The Antigravity A1 drone: 8K 360-degree video, FPV goggles, and controls that feel like you're playing GTA 5. Seriously.
It even *magically* removes itself from footage! But don't worry, the "press-once-press-it-again-and-hold-and-pray" pairing ritual for its software issues keeps things grounded.
Would you trade cutting-edge features for bulletproof stability, or is the future always a bit buggy?
#DroneLife #TechReview #FPV #SoftwareBugs #Innovation
Link: https://www.engadget.com/cameras/antigravity-a1-drone-review-140026021.html?src=rss
Antigravity A1 drone review: FPV flying unlike anything else

With 360-degree cameras, FPV goggles and an innovative, accessible control style, this debut drone is a lot of fun.

Engadget

Hal fixing a light bulb (from Malcolm in the Middle S03E06 - Health Scare)

https://prtb.fname.ca/w/4cCFptNYZUAQbJwZnYHyAA

Hal fixing a light bulb (from Malcolm in the Middle S03E06 - Health Scare)

PeerTube

Waymo's robotaxi decided school bus rules were optional, prompting regulators to ask questions. Naturally, Waymo's already pushed a 'software update.' Because isn't that always the first line of defense? What's the most *critical* bug fix you've deployed that totally wasn't a hotfix?

#Waymo #Robotaxi #TechNews #SoftwareBugs #DevLife
Link: https://techcrunch.com/2025/10/20/regulators-probe-waymo-after-its-robotaxi-drove-around-a-stopped-school-bus/

Regulators probe Waymo after its robotaxi drove around a stopped school bus | TechCrunch

The incident happened in Atlanta, Georgia earlier this month. Waymo says it has already updated the software on its robotaxis.

TechCrunch
Ah, another *riveting* tale of tech wizards turning software quirks into security nightmares 🎩✨. NVIDIA's drivers are as stable as a unicycle on a tightrope, and #Quarkslab is here to make sure everyone knows it, because apparently, there are not enough blogs to repeat their name 🥱📝.
https://blog.quarkslab.com/./nvidia_gpu_kernel_vmalloc_exploit.html #techsecurity #softwarebugs #NVIDIA #cybersecurity #tales #HackerNews #ngated
Oops! It's a kernel stack use-after-free: Exploiting NVIDIA's GPU Linux drivers - Quarkslab's blog

This article details two bugs discovered in the NVIDIA Linux Open GPU Kernel Modules and demonstrates how they can be exploited. The bugs can be triggered by an attacker controlling a local unprivileged process. Their security implications were confirmed via a proof of concept that achieves kernel read and write primitives.

When your car's too smart for its own good: NHTSA is probing Tesla over door handles that *aren't* opening. Nine reports, including kids stuck inside. Is this a bug or an extreme anti-theft measure? What's the weirdest tech glitch you've encountered?
https://techcrunch.com/2025/09/16/tesla-probed-for-potentially-faulty-door-handles/
#Tesla #EV #TechNews #SoftwareBugs #CarSafety
Tesla probed for potentially faulty door handles | TechCrunch

The National Highway Traffic Safety Administration has received nine reports from owners who couldn't access their cars, including from parents whose children were stuck inside.

TechCrunch

Déjà vu! The DOJ has filed *another* lawsuit against Uber for alleged discrimination against riders with disabilities, especially those with service animals or wheelchairs. Uber insists on its zero-tolerance policy.

When will this 'feature' be truly fixed?
#UberLawsuit #DisabilityRights #TechForGood #SoftwareBugs #Accessibility
https://www.engadget.com/transportation/the-doj-sues-uber-again-for-allegedly-discriminating-against-people-with-disabilities-195442362.html?src=rss

The DOJ sues Uber (again) for allegedly discriminating against people with disabilities

The government said the company's drivers 'routinely refuse to serve individuals with disabilities.'

Engadget
🖥️ OMG, CPU cores can be odd? Who knew? 🧐 Apparently, Xe Iaso just discovered software has bugs, shocking absolutely no one in the history of computing. We eagerly await their next revelation that water is wet. 💧🔍
https://anubis.techaro.lol/blog/2025/cpu-core-odd/ #CPUcores #SoftwareBugs #TechNews #ComputingHumor #XeIaso #HackerNews #ngated
Sometimes CPU cores are odd | Anubis

TL;DR: all the assumptions you have about processor design are wrong and if you are unlucky you will never run into problems that users do through sheer chance.

Ah, yes, the Therac-25 incident... because who doesn't enjoy a quick jaunt down memory lane to when a software bug was (literally) a killer feature? 💀🖥️ It's like the article took a wrong turn into a time machine and forgot to get out, leaving us with a listicle more scrambled than an amateur coder's first Python script. 🙄⌨️
https://thedailywtf.com/articles/the-therac-25-incident #Therac25 #SoftwareBugs #TechHistory #ProgrammingFails #KillerFeature #HackerNews #ngated
The Therac-25 Incident

A few months ago, someone noted in the comments that they hadn't heard about the Therac-25 incident. I was surprised, and went off to do an informal survey of developers I know, only to discover that only about half of them knew what it was without searching for it. I think it's important that everyone in our industry know about this incident, and upon digging into the details I was stunned by how much of a WTF there was. Today's article is not fun, or funny. It describes incidents of death and maiming caused by faulty software engineering processes. If that's not what you want today, grab a random article from our archive, instead. When you're strapping a patient to an electron gun capable of delivering a 25MeV particle beam, following procedure is vitally important. The technician operating the Therac-25 radiotherapy machine at the East Texas Cancer Center (ETCC) had been running this machine, and those like it, long enough that she had the routine down.

The Daily WTF

Do AI models help produce verified bug fixes?

"Abstract: Among areas of software engineering where AI techniques — particularly, Large Language Models — seem poised to yield dramatic improvements, an attractive candidate is Automatic Program Repair (APR), the production of satisfactory corrections to software bugs. Does this expectation materialize in practice? How do we find out, making sure that proposed corrections actually work? If programmers have access to LLMs, how do they actually use them to complement their own skills?

To answer these questions, we took advantage of the availability of a program-proving environment, which formally determines the correctness of proposed fixes, to conduct a study of program debugging with two randomly assigned groups of programmers, one with access to LLMs and the other without, both validating their answers through the proof tools. The methodology relied on a division into general research questions (Goals in the GoalQuery-Metric approach), specific elements admitting specific answers (Queries), and measurements supporting these answers (Metrics). While applied so far to a limited sample size, the results are a first step towards delineating a proper role for AI and LLMs in providing guaranteed-correct fixes to program bugs.

These results caused surprise as compared to what one might expect from the use of AI for debugging and APR. The contributions also include: a detailed methodology for experiments in the use of LLMs for debugging, which other projects can reuse; a finegrain analysis of programmer behavior, made possible by the use of full-session recording; a definition of patterns of use of LLMs, with 7 distinct categories; and validated advice for getting the best of LLMs for debugging and Automatic Program Repair"

https://www.arxiv.org/abs/2507.15822

#AI #GenerativeAI #LLMs #Debugging #Programming #APR #SoftwareDevelopment #SoftwareBugs

Do AI models help produce verified bug fixes?

Among areas of software engineering where AI techniques -- particularly, Large Language Models -- seem poised to yield dramatic improvements, an attractive candidate is Automatic Program Repair (APR), the production of satisfactory corrections to software bugs. Does this expectation materialize in practice? How do we find out, making sure that proposed corrections actually work? If programmers have access to LLMs, how do they actually use them to complement their own skills? To answer these questions, we took advantage of the availability of a program-proving environment, which formally determines the correctness of proposed fixes, to conduct a study of program debugging with two randomly assigned groups of programmers, one with access to LLMs and the other without, both validating their answers through the proof tools. The methodology relied on a division into general research questions (Goals in the Goal-Query-Metric approach), specific elements admitting specific answers (Queries), and measurements supporting these answers (Metrics). While applied so far to a limited sample size, the results are a first step towards delineating a proper role for AI and LLMs in providing guaranteed-correct fixes to program bugs. These results caused surprise as compared to what one might expect from the use of AI for debugging and APR. The contributions also include: a detailed methodology for experiments in the use of LLMs for debugging, which other projects can reuse; a fine-grain analysis of programmer behavior, made possible by the use of full-session recording; a definition of patterns of use of LLMs, with 7 distinct categories; and validated advice for getting the best of LLMs for debugging and Automatic Program Repair.

arXiv.org