Google has begun rolling out Android 17, the June Pixel Feature Drop, and Wear OS 7 simultaneously across supported Pixel phones and watches. Highlights include floating app bubbles, improved foldable multitasking and gaming, tighter location and contact permissions, stronger lost-device protections, new Pixel AI tools, and up to 10% better Pixel Watch battery life. PhoneArena reports: Pixel owners are the clear winners, since everything here reaches Pixel first and a lot of it goes back to the Pixel 6. Fold owners get the most toys, with the Bubble Bar and foldable gaming mode built for the big screen. Watch wearers get the quietly important upgrade. Better battery and Live Updates make an everyday wearable easier to rely on, especially if you keep it on overnight. Google's latest Pixel Drop combines several AI-powered tools with a broader slate of Android 17 upgrades. Pixel owners gain Lyria 3 for generating music from text or images, Gemini Omni for creating custom video clips, enhanced call translation and screening, AirDrop-compatible Quick Share, expanded Magic Cue support, and conversational photo editing.
Android 17 builds on those additions with floating app Bubbles, selfie-camera Screen Reactions, and a split-screen gaming mode for foldables, while also strengthening privacy and security with more granular location and contact permissions, improved lost-device protection, tighter PIN-guessing limits, and enhanced threat detection.
Other additions include expanded parental controls, separate assistant volume and app memory settings, and an option to hide app names for greater privacy.
You can read more about everything new in Android 17 in Google's blog post.
An anonymous reader quotes a report from MacRumors: Apple is raising its prices to offset the high cost of memory and storage, CEO Tim Cook told The Wall Street Journal. Apple is no longer able to absorb the increased prices and will need to pass some of the cost on to consumers. "Unfortunately, price increases are unavoidable," said Cook. "We're doing our best to mitigate the huge increases that are being passed to us, and we've been trying to shield our customers from the increases, but the situation has become unsustainable."
Growing demand for memory and storage chips from AI companies has led to chip shortages and higher costs. The Wall Street Journal suggests Apple will need to increase device costs "substantially" to maintain its current profit margins given the cost of memory chips and SSDs. Research firm TechInsights claims Apple will need to make the iPhone 18 Pro around $270 more expensive to keep its existing profit margin.
Apple is struggling more with memory chips, but storage chips are also an issue. "There's less supply at a time when consumers want devices and the memory guys are passing along huge price increases," Cook told The Wall Street Journal. Cook said Apple will use its cash to increase memory supply, but he did not give details on what that means. Apple does not plan to create its own memory and storage factories. "We can't do everything," Cook said. "We know what we're good at." Cook likened the memory shortages to a hundred-year flood. "I've never seen anything like it in any area in over 40 years," he said.
Further reading: Smartphone Market To Shrink 15% This Year Due To Memory Crisis
InfoWars founder Alex Jones speaks to the media outside Waterbury Superior Court during his trial on September 21, 2022. | Getty Images
The Onion's InfoWars officially has a launch date: On July 2nd, the conspiracy network previously run by Alex Jones will return as a comedy and media platform. The reboot comes more than a year and a half after news broke that the satirical news site was working to acquire the property owned by Jones, a conspiracy theorist who notably targeted victims of the Sandy Hook school shooting.
"Developed with the support of the Sandy Hook families, the new InfoWars is being reimagined as a comedy and cultural platform featuring original programming, guest talent, and new comedic voices," a press release reads. "The project reflects The Onion's broa …
When three Amazon software engineers testified earlier this month at Seattle City Council hearings about data centers, they started their testimony by citing a city law barring employment discrimination over political speech. Now, they're accusing their employer of breaking that law by retaliating against them.
On June 10th - one week after the hearing, and one day after the City Council passed a milestone moratorium on data centers - Patrick Schloesser, Darius Irani, and Liesl Wigand were each called into an impromptu meeting with Amazon's "Employee Relations." HR representatives told the employees that the company was investigating them …
This week, egghead.io cofounder John Lindquist joined host YK Sugi, founder of CS Dojo and developer experience manager at Eventual, to cover the latest AI news. First on the agenda was the contested release of Claude Fable 5. They also examined the financial shifts reshaping the technology industry, including the rising costs associated with agentic coding loops. Then John outlined the framework he uses to build in the agent era without starting from scratch every time.
Watch the full episode here:
Claude Fable 5: 3 days, a government order, and a lot of unanswered questions
The dispute about what actually happened is unresolved. Anthropic’s position is that the reported issue was a narrow jailbreak that had been previously identified and was present across public models generally, and not a serious security threat. An independent researcher who reviewed the report described it as defensive prompting that surfaced known vulnerabilities and called the response an overreaction. Neither side has published the technique or prompt, so there’s no way to evaluate the claim independently. But as John put it, “It sets a very strange precedent going forward, as models are released, that governments can step in and control what private companies can and cannot do with their model.”
Another new precedent: Fable 5 wasn’t built on the Opus or Sonnet architecture, which means comparisons to prior Anthropic models or contemporaries don’t tell us much. But initial impressions were positive, including from YK and John, and Fable 5 quickly reached the top of the Arena leaderboard in the text, agents, and web dev code categories. However, the model also had a purposeful limitation: On questions related to AI and machine learning training specifically, it was designed to underperform (without signaling this to users), apparently to prevent competitors from using it to improve their own models. Intentional capability suppression in a commercial model, without disclosure, is a different kind of product decision than a safety guardrail. Whether that approach becomes more common as competitive stakes rise is an open question.
Tokens burn fast when the loop isn’t ready for them
Last week, SpaceX went public in the largest IPO in history. The company finalized its acquisition of Cursor in a $60 billion all-stock deal shortly after. (That last one happened after this episode aired—we’ll talk more about it on Monday.) Both OpenAI and Anthropic have filed to go public as well, and Google raised roughly $160 billion through equity and a 100-year bond. A significant share of that capital is flowing toward AI coding infrastructure.
YK brought up another, less celebratory, financial story that’s been making the rounds: Uber burned through its full 2026 AI tools budget by April, mostly on Claude Code and Cursor, and Andrew Macdonald, the company’s COO, acknowledged they couldn’t link that spending to a measurable increase in useful customer features. Uber subsequently put a $1,500 per month per employee cap in place.
John flagged projects inefficiently utilizing agentic loops as one possible cause for wasteful token spend. Most developers deploying agents against existing codebases haven’t built the tooling those agents need to work efficiently, so agents burn tokens doing work that dead-ends, repeating context, or generating code that requires significant debugging. He explained:
If you take a legacy codebase and you throw agents against it with loops, you haven’t set up a proper agent environment. It’s so quick to burn tokens because. . .the agents don’t have the tools to work with.
The conversation in developer communities so far has focused almost entirely on what agents can generate. But as more organizations move from experimentation to production-scale deployment, building logging, verification, and proper error surfaces into agent tooling is what will determine whether token spend maps to real output. Otherwise, we’ll likely see more companies go the way of Uber.
Ingredients beat inference: A practical framework for building in the clone wave
For most developer workflows today, buy-versus-build leans toward building in a way it didn’t even a year or two ago. As John noted, “It’s so easy to build apps and workflows now where there are so many amazing production apps out there, apps on your phone, apps on your desktop, software as a service, that are trivial to copy and clone.” He uses the term the “clone wave” to describe this expanding set of open source equivalents to consumer software products that can now be cloned, forked, or replaced and get you 99% of the way to your use case.
The principle that drives the clone wave is “ingredients beat inference.” If you ask an agent to build a feature from scratch, it infers a solution with no external reference. If you give it an existing open source implementation to start from, it can adapt, translate, and integrate that code far faster and more reliably. The ingredients approach also helps with the 43% of AI-generated code that needs debugging in production, per a figure YK cited earlier in the episode.
The GitHub CLI plays a central role in this workflow. John explained that because agents understand the GitHub CLI natively, you can give an agent a search task and let it find implementations it wouldn’t have generated itself. Language mismatch isn’t a blocker, because agents translate between languages and libraries well. And tools like DeepWiki from Cognition let agents explore and understand a repo’s structure before cloning or forking it, so the evaluation step doesn’t require local setup.
The framework extends to how you build the last 20% that isn’t available as an ingredient. This is the part that’s specific to your use case; John described it as “that extra bit that you’re building on top of it to make it into the custom product and project for either yourself or for your users.” John’s bigger point is that the tools you build for yourself should also be usable by your agents. Expose endpoints and logging. Give agents the ability to read state and errors. An agent that can control a tool but not debug it will eventually stop in ways that are hard to diagnose.
John walked through cmux to demonstrate what an agent-native workspace looks like in practice. cmux is a terminal multiplexer built with agentic workflows in mind: it exposes a CLI that agents can control directly, so you can open a terminal pane, have that pane spawn another, and have the two read from and write to each other. In practice that means you can run Claude Code in one pane, Codex in another, and a third pane reading output from both, with each agent able to observe the others’ state.
Agents need more than the ability to run commands. They need to read logs, check errors, and confirm state before taking the next step. A workspace that exposes those surfaces gives agents a feedback loop. This tenet is applicable to tools across the company. Organizations that treat their internal tooling as agent-accessible infrastructure are building something that compounds. Those treating agents as black-box code generators are taking on technical debt they may not see until causes issues later on.
What’s next
SpaceX’s acquisition of Cursor turns the coding-agent race into something much larger than an IDE fight. Cursor may be positioning itself as a new GitHub for the agentic era, where agents write, review, test, repair, and govern code. At the same time, Salesforce’s $3.6B acquisition of Fin shows the same pattern inside enterprise software: Buyers want packaged workflows that solve real support, sales, and operations problems rather than abstract “agents.”
Next week, host Ksenia Se examines these stories and more through the lens of who owns the loop where AI does the work. Join us to find out why the next phase of AI will be about who controls the infrastructure, economics, and trust layer.
Our episodes are free and open to all through the end of June if you’d like to attend live—register here. And we’ll continue to publish our takeaways here on Radar each Friday and share full episodes on YouTube, Spotify, Apple, or wherever you get your podcasts.
More people are contributing to open source than ever, most of them trying to help. The challenge is keeping up with the volume. Creating a pull request has never been easier. Reviewing one still takes a human about as long as it ever did. When great contributions and low-quality noise land in the same queue, the ones that deserve attention are harder to find.
That’s why we’ve introduced pull request limits. It takes on the problem we hear most: too many incoming pull requests, too much low-quality noise, and too few ways to manage the flow.
How it works
A pull request limit sets the maximum number of pull requests a user without write access can have open at once in your repository. Hit the limit, and you must close or merge one before opening another. Pull requests opened by Copilot or another AI agent will counts toward your limit. Trusted contributors can be placed on a bypass list, where they are exempted from limits, but don’t gain full contributor access. Draft pull requests will not count towards your limit.
GitHub already has interaction limits, but those are temporary cooldowns. These new pull request limits are persistent and configurable—giving maintainers the control they told us they were missing.
A cap also changes how contributors behave. When anyone can open a pull request in seconds, a polished change and a rough draft look the same in the queue. But when only a few pull requests can be open at once, a contributor must be selective and prioritize which contributions they want to be reviewed. That first judgment call happens before the pull request reaches you, and a smaller pool makes good work easier to spot.
It’s helped us want to review pull requests again. Knowing that someone hasn’t just opened 5–10 pull requests that are slop makes it much easier to want to look. Going forward we expect it to help us manage our backlog and ensure the things people are working on are the things we need.
Nicholas Tindle, AutoGPT
This feature is great. We’ve had problems on Homebrew for a while with enthusiastic users submitting many pull requests that need near identical review. AI further accelerated it. This allows us to still have outside contribution and maintainers contribute more while gating users to a level of pull requests we can cope with.
Mike McQuaid, Homebrew
At OpenClaw we get a huge volume of pull requests from the community and had to build our own bots for fighting spam. We are super glad GitHub has been able to develop out-of-the-box solutions for maintainers now to manage this volume.
Vincent Koc, OpenClaw
The cost to create outran the cost to review
These limits are especially crucial right now because of a change in the ecosystem. In January 2023, developers merged about 25 million pull requests a month across GitHub. Today that number tops 90 million—a roughly 3.6x increase. More people are building in the open than at any point in GitHub’s history.
Most contributions come in good faith, and even good-faith work can pile up faster than one volunteer can answer. In February, we wrote that open source was hitting its own Eternal September. A pull request limit gives maintainers some of that attention back, without closing the door on the next contributor.
What’s coming next: More controls for managing contributions
Pull request limits are just the first step. The same feedback is pointed straight at what comes after: more flexible, more granular control over how contributions flow in.
Archiving pull requests (shipping soon): Repository admins will be able to archive pull requests, hiding low-quality or spammy pull requests out of the main pull request view. Archived pull requests stay accessible to admins, but can be filtered out of the default list. We chose archive over delete on purpose: some organizations can’t permanently delete pull requests for legal or compliance reasons, and many maintainers want to keep them for context.
Issue limits (in development): The controls you now have on pull requests will be applied to issues: per-repository caps on how many open issues a user without write access can have at once, with a bypass list, plus an option to restrict issue creation to collaborators.
Smarter bypass signals (up next): The goal is less manual trust management. Instead of curating a bypass list by hand, you could let contributors clear a limit automatically based on real signals: a previously merged pull request in the repo, account age, or organization membership. That frees maintainers from curating lists by hand and gives them more time to focus on the work itself.
Cross-repository controls (exploring): A per-repository cap helps with repeated activity in one project, but it does nothing when someone opens pull requests across hundreds of repositories at once. We’re exploring ways to catch contributors who spray pull requests across multiple repositories, whether through trust signals, rate limiting, or other cross-repository controls.
Thank you
Open source runs on the people who show up every day. To everyone who reviews pull requests late at night, mentors a first-time contributor, triages a backlog, files issues, or tells us where our tools fall short: thank you. You shaped this feature, and your input is critical in helping us decide what comes next. We’ll keep building with you.
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