from the vibe-code-your-social-experience dept

Disclaimer: This post talks about Bluesky and an offering from Bluesky and I am on the Bluesky board. Take everything I say with whatever size grains of salt you feel is appropriate.

I’ve written a few times now about how I think that AI tools, used carefully and thoughtfully, represent our best chance at taking back control over the open web. I know this is not a popular opinion with many Techdirt readers, but I’m hoping some of you will read through this to try to understand and engage with the points I’m making here. I truly do believe that if used well and appropriately, these tools can serve to put power back into the hands of users, rather than giant centralized companies who are more interested in exploiting your attention.

Over the last few weeks I’ve been playing around with an AI-powered tool that Bluesky has released (much to the chagrin of many users) to a relatively small group of early beta testers. I think the negative reaction to the product announcement is understandable, given the general distrust of all AI tools, but it’s really worth examining what this tool is and what it can enable, including really empowering people to take back control over their own social experience. It literally gives you a path to routing around Bluesky’s own design features if you don’t like them.

Yes, a lot of AI is overhyped garbage being shoved at people who don’t want it — but that doesn’t mean the underlying tools can’t be useful when applied carefully by those who choose to use the tools appropriately.

It means not outsourcing your brain to the tool, but rather using it the way any skilled person automates some aspect of work that they do. I’ve sanded and restained the floors of my house, and while I could have done the whole thing by hand with a stack of sandpaper, it was helpful to rent a floor sander from a local hardware store, learn how to use it properly, and then use it so that I could finish the job in a day rather than weeks. I view AI tools the same way. If you learn how to use them properly, as an assistive tool rather than a replacement for your brain, they can help you accomplish useful things.

Let me give an example: a couple of weeks ago, law professor Blake Reid wrote a short thread on Bluesky about how he needed to take a break from social media, because he worried that it was eating up too much of his time and he was better off just stopping cold turkey, to avoid getting sucked into unproductive discussions that push him to (as he put it) “get over my skis” in engaging in conversations where he’s tempted to weigh in despite not having much expertise (a common thing on social media). It’s a worthwhile thread.

But in that thread he mentioned that he was hopeful that maybe some day technology itself could help him use social media in a healthier way, to dial back how much time he spent on it, and get him focused on the more productive and useful discussions (which he admits also happen regularly on Bluesky).

What was amusing to me was that the only reason I saw that post by Reid was because I’ve been beta testing a new tool that… kinda does that. When he wrote that thread, I was actually on vacation, hiking in the National Parks in Utah, and mostly offline. But in the evenings, I would check in, and rather than sorting through everything I missed on social media that day, I had a tool just show me things that I would find useful that I might have missed.

But using an AI tool, I had built an entirely personalized news aggregator, which had access to my Bluesky account, Techdirt’s RSS feed, and the knowledge that I had been out all day and wanted not just a summary of what news might be interesting to me as the editor of Techdirt, but also what people on Bluesky were saying about it. Here’s a screenshot of what my first attempt at this looks like:

The tool that let me do this is an advanced version of Attie, which I also recognize is extremely controversial among users on Bluesky, many of whom vocally have expressed their hatred of the very idea of it when it was announced last month. But, my main interest is in figuring out to empower users who want to take control over their own social experience, and this seems like a clear example of that. I’ll note that this version of Attie has not yet rolled out to most of the beta testers (I believe some have access to it — but this is one small benefit of being on the board).

Honestly, I think the way Bluesky announced Attie may have done it an injustice, positioning it as a kind of AI-powered feed generator. There are multiple other feed generator tools for Bluesky out there, many of which are really fantastic. For a while now I’ve used both Graze.social and Surf.social to make AI-powered feeds (which never seemed to generate much controversy).

But generating feeds alone isn’t all that interesting. With the more advanced version of Attie, I can take much more control over my entire social experience. The fact that with a single prompt I could build that personalized aggregator (based not just on my own feed, but Techdirt’s RSS) is something more powerful, including the fact that the tool knows to summarize a whole days’ worth of posts, because I’m trying to see in a glance if there’s anything relevant for Techdirt and I’d been offline the entire day.

Rather than just letting a single company (in this case Bluesky) define my entire experience for me, I can vibe-code my social experience. I can tell it not just the types of content I want to see, but how I want to see it. And for what reason. And how much (or how little) content to show me. And with what context around it. It’s all based on what I expressly want. Not what any company thinks I want.

And I keep experimenting with other versions of this as well. In one test, I had it also try to summarize stories and tell me why it thought I’d find them useful for Techdirt:

In this case it not only found a story that is interesting to me, but it suggested multiple sources for me to read about it, even noting (for example) that Professor Eric Goldman’s blog post is “the definitive blog post” for my coverage (it’s not wrong).

I go back to the piece I wrote a little while back about the kind of learned helplessness of social media users. We’ve had two decades of billionaires deciding exactly how they wanted to intermediate your social experience. How your feed looks. What kind of algorithm you’ll see. What sorts of content will be put in your feed. They got to focus on engagement maxxing. You just had to deal with it.

In such a world, the only thing users felt they could do in response was to yell. They could yell at the CEOs of these platforms. Or at the government, telling them to yell at the CEOs of these platforms.

But with an AI tool that explores an open social ecosystem, you don’t need to yell at a CEO or a regulator. You can just tell the tool what you want, what you don’t want, how you want (or don’t want) to see it, and what context would be useful. It puts you in control.

And yes, sometimes it makes mistakes. It can recommend a story I’m not interested in. But, then I can just tell it that such and such story isn’t useful and why… and it will update the system for me.

Once again, I understand that some people hate any and all uses of AI. And I’m not suggesting you have to run out and use the tools yourself. You do you. But showing concrete use cases where these tools actually deliver more user agency — more control over your online environment, rather than deferring to the whims of any particular company — matters.

The larger point here isn’t really about Attie specifically (indeed, anyone could build their own version of this thanks to open protocols). It’s that for two decades, users have been trained to believe their only options are to accept whatever a platform gives them, or yell loudly enough that someone powerful might change it. That’s the learned helplessness I wrote about earlier, and it’s corrosive.

Tools like this — built on open protocols, not locked inside a corporate walled garden — represent a different path. One where you don’t petition a billionaire for a better feed algorithm. You don’t petition the government to try to put time limits on social media. You just build the experience you want. You tell it to make you a better interface that matches what you want. You tell it you don’t want to spend that much time. That’s what “protocols, not platforms” actually looks like in practice, helped along by agentic tools, and it’s why I think this matters well beyond whether any particular AI tool is good or not.

Filed Under: ai, attie, custmization, decentralization, vibe coding

Companies: bluesky

Summary: Sundar Pichai opened Cloud Next 2026 with Google Cloud at $70 billion in annual revenue, 48% growth, a $240 billion backlog that doubled in a year, and $175-185 billion in planned capital expenditure. The Gemini app has 750 million monthly users, AI Overviews reach two billion, and the Gemini API processed 85 billion requests in January alone. Pichai framed the conference around Search evolving from a retrieval engine into an “agent manager” and announced the Universal Commerce Protocol with Shopify, Target, and Walmart, while positioning Google’s full-stack integration from custom silicon to consumer distribution as the advantage competitors cannot replicate.

Sundar Pichai opened Google Cloud Next 2026 on Tuesday with a set of numbers that reframe the competitive dynamics of enterprise AI. Google Cloud is now generating more than $70 billion in annual revenue, growing at 48% year on year, with a backlog of $240 billion, up 55% and more than double the roughly $155 billion of a year ago. The number of billion-dollar deals Google Cloud signed in 2025 exceeded the combined total of the three previous years. Existing customers are outpacing their own commitments by 30%, spending faster than they contracted. Google has committed $175 billion to $185 billion in capital expenditure for 2026, nearly doubling the $91.4 billion it spent last year. Pichai described the moment as “a fundamental rewiring of technology and an accelerant of human ingenuity.” The money suggests he may not be exaggerating.

The keynote, titled “The Agentic Cloud,” was less a product launch than a thesis statement. Google is positioning itself not as a cloud provider that offers AI but as the operating system for what it calls the agentic enterprise: a model in which AI agents handle routine business operations autonomously, communicate with each other across platforms, and interact with the physical world through commerce, search, and real-time data. The pitch is that Google is the only company that controls every layer of that stack, from the custom silicon that runs inference, to the frontier models that power reasoning, to the cloud platform that hosts the agents, to the productivity suite and search engine through which three billion users interact with them.

The scale of the machine

The Gemini app has reached 750 million monthly active users as of the fourth quarter of 2025, up 100 million from the previous quarter. AI Overviews, Google’s AI-generated search summaries, reach two billion monthly users across more than 200 countries and drive 10% more search queries globally. AI Overviews now trigger on approximately 48% of all tracked queries, up from 31% in February 2025, a 58% increase in a year. The Gemini API processed 85 billion requests in January 2026, a 142% increase from 35 billion in March 2025. Eight million paid Gemini Enterprise seats are deployed across 2,800 companies. Thirteen million developers are building with Google’s generative models. Gemini 3 Pro has had, in Pichai’s words, “the fastest adoption of any model in our history.”

These are not cloud metrics. They are platform metrics. Google is arguing that its advantage over AWS, Azure, OpenAI, and Anthropic lies not in any single product but in the fact that it reaches more users, processes more queries, and touches more surfaces than any competitor. Search alone handles more than a billion shopping interactions per day. Workspace has more than three billion users. Android runs on billions of devices. The thesis is that when AI agents become the primary interface for work and commerce, the company with the largest existing surface area wins, because the agents need somewhere to run, something to connect to, and someone to serve.

Search becomes the agent manager

Pichai’s most consequential framing may have come in a podcast appearance earlier this month: “A lot of what are just information-seeking queries will be agentic in Search. You’ll be completing tasks. You’ll have many threads running.” He described Search evolving from a retrieval engine into an “agent manager,” an orchestration layer that dispatches AI agents to complete tasks on a user’s behalf rather than returning a list of links.

The infrastructure for this is already being built. Google announced the Universal Commerce Protocol at NRF in January, an open-source standard for agentic commerce co-developed with Shopify, Etsy, Wayfair, Target, and Walmart. More than 20 partners have endorsed it, including Adyen, American Express, Best Buy, Flipkart, Macy’s, Mastercard, Stripe, The Home Depot, Visa, and Zalando. UCP is built on REST and JSON-RPC transports with the Agent2Agent protocol, Model Context Protocol, and a new Agent Payments Protocol built in. It lets AI agents treat any participating store as a programmable service, with the merchant remaining the merchant of record. Pichai, who described himself as “an indecisive shopper,” said he is “looking forward to the day when agents can help me get from discovery to purchase.”

The implications for the advertising industry are significant. If Search shifts from showing links that users click to dispatching agents that complete purchases, the entire cost-per-click model that funds Google’s advertising business, and by extension the businesses of every company that advertises on Google, changes. Retailers are already deploying AI-powered shopping through Gemini, ChatGPT, and Copilot. The question is whether agentic commerce cannibalises Google’s own advertising revenue or whether Google can capture a larger share of the transaction itself. UCP suggests Google is betting on the latter.

The full-stack argument

The competitive positioning at Cloud Next was unusually direct. Thomas Kurian said competitors are “handing you the pieces, not the platform,” leaving enterprise teams to integrate components themselves. The claim rests on Google’s vertical integration: Ironwood TPUs and the forthcoming eighth-generation split into Broadcom-designed training chips and MediaTek-designed inference chips provide the silicon. Gemini 3 Pro, 3 Flash, and 3.1 Pro provide the models. The Gemini Enterprise Agent Platform, formerly Vertex AI, provides the developer tools and runtime. Workspace Studio provides the no-code agent builder. Search and Android provide the consumer distribution. No other company assembles all of these under one roof.

The argument has a specific target: Microsoft Copilot, which despite being embedded in virtually every Fortune 500 company has struggled with adoption. Only 3.3% of Microsoft 365 users with Copilot access actually pay for it, and its accuracy net promoter score deteriorated to negative 24.1 by September 2025. Google’s eight million paid Gemini Enterprise seats in roughly four months represents a faster trajectory, though from a much smaller base. GitHub has frozen new Copilot sign-ups because agentic coding sessions consume more compute than users pay for, illustrating why owning the silicon layer, as Google does, is not just a technical advantage but an economic one.

The capital question

The $175 billion to $185 billion in planned capital expenditure is the number that makes the rest of the strategy credible or alarming, depending on how the next two years unfold. Roughly 60% goes to servers and 40% to data centres and networking equipment. Combined with Microsoft, Meta, and Amazon, total big tech AI infrastructure spending is approaching $700 billion this year, a figure large enough to reshape energy markets and strain power grids. Pichai acknowledged on the fourth-quarter earnings call that the “top question is definitely around compute capacity and all the constraints, be it power, land, supply chain,” and expects Google to remain supply-constrained through 2026.

The backlog provides the justification. At $240 billion, it represents more than three years of current revenue contracted but not yet delivered. Thirteen product lines each generate more than $1 billion in annual revenue. The ServiceNow deal alone was worth $1.2 billion over five years. If the demand is real, and the backlog suggests it is, then the capital expenditure is not a gamble but an obligation: the cost of building the infrastructure to fulfil commitments already made.

Google Cloud holds roughly 11% of the cloud infrastructure market, behind AWS at 31% and Azure at 25%. The gap has narrowed: Google grew at 48% in the fourth quarter of 2025, the fastest of the three, and achieved sustained profitability for the first time. But the gap remains. What Pichai presented at Cloud Next is not a plan to close that gap through incremental cloud sales. It is a plan to redefine what the cloud is, from a place where companies store data and run workloads to a platform where AI agents perform work, make decisions, complete purchases, and coordinate with each other across organisational boundaries. If that transition happens, the company that built the agents, the models, the chips, the protocols, and the distribution channels stands to capture a share of the value that the current market share numbers do not reflect. That is the bet. Cloud Next 2026 is the moment Google made it explicit.

For decades, playing a video game was like following someone’s elaborate script. Every character and branching path was meticulously created by a developer. While impressive, these environments were ultimately finite and predictable. They had boundaries, not just on the map, but in their very code. Modern reality has changed it. Artificial intelligence is transforming the virtual world from static landscapes to dynamic systems with no pre-written steps. The gaming environment is becoming smart, and the players enjoy total immersion and engagement in the process.

Beyond the script: creating characters that think

The most noticeable impact of AI falls on the inhabitants of these virtual worlds, Non-Player Characters (NPCs). We’ve all seen a classic city guard who repeats the same line of dialogue endlessly or an enemy running along a predictable path. Modern AI leaves these simplistic automatons behind.

Instead of a rigid script, today’s NPCs perceive and react to the world around them. They utilize complex algorithms to navigate difficult environments, find cover, or coordinate group attacks. More impressively, they learn from player behavior. Imagine an enemy that notices you always use stealth and begins setting traps. This creates a much more engaging experience, the world feels less like programmed challenges and more like intelligent agents with their own goals.

• Dynamic pathfinding: Characters don’t follow predefined routes. They can analyze the environment in real time and figure out the best way to the destination point. Remarkably, they cope with that even if the terrain changes suddenly.
• Behavioral trees: Developers apply complex decision-making models. This allows NPCs to choose from a wide range of actions based on current situations, making them highly unpredictable.
• Machine learning: Some advanced systems train NPCs by having them observe human players. This allows them to adopt effective strategies that a developer might never have programmed manually.

Worlds without end: the magic of procedural generation

Creating a whole world where gamers will learn to survive takes much time and effort. Building every tree or mountain manually is a rigorous task. AI-driven Procedural Content Generation (PCG) turns out to be a solution here. Designers, technical artists, and engineers use the PCG as a toolset of helpful components. The framework creates game content automatically and generates believable environments.

AI technologies allow designers to avoid manually scattering random trees if they need to depict a credible forest landscape. Instead, the AI algorithm learns the rules of a forest ecosystem. The combination of realistic views and the engineer’s initial intent in the setting captures players and makes them enjoy the game. For example, No Man’s Sky used PCG to create a virtual galaxy with billions of planets. Planets have their unique flora and fauna. Players can fight with alien species or trade with them to get necessary resources or equipment. The game fosters a sense of exploration and impresses with its scale. The future of AI in GameDev lies in this ability to create believable worlds.

A game that knows you: the personalized experience

It is interesting to play a game as long as it is unpredictable. AI allows for tailoring playing experiences to individuals. This is possible due to the AI analyzing the skill levels, performance, and preferences of players. The game adapts to your style of playing and makes subtle adjustments to the game in real time. This is far more than just a simple “easy, normal, hard” difficulty setting.

• Dynamic difficulty adjustment: The system detects your performance and adjusts the game levels accordingly. For example, it might slightly reduce enemy numbers or provide more resources. Vice versa, if you’re doing well, the algorithm keeps the challenge.
• Personalized content: It’s great to know your decisions impact the storyline of the game. AI might notice you prefer a certain weapon type and start dropping more powerful versions of it. In narrative games, it can alter future plot points based on the choices and emotional reactions it observes from the player. Besides, the system might adapt in-game rewards to players’ preferences. For example, you can receive new gear, abilities, or characters.  
• Social customization: AI may suggest players with the same skill levels to keep the competitive environment. At the same time, it may also offer personalized NPCs, which adds to the general immersive experience.

Conclusion

To summarize what was mentioned before, AI allows for never having the same gaming experience twice. This makes gameplay exciting for gamers, yet the development process becomes challenging and demands high competence from the specialists. Therefore, game studios partner with a specialized AI development company in the United States to create unforgettable playing grounds. And the amazing news is that it is only the beginning. AI continues to develop and inspire improvements in all the spheres where it is applied.

Whether you are in the cyber field or not, skills in security have a range of benefits that can empower virtually every career.

The cybersecurity space is endlessly evolving as new technologies, protocols and systems demand heightened security measures. Unfortunately, malicious people can circumvent cybersecurity tech almost as quickly as it is invented, so it takes time and commitment to stay on top of skills in this space.

Upskilling in cybersecurity shouldn’t cost an arm and a leg, nor should it be inconvenient to your day-to-day life. With that in mind, SiliconRepublic.com has created a list of some of the most interesting cybersecurity courses designed to suit almost every lifestyle and budget, whether you are a novice, a student or a veteran professional.

Cisco Networking Academy 

Cisco Networking Academy is running an Introduction to Cybersecurity course. The free, six-hour, self-paced course covers the basics and aims to give students and professionals the skills needed to protect their personal digital life and gain insights into the biggest security challenges facing companies, governments and educational institutions. There is also a paid option that is instructor-led and may be better suited to a student or professional looking for additional structure. 

There is also a three-hour Cybersecurity Landscape course that will give learners a greater understanding of cybersecurity core concepts, risk management and what it takes to develop a career as a SOC analyst. The academy noted that this is the first course in its Cybersecurity Defense Analyst Career path.

Coursera

Education platform Coursera has dozens of free, inexpensive and slightly more pricey courses open to professionals and students looking to advance their cybersecurity knowledge. For example, there is a CompTIA Security course for people at an intermediate level. At 10 hours a week, the flexible course roughly takes two weeks to complete, enrollment is free and areas covered include cyberthreat intelligence, incident response, risk management, governance and cryptography, among others. 

There is also a Certified Cloud Security Professional course. Similarly it is for people at an intermediate level, can be undertaken in 10 weeks and is free to take part in. Skills gained from the various modules include, IT security architecture, cloud technologies, cybersecurity, computer science and encryption. 

There are also a range of options that, if you are unsure about what you are looking for, offer free trials. Consider courses such as Introduction to Cybersecurity Essentials, Google Cybersecurity, IBM Cybersecurity Analyst and Cybersecurity for Everyone.

EdX

Learning resource EdX has plenty of courses for cybersecurity students to avail of, including an IBM Beginners Guide to Cybersecurity course, Cybersecurity Basics: Tools and Cyberattacks course and the Launch your Cybersecurity Career course. 

Advanced professionals have options such as Cybersecurity Fundamentals and Cybersecurity Risk Management. Both courses take eight weeks on average to complete, with a commitment of roughly 10 to 12 hours per week and come with a certificate to show you are now qualified, provided you pass the course. 

TryHackMe

TryHackMe is a gamified, hands-on cybersecurity training platform designed for people of all skill levels. The browser-based resource offers users guides, challenges and learning content for complete beginners as well as seasoned hackers.

Students and professionals can enroll in programmes such as Cyber Security 101, Pre Security, Jr Penetration Tester, Soc Level 1, Web Fundamentals and AI Security among others. Many of the initial courses are free and for more advanced options there are monthly payment options. Users can also benefit from a wide community of peers with a common interest and take part in group challenges to advance skills and knowledge. 

Udemy

Educational platform Udemy has a plethora of learning opportunities. The Stay Secure: Modern Information Security Awareness course can help users learn cybersecurity skills to prevent cyberthreats, avoid phishing scams, strengthen passwords and stay safe online. The course is for beginners, is free and consists of 15 lectures.

There is also an SOC Analyst Essentials: Introduction to Cybersecurity programme, where learners explore SOC basics, cybersecurity tools, threat detection, malware analysis and core skills for aspiring SOC analysts. There are 20 lectures and will take, on average, an hour and a half to complete. 

Additional courses include Cybersecurity Defence Fundamentals Encryption and OS Security, Cybersecurity Fundamentals: Don’t get Hacked, and Cybersecurity at Work: Stay Safe Online For Employees. 

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