标签：#go

I’ve been making massive headway on tgo, my TypeScript to Go compiler library, but it is forcing me to confront some hard realities about how I manage systems, AI, and even people. The Cost of Scaling Complexity Since the last devlog, I’ve added full support for Node libraries— fs , path , process , and a few others. Right now, I’m in the trenches trying to compile Lodash, argparse , and date-fns . I pushed date-fns to the side for a minute because Lodash is proving to be the perfect stress test. It is, frankly, obnoxious. In some cases, the code is just very poorly written. Lodash has 316 different entry points. Right now, 122 are failing. But dealing with this massive, complex library has forced me to completely overhaul my test runner. I’ve built it so that you can choose specific entry points and compile only what you need—similar to how ES bundle works. I’ve also implemented heavy caching. If you are continually rebuilding, it won't re-compile the source to Go every single time; it just handles the binary compilation unless something actually changed. It’s significantly faster. But as this project scales, the sheer complexity is threatening to break the system—and by the system, I mean the AI I am using to build it. Process is Survival I do most of this development through AI, and getting an LLM to consistently output good software engineering without breaking existing features is incredibly difficult. I was constantly blowing out the context window. Even at 200,000 tokens, it wasn't enough. By the time the AI figured out what to do, it would start summarizing the context and immediately start doing a terrible job. This forced me to narrow down all possibilities. I realized there are really only four things I am ever asking the AI to do: Update the test runner. Fix a bug. Implement a new feature. Work on a library. That’s it. I defined strict workflows for those four pathways. If I ask it to fix a bug, it has to run the specific test, read the JavaScript, read

Android 17 is getting a dedicated gaming mode for foldables that will put a virtual gamepad with touch controls on half of your screen to theoretically make it easier to play games. With foldable gaming mode, which is set to launch in the coming months, the virtual controller emulates physical button presses at a system […]

YouTube Shorts is getting a makeover.

It took 20 years, but the Finance app arrives just in time to be packed full of AI.

Earlier this month, a German court ruled that Google is liable for its AI search summaries. Rejecting defenses like “users can check for themselves,” and that they generally know “that information generated with AI should not be blindly trusted,” the court held that the AI’s summaries are reflections of the company and “above all an expression of Google’s business activities.” This is the latest skirmish in a decades-old battle over internet publishing. Historically, there were two different types of information distributors: carriers and publishers. A phone company is a carrier. It’ll transmit whatever you say, even discussions about committing a crime. Words are words, and the phone company does not know—nor is it liable for—the words you choose to speak. A newspaper, on the other hand, is a publisher. It decides the words it publishes, and what quotes to include in its articles. If those words or quotes are defamatory or otherwise illegal, it’s liable...

Google Finance is a standalone Android app now, with an iOS version on the way.

Users will be able to access their watchlists, real-time market data, live financial news, and Google's AI-powered "Key Moments" feature, which explains why stocks moved.

The Department of Commerce declined to give the Chinese-owned automaker a special authorization to keep selling EVs in the U.S.

The Department of Transportation wants to remove the brake-pedal requirement for vehicles "designed to be driven exclusively by automated driving systems."

Google will allow developers to offer alternative billing in the US, the UK and Europe starting on June 30.

This is a fascinating explotation of how LLMs fall for prompt injection attacks. It turns out that they learn to recognize the style of text in different role/instruction blocks, and not just the tags. Their conclusion: Role tags were a formatting trick that became the security architecture and the cognitive scaffolding of modern LLMs. We’ve shown that this architecture doesn’t survive into the model’s actual representations, and that such role confusion is linked to prompt injection. Unless LLMs achieve genuine role perception, we think injection defense will remain a perpetual whack-a-mole game. And the continuous nature of role boundaries opens the threat of injections designed to subtly shift LLM states through seemingly innocuous text, legally and at scale...

Google’s Search history update stores media uploads from your interactions, like images used in reverse image searches, for training its AI models.

Top AI researchers Jonas Adler and Alexander Pritzel are leaving Google for Anthropic, following departures from top scientists Noam Shazeer and John Jumper.

Hey dev community! 👋 As developers, our inboxes often turn into a graveyard of job alerts (LinkedIn, Indeed, ZipRecruiter) and tech newsletters we subscribe to with the intention of "reading later" but never actually open. The result? Important emails get lost, and we get the dreaded "Account storage is almost full" notification. Recently, I hit that wall. I had thousands of accumulated emails. While Gmail allows you to create filters for incoming mail, it doesn't have a native feature to say: "Delete this email automatically after 7 days" . So, I decided to solve it the way we solve everything: by writing some code. 🛠️ The Solution: Google Apps Script + JavaScript Since the Google Workspace ecosystem runs on a JavaScript-based environment, I put together a custom script. Fun fact: a simple loop originally failed due to Google's strict 6-minute execution limit. To fix this, I optimized the code to process emails in batches of 100 , preventing the server from timing out. Here is the final production-ready script: function cleanSpamTsunami() { // 1. Loop to delete ALL Job Board emails in batches of 100 var continueJobSearch = true; while (continueJobSearch) { var jobThreads = GmailApp.search('computrabajo OR indeed OR linkedin OR OCC OR neuvoo OR talent.com OR jooble', 0, 100); if (jobThreads.length > 0) { Logger.log('Deleting a batch of ' + jobThreads.length + ' job alert emails...'); GmailApp.moveThreadsToTrash(jobThreads); } else { Logger.log('No more job alerts found!'); continueJobSearch = false; // Break the loop } } // 2. Loop to delete old Newsletters (older than 7 days) in batches of 100 var continueNewsletters = true; while (continueNewsletters) { var newsletterThreads = GmailApp.search('unsubscribe OR "cancelar suscripción" older_than:7d', 0, 100); if (newsletterThreads.length > 0) { Logger.log('Deleting a batch of ' + newsletterThreads.length + ' old newsletters...'); GmailApp.moveThreadsToTrash(newsletterThreads); } else { Logger.log('No more old newslett

Indie movie fans are upset about Google DeepMind’s $75 million investment in the studio, which comes as AI companies are deepening their influence in Hollywood.

Google's GKE Labs has introduced OpenRL, an open-source project that provides a self-hosted API for post-training and fine-tuning Large Language Models (LLMs) on standard Kubernetes clusters. By Sergio De Simone

While the court still hasn't signed off on the massive settlement resolving Epic's antitrust lawsuit against Google for having a monopoly over Android's app store with Google Play, the tech giant says it will start rolling out changes to the way it handles billing for developers worldwide. As announced in March, the flat 30 percent […]

A few additional markets will get the lower fees this year ahead of a global rollout in 2027.

Math.random() returns a number between 0 and 1, and roughly nobody reading this could explain what happens between the call and the return. That is fine, fine right up until the output decides who gets money, and then it becomes one of the genuinely hard problems in applied software, the kind that regulated industries build entire testing labs around. Start with the thing most people get wrong: a sequence that passes for random and a fair sequence are different claims, and your users cannot tell them apart by staring at outputs. The users will never catch the difference and that is the whole problem in one sentence. This is why fairness in any real-money system, an online casino being the sharpest example, is a verification problem long before it is a math problem. Pseudorandom generators are deterministic. A PRNG eats a seed, runs it through fixed arithmetic, and spits out numbers that sail through statistical randomness tests while being completely predetermined by that seed. Mersenne Twister is the poster child: excellent distribution, used everywhere by default for years, and from a few hundred observed outputs you can reconstruct its internal state and predict the rest. For a Monte Carlo simulation, who cares! For anything where a human has a financial reason to guess your next number, you just shipped a vulnerability and called it a feature. What you want when stakes exist is a CSPRNG. The guarantee that matters: even with a long history of outputs, an attacker cannot compute the next one or recover the internal state. crypto.randomBytes() in Node. crypto.getRandomValues() in the browser. They sit one autocomplete away from the unsafe option and offer wildly different guarantees, which is exactly why this bug ships so often. The safe call and the dangerous call look like fraternal twins. ** The part players actually rely on ** Say you build it correctly: a proper CSPRNG, real entropy, no timestamp nonsense. You know it is fair but now prove it to a stranger wh

Technology companies are extending AI beyond code generation into earlier stages of the software lifecycle, including PRD validation, design inputs, and code review. Initiatives from Uber, DoorDash, and Cloudflare highlight a shift toward AI-driven governance layers that evaluate engineering artifacts before implementation while preserving human oversight across the development pipeline. By Leela Kumili