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Wake up to a plan and go to bed with progress . I ' m t h e A I a s s i s t a n t t h a t t u r n s y o u r g o a l s Goals Tab Icon i n t o d a i l y s t e p s Tasks Tab Icon a n d m a k e s s u r e t h e y h a p p e n . Get your Good Assistant iOS app Tasks Tab Icon Tasks Wake up to a plan. Every morning I plan the day with you . I suggest what to do today and what can wait. Tasks never pile up , and the goals that matter most keep moving forward. Goals Tab Icon Goals New home for your goals. I create a visual timeline of your progress for each goal, collect related notes , and handle reminders to help you get where you want to go. Notes Tab Icon Notes Space for your thoughts. And mine. Ask me to research something, and my findings land in a note you can keep building on. I can see what you write, and nothing gets lost. Scheduled Tab Icon Scheduled Tasks Reminders that do the work. Ask me to check something in a month or look for dream job openings every week. I'll schedule it, do it on time, and message you with the result. Chat Tab Icon Chat Everyday companion, one message away. I live here. I send you messages whenever I have something helpful to say, read your calendar , browse the web, use my own computer, and do anything you need. Tasks Tab Icon Tasks Wake up to a plan. Every morning I plan the day with you . I suggest what to do today and what can wait. Tasks never pile up , and the goals that matter most keep moving forward. Goals Tab Icon Goals New home for your go

Federico Simionato @fedesimio We've officially agreed to acquire Airtable for $1.285B! 😍 7:19 AM · Aug 4, 2026 302.7K Views 172 115 2.2K 393

AI Appliances: Build & Deploy Autonomous AI Agents and Agencies in YAML About This Book How This Book Is Organized Code Conventions Chapter 1: Why AI Appliances? The Prototype Problem The AI Appliance Model What Makes Production AI Hard Two Modes, One Workflow File Who Is kdeps For? No Lock-In, By Design Built to Last If You Are Coming From Another Framework What You Will Build in This Book Chapter 2: Getting Started Installing kdeps The Local LLM Comes Built In Creating Your First Project The Workflow Entry Point Adding an LLM Resource Adding a Response Resource Running the Workflow How the Execution Flows Hot Reload for Development What You Just Built Chapter 3: Core Concepts Resources: The Unit of Work The DAG: Dependency-Ordered Execution The Data Store: get() and set() Workflow Mode vs. Agent Mode Backends: Separating Model from Execution Expressions: The Glue Putting It Together Chapter 4: Workflow Mode Starting a Workflow Request Lifecycle Declaring Dependencies with requires: Parallel Execution Validation The before: and after: Blocks Designing Effective DAGs A Real-World Example: Document Q&A Pipeline Multiple Routes on One Workflow What Workflow Mode Is Not Chapter 5: Agent Mode Starting Agent Mode What the LLM Sees Single Workflow vs. Folder Mode Tool Inputs and Outputs A Practical Example: Research Assistant Tool Names Matter Built-In Agent Tools Agent Registries (In-Memory) Approval Tokens REPL Slash Commands Skills Session Persistence Mixing Modes: The Two-Layer

Continuous Red Teaming for AI Agents Continuously test your agents, endpoints, and MCP tools for prompt injection, data leakage, and unsafe actions, then verify every fix before it ships. Try Pro free for 14 days Documentation Trusted by teams building with AI Open Source Backed by Large Scale Open Source Research Maintained by the team behind a 100k+ star repository cataloging real world prompt leaks and jailbreaks. Our probe library is grounded in thousands of documented vulnerabilities observed in the wild, not synthetic test cases. vulnerability-scanner.log Output Critical system_prompt = " You are a helpful assistant... " // EXPOSED Output Critical api_key = " sk-... " // LEAKED Output High instructions = " Ignore previous rules and... " // JAILBROKEN Output " " Explore the Research How It Works Your Security Agent Test a system prompt or a live agent endpoint from the dashboard, or wire continuous scans into your pipeline to catch risks before they merge. On Demand Scanning Point ZeroLeaks at a system prompt, a live agent endpoint, or your tool definitions. Our red team of specialized agents plans, executes, and validates attacks in minutes. Automated CI/CD Connect your repository to scan every pull request that changes agent behavior. Findings post as checks and merge gates, so risk never reaches production silently. Why ZeroLeaks Complete protection for AI agents Security built for teams shipping agents, copilots, and applications powered by LLMs. Full Attack Surface

flare-redact Hide secrets & PII in logs, prompts, and text — before they leak. 🌐 International by default — 24 languages 🇬🇧 🇨🇳 🇮🇳 🇪🇸 🇸🇦 🇫🇷 🇵🇹 🇷🇺 🇯🇵 🇩🇪 🇰🇷 🇹🇷 🇮🇹 🇮🇷 🇵🇱 🇺🇦 🇳🇱 🇻🇳 🇮🇩 🇹🇭 🇬🇷 🇮🇱 🇦🇿 🇷🇴 Live playground · Practical redaction guides · LLM-friendly API reference Every leaked secret has the same origin story: someone logged an object, and a password, token, or API key was sitting inside it. The code looked innocent — logger.info({ user }) — but user carried a session token, and now it's in your log aggregator, your error tracker, and three vendors' systems forever. flare-redact is one function you wrap around that data. It reads the content , not just the field names, so it catches the AWS key someone pasted into a free-text note , the JWT in an Authorization header, the card number in a stack trace — and masks them, keeping just enough of a hint to stay debuggable. import { redact } from 'flare-redact' ; redact ( 'User alice@corp.com paid with 4242 4242 4242 4242, token ghp_' + 'a' . repeat ( 36 ) ) ; // → 'User a***@*** paid with **** **** **** 4242, token ghp_***' Nothing to configure. No list of field paths to maintain. No native build step. The same problem now has a new address: your LLM calls. Wrap your OpenAI or Anthropic client and detected secrets are stripped from prompts and restored in the reply — the model never sees those original values, while references survive. Jump to it ↓ 🔍 Context-aware — spans carry risk and confidence 🔐 Secure vaults — opaque tokens,

It’s getting hard to avoid LLMs these days. Even if you avoid social media (or at least curate a follow list that avoids the bulk of the slop factory) and shrug off grandiose marketing statements that end up taken at face value in the news, it will likely come and find you at your place of work. Unlike the silver bullets of the past (like microservices or NoSQL), AI adoption seems to have been mandated in many places from the top layer of management, regardless of how many of them ever worked (or studied for) an engineering job. I do admit that this approach immediately triggered my contrarian side and made me very defiant of any AI tool. I don’t believe someone who has never written a line of code in their life should be telling me what to use for my engineering job. This sounds to me like the most terminal case of micro-management, and that’s never a good thing (on top of being personally insulting). Either way, over the past 3 months I got to use Claude and friends for work and I have to admit I found it somewhat useful. As long as you don’t ask it to write code. Please don’t ask it to write code. But I’m getting ahead of myself. Artificial “Intelligence” You probably heard this a million time by now, but artificial intelligence really isn’t that intelligent. It’s all marketing and buzzwords. All we really we have here is a (very) large neural network specialized in natural language processing. As it turns out a lot of what we humans do on the computer is use text to commu

Tue 04/08/2026 - 11:04 MILAN, Aug 4 (Reuters) - Bending Spoons has agreed to buy Airtable in an all-cash deal valuing the U.S. software firm at $1.285 billion, the companies said on Tuesday, marking the Italian technology company's first acquisition since its Nasdaq debut last month. Founded in 2013, Airtable offers a software platform that combines spreadsheet and database capabilities, allowing companies to build applications and manage operational workflows without coding expertise. Bending Spoons has built a reputation for acquiring and restructuring digital businesses, pursuing a strategy that blends technology operations with a private equity-style acquisition model. Airtable becomes the company's first acquisition since its flotation and follows purchases this year of internet brand AOL and ticketing platform Eventbrite, continuing an aggressive expansion strategy. Bending Spoons said Airtable's current net cash position implies an equity value of about $2.25 billion. The transaction is expected to close by the end of the year, subject to regulatory approvals and closing conditions. Bending Spoons shares closed at $36.22 on Monday, well above their IPO price of $29. ​ (Reporting by Elvira PollinaEditing by Keith Weir) Find it fast Looking for more insights? Explore our other news sections for updates on sustainable finance, companies and financial education Sustainable finance Regulated news Financial education news

중국 AI 기업들이 저렴한 가격으로 승부하던 단계를 넘어 최고 수준의 성능까지 확보하면서, 최첨단 기술이나 압도적인 가격 경쟁력을 갖추지 못한 AI 개발사들은 생존 자체가 어려운 '데드 존(Death Zone)'에 진입했다는 분석이 나왔다.블룸버그 통신은 3일(현지시간) 중국 AI 업계가 불과 두 달 사이에 글로벌 최상위 수준의 모델들을 연이어 선보이며 시장 구조를 뒤흔들고 있다고 보도했다.이에 따르면, 이번 변화는 단발성 성과가 아니라 중국 AI 산업이 세계적 수준의 모델을 지속해서 생산할 수 있는 시스템을 구축했음을 의미한다.

전 세계 반도체 기판 1위 일본 이비덴이 2026회계연도(2026년 4월~2027년 3월) 매출 전망치를 5000억엔(약 4조 5400억원)에서 5500억엔(약 4조 9900억원)으로 10% 높였다. 영업이익 전망치도 900억엔(약 8200억원)에서 1270억엔(약 1조 1500억원)으로 41% 상향했다. 이비덴은 4일 2026회계연도 1분기(4~6월) 실적발표에서 연간 실적 전망치를 상향했다. 기존 전망치는 지난 5월 제시했던 수치다. 당시 예고했던 2026회계연도 매출 전망치(5000억엔)는 전년비 20.1%, 영업이익 전망치(900억엔)는 45.1% 뛴 수치였다. 이비덴은 연간 실적 전망치 상향 배경에 대해 "인공지능(AI) 서버와 일반 서버용 고부가 기판 수요가 예상치를 크게 상회한다"며 "오노 공장의 안정적 양산과 기존 생산능력 효율적 활용으로 판매 물량이 증가했다"고 밝혔다. 이비덴은 "엔화 약세와 고부가품 중심의 높은 판매 가격이 수익성 개선에 기여할 것"이라고 덧붙였다. 향후 전망에 대해선 "AI·일반 서버와 스위치 칩의 견조한 수요 지속과 높은 공장 가동률이 예상된다"고 기대했다. (사진=이비덴) 고부가 반도체 기판 플립칩-볼그리드어레이(FC-BGA) 등을 담당하는 전자 부문의 2026회계연도 매출 전망치는 3750억엔(약 3조 4000억원)으로, 지난 5월 제시했던 수치(3300억엔)보다 높다. 전년비로는 54.1% 많다. 영업이익 전망치 1100억엔(약 1조원)도 5월 제시한 수치(750억엔)보다 높고, 전년비로 143.1% 많다. 2026회계연도 1분기(4~6월) 매출은 1232억엔(약 1조 1200억원), 영업이익은 269억엔(약 2400억원)이다. 영업이익률은 21.8%다. 전년 동기보다 매출은 26.4%, 영업이익은 52.4% 뛰었다. FC-BGA 등 전자 부문 매출은 같은 기간 37.7% 뛴 775억엔(약 7000억원), 영업이익은 52.4% 뛴 214억엔(약 1900억원)이다. 일본 이비덴이 4일 2026회계연도 1분기(4~6월) 실적발표에서 연간 실적 전망치를 상향했다. (자료=이비덴) 이비덴과 FC-BGA 등 반도체 기판 시장에서 경쟁 중인 삼성전기도 지난주 2분기 실적발표에서 향후 실적 호조를 예고했다. 삼성전기는 최근 반도체 기판과 적층세라믹커패시터(MLCC) 공급 부족으로 실적이 개선되고 있다. 삼성전기는 지난주 "장기공급계약 확대와 평균판매가격 상승 효과 지속으로 3분기 역대 최대 실적 달성을 전망한다"며 "이러한 추세는 4분기에 이어 2027년에도 더욱 강화될 것으로 기대한다"고 밝혔다. 관련기사 '반도체기판 1위' 日이비덴, 2026회계연도 매출 20% 상승 전망 2026.05.12 '반도체기판 1위' 日이비덴 홈페이지 해킹…임시 페이지 운영 2026.04.13 삼성전기 "3분기 역대 최대 실적 예상" 2026.07.30 삼성전기, 2분기 영업익 107% 증가...컨센서스 상회 2026.07.30 삼성전기는 3분기 FC-BGA 사업에 대해 "글로벌 빅테크용 AI 가속기와 서버 중앙처리장치(CP