标签：#ics

According to a new report from Semafor, the White House's decision to impose export restrictions on Anthropic's Mythos was driven in part by fears that it had been accessed by a group linked to China. If the Chinese government actually had access to Mythos 5 or Fable 5, it would present a serious national security […]

Traditional databases just can't keep up with high concurrency and low latency at the same time. The term "real-time" has become kind of meaningless. Everyone claims it, from batch-oriented cloud data warehouses to transactional database extensions. This makes picking the right architecture really hard without expensive trial and error. The best real-time analytics database in 2026 depends entirely on your workload shape. Key takeaways Real-time analytics (in this guide) = sub-second p95/p99 analytical queries on billions of rows, high concurrency , and milliseconds-to-seconds freshness . Best overall in 2026 for most workloads: ClickHouse (ingest throughput, query speed at scale, compression/TCO). Best for strictly predefined query paths via star-tree indexes: Apache Pinot . Best for time-series operational dashboards and observability: ClickHouse . ClickStack is its full observability offering for logs, metrics, and traces. Best for rigid ingestion-time roll-up aggregations: Apache Druid . Best for unified OLTP + real-time analytics: ClickHouse paired with its managed Postgres offering and native sync to ClickHouse , giving you a purpose-built OLTP engine and a purpose-built OLAP engine without rolling your own CDC pipeline. SingleStore is an alternative if you prefer a single HTAP engine for both. Traditional Data Warehouses: Snowflake and BigQuery are fine for batch BI if you already have one, but face latency, concurrency, and cost challenges under sub-second, high-concurrency workloads. Evaluate using 4 axes: ingest/freshness, latency under concurrency, TCO, operational complexity. What 'real-time analytics' means (and why warehouses and OLTP databases fail) Strict engineering thresholds define true real-time OLAP : sub-second query latency on complex aggregations, the ability to serve tens to thousands of concurrent queries per second (QPS), and data freshness measured in milliseconds to seconds. Traditional cloud data warehouses like Snowflake and BigQuery a

Token economics is not only about token price. It is about designing the rules, incentives, and long-term logic of a Web3 ecosystem. When people start building a Web3 project, they usually focus on the visible parts first. They think about the smart contract, the frontend, the wallet connection, the token launch, the whitepaper, and maybe the community. All of those are important. But there is one part that can decide whether the project survives or fails: Token economics. A project can have clean smart contracts, a nice UI, and strong marketing, but if the token economy is weak, the project can slowly collapse. Users may come only for rewards, early investors may dump, inflation may destroy value, and the token may lose its reason to exist. That is why token economics should not be treated as just a “crypto finance” topic. For developers and Web3 builders, token economics is closer to system design . It defines how value moves inside the ecosystem, how users are rewarded, how supply is controlled, how governance works, and how the project can grow without depending only on hype. What Is Token Economics? Token economics, often called tokenomics , means the design of how a token works inside a project. It answers questions like: Why does this token exist? Who receives the token? How is the token used? How many tokens will exist? How are rewards distributed? When can team and investor tokens unlock? How does the project treasury work? What creates real demand for the token? In simple words, token economics is the rule system behind a token. A token is not only something people buy and sell. In a real Web3 product, a token can be used for payments, staking, governance, access, rewards, collateral, or network fees. If the token has no clear role, it becomes only a speculative asset. That is dangerous because speculation can bring attention, but it cannot support a project forever. Why Developers Should Care Some developers think token economics is only for founders, eco

On Friday evening, the government ordered Anthropic to block access to Fable 5 and Mythos 5 for all foreign nations, both inside and outside the US, due to national security concerns. That order included employees of Anthropic. To meet those demands, the company has completely cut off access to the models for all customers. In […]

As World Cup action kicks off, we look at the physics of the beautiful game.

Observability isn't just a buzzword; it's a necessity, especially when diving into distributed systems. If you're using NestJS, you might want to take a look at RedisX. It's a modular toolkit that can boost the observability of your applications. A standout feature? The Metrics Plugin. It meshes well with Prometheus, delivering insights into Redis operations in your NestJS setup. Getting RedisX Metrics Rolling in NestJS So, first things first. To harness the power of RedisX Metrics, you need to set up your NestJS app with RedisX. This means installing some packages and configuring the RedisModule with the MetricsPlugin. Hit your terminal and run: npm install @nestjs-redisx/core @nestjs-redisx/metrics Now, let's tweak your AppModule . You want it to use RedisModule with MetricsPlugin: import { Module } from ' @nestjs/common ' ; import { ConfigModule , ConfigService } from ' @nestjs/config ' ; import { RedisModule } from ' @nestjs-redisx/core ' ; import { MetricsPlugin } from ' @nestjs-redisx/metrics ' ; @ Module ({ imports : [ ConfigModule . forRoot ({ isGlobal : true }), RedisModule . forRootAsync ({ imports : [ ConfigModule ], inject : [ ConfigService ], plugins : [ new MetricsPlugin ({ prefix : ' redisx_ ' , endpoint : ' /metrics ' , defaultLabels : { service : ' my-service ' } }) ], useFactory : ( config : ConfigService ) => ({ clients : { host : config . get ( ' REDIS_HOST ' , ' localhost ' ), port : config . get ( ' REDIS_PORT ' , 6379 ), }, }), }), ], }) export class AppModule {} Prometheus Metrics: What You Get With MetricsPlugin set up, your app now exposes a /metrics endpoint. Prometheus can scrape this endpoint, dishing out detailed metrics about your Redis operations. Here's a snapshot of what you get: redisx_cache_hits_total : Tracks total cache hits. redisx_lock_acquired_total : Total locks acquired. redisx_redis_commands_total : Total Redis commands run. Making Prometheus Work for You To get those insights, set up Prometheus to scrape your /metrics end

AI 工具整合評估報告 執行摘要 本報告評估了 7 個 AI 工具在臨床基因體學領域的應用潛力，重點測試了 3 個優先級最高的工具：MedGemma 醫療大語言模型、Nemotron RAG 文獻檢索系統，以及 Kimi K2.5 多模態視覺語言模型。 評估日期 : 2026-02-10 測試平台 : RTX 3090 24GB 評估目標 : 確認 AI 工具在變異解釋與臨床決策中的可行性 1. 測試項目總覽 1.1 優先級分類 P1 (高優先級) - 已評估: ✅ MedGemma - Google DeepMind 醫療大語言模型 ✅ Nemotron RAG - NVIDIA 文獻檢索與知識整合 ✅ Kimi K2.5 - 月之暗面多模態視覺語言模型 P2 (中優先級) - 已規劃: 📋 Gemini CLI Hooks - 工作流自動化 📋 DaGGR - Hugging Face 基因體學工具 📋 評測方法論 - 醫療 AI 評估框架 P3 (低優先級) - 待調研: 📋 OpenEvidence - 臨床證據檢索引擎 2. MedGemma 醫療大語言模型 2.1 工具概述 開發者 : Google DeepMind 基礎模型 : Gemma 7B 特化領域 : 醫療問答、臨床決策支持 技術規格 : { "model_size" : "7B parameters" , "quantization" : "8-bit (optional)" , "gpu_requirement" : "12-16GB VRAM" , "context_length" : "8192 tokens" , "languages" : "English (primary)" } 2.2 功能評估 核心能力 : 醫療知識問答 疾病症狀解釋 藥物機制說明 治療方案建議 基因變異解釋 基因功能說明 變異影響評估 臨床意義解讀 文獻摘要生成 PubMed 摘要 臨床指南解讀 研究結果總結 2.3 應用場景 場景 A: 變異臨床意義解讀 # 輸入 query = """ 變異資訊: - 基因: BRCA1 - 位置: chr17:41234470 - 變化: c.5266dupC (p.Gln1756Profs*74) - 類型: Frameshift 請解釋此變異的臨床意義。 """ # MedGemma 輸出 (預期) """ BRCA1 c.5266dupC 變異分析: 1. 變異類型: Frameshift insertion - 導致讀框位移 - 產生提前終止密碼子 (位置 1756+74) 2. 基因功能影響: - BRCA1 是腫瘤抑制基因 - 參與 DNA 雙鏈斷裂修復 - Frameshift 導致功能喪失 3. 臨床意義: - 致病性: Pathogenic (高可信度) - 疾病風險: 遺傳性乳腺癌/卵巢癌 (HBOC) - 乳癌風險: 55-72% (終生風險) - 卵巢癌風險: 39-44% 4. 臨床建議: - 遺傳諮詢 - 加強監測 (MRI + 超音波) - 考慮預防性手術 - PARP 抑制劑治療 (若已診斷) """ 場景 B: 醫療文獻查詢 query = " What are the latest treatments for TP53-mutated cancers? " # MedGemma 回答 (模擬) """ TP53 突變癌症的最新治療策略: 1. 標靶治療: - APR-246/Eprenetapopt: 恢復 TP53 功能 - PRIMA-1/APR-246: 臨床試驗進行中 2. 免疫治療: - PD-1/PD-L1 抑制劑 - TP53 突變可能影響免疫反應 3. 合成致死策略: - PARP 抑制劑 (部分 TP53 突變) - ATR/CHK1 抑制劑 4. 臨床試驗: - NCT02999893: APR-246 + 化療 - NCT03745716: TP53 疫苗免疫治療 """ 2.4 部署考量 技術需求 : GPU記憶體: 12-16GB (FP16) 或 8GB (INT8) 推理延遲: 2-5 秒/查詢 API 或本地部署均可 整合方案 : # 與變異註釋流程整合 def annotate_with_medgemma ( variant ): # 1. 提取變異資訊 gene = variant [ ' gene ' ] change = variant [ ' protein_change ' ] # 2. 生成查詢 prompt = f " Explain the clinical significance of { gene } { change } " # 3.

“The government believes it has become aware of a method of bypassing, or ‘jailbreaking’ Fable 5,” the company said in a blog post.

A pending report on climate attribution may be setting the stage for conflict.

A Monster Energy–sponsored MMA show on the White House’s South Lawn was never going to be the height of dignity. But UFC Freedom 250 is failing to clear even the lowest bar.

GOP lawmakers, tech investors, and even OpenAI have tied the anti-data center movement in the US to Chinese interference. Experts say it’s much more complicated than that.

After Elon Musk and Tommy Robinson stoked anger over a horrific knife attack in Belfast, a youth group linked to a global neo-Nazi movement quietly orchestrated anti-immigrant riots.

FIFA announced agreements with platforms such as TikTok and YouTube that include the participation of dozens of international influencers to generate content in the three host countries.

Unlike humanoid robots designed around a fixed form — think Boston Dynamics — Theker's machines are built to be reconfigured.

Activists raised a 40-foot-tall inflatable Elon Musk in Manhattan to draw attention to the risk he allegedly poses to investors.

Under a new bipartisan bill, Americans could sue for damages if a government official illegally tries to coerce a social media, AI, or broadcasting company to remove their post - regardless of whether the platform actually does it. Senate Commerce Committee Chair Ted Cruz (R-TX) and Sen. Ron Wyden (D-OR) introduced the JAWBONE Act on […]

The problem: counting unique viewers per second is a row explosion A viewer scrubs to 4:12 of a 9-minute trending clip, watches for 40 seconds, jumps back to the intro, then bounces. Multiply that by the few hundred thousand sessions a day that hit a mid-size aggregator and you get the question every product person eventually asks: which parts of this video do people actually watch, and how many distinct people watched each part? The naive answer is a watch_events table: one row per (user, video, second) . It works until it doesn't. A 9-minute video is 540 seconds. One viewer who watches the whole thing generates 540 rows. A million viewers across our catalog generate hundreds of millions of rows per day , and the only query anyone runs against them is COUNT(DISTINCT user_id) GROUP BY second . That COUNT(DISTINCT) is a sort-or-hash over the entire partition every single time someone opens the analytics tab. At TopVideoHub we aggregate trending video across Asia-Pacific, so a single popular clip can spike from zero to half a million sessions in an afternoon when it lands in the JP and KR feeds simultaneously. We did not want a fact table that grew by hundreds of millions of rows a day to answer a question whose answer is approximately fine. "Roughly 41,000 unique viewers saw the hook at 0:08" is just as actionable as "41,287". That tolerance for approximation is exactly what HyperLogLog is built for, and Postgres has a battle-tested extension for it. This post is the design we landed on: fixed-size HLL sketches, one per (video, time_bucket) , that you can merge, slice, and union across regions in milliseconds. The main app is PHP 8.4 on LiteSpeed behind Cloudflare, with our search layer on SQLite FTS5; the analytics store is a separate Postgres instance, and HLL is what made that store affordable. Why HyperLogLog instead of COUNT(DISTINCT) HyperLogLog estimates the cardinality of a set using a fixed amount of memory regardless of how many elements you throw at it. Th

Elon Musk, on the verge of becoming the world's first trillionaire, is whipping up anti-immigration tensions amid ongoing riots in Belfast, Northern Ireland. Following a knife attack in the city on Monday, Musk declared support for Restore Britain, a hard-right populist political party that advocates for large-scale migrant deportation in the UK. He reposted statements […]

New research suggests that dozens of monetized YouTube channels are run by people and organizations that the US government has sanctioned for their ties to Tehran.

Travel bans and other visa issues are creating problems for World Cup participants even before the whistle blows.