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AI 资讯

Build Resilient Web3 Data Pipelines in Go with tokenterminal-go

When a Go application needs on-chain and protocol-level data, the HTTP request itself is usually the easy part. The difficult work starts afterward: defining request models, decoding inconsistent payloads, respecting rate limits, recovering from transient failures, and deciding what to do when one part of a multi-metric query succeeds while another part does not. Those concerns can quietly turn a small integration into a maintenance burden. tokenterminal-go is an open-source, production-oriented Go SDK for Token Terminal API v2 that aims to remove that plumbing. The project supports all 24 documented API routes across Assets, Projects, Market Sectors, Metrics, and Datasets; it requires Go 1.21 or newer and uses only the Go standard library. 1 It is a focused choice for engineers building internal analytics services, data jobs, dashboards, research tooling, or any application that needs Token Terminal data without hand-rolling an HTTP client. The practical promise: keep the integration idiomatic and type-aware, while the client handles the failure modes that normally appear only after an application reaches real traffic. Why an SDK matters here Token Terminal’s API gives programmatic access to its data, but it requires an API key and an API-enabled plan. 2 That makes the client layer part of the application’s operational surface: it needs to handle credentials, request timeouts, rate limits, pagination or filtering parameters where relevant, and failures that should not crash a larger data pipeline. The library addresses these needs with a small, deliberate design. Its client methods take a context.Context , its response envelopes use generic Result[T] types, and its errors can be inspected with standard Go mechanisms such as errors.Is and errors.As . 1 In other words, callers can keep control of cancellation and business policy instead of receiving opaque, string-only errors. Capability What it means in practice Why it is useful Zero external dependencies The packag

2026-08-05 原文 →
AI 资讯

Google Assistant will disappear from your phone next month

Google Assistant's days have been numbered ever since Gemini arrived on the scene, and its time is now up. Google has announced that it will be removing access to Assistant on Android phones and tablets, along with paired devices like smartwatches or headphones, from September 4th. The announcement came in an email apparently sent to […]

2026-08-05 原文 →
AI 资讯

Vulnerabilities in Car Anti-Theft Device

This is disturbing: …a team of security researchers at UC San Diego, who found that a model of aftermarket car alarm known as the KARR Security System, installed in more than 2 million vehicles across the US by their estimate, can let any hacker within Bluetooth range send radio commands to silently unlock the car at will, turn off its alarm, honk the car’s horn or flash its lights, or even disable its ignition and leave a driver stranded.

2026-08-05 原文 →
AI 资讯

Iran Cyberattacks Against Minnesota Water Systems

Attribution is preliminary , and so far it seems no real damage. And it seems like this is a campaign that has targeted at least seven states . And, because this is where the US is right now, Trump doesn’t believe it’s Iran and that Minnesota…I guess…hacked itself. “I think I blame it on Minnesota because they’re grossly incompetent,” Trump said. “I would blame it on Minnesota and the governor, the corrupt governor of Minnesota. They like to say, ‘Oh, it’s Iran.’ Iran should be so lucky. Iran’s got bigger problems than worrying about Minnesota.”...

2026-08-05 原文 →
AI 资讯

Building the foundation Claudius runs on

This tutorial was written by Néstor Daza . This is the third article in a series about building Claudius , my own Claude-based chatbot ( Github ). The previous article discussed the MongoDB data model to use for the app. The previous article decided the shape of the data. None of it matters until the app around it is working, and getting it there is the unglamorous half of this phase. It comes down to three things: an identity system the client cannot tamper with, proof that Claudius can reach the two services it depends on, and the deployment realities that decide whether any of it runs at all. This is the boring work that quietly decides whether a project survives contact with production. Identity: the client never gets a vote Any Google account on Earth can sign into Claudius safely because a user's role is never something the client sends. It is decided on the server every time. One piece of this lives outside the code. The Google provider needs an OAuth (Open Authorization) client that you register once in the Google Cloud Console, and the client identifier and secret from that registration are set in corresponding env variables. These setup steps live in the Auth.js and Google documentation, so I am not repeating them here. Sign-in runs on Auth.js v5 with the Google provider and the MongoDB adapter. There are three roles, admin, member, and guest, and they resolve in exactly one place on the server, with a clear precedence: export async function resolveRole ( email : string | null | undefined ): Promise < Role > { if ( ! email ) return " guest " ; const normalized = email . toLowerCase (); if ( normalized === env . ADMIN_EMAIL . toLowerCase ()) return " admin " ; const settings = await settingsCol (); const allowlist = await settings . findOne ({ _id : " allowlist " }); if ( allowlist && " emails " in allowlist ) { const allowed = allowlist . emails . some (( e ) => e . toLowerCase () === normalized ); if ( allowed ) return " member " ; } return " guest " ; }

2026-08-04 原文 →
AI 资讯

The Asus Chromebook Plus CX34 is at one of its lowest prices

The Asus Chromebook Plus CX34 is a dependable laptop that doesn’t cost a fortune, despite being nearly three years old. It’s cheaper than usual right now, and you have a few options in the sub-$400 range. The option with the most storage is currently on sale for $399.99 (about $100 off recent prices) at Amazon. […]

2026-08-04 原文 →
AI 资讯

Some Claude Chats Are Searchable on Google

And it’s personal information (alternate link ): The exposed data includes an AI-powered therapy app that someone appears to have vibe-coded, notes on meetings, and a dashboard someone made apparently to analyze medical billing data. Exposed chats reportedly include private cryptocurrency wallet keys and personal information like peoples’ addresses. What seems to be the issue is a user setting about data sharing. Anthropic’s position is that it’s not their problem : “We give people control over sharing their Claude conversations publicly, and in keeping with our privacy principles, we do not share chat directories or sitemaps with search engines like Google,” the company said in a statement. “These shareable links are not guessable or discoverable unless people choose to share them themselves. When someone shares a conversation, they are making that content publicly accessible, and like other public web content, it may be archived by third-party services.”...

2026-08-04 原文 →
AI 资讯

Can Reddit fend off a new wave of AI SEO spam?

Earlier this year, a Reddit user had asked members of a skincare-focused subreddit if anyone had tried a specific hypochlorous acid spray, a product often used for acne. There were dozens of responses; one from a user named Primary-Taro4254 seemed innocuous enough, at least at first. "I haven't personally tried [that brand] so I can't […]

2026-08-04 原文 →
开发者

How Market Sessions Influence an Algorithmic Trading Platform

An algorithmic trading platform doesn't operate in isolation it responds to the changing conditions of the financial markets. One of the biggest factors affecting automated trading performance is the market session. Liquidity, volatility, trading volume, and price movements can vary significantly throughout the trading day, influencing how an algorithmic trading platform executes trades. Understanding how different market sessions impact automated trading can help traders choose the right strategies, manage risk more effectively, and improve overall trading performance. What Are Market Sessions? A market session refers to a specific period during which a stock exchange is open for trading. In India, the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) follow a structured trading schedule that includes the pre-open session, regular trading hours, and post-closing session. Each session has unique market characteristics, making it important for traders to understand how their automated strategies may behave during these periods. Why Market Sessions Matter in Algorithmic Trading An algorithmic trading platform follows predefined rules, but the market environment changes throughout the day. A strategy that performs well during high-volume periods may struggle when trading activity is low. Market sessions influence several key factors, including: Trading volume Market liquidity Price volatility Bid-ask spreads Order execution quality Recognizing these differences allows traders to build strategies that are better suited to specific market conditions. Pre-Open Session The pre-open session is used to determine the opening price of securities before regular trading begins. During this period: Orders are collected but not executed immediately. Prices may fluctuate as the market discovers the opening level. Liquidity can be limited. Large overnight news events may influence price movements. Most intraday automated strategies are designed to become active only afte

2026-08-04 原文 →
AI 资讯

EU GPAI Code of Practice: What Signatories Commit to Under the AI Act

The European Union's voluntary General-Purpose AI Code of Practice gives providers of general-purpose AI models a practical framework for supporting compliance with the EU AI Act. Finalised in July 2025, the code addresses transparency, copyright, and safety and security. Its public signatory list includes major AI and technology companies, but official EU material does not support claims that roughly 190 organisations have signed the GPAI code. The distinction matters for companies assessing AI suppliers. Signing the code can signal engagement with the EU's emerging governance expectations, but it is not a substitute for examining a provider's specific commitments, documentation, and product-level controls. The European Commission describes the code as a voluntary instrument, and its official GPAI Code of Practice page states that the signatory process and public information continue to be updated. What the GPAI Code of Practice covers The code is designed for providers of general-purpose AI models, a category that can include models used across multiple downstream applications. Rather than creating a separate legal regime, it is intended to help providers demonstrate how they can meet relevant AI Act obligations . Its three chapters cover different aspects of provider responsibility: Transparency: commitments related to information and documentation that can help downstream providers understand and use general-purpose AI models appropriately. Copyright: measures intended to address copyright-related obligations for providers of general-purpose AI models. Safety and security: commitments focused on managing risks associated with the most capable models, including systemic-risk considerations where applicable. Code chapter Primary focus Why it matters to AI buyers Transparency Provider information and documentation Helps buyers assess whether a model provider can supply information needed for downstream use. Copyright Copyright-related provider commitments Relevant

2026-08-04 原文 →
AI 资讯

Running Celery in Production: What We Do Differently After Years of Real Projects

The first time we deployed Celery to production on a client project, we thought we had done everything right. We had workers running, tasks queuing, and Redis as the broker. Six weeks later, the task queue was backed up with 40,000 unprocessed jobs, the workers had silently died, nobody knew, and a batch of client invoices had not been generated for two weeks. That was four years ago. Since then we have deployed Celery on dozens of projects and we have learned what actually goes wrong — not in development, where everything works, but in production, where things fail in ways you do not anticipate. This post covers the configuration and operational patterns we now use on every Celery deployment. Why tasks fail silently (and how to stop it) The most dangerous thing about Celery is how quietly it can fail. A worker process dies, the task queue fills up, and your application keeps accepting work and sending it to a queue that nobody is processing. No exception is raised. No alert fires. Users notice eventually, or you notice when a daily report does not arrive. The fix has two parts: monitoring and task acknowledgement configuration. Task acknowledgement By default, Celery acknowledges a task (removes it from the queue) as soon as a worker picks it up, before the task runs. If the worker dies mid-task, the task is lost. # celery.py app = Celery ( ' myproject ' ) app . conf . update ( # Only acknowledge after the task completes successfully task_acks_late = True , # If a worker dies, reject the task back to the queue task_reject_on_worker_lost = True , # Limit memory — workers that leak memory will restart cleanly worker_max_memory_per_child = 200_000 , # 200MB in KB # Limit tasks per child process to prevent long-running workers # from accumulating state worker_max_tasks_per_child = 1000 , ) With task_acks_late=True , a task that is picked up by a dying worker will be requeued and picked up by another worker. The task might run twice (more on that shortly), but it will n

2026-08-04 原文 →