The team from Bluesky has built another app — and this time, it’s not a social network, but an AI assistant that allows you to design your own algorithm, create custom feeds, and, one day, vibe-code your own app.

At the Atmosphere conference over the weekend, Bluesky’s former CEO, Jay Graber, now chief innovation officer, and Bluesky CTO Paul Frazee, presented the AI app, called Attie, for the first time. Conference attendees will become the initial beta testers for the new experience, which leverages Anthropic’s Claude under the hood to create an agentic social app built on Bluesky’s underlying protocol, the AT Protocol (or atproto for short).

“It’s a new product — it’s not a part of the Bluesky app,” explains interim CEO Toni Schneider in an interview. (In addition to his CEO role, Schneider is a partner at Bluesky backer True Ventures.) “We’ve launched a lot of things inside Bluesky — Starter Packs and custom feeds, and all those kinds of things. This is a standalone product, and it’s the first one that’s built by Jay’s new team.”

With Attie, anyone will be able to build their own custom feed just by typing in commands in natural language, the same as if they’re chatting with any other AI chatbot. To use the app, people will sign in with their Atmosphere login (meaning their login for any app that runs on atproto, which includes Bluesky). Attie will immediately understand what you’ve been talking about, what sort of things you like, and more, because Bluesky and the wider ecosystem are open systems that share data across apps.

You can ask Attie questions, like what posts you might like to see or repost, and you can use the app to curate your own custom feed, personalized to you.

“You control it, you shape it, without having to write code or know how to set up these feeds,” Schneider says. “It’s the beginning of just having a lot more people be able to build on top of the Atmosphere.”

Plus, he adds, “It is an AI product, but it’s an AI product that’s very people-focused … We think AI is a very powerful technology, but we want to make sure that we use it to build things that really benefit people.”

At launch, Attie can be used to build and view these feeds, which will later become available to you within Bluesky or any other atproto app. Over time, the plan is to allow Attie’s users to vibe-code their own social apps as well as build tools for other people.

Schneider says that Graber and her team began working on the app a few months ago, which was around the same time she decided to return to building, instead of running the company.

“I think she realized that there was so much more that she wanted to build, and just doing the CEO job kept her busy, and she felt like she wanted more time,” Schneider tells TechCrunch. “As she spent more time, [and] got freed up, I think it became clear that this is her happy place. She’s an amazing leader and visionary, and we want her building more things and not worrying about operating the company,” he says.

Graber says today, AI is being used by the major platforms to serve themselves, not their users, by trying to increase people’s time spent in their apps, harvesting data, and controlling their algorithms.

“We think AI should serve people, not platforms,” Graber said in her announcement of Attie. “An open protocol puts this power directly in users’ hands. You can use it to build your own feeds, create software that works the way you want it to, and find signal in the noise.”

Graber’s decision to once again focus on protocol and product was followed by the company’s announcement that it now has $100 million in additional funding from a round that closed last year. The team hopes that news serves as a signal to the wider community that Bluesky will continue to be around.

“It means we have three-plus years of runway, which is great. That means stability and security for the rest of the ecosystem,” Schneider tells TechCrunch. It also means that Bluesky’s team has time to tackle the bigger challenges ahead, which include adding privacy controls to the protocol and finding a way to monetize the social network of 43.4 million users.

One thing that Schneider assures us is not in the works, however, is any crypto integration — despite the financial backing from multiple crypto investors. That’s something that had worried some Bluesky users, who feared the app would be filled with crypto scams or become a payment tool.

“It’s the kind of investors who were attracted to crypto because of its decentralization, and they were investing in things built on the blockchain that were super decentralized,” Schneider says of Bluesky’s backers in the crypto space. “This is decentralized social, so it fits those who are invested to believe in the platform and the ecosystem opportunity.”

Instead, the company may experiment with other means of monetization. The team hasn’t yet decided if Attie will ultimately require a fee, as it’s only a private beta for the time being. Other ideas being batted around include subscriptions and hosting services for those who want to host their own communities on the protocol.

Schneider, the former CEO of Automattic, the home of publishing platform WordPress.com, sees the potential for the Atmosphere as being similar to WordPress in this way.

“At the center of [the Atmosphere] is a completely open system, so anybody can participate,” he says. “You can have all of these independent, decentralized pieces that work together. With WordPress, that turned into a huge ecosystem with billions of dollars — over $10 billion a year, now — flowing through it.”

Schneider continues, “So it’s gotten very big, even though it’s completely decentralized. And this is what we’re hoping for, for the Atmosphere to have that similar ability for lots of these apps and services to coexist and work together and build an ecosystem.”

Looking for the most recent Strands answer? Click here for our daily Strands hints, as well as our daily answers and hints for The New York Times Mini Crossword, Wordle, Connections and Connections: Sports Edition puzzles.

Today’s NYT Strands puzzle is fairly difficult. Some of the answers are difficult to unscramble, so if you need hints and answers, read on.

I go into depth about the rules for Strands in this story. 

If you’re looking for today’s Wordle, Connections and Mini Crossword answers, you can visit CNET’s NYT puzzle hints page.

Read more: NYT Connections Turns 1: These Are the 5 Toughest Puzzles So Far

Hint for today’s Strands puzzle

Today’s Strands theme is: A bit peckish?

If that doesn’t help you, here’s a clue: Feathered friends’ food.

Clue words to unlock in-game hints

Your goal is to find hidden words that fit the puzzle’s theme. If you’re stuck, find any words you can. Every time you find three words of four letters or more, Strands will reveal one of the theme words. These are the words I used to get those hints, but any words of four or more letters that you find will work:

• FORT, FORTS, SEEN, STORE, BATE, RATE, FILE, LIFE, LIFER, TIES, FORTH, SILL

Answers for today’s Strands puzzle

These are the answers that tie into the theme. The goal of the puzzle is to find them all, including the spangram, a theme word that reaches from one side of the puzzle to the other. When you have all of them (I originally thought there were always eight, but learned that the number can vary), every letter on the board will be used. Here are the nonspangram answers:

• BUGS, SUET, FRUIT, SEEDS, MILLET, NECTAR, BERRIES

Today’s Strands spangram

Today’s Strands spangram is FORTHEBIRDS. To find it, start with the F that’s the first letter to the left on the top row, and wind over and down.

Australian researchers have pulled off something that quantum theory predicted but nobody had managed to actually observe in matter until now. Working with pairs of helium atoms, they captured the particles existing in two different locations simultaneously, their behavior frozen in a way that has no equivalent in everyday experience. It is the first direct observation of this phenomenon in matter rather than light, and it opens a new window into how the fundamental building blocks of our world actually behave.

The team at Australian National University started with a cloud of helium atoms cooled to just above absolute zero, at which point the atoms slow down enough to behave more like overlapping waves than solid particles. Releasing the cloud from its magnetic trap allowed two groups to collide head on, and that collision created exactly the right conditions for something remarkable. Measure one atom moving in a particular direction and its counterpart will instantly appear moving the opposite way, no matter how far apart the two have traveled.

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To confirm the atoms were genuinely sharing an existence across two locations rather than simply traveling on predictable paths, the team sent the scattered pairs through an apparatus using laser pulses that acted like a half silvered mirror, splitting each atom along two separate routes at the same time. The atoms were then allowed to fall briefly before detectors recorded exactly where each one landed. The interference pattern that emerged left no room for doubt. Each atom had traveled both paths simultaneously right up until the moment it was measured, with one member of a pair appearing on the left side of the detector while its partner showed up on the right, yet the data made clear that both had been exploring both locations all along.

The significance of the result comes down to what the atoms actually are. Previous experiments of this kind used photons, which have no mass and are unaffected by gravity. Helium atoms are a different matter entirely, heavy enough to feel the pull of the Earth, yet they still display this split existence. The connection between the pairs was strong enough to violate Bell’s inequality, a well established test that rules out any classical explanation for the behavior. Measuring one atom instantly determined the state of its partner regardless of the distance between them, exactly as quantum theory has always predicted but nobody had seen demonstrated with matter until now.

Lead researcher Yogesh Sridhar spent years refining the setup because earlier attempts always fell short. “Experimentally, it is extremely hard to demonstrate this,” he said. His colleague Dr. Sean Hodgman put the strangeness into plain words: “It is really weird for us to think that this is how the universe works. You can read about it in a textbook, but it is really weird to think that a particle can be in two places at once.”
[Source]

WhatsApp is rolling out multiple features designed to make the app easier to use, including AI-powered message replies and photo retouching, support for two accounts on iOS, and chat history transfer between iOS and Android devices.

Meta said that after the new updates, users will be able to touch up images in the chat before sharing them with contacts or in groups using Meta AI.

The Writing Help feature enables users to quickly draft a response based on the active conversation, with Meta saying it uses Private Processing to ensure messages are completely private.

“Writing Help is built on top of Private Processing technology, which allows you to leverage Meta AI to generate a response without Meta or WhatsApp ever reading your message or the suggested re-writes,” Meta states.

The company says WhatsApp now also helps find large media files in any chat, quickly freeing up space without deleting entire conversations.

Additionally, WhatsApp allows two WhatsApp accounts to be logged in at the same time on iOS (an option that was already available on Android devices), and its chat transfer feature now supports moving message history from iOS to Android (including individual and group chats, call history, channel and community history, and more).

“Now, with just a few taps, your conversations, photos, and videos easily come with you no matter what device you’re using,” Meta said.

​Earlier this month, WhatsApp also introduced parent-managed accounts for pre-teens, a feature that allows parents to decide who can contact them and which groups they can join.

Meta also introduced new WhatsApp anti-scam protections that warn users when behavioral signals suggest an incoming device-linking request may be fraudulent.

These scam defenses were added soon after the Dutch intelligence agencies warned that Russian state-backed hackers had been targeting Dutch government employees in phishing attacks aimed at their Signal and WhatsApp accounts.

In January, Meta also began rolling out a new WhatsApp lockdown security feature designed to protect journalists, public figures, and other high-risk individuals from sophisticated threats, including, but not limited to, spyware attacks.

Automated pentesting proves the path exists. BAS proves whether your controls stop it. Most teams run one without the other.

This whitepaper maps six validation surfaces, shows where coverage ends, and provides practitioners with three diagnostic questions for any tool evaluation.

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Many people have tried AI tools and walked away unimpressed. I get it — many demos promise magic, but in practice, the results can feel underwhelming.

That’s why I want to write this not as a futurist prediction, but from lived experience. Over the past six months, I turned my engineering organization AI-first. I’ve shared before about the system behind that transformation — how we built the workflows, the metrics, and the guardrails. Today, I want to zoom out from the mechanics and talk about what I’ve learned from that experience — about where our profession is heading when software development itself turns inside out. 

Before I do, a couple of numbers to illustrate the scale of change. Subjectively, it feels that we are moving twice as fast. Objectively, here’s how the throughput evolved. Our total engineering team headcount floated from 36 at the beginning of the year to 30. So you get ~170% throughput on ~80% headcount, which matches the subjective ~2x. 

Zooming in, I picked a couple of our senior engineers who started the year in a more traditional software engineering process and ended it in the AI-first way. [The dips correspond to vacations and off-sites]:

Note that our PRs are tied to JIRA tickets, and the average scope of those tickets didn’t change much through the year, so it’s as good a proxy as the data can give us. 

Qualitatively, looking at the business value, I actually see even higher uplift. One reason is that, as we started last year, our quality assurance (QA) team couldn’t keep up with our engineers’ velocity. As the company leader, I wasn’t happy with the quality of some of our early releases. As we progressed through the year, and tooled our AI workflows to include writing unit and end-to-end tests, our coverage improved, the number of bugs dropped, users became fans, and the business value of engineering work multiplied.

From big design to rapid experimentation

Before AI, we spent weeks perfecting user flows before writing code. It made sense when change was expensive. Agile helped, but even then, testing multiple product ideas was too costly.

Once we went AI-first, that trade-off disappeared. The cost of experimentation collapsed. An idea could go from whiteboard to a working prototype in a day: From idea to AI-generated product requirements document (PRD), to AI-generated tech spec, to AI-assisted implementation. 

It manifested itself in some amazing transformations. Our website—central to our acquisition and inbound demand—is now a product-scale system with hundreds of custom components, all designed, developed, and maintained directly in code by our creative director. 

Now, instead of validating with slides or static prototypes, we validate with working products. We test ideas live, learn faster, and release major updates every other month, a pace I couldn’t imagine three years ago.

For example, Zen CLI was first written in Kotlin, but then we changed our mind and moved it to TypeScript with no release velocity lost.

Instead of mocking the features, our UX designers and project managers vibe code them. And when the release-time crunch hit everyone, they jumped into action and fixed dozens of small details with production-ready PRs to help us ship a great product. This included an overnight UI layout change.

From coding to validation

The next shift came where I least expected it: Validation.

In a traditional org, most people write code and a smaller group tests it. But when AI generates much of the implementation, the leverage point moves. The real value lies in defining what “good” looks like — in making correctness explicit.

We support 70-plus programming languages and countless integrations. Our QA engineers have evolved into system architects. They build AI agents that generate and maintain acceptance tests directly from requirements. And those agents are embedded into the codified AI workflows that allow us to achieve predictable engineering outcomes by using a system.

This is what “shift left” really means. Validation isn’t a stand-alone function, it’s an integral part of the production process. If the agent can’t validate it’s work, it can’t be trusted to generate production code. For QA professionals, this is a moment of reinvention, where, with the right upskilling, their work becomes a critical enabler and accelerator of the AI adoption. 

Product managers, tech leads, and data engineers now share this responsibility as well, because defining correctness has become a cross-functional skill, not a role confined to QA.

From diamond to double funnel

For decades, software development followed a “diamond” shape: A small product team handed off to a large engineering team, then narrowed again through QA.

Today, that geometry is flipping. Humans engage more deeply at the beginning — defining intent, exploring options — and again at the end, validating outcomes. The middle, where AI executes, is faster and narrower.

It’s not just a new workflow; it’s a structural inversion.

The model looks less like an assembly line and more like a control tower. Humans set direction and constraints, AI handles execution at speed, and people step back in to validate outcomes before decisions land in production.

Engineering at a higher level of abstraction

Every major leap in software raised our level of abstraction — from punch cards to high-level programming languages, from hardware to cloud. AI is the next step. Our engineers now work at a meta-layer: Orchestrating AI workflows, tuning agentic instructions and skills, and defining guardrails. The machines build; the humans decide what and why.

Teams now routinely decide when AI output is safe to merge without review, how tightly to bound agent autonomy in production systems, and what signals actually indicate correctness at scale, decisions that simply didn’t exist before.

And that’s the paradox of AI-first engineering — it feels less like coding, and more like thinking. Welcome to the new era of human intelligence, powered by AI.

Andrew Filev is founder and CEO of Zencoder

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