs predictable.

[Kevin O'Leary aka Mr. Wonderful @kevinolearytv]

If I were 25 today, I'd focus on two massive opportunities: Al implementation and data center development. Small businesses are desperate to adopt Al but need help executing it-that's your chance to step in and solve a huge pain point. And data centers? The demand is off the charts. Real estate meets tech in the most lucrative way. This is where the future's heading, Don't miss it.

[Andrew Ng @AndrewYNg]

Will AI create new job opportunities? My daughter Nova loves cats, and her favorite color is yellow. For her 7th birthday, we got a cat-themed cake in yellow by first using Gemini’s Nano Banana to design it, and then asking a baker to create it using delicious sponge cake and icing. My daughter was delighted by this unique creation, and the process created additional work for the baker (which I feel privileged to have been able to afford).

Many people are worried about AI taking peoples’ jobs. As a society we have a moral responsibility to take care of people whose livelihoods are harmed. At the same time, I see many opportunities for people to take on new jobs and grow their areas of responsibility.

We are still early on the path of AI generating a lot of new jobs. I don't know if baking AI-designed cakes will grow into a large business. (AI Fund is not pursuing this opportunity, because if we do, I will gain a lot of weight.) But throughout history, when people have invented tools that unleashed human creativity, large amounts of new and meaningful work have resulted. For instance, according to one study, over the past 150 years, falling employment in agriculture and manufacturing has been “more than offset by rapid growth in the caring, creative, technology, and business services sectors.”

AI is also growing the demand for many digital services, which can translate into more work for people creating, maintaining, selling, and expanding upon these services. For example, I used to carry out a limited number of web searches every day. Today, my agents carry out dramatically more web searches. For example, the Agentic Reviewer, which I started as a weekend project and Yixing Jiang then helped make much better, automatically reviews research articles. It uses a web search API to search for related work, and this generates a vastly larger number of web search queries a day than I have ever entered by hand.

The evolution of AI and software continues to accelerate, and the set of opportunities for things we can build still grows every day. I’ve stopped writing code by hand. More controversially, I’ve long stopped reading generated code. I realize I’m in the minority here, but I feel like I can get built most of what I want without having to look directly at coding syntax, and I operate at a higher level of abstraction using coding agents to manipulate code for me. Will conventional programming languages like Python and TypeScript go the way of assembly — where it gets generated and used, but without direct examination by a human developer — or will models compile directly from English prompts to byte code?

Either way, if every developer becomes 10x more productive, I don't think we’ll end up with 1/10th as many developers, because the demand for custom software has no practical ceiling. Instead, the number of people who develop software will grow massively. In fact, I’m seeing early signs of “X Engineer” jobs, such as Recruiting Engineer or Marketing Engineer, which are people who sit in a certain business function X to create software for that function.

One thing I’m convinced of based on my experience with Nova’s birthday cake: AI will allow us to have a batter life!

[Original text: https://t.co/yws8drSTfO ]

[Andrew Ng @AndrewYNg]

Job seekers in the U.S. and many other nations face a tough environment. At the same time, fears of AI-caused job loss have — so far — been overblown. However, the demand for AI skills is starting to cause shifts in the job market. I’d like to share what I’m seeing on the ground.

First, many tech companies have laid off workers over the past year. While some CEOs cited AI as the reason — that AI is doing the work, so people are no longer needed — the reality is AI just doesn’t work that well yet. Many of the layoffs have been corrections for overhiring during the pandemic or general cost-cutting and reorganization that occasionally happened even before modern AI. Outside of a handful of roles, few layoffs have resulted from jobs being automated by AI.
Granted, this may grow in the future. People who are currently in some professions that are highly exposed to AI automation, such as call-center operators, translators, and voice actors, are likely to struggle to find jobs and/or see declining salaries. But widespread job losses have been overhyped.

Instead, a common refrain applies: AI won’t replace workers, but workers who use AI will replace workers who don’t. For instance, because AI coding tools make developers much more efficient, developers who know how to use them are increasingly in-demand. (If you want to be one of these people, please take our short courses on Claude Code, Gemini CLI, and Agentic Skills!)

So AI is leading to job losses, but in a subtle way. Some businesses are letting go of employees who are not adapting to AI and replacing them with people who are. This trend is already obvious in software development. Further, in many startups’ hiring patterns, I am seeing early signs of this type of personnel replacement in roles that traditionally are considered non-technical. Marketers, recruiters, and analysts who know how to code with AI are more productive than those who don’t, so some businesses are slowly parting ways with employees that aren’t able to adapt. I expect this will accelerate.

At the same time, when companies build new teams that are AI native, sometimes the new teams are smaller than the ones they replace. AI makes individuals more effective, and this makes it possible to shrink team sizes. For example, as AI has made building software easier, the bottleneck is shifting to deciding what to build — this is the Product Management (PM) bottleneck. A project that used to be assigned to 8 engineers and 1 PM might now be assigned to 2 engineers and 1 PM, or perhaps even to a single person with a mix of engineering and product skills.

The good news for employees is that most businesses have a lot of work to do and not enough people to do it. People with the right AI skills are often given opportunities to step up and do more, and maybe tackle the long backlog of ideas that couldn’t be executed before AI made the work go more quickly. I’m seeing many employees in many businesses step up to build new things that help their business. Opportunities abound!

I know these changes are stressful. My heart goes out to every family that has been affected by a layoff, to every job seeker struggling to find the role they want, and to the far larger number of people who are worried about their future job prospects. Fortunately, there’s still time to learn and position yourself well for where the job market is going. When it comes to AI, the vast majority of people, technical or nontechnical, are at the starting line, or they were recently. So this remains a great time to keep learning and keep building, and the opportunities for those who do are numerous!

[Original text; https://t.co/zbIhZHfCC0 ]

[Andrew Ng @AndrewYNg]

Another year of rapid AI advances has created more opportunities than ever for anyone — including those just entering the field — to build software. In fact, many companies just can’t find enough skilled AI talent. Every winter holiday, I spend some time learning and building, and I hope you will too. This helps me sharpen old skills and learn new ones, and it can help you grow your career in tech.

To be skilled at building AI systems, I recommend that you:
- Take AI courses
- Practice building AI systems
- (Optionally) read research papers

Let me share why each of these is important.

I’ve heard some developers advise others to just plunge into building things without worrying about learning. This is bad advice! Unless you’re already surrounded by a community of experienced AI developers, plunging into building without understanding the foundations of AI means you’ll risk reinventing the wheel or — more likely — reinventing the wheel badly!

For example, during interviews with job candidates, I have spoken with developers who reinvented standard RAG document chunking strategies, duplicated existing evaluation techniques for Agentic AI, or ended up with messy LLM context management code. If they had taken a couple of relevant courses, they would have better understood the building blocks that already exist. They could still rebuild these blocks from scratch if they wished, or perhaps even invent something superior to existing solutions, but they would have avoided weeks of unnecessary work. So structured learning is important. Moreover, I find taking courses really fun. Rather than watching Netflix, I prefer watching a course by a knowledgeable AI instructor any day!

At the same time, taking courses alone isn’t enough. There are many lessons that you���ll gain only from hands-on practice. Learning the theory behind how an airplane works is very important to becoming a pilot, but no one has ever learned to be a pilot just by taking courses. At some point, jumping into the pilot's seat is critical! The good news is that by learning to use highly agentic coders, the process of building is the easiest it has ever been. And learning about AI building blocks might inspire you with new ideas for things to build. If I’m not feeling inspired about what projects to work on, I will usually either take courses or read research papers, and after doing this for a while, I always end up with many new ideas. Moreover, I find building really fun, and I hope you will too.

Finally, not everyone has to do this, but I find that many of the strongest candidates on the job market today at least occasionally read research papers. While I find research papers much harder to digest than courses, they contain a lot of knowledge that has not yet been translated to easier-to-understand formats. I put this much lower priority than either taking courses or practicing building, but if you have an opportunity to strengthen your ability to read papers, I urge you to do so too. I find taking courses and building to be fun, and reading papers can be more of a grind, but the flashes of insight I get from reading papers are delightful.

Have a wonderful winter holiday and a Happy New Year. In addition to learning and building, I hope you'll spend time with loved ones — that, too, is important!

[Original text: https://t.co/MaWDs0AbzG ]

[Johnny B. =) @JohnnyB95]

@AndrewYNg @jyx_su Great idea! This is helpful for any student. May I ask what is ICLR ?

[Andrew Ng @AndrewYNg]

Is there an AI bubble? With the massive number of dollars going into AI infrastructure such as OpenAI’s $1.4 trillion plan and Nvidia briefly reaching a $5 trillion market cap, many have asked if speculation and hype have driven the values of AI investments above sustainable values. However, AI isn’t monolithic, and different areas look bubbly to different degrees.
- AI application layer: There is underinvestment. The potential is still much greater than most realize.
- AI infrastructure for inference: This still needs significant investment.
- AI infrastructure for model training: I’m still cautiously optimistic about this sector, but there could also be a bubble.

Caveat: I am absolutely not giving investment advice!

AI application layer. There are many applications yet to be built over the coming decade using new AI technology. Almost by definition, applications that are built on top of AI infrastructure/technology (such as LLM APIs) have to be more valuable than the infrastructure, since we need them to be able to pay the infrastructure and technology providers.

I am seeing many green shoots across many businesses that are applying agentic workflows, and am confident this will grow. I have also spoken with many Venture Capital investors who hesitate to invest in AI applications because they feel they don’t know how to pick winners, whereas the recipe for deploying $1B to build AI infrastructure is better understood. Some have also bought into the hype that almost all AI applications will be wiped out merely by frontier LLM companies improving their foundation models. Overall, I believe there is significant underinvestment in AI applications. This area remains a huge focus for my venture studio, AI Fund.

AI infrastructure for inference. Despite AI’s low penetration today, infrastructure providers are already struggling to fulfill demand for processing power to generate tokens. Several of my teams are worried about whether we can get enough inference capacity, and both cost and inference throughput are limiting our ability to use even more. It is a good problem to have that businesses are supply-constrained rather than demand-constrained. The latter is a much more common problem, when not enough people want your product. But insufficient supply is nonetheless a problem, which is why I am glad our industry is investing significantly in scaling up inference capacity.

As one concrete example of high demand for token generation, highly agentic coders are progressing rapidly. I’ve long been a fan of Claude Code; OpenAI Codex also improved dramatically with the release of GPT-5; and Gemini 3 has made Google CLI very competitive. As these tools improve, their adoption will grow. At the same time, overall market penetration is still low, and many developers are still using older generations of coding tools (and some aren’t even using any agentic coding tools). As market penetration grows — I’m confident it will, given how useful these tools are — aggregate demand for token generation will grow.

I predicted early last year that we’d need more inference capacity, partly because of agentic workflows. Since then, the need has become more acute. As a society, we need more capacity for AI inference.

Having said that, I’m not saying it’s impossible to lose money investing in this sector. If we end up overbuilding — and I don’t currently know if we will — then providers may end up having to sell capacity at a loss or at low returns. I hope investors in this space do well financially. The good news, however, is that even if we overbuild, this capacity will get used, and it will be good for application builders!

AI infrastructure for model training. I am happy to see the investments going into training bigger models. But, of the three buckets of investments, this seems the riskiest. If open-source/open-weight models continue to grow in market share, then some companies that are pouring billions into training models might not see an attractive financial return on their investment.

Additionally, algorithmic and hardware improvements are making it cheaper each year to train models of a given level of capability, so the “technology moat” for training frontier models is weak. (That said, ChatGPT has become a strong consumer brand, and so it enjoys a strong brand moat, while Gemini, assisted by Google's massive distribution advantage, is also making a strong showing.)

I remain bullish about AI investments broadly. But what is the downside scenario — that is, is there a bubble that will pop? One scenario that worries me: If part of the AI stack (perhaps in training infra) suffers from overinvestment and collapses, it could lead to negative market sentiment around AI more broadly and an irrational outflow of interest away from investing in AI, despite the field overall having strong fundamentals. I don’t think this will happen, but if it does, it would be unfortunate since there’s still a lot of work in AI that I consider highly deserving of much more investment.

Warren Buffett popularized Benjamin Graham’s quote, “In the short run, the market is a voting machine, but in the long run, it is a weighing machine.” He meant that in the short term, stock prices are driven by investor sentiment and speculation; but in the long term, they are driven by fundamental, intrinsic value. I find it hard to forecast sentiment and speculation, but am very confident about the long-term health of AI’s fundamentals. So my plan is just to keep building!

[Original text: https://t.co/psPlIFRJsi ]

[Google Gemini @GeminiApp]

Our most anticipated launch of the year is here.

- Gemini 3, our most intelligent model

- Generative interfaces, for perfectly designed responses

- Gemini Agent, made to complete complex tasks on your behalf

See how Gemini 3 can help you learn, build & plan anything 🧵

[NotebookLM @NotebookLM]

The moment you've ACTUALLY been waiting for... Introducing Deep Research!

Rolling out now, Deep Research browses hundreds of sites to craft an organized report AND gives you an annotated list of sources for deeper exploration, all of which you can add directly to your notebook.

[Satya Nadella @satyanadella]

I’ve been thinking a lot about what the net benefit of the AI platform wave is. The real question is how to empower every company out there to get more out of this platform shift and build their own AI native capabilities and enterprise value (vs inadvertently just transfer their unique value to the tech sector!!).

Bill famously said a platform is when the economic value of everybody that uses it exceeds the value of the company that creates it. That’s the essence of the positive-sum future.

Even in our somewhat zero-sum mindset industry, we can create partnerships that create value for all parties involved. Our partnership with OpenAI is a great example. Our investment helped them scale; their research accelerated our own innovation. That’s what healthy platforms and partners do—they catalyze and compound progress.

There’s no better proof than what we announced just this week. The world’s first AI superfactory was co-designed with OpenAI and informed by three generations of AI supercomputers we built for frontier model training and inference. It was also a result of working closely with Nvidia and getting better at the full stack optimization from model architecture to micro-architecture of the chip and everything between three companies!

We also did the work to bring AMD into the fleet doing inference of GPT models, which enabled them to get up to speed on their own software stack for AI.

And now all this infrastructure will scale to support every startup to enterprise doing their own training to inference.

You can see the same dynamic in coding. Thanks to AI, the category itself has expanded and may ultimately become one of the largest software categories. I don’t ever recall any analyst ever asking me about how much revenue Visual Studio makes! But now everyone is excited about AI coding tools. This is another aspect of positive sum, when the category itself is redefined and the pie becomes 10x what it was! With GitHub Copilot we compete for our share and with GitHub and Agent HQ we also provide a platform for others.

Of course, the real test of this era won’t be when another tech company breaks a valuation record. It will be when the overall economy and society themselves reach new heights.

When a pharma company uses AI in silico to bring a new therapy to market in one year instead of twelve. When a manufacturer uses AI to redesign a supply chain overnight. When a teacher personalizes lessons for every student. When a farmer predicts and prevents crop failure. That’s when we’ll know the system is working.

Let us move beyond zero-sum thinking and the winner-take-all hype and focus instead on building broad capabilities that harness the power of this technology to achieve local success in each firm, which then leads to broad economic growth and societal benefits. And every firm needs to make sure they have control of their own destiny and sovereignty vs just a press release with a Tech/AI company or worse leak all their value through what may seem like a partnership, except it's extractive in terms of value exchange in the long run.

We know that the Internet wave had tremendous positive sum impact in the world, and yet we also had some sectors that got hollowed out like local media. This time around we have the opportunity to ensure broad diffusion of this tech with choice and control that is distributed to ensure positive sum outcomes across the board.

At the end of the day, this new technological wave gives us the opportunity to dream bigger and set higher ambitions for what we can collectively achieve. Each of us will need to play our part!

[Nozz @NoahEpstein_]

🚨 Anthropic just dropped something that makes $10K automation consultants look like clowns.

It's called "Skills" and it's basically how Claude becomes a specialist in YOUR exact workflow without re-prompting every damn time.

Not another "AI update" - this is the actual framework companies like Box, Notion, and Rakuten are using to:

→ Cut day-long reports to 1 hour (8× faster)
→ Apply brand standards automatically to every file
→ Build agents that remember your procedures forever

The kicker? It's 100% FREE if you already have Claude Pro/Team/Enterprise.

While everyone's paying n8n consultants $15K for basic automations, Claude just learned to do specialised work through simple Markdown files.

A finance team can go from spending 8 hours on reports to finishing in 1 hour. Same day. Zero retraining.

This is the "custom instructions" killer nobody saw coming.

Comment "SKILLS" and I'll send you the setup guide + best custom skills to replace your overpriced consultants.