标签：#go

State agency's delay could mean free robotaxi rides in company’s new Ojai vehicle for a few months.

GitHub热门项目 | Fully autonomous AI Agents system capable of performing complex penetration testing tasks | Stars: 19,091 | 454 stars today | 语言: Go

Because of the way they are trained, large language models capture only a slice of human language. They’re trained on the written word, from textbooks to social media posts, and our speech as captured in movies and on television. These models have minimal access to the unscripted conversations we have face to face or voice to voice. This is the vast majority of speech, and a vital component of human culture. There’s a risk to this. The increased use of large language models means we humans will encounter much more AI-generated text. We humans, in turn, will begin to adopt the linguistic patterns and behaviors of these models. This will affect not just how we communicate with one another, but also how we ...

Google shipped AlloyDB AI functions GA with a proxy model architecture that trains a lightweight local model from LLM outputs, then runs queries at database speed without external calls. Smart batching delivers 2,400x throughput improvement. The proxy model reaches 100,000 rows per second in preview, but benchmark numbers apply only to ai.if in internal testing. By Steef-Jan Wiggers

Are you building a trading dashboard, a market sentiment tracker, or a financial data pipeline in Go? If so, you know that gathering reliable social intelligence and market data is often a complex, messy process. You have to juggle raw HTTP requests, decode deeply nested JSON payloads, and manually handle rate limits. But what if you could access a wealth of crypto and stock social intelligence idiomatically, right where your Go code lives? Enter lunarcrush-go , a powerful, zero-dependency SDK designed to seamlessly integrate the LunarCrush API v4 into your Golang applications. In this article, we will explore why lunarcrush-go is the ultimate tool for developers looking to tap into social and market intelligence, how to get started in under 60 seconds, and why its zero-dependency architecture makes it a robust choice for production workloads. Why LunarCrush? Before diving into the SDK, it is worth understanding what LunarCrush brings to the table. LunarCrush goes beyond traditional price charts. It measures what the internet is actually saying about Bitcoin, Ethereum, Tesla, and thousands of other assets. By analyzing social buzz, creator impact, and overall market sentiment across various platforms, LunarCrush provides a holistic view of the market 1 . Whether you want to know the Galaxy Score of a specific coin, track the hourly social time-series of a stock, or get AI-generated insights on a trending topic, LunarCrush has you covered. Introducing lunarcrush-go The lunarcrush-go library was built with one primary goal: to provide clean, typed, and production-ready access to every LunarCrush endpoint without pulling in a single third-party dependency. It speaks Go natively, meaning you do not have to wrestle with raw JSON or hand-roll your own retry loops. Key Features Here is what makes lunarcrush-go stand out: Complete API Coverage: The SDK supports every LunarCrush endpoint, including Coins, Stocks, Topics, Categories, Creators, Posts, Searches, AI summaries, a

The caller ID company says users are increasingly ignoring and blocking calls from India's dedicated business number series.

The saga of Musk's tussle with the SEC over how he disclosed his growing stake in Twitter (now X) has come to an end.

The feature can do things like apply cinematic relighting to brighten up a dark clip, swap out a plain background for something fun, or add artistic styles to videos.

The company's streaming devices aren't quite keeping up with competitors in several important categories.

It allows you to reimagine videos stored in Google Photos using Gemini Omni.

Android Bench is evolving, and developers can help guide that process.

Google's upcoming Pixel lineup might cost more than last year's. A report from Dealabs spotted by 9to5Google suggests that Google could raise the starting price of its 41mm Pixel Watch 5 to $399, while adding LTE could bump the price to $499. That's a $50 jump from the base Pixel Watch 4, which starts at […]

GitHub热门项目 | Declarative Continuous Deployment for Kubernetes | Stars: 23,361 | 20 stars today | 语言: Go

Last week, national security agencies from the Five Eyes—that’s the rich, English-language-speaking countries club—jointly released a statement warning of the increasing cyber risks of AI models: in particular, their ability to autonomously hack into systems and networks. The statement was more measured than some of the breathless headlines about it, and the advice they gave is pretty much the standard advice everyone gives—albeit with newfound urgency. Internet risks are nothing new, and cyberattacks—both large and small—have been a significant issue since long before the current crop of generative AI models...

Scammers are hijacking government websites to upload ads for “leaked” OnlyFans content. Thousands of copyright complaints from adult creators are helping people avoid malicious links.

I have a simple rule when I work with a team: if a vulnerability makes it into main, the process...

Nine months ago I wrote a post on here claiming my ENS identity architecture had reached "85% Knowledge Panel trigger probability." Two things happened since. Google's Knowledge Graph actually minted an entity node for me. And I learned that the 85% number was fiction — mine. This is the honest retrospective. The timeline, with receipts Date What happened Aug 2025 ookyet.com first indexed Oct 2025 Entity markup shipped: Person @graph , Dentity verification, ENS identifiers. The "85%" post Jun 2026 Search Console turned red: Q&A errors, Profile page: Invalid object type . Cleanup Jun 28, 2026 Fixed markup deployed. Then: hands off Jul 2, 2026 Knowledge Graph Search API returns a machine-minted Person node for ookyet Jul 7, 2026 Search Console fully green: ProfilePage valid, indexed pages up, zero 404s Still no Knowledge Panel. Keep reading — that part matters. What Google actually built You can reproduce this: curl "https://kgsearch.googleapis.com/v1/entities:search?query=ookyet&limit=10&key=YOUR_API_KEY" { "result" : { "@id" : "kg:/g/11z806my44" , "name" : "Qifeng Huang" , "@type" : [ "Person" ] } } Three details in that tiny response taught me more than anything I shipped in 2025. The /g/ MID is machine-minted. You can't register one, buy one, or submit one. Google's entity reconciliation creates it when enough independent sources agree that a person exists. This is the mechanical prerequisite for a Knowledge Panel — the entity has to exist in the graph before anything can be displayed about it. The node's name is my real name, not my handle. My site declares name: "ookyet" . The node says "Qifeng Huang" — pulled from the high-authority anchors (LinkedIn, ORCID), not from my self-declaration. Third-party corroboration outweighs anything you say about yourself. Expected, and honestly a relief: the graph is working as designed. The Knowledge Graph holds 8 distinct people named Qifeng Huang. Query any of them by real name and you get a crowded namespace. Query ookyet

Part 1 of "Trust the Machine" -> a series on building AI infrastructure that is secure, compliant, and governable by design. Most organizations can produce an accurate catalog of the web services they operate. Far fewer can produce an equivalent catalog of the AI systems they run — the models, fine-tunes, retrieval pipelines, agents, and third-party AI APIs now embedded throughout their products and internal tooling. This asymmetry defines the state of AI security in 2026. Adoption has outpaced oversight. Industry reporting this year has described a surge in enterprise AI activity on the order of 83% year over year, with governance and visibility lagging well behind. The consequence is a large and only partially mapped attack surface — one that many organizations cannot fully enumerate, let alone defend. Every mature security program rests on a single first principle: you cannot protect what you cannot see. Artificial intelligence is no exception. Before threat-modeling an agent or authoring a guardrail, an organization must be able to answer a deceptively difficult question: what AI is running across the environment, and who is accountable for it? This post examines how to build that answer. The rise of shadow AI Shadow IT — the unsanctioned adoption of tools outside official channels has been a recognized challenge for decades. Shadow AI is its faster-moving successor, and it appears in more forms than most inventories are designed to detect: Embedded API calls. A product team integrates a hosted model in a few lines of code and an API key, with no formal review. Copilots and assistants enabled across existing SaaS platforms, frequently activated by the vendor rather than the customer. Fine-tunes and adapters trained on internal data and stored in locations that fall outside standard scanning. Agents and automations that have incrementally acquired the ability to act—filing tickets, sending communications, initiating transactions—one permission at a time. Model de

I spend a lot of time preaching architecture and constraints, so it is always nice when a side project gives me receipts. Adding this new feature to DumbQuestion.ai was a good reminder that a well-structured first version lets you spend your next iteration on value, not repair. Below, you will find a few relatively simple challenges and how thoughtful, upfront design made the changes effortless. To vibe or not to vibe ... Many developers jump right in and just rip out an app, ship fast, let the coding agent sort it out, come back and deal with it later. To be fair, that absolutely can get you to first release faster. But even on a solo project, a little proper SDLC discipline pays back later when you want to extend the product without turning every feature into a rescue mission, which is a theme that already runs through how I have been building DumbQuestion.ai. Extend this to the enterprise and you turn a little upfront effort into potential huge savings on token spend Roasting starup pitches (for sport) ... The core idea for Startup Roast was simple enough: take a startup pitch, roast it, and add a reality-check section so the output is not just mockery for mockery’s sake. To illustrate (and avoid just vaguely describing the feature) I picked a random but highly upvoted pitch from Product Hunt: Vida . Vida, which pitches itself as an “AI clone” that learns how you work, remembers what matters, and becomes a “second you,” with early use cases like Reply Rescue, Prompt Rescue, Resume Rescue, Workspace Cleanup, and Daily Wrap. This is a pretty common target use case of agentic AI making it a solid candidate. If you want to skip ahead, here's an example roast for Vida. Combining a preliminary web "market search" into the content yielded a result that was not just sarcastic, but informed. The roast hit the obvious AI-clone positioning, questioned whether the product was really a clone versus a macro suite, and then turned the market context into a sharper Reality Check

Open source models’ success isn’t coming at the expense of frontier labs. Instead, they each seem to capture two phases of the same life cycle.