标签：#go

Google's selfie videos can be used for account access, AI Avatars, and age verification.

The European Commission claims that Google boosted its own apps and products to the top of search rankings to the detriment of its competitors.

Google becomes third tech giant to face huge fines under the Digital Markets Act.

Google continues to report big quarterly revenue, but its AI spending has skyrocketed.

The tech giant says selfie videos give users more options to sign in if they're ever locked out or don't have access to their usual phone or computer.

Hey Techie! 🌸 Welcome to my Go series! I'll be sharing what I'm learning in ways that make sense to me, the mistakes I make and the "aha!" moments that help everything click. Whether you're learning Go too or just curious about it, I hope you'll pick up something along the way. Feel free to add any insights or experiences in the comments. Today's topic is... drumroll, please! Slices . Let's dive in! So, what exactly is a slice? When I first came across slices in Go, I thought they were another name for arrays. Turns out, they're not! A slice is internally represented by a small data structure called a slice header . Instead of storing the elements themselves, the slice header stores a pointer to the underlying array, along with its length and capacity . This realization helped me understand why modifying a slice can also modify the original array. Another interesting and convenient thing is how flexible slices are compared to arrays which are fixed size. Slices can grow using functions like append() or be resliced to work with a smaller portion of the underlying array. This flexibility is one of the reasons slices are used so frequently in Go.

Gemini had over 750 million monthly users in February.

When a new platform team set out on implementing their roadmap through forced workflows with poor documentation, developer experience declined. Success came from simplifying governance, prioritizing what matters, and rolling out compliance incrementally through prevention, detection, and communication. Empathy, focus, and shared purpose drove successful adoption. By Ben Linders

New paper: “ Encryption and Globalization 15 Years Later: End-to-End Encryption and the Third Round of the ‘Going Dark’ Debate “: Abstract : This Article updates and expands on 2012 research on encryption and globalization, analyzing what the authors call “Round 3” of the Going Dark Debate: the current controversies over end-to-end encryption (E2EE). Governments around the world have proposed, and in some cases enacted, laws limiting E2EE for law enforcement and national security purposes. This Article explains the underlying technologies and market developments for a law and policy audience to assess those proposals critically. The Article proceeds in three parts tracking three rounds of the Going Dark Debate. Round 1 covers the Crypto Wars of the 1990s, when U.S. export controls on strong encryption ultimately fell in 1999. Round 2 covers the period roughly 2010 to 2015, when encryption-in-transit became widespread but lawful access remained available through cloud providers, giving rise to what the authors called a “golden age of surveillance” rather than a period of going dark. Round 3 addresses the current debate over E2EE, where no entity between sender and recipient can read the plaintext...

The European Union has fined Google's parent company Alphabet €890 million (about $1 billion) for two separate violations of the bloc's Digital Markets Act (DMA). One penalty is for giving its own products preferential treatment in search results, while the other is for blocking Android developers from sending users to alternate payment options. A €460 […]

The next time you’re locked out of your Google account, you can use your face as part of the account recovery process.

Google announced yet another way to gain access to your account if you get locked out, forget your password, or don't have access to the phone, computer, or two-factor authentication apps you typically use. As the name implies, selfie video lets you capture a short video of your face on a mobile device or PC, […]

Ink & Switch published bijou64, a variable-length integer encoding where every number has exactly one byte representation, closing the canonicality bug class behind attacks on PKCS#1, JWT libraries, and Bitcoin. The design also decodes two to ten times faster than LEB128. Community ports to Elixir, Go, Perl, and Java followed, while HN commenters debated SIMD performance and residual range checks. By Steef-Jan Wiggers

Every morning, an online store receives the previous day’s orders from a marketplace partner. The file comes in JSON format. The company needs to add those orders to its main MongoDB orders collection. The sales manager also needs a CSV report that can be opened in Excel. That sounds like a small task. Import the file, copy the documents, export the report. But in practice, a few things can break the process. A date can be imported as a string. A field can have the wrong name. One batch may use total , while the main collection uses totalAmount . A temporary collection can keep old records and trigger duplicate key errors. A CSV export can create null values because the mapping points to fields that do not exist. And then there is customer data. The manager may need the sales numbers, but they probably do not need real customer names or internal customer IDs. This article walks through a real daily workflow: Import marketplace JSON ↓ Store the batch in a temporary MongoDB collection ↓ Copy the orders into the main orders collection ↓ Mask customer fields during export ↓ Create a CSV report The goal is not just to move data from JSON to CSV. The goal is to make the process repeatable, easier to check, and safer to share. The workflow The workflow has three jobs: Import Yesterday Orders ↓ Add Orders to Main ↓ Export Daily Sales Report The important part is the parent relationship between the jobs. Add Orders to Main depends on Import Yesterday Orders , so it only runs after the JSON file is imported successfully. Export Daily Sales Report depends on Add Orders to Main , so the CSV is created only after the main orders collection has been updated. This prevents the report from being generated when data is missing or incomplete. The incoming JSON file The partner sends a file with yesterday’s completed orders. A single order looks like this: { "orderId" : "ORD-2026-07-201" , "customerId" : "CUST-1003" , "customerName" : "Sofia Rossi" , "orderDate" : "2026-07-21T08:20:00

TL;DR : most production AI tasks are not LLM tasks. To triage my email, I replaced a 7-billion-parameter model with a tiny classifier in Go. The rule fits in one sentence. Rules first, a small model next, the LLM only as a last resort. The result: no GPU, sub-millisecond inference, and a cloud call that became rare. Here is how, with the real numbers. This article is for developers who put an LLM in production and pay the bill. Not a demo. Most AI tasks are not LLM tasks In 2026, the default reflex is to wire a big model into everything. A question comes in, you call the LLM. But many tasks do not need it. Filing an email under "work" or "newsletter" is classification. A problem solved for twenty years, long before LLMs. To classify is to pick a label from a short, stable list. To generate text is something else. The first job needs a small model. The second earns a big one. The rule I defend fits in one sentence. Put the LLM last. The setup I built an agent that triages my inbox. It is a daemon. It reads new messages and files each one into a category: work, notification, newsletter, promo, and a few more. Nothing secret, just my real mailbox, with years of mail. The first version handed every email to a local LLM. A 7-billion-parameter model, Qwen 2.5 7B, served by Ollama on a GPU. Ollama is a tool that runs an LLM on your own machine. It worked. But the price was heavy. A GPU on all the time. One more container to watch. And an absurd slowness for the question asked. One day I asked myself: does deciding "is this a newsletter?" really need 7 billion parameters? No. The answer sits in two or three words from the sender and the subject. So I rethought the whole thing. Three layers, from cheapest to most expensive Every email goes through three layers, in order. It stops at the first one that can answer. Deterministic rules. Instant, exact, no cost. A small model. Sub-millisecond, on CPU. The LLM. Only if the small model is unsure. The routing code fits in a few lin

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

I have released the Sovereign package on Github. It deploys Lemmings into up to 3 cloud regions for your choice in configuration flavor with cost estimations through dry-runs and aggregating the report into one final report as lemmings come and go in the result of the load test. I built it for organizations that plan on using AI to build something, not hire somebody like me who helped write The Library to do it for them. You can hire me in consulting if you need help, but it's available now. But, before you spend $50,000 on a television ad driving people to your new app that you just built after spending $50,000 on tokens, why not run Lemmings and Sovereign first? It's 100% free and does not involve me at all in order for you to read through the extensive README.md files and comments in the code for you to understand what to do to run it and adapt to its results. Enjoy using it! There - that is the post. Now - 🙌🏻 - Ask me anything 🙇🏻 👇🏻

Google's cloud business is thriving, as companies adopting its AI and AI infrastructure services help the tech giant to report record profits.

There’s a debate going on in the Trump administration over how to handle increasingly powerful Chinese AI models.

Treasury Secretary Scott Bessent warned the U.S. government could sanction Chinese AI companies after White House officials accused Moonshot of distilling Anthropic's Fable model to develop Kimi K3.