标签：#Go

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.

Some Android users find the security feature to be too restrictive. Here's how to disable it and what you risk by doing so.

The new feature lets users wirelessly transfer more data types from an iPhone without needing to download a separate app.

Samsung has given us our first chance to check out its upcoming smart glasses in person, revealing two new designs and the first specs in the process, including an impressive 9-hour battery life. The glasses, developed in collaboration with Google and the eyewear brands Gentle Monster and Warby Parker, are due to launch this fall. […]

GitHub热门项目 | Easily and securely send things from one computer to another 🐊 📦 | Stars: 37,203 | 737 stars today | 语言: Go

Google and Samsung have revealed the Gemini-powered AI tools coming to the new Galaxy foldables and smartwatches.

Harrowing story of an identity theft victim. Yes, the person made a mistake—they gave the scammer a two-factor authentication code that allowed the scammer to take over their email address. But the real story here is how, for many of us, the security of most of our accounts hangs on the security of our email accounts.

Google Play's 14-day closed test isn't just an ops requirement — it's 14 days when real people will use your app for the first time. If your design isn't ready for that scrutiny, you're getting expensive feedback on the wrong things. Here's what design should have shipped before day one of the closed test. Why 'good enough for testers' isn't The temptation: 'they're just testers, we'll polish for production launch.' What actually happens: Testers give feedback on rough UI, not the product. You lose the signal. Screenshots make it to social. First impression matters. The 14-day counter resets if engagement dips. Rough UX = lower engagement. Ship the closed test with launch-quality design or you're paying the 3-week price for lower-quality data. The five artefacts 1. Onboarding flow. Every tester's first 90 seconds. Empty states, permissions requests, initial value proof — all designed, not 'I'll clean it up later'. 2. All error states. Network fail, permission denied, invalid input. Real users hit these on day 1. Placeholder 'something went wrong' text loses trust fast. 3. Play Store listing assets. Screenshots, feature graphic, description. Play requires them before you can even publish to closed testing. Don't leave this for day 13. 4. Empty states for every screen. 'You have no [thing] yet' + 'here's how to get started'. Most closed-test users are seeing your app the first time with zero data — every empty state matters. 5. A visible feedback mechanism. In-app 'Send feedback' button or shake-to-report. Testers WILL find bugs; make it easy to report while they're in the moment. Designing the in-app feedback loop Testers who have to remember to email you get around to it maybe half the time. Testers who can tap 'Send feedback' from inside the app respond 5-10x more often. Design this before day 1: Persistent 'feedback' entry point (settings screen, help menu, floating button in beta builds). Feedback form is 3 fields max: (1) What were you trying to do? (2) What hap