locater16 shares a report from IEEE Spectrum: One morning in 2019, Adebayo Alonge was in a Cape Town hotel room, preparing to demonstrate his startup's AI answer to a serious problem in African health care: counterfeit medication, which kills thousands of people across the continent every year. The RxScanner is a handheld spectrometer that scans a pill with infrared light, then sends the item's molecular profile to an AI model equipped with a pharmaceutical database. In seconds, the AI identifies the medication from its molecular profile -- or reports that it's phony. Pharmacies were using the system in more than a dozen countries, including Ghana, Kenya, Myanmar, and Alonge's native Nigeria. But that morning in South Africa, it didn't work. "I was shocked," Alonge says... So Alonge immediately asked his engineers to shrink the AI model down to a smaller, low-power, unconnected version that could run entirely on his Android phone. They produced it 2 hours later, and that saved the demo. More importantly, the work birthed a new version of his device, which can authenticate a pill in places without broadband, computers, or even reliable electricity. It also turned Alonge into an advocate for this kind of "small AI." "The article goes on to detail other immediately useful 'small' AI applications without any subscription or billion dollar data centers needed," writes locator16. For example, Bala Murugan and colleagues at Vellore Institute of Technology in India developed a drone-based system that photographs cashew plants and identifies disease-indicating splotches on the plants. The key advantage is that all processing happens on the drone itself, so farmers do not need a computer, broadband connection, or cloud server access. In a Uruguayan vineyard, researchers developed small-AI systems to identify ant infestations. The article doesn't go deep into the deployment details, but it presents this as another example of a narrow, localized model trained to recognize a specific agricultural threat. Small AI has also been used to detect the presence of malaria-carrying mosquitoes in multiple countries. This is especially useful in regions where public-health teams may lack reliable network access or expensive lab infrastructure, but still need fast, local detection. In parts of Brazil without access to more complex medical equipment, researchers have used small AI to run electrocardiograms from an Arduino device. The article also describes Marcelo Jose Rovai's work on a TinyML model that generates electrocardiograms in a patient simulator lab. Rovai also describes a newer experiment using an Arduino UNO Q with a Qualcomm chipset. The device runs a language model locally, collects sensor data, and analyzes it to detect tiny pools of water where mosquitoes might breed -- while using only about 3 watts of power.

Read more of this story at Slashdot.

The Supreme Court allowed Texas to enforce a law requiring app stores to verify users' ages and obtain parental consent before minors can download apps. Tech industry groups argue the law broadly restricts young people's access to digital speech, but the court let a 5th Circuit order stand without explanation or noted dissents. CNN notes that the Supreme Court's decision "doesn't resolve the case but rather will allow Texas to enforce the law while the litigation continues to play out." From the report: "A minor child who downloads a software application from an app store agrees to contractual terms of service, including whether the child's location will be tracked, whether the child's privacy will be protected, whether information from the child's phone can be sold by the developer, and whether the child waives the right to sue," Texas told the Supreme Court in urging the court to allow its law to take effect. But the Computer & Communications Industry Association, a trade group whose members include Apple and Google, said the law would effectively bar young people from accessing a wide range of content, "be it a book by Ernest Hemingway or J.K. Rowling, a Taylor Swift album, or a subscription to National Geographic." Allowing the law to take effect, the group said, would have "profound consequences for the protection of digital speech." [...] In the new case, involving Texas' age verification for apps, a federal district court blocked the law's enforcement in December -- days before it was set to take effect. But a three-judge panel of the conservative 5th US Circuit Court of Appeals put that decision on hold in early June, allowing the state to enforce it. By declining to take up the emergency appeal from the computer and student groups, the Supreme Court has left the 5th Circuit's decision in place.

Read more of this story at Slashdot.

Amazon has cut a total of 57 jobs in Washington state across various teams, including roles at the director and senior manager levels, according to a filing made public Monday morning.

People impacted by the cuts include 16 software engineers as well as product managers and creative marketing employees working in Seattle and Bellevue offices. Nine remote employees, including investigation specialists and risk managers, were also let go.

Employees were notified of the layoffs throughout May and in early June, according to an Amazon filing with the Employment Security Department, released Monday under the Worker Adjustment and Retraining Notification (WARN) Act. The roles are scheduled to end in August.

“[W]e filed a WARN notice because a few businesses across the company made organizational changes that each impacted a small number of employees — in most cases fewer than five employees per business,” said Brad Glasser, an Amazon spokesperson, via email.

WARN notifications are triggered by state law when more than 50 Washington-based employees in total are laid off over a period of 30 days.

“We don’t make decisions like this lightly, and we’re committed to supporting the employees who were impacted,” Glasser added.

It’s a sign of the broader belt-tightening across the tech industry. Microsoft separately cut more than 600 jobs in Washington state on Monday morning, part of global layoffs eliminating 4,800 roles across the Redmond company, primarily in sales, consulting and gaming.

The latest Amazon cuts follow layoffs of 2,198 Washington-based employees in February and 2,303 in October 2025. Globally, the company has eliminated roughly 30,000 positions in the past year, cumulatively amounting to the the largest workforce reduction in its history.

The multiple rounds of layoffs have hit wide-ranging positions and divisions, with software engineers the hardest hit. Corporate support, commercial functions, legal, tax, and ad sales positions have all seen cuts, as have Amazon’s core technology organization, gaming division and robotics unit.

The previous larger cuts were part of an effort to “reduce layers, increase ownership, and remove bureaucracy,” according to a memo sent to employees and posted online earlier this year by Beth Galetti, senior vice president of people experience and technology.

Amazon’s corporate roles numbered around 50,000 in the Seattle area.

Tech giants nationwide have made round after round of job cuts in the past year as they pour billions into AI data center expansions and gain labor efficiencies through the use of artificial intelligence.

Amazon reported $181.5 billion in sales for the first quarter of this year, up 17% from a year earlier. Profits came in at $30.3 billion, boosted by gains tied to the value of its investment in Anthropic.

There’s a common fear about what AI could do to open source. Coding agents take over the beginner-friendly issues that help new contributors get started; the code they generate is harder to maintain, and eventually, the pipeline of new maintainers dries up. It’s a plausible scenario. But according to new research, it doesn’t appear to be what’s actually happening. 

​A study submitted to arxiv.org on July 2 out of Peking University tracked 1,888 GitHub repositories that adopted AI coding agents, tools like Cursor and Claude Code, to see how those projects changed after AI entered the workflow. The researchers treated adoption as the point when a project committed its first agent configuration file, something like a .cursorrules or CLAUDE.md, and then compared those projects against a matched set that never adopted.

They used difference-in-differences, which is basically the gold standard for separating what a tool actually caused from whatever was already going on in a project before the tool showed up. And what they found was…not much. Newcomer participation held steady or crept slightly upward. Under the most conservative statistical specification, the worst they could find was a 1.5% dip that didn’t approach statistical significance.

Complexity up, contributors steady

​​Cyclomatic complexity, which counts the number of independent paths through a function, ticked up 3% to 4% across all languages after adoption. Cognitive complexity, the trickier metric that penalizes heavily nested logic and tangled control flow, jumped by about 11% in Python projects. That sounds bad until you compare it to a Carnegie Mellon study published last year, which found that Cursor adoption drove a 41% increase in the same metric. The Peking University team, working with a larger and more established set of projects and tighter statistical controls, landed at roughly a quarter of that earlier estimate. 

But where the study really gets interesting is that instead of just reporting complexity and newcomer numbers as two separate findings that happen to live in the same PDF, they locked the analysis to the exact 128 Python projects where complexity actually increased.

On those same repos, newcomer entry didn’t decline, retention held steady, and the active contributor base grew. Both effects are real, but they don’t seem to be connected. AI-generated code is getting a little more complicated, yet that extra complexity doesn’t appear to be discouraging newcomers, at least at the levels this study found. 

AI-generated code is getting a little more complicated, yet that extra complexity doesn’t appear to be discouraging newcomers, at least at the levels this study found.

What the study excluded

There are a few important caveats. The biggest issue is that the study focused on established open-source projects. Nearly two-thirds of the repositories that adopted AI coding tools did so almost as soon as they were created, leaving researchers with no meaningful pre-AI baseline for comparison. Those repositories were excluded from the main analysis, which instead focused on 603 projects with at least six months of history before AI was introduced. 

The researchers also examined the full dataset, in which newcomer participation appeared to decline. But a closer look showed those projects were already losing contributors before they adopted AI, making it impossible to blame the decline on the tools themselves.

Measuring adoption’s blind spots

There’s another limitation, too. The study measures AI adoption by looking for configuration files associated with tools like Cursor, not by tracking how often developers actually used them. That means it can tell us what happened after projects adopted AI, but not whether teams that relied heavily on AI experienced different outcomes than teams that only experimented with it.

GitHub says merged pull requests across the platform have grown from about 25 million per month in early 2023 to roughly 90 million a month today.

GitHub has started responding, too. The company recently introduced limits on the number of open pull requests that outside contributors can have at once, along with new tools to help maintainers sort through growing review queues.

The crowding-out fear is, for now, put to rest.

Where this leaves us

The crowding-out fear is, for now, put to rest. In established open-source projects, at current adoption levels, AI agents are not pushing newcomers out the door. However, pull request volume has nearly quadrupled. The code arriving in those PRs is a bit more complicated. The people submitting it may not fully grasp what they’re proposing. And all of that lands on maintainers whose ranks are not growing at anything like the same pace.

The researchers say future work should examine how heavily projects rely on AI coding tools and find better ways to study repositories that were effectively born with AI. Those are important next steps. But the question that feels most pressing is: Not whether AI changes who shows up to contribute, but whether it changes the effort required to maintain an open-source project over time.

Not whether AI changes who shows up to contribute, but whether it changes the effort required to maintain an open-source project over time.

The post A new study just debunked the biggest fear about AI and open source appeared first on The New Stack.

Back in 2022, I brought home a new 2023 Nissan Leaf S and I’ve had a blast driving it since. In hindsight, it was one of my best purchases in a long time– it’s super fun to drive, and other than tires/alignment it has required zero maintenance (not even refilling the wiper fluid!) in the 45 months that I’ve driven it almost 38K miles. It’s a great car.

But.

• The Leaf’s range is not awesome. While its efficiency is high (I average 4.2 miles per KW), that’s partly achieved with a small 40KW battery pack, and the extremely limited fast-charger support (I’ve fast-charged perhaps 4 times) means that it’s completely impractical for road trips.
• It’s small. My 13yo now rides in the front seat, and my 10yo is pretty crammed in the back.
• It lacks features. No remote start or charge monitoring, no built-in CarPlay, small internal screen. Black cloth interior.

As early as one year in after getting my Leaf, I was already thinking about upgrading to something bigger in a year or two. I was excited that the 2026 Leaf fixes many of the problems of mine (bigger battery, nicer features, winning charger) but was disappointed to see that it’s got a backseat that seems even smaller than that of the 2023. I pondered waiting for a Tesla Model YL (the upcoming extended length version of the Y) but I’ve been souring on the Tesla because they’re everywhere nowadays and the look doesn’t appeal to me as it once did.

On June 27th, I bought an Equinox EV AWD in RipTide Blue with the cloud grey interior:

A week prior, I’d test-driven a Blazer EV and was disappointed that driving it felt … pokey. It was big and comfortable, yes, but driving it felt more like my CX-5 than my sporty Leaf, despite the 220HP electric motor. I’d initially been skeptical of going to the all wheel drive model as it cost more ($3300) and dropped estimated range (from 319 to 307), but online reviewers raved that the additional 80HP gave it the “electric car zip” that I was looking for.

So the following Saturday, as my kid warmed up at his out-of-town swim meet, I drove up from San Antonio to San Marcos (a lot further than I realized) to test drive the AWD LT2 trim. As soon as I sat down in its big comfy grey seat, I knew I wanted it. Ten-seconds off the lot in the test drive, I dropped the pedal and got that roller-coaster takeoff feel I was craving. The price was good (about $10K under sticker for the Riptide Blue one I wanted that had been used as a courtesy vehicle). Then it was just an annoying amount of paperwork before I got the keys and headed back to the meet.

I’ve only got some initial impressions, but here they are:

• The cabin is really nice (quiet, high-riding, spacious)
• The giant Google-powered infotainment display is really great (with a few annoyances: no CarPlay, many features locked out while driving)
• 360 degree cameras are awesome
• Fast-charging at up to 150KW (I consistently got 125KW at EvGo and Buc-ee’s) makes it a practical road-trip car
• Remote start/AC is awesome, but the myChevrolet app is still annoyingly limited (e.g. cannot override charge schedule to say “Charge fully now”)
• Having three cars is a hassle. I’d been planning for the kids to get my hand-me-downs, but it’s going to take a few years.
• The CCS charge port is much more broadly supported than the CHAdeMO, but they really ought to throw in a NACS converter (the 2027 model is rumored to be switching to that).
• WeatherTech floor mats are a worthwhile upgrade.
• Selling heated seats and steering wheels in Texas (in the summer!) has to be a hard proposition. They probably should sell a “Hot Weather” config instead.

In the nine days I’ve owned the Equinox EV, I’ve driven it to and from San Antonio twice and around town a bit, but I’ve been driving my Leaf more than I expected– it’s still fun, easy to park, and 33% more energy efficient than the Equinox.

So, why I bought, in order of reasons:

1. I need the range. I bought a house a bit further out from the city and I’ll be moving sometime in August.
2. The Equinox offers more room for my family.
3. I wanted it. :)

I’ll expect I’ll update this post over the next few months when I’ve got more informed things to say.

-Eric

My Car History:

• 1990 Pontiac Grand Am (thanks, Grandma!)
• 2003 Corolla LE 130hp (thanks, Microsoft!)
• 2013 CX5 155hp (thanks, Telerik!)
• 2023 Leaf S 147hp
• 2026 Equinox EV AWD 300hp

Hello Windows Insiders, We have new releases today with builds across Beta and Experimental. New builds this week Today we are releasing new Windows 11 Insider Preview Builds. As a reminder, all Insiders can find the release notes for your device based on the new channel system, even if you haven’t moved yet. This is to make finding build information as easy as possible during the transition. See your channel release notes here:

• Beta: Build 26220.8764
• Experimental: Build 26300.8782

For those on other specific build versions, here are today’s new builds and release notes:

• Beta (26H1): Build 2820.2380
• Experimental (26H1): Build 28120.2387
• Experimental (Future Platforms): We do not have a new build for this channel today, keep an eye out for future announcements.

Notable new features:

[Cloud Recovery]

Release channel: Experimental

• We're introducing Cloud rebuild, a new recovery option that restores a Windows 11 PC to a clean, known-good state by performing a full OS reinstall, even when Windows won't boot. Unlike Reset this PC, Cloud rebuild downloads both the target Windows image and the device's drivers from Windows Update, so the device comes back fully functional without USB media, without a custom image, and without depending on the health of the currently installed OS.

[caption id="attachment_179033" align="aligncenter" width="811"] The new Cloud rebuild option in Windows Recovery Environment (WinRE), enabling a full Windows reinstall using files downloaded from Windows Update.[/caption] To learn more, see https://aka.ms/CloudRebuild.

[Account Control]

Release channel: Experimental

• The Windows 11 Account Control flyout has a refreshed, modern design and a clear subscription badge to make the user's account status instantly visible. This helps users easily identify their plan, discover benefits, and explore upgrades.

[caption id="attachment_179034" align="aligncenter" width="1024"] UI showing the refreshed Account Control flyout with subscription badges, account benefits, and storage details.[/caption] Thanks, Stephen and the Windows Insider Program team