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Most security tools for AI agents check one message at a time. Arc Gate tracks the whole conversation. That matters because the attacks that actually work in production don’t happen in one message. They happen across 8 turns. Each one looks clean. By the time the payload arrives your agent is already primed to execute it. I built Arc Gate using a geometric framework from my own research to detect adversarial behavioral drift across a full session — not just flag individual messages. When a conversation starts drifting toward something dangerous, it catches the pattern before the attack completes. I’m looking for 3 teams running real agents to test it against actual workflows and tell me where it breaks. Not chatbot wrappers. Agents with real tool access. Browser use, email actions, MCP servers, internal copilots, workflow automation. No charge. No sales call. Just feedback from people close to production. Comment or DM me if that’s you. Platform: https://bendexgeometry.com GitHub: https://github.com/9hannahnine-jpg/arc-gate Demo: https://web-production-6e47f.up.railway.app/demo submitted by /u/Turbulent-Tap6723 [link] [留言]

Even in a future of robot taxis, someone still has to return the things passengers leave behind.

Crypto APIs are no longer just tools for fetching Bitcoin prices. In 2026, they are becoming the...

A few years ago, we started adapting our websites for mobile devices. Then we adapted them for...

The sequel is headed to PS5 in 2027.

A government committee says that the country’s growing dependence on the data analytics company is a serious liability.

Hot take: if I wanted to gather data via the internet, and I’m writing scripts/code to speed up the process, I have to follow some basic rules (ie look at the sitemap, find relevant robots.txt, follow that websites preference and rules). But it seems any AI-agent I’ve used does not give af about rules and limits, and is totally cool building me a scraper that will perform hundreds of thousands of requests without regards to the website owner’s preference. Given it’s widely known you can use AI for simple coding tasks I can easily see a future where ordinary individuals are operating their own scrapers. Especially in gathering high-value information that “seems easy to get” like google search rankings, or job data. This creates an obvious nightmare for Google, ATS platforms, and just about every website on the internet if everyone and their mother starts spinning up Playwright sessions in Python. I’m deadset on this being a responsbility of AI providers (anthropic, open ai, anysphere, etc). But how are these companies supposed to balance this without implementing guardrails that heavily limit their products? Maybe this has been solved and someone can feed my curiosity. submitted by /u/TacoTuesdayX [link] [留言]

One hardware announcement and several software highlights from Microsoft Build.

The cybersecurity company is nearing a $300 million round led by Evolution Equity Partners.

After playing as Kratos and Atreus, now we get to be Faye.

The assistant that learns how you work, then gets to work. Discussion | Link

The agent that grows with you Discussion | Link

Sony ended its big State of Play showcase with a major reveal: the next God of War. The new title is called God of War Laufey, and is once again developed by Sony's Santa Monica Studio. Currently, the game doesn't have a date, but it's coming to the PS5 whenever it does launch. This time, […]

You can pre-order the Faden siblings' next adventure now.

Caching in e-commerce is never just about speed. A fast storefront is useful only if it still shows the right price, the correct stock level, and the right experience for the current customer. That is why caching in a Next.js storefront can be deceptively hard. Some data should be shared broadly and kept warm for SEO and performance. Some data should be refreshed often. Some should never be shared between users at all. Next.js 16 gives teams a much clearer toolbox for solving this problem with Cache Components, use cache , tag-based invalidation, and explicit cache lifetime controls. Used properly, these features let you keep pages fast without drifting into stale commerce data. In this guide, I will walk through a practical way to think about caching in a modern storefront and show how to combine use cache , cacheLife , and revalidateTag for real e-commerce use cases. Why Caching Is Harder in E-Commerce Than in a Typical Content Site On a standard marketing site, most content changes infrequently. If a page is cached for a few minutes or even a few hours, the business impact is usually negligible. Commerce systems work differently. The same product page may contain: stable product descriptions and category copy semi-dynamic data such as price, availability, shipping estimates, or promotion labels private data such as cart state, recently viewed items, or customer-specific pricing Treating all of that data the same way leads to one of two bad outcomes: You cache too aggressively and serve stale prices or availability. You disable caching everywhere and lose the performance benefits that help SEO and conversion. The better approach is to split your data by volatility and audience. The Three Cache Boundaries That Matter Most For most commerce projects, the cleanest mental model is to divide data into three groups. 1. Stable catalog content This is the part of the page that usually changes only when content editors or merchandisers update the catalog. Examples: product

Control Resonant, the upcoming sequel to Remedy Entertainment's Control, will be released on September 24th, 2026, according to a trailer that premiered during Tuesday's PlayStation State of Play show. The trailer also gave a preview of the game's story, which stars Dylan Faden, who was imprisoned for much of the first game. He will be […]

For the AI artists here who like creating painterly / impasto style work, what generators are you guys using? Just curious since I'm much more into that theme as opposed to realism, but it feels like most generators cater to realism now. submitted by /u/geekedprompts [link] [留言]

Software testing is changing quickly. Teams are dealing with faster release cycles, more AI-assisted development, more complex browser behavior, and higher expectations around product quality. I collected a few practical articles that cover different parts of modern QA, test automation, developer workflows, and testing strategy. Recommended reads How to Test AI Agents for Tool Use, Memory, and Recovery Paths A practical framework for testing AI agents for tool use, memory retention, retries, and recovery paths, with concrete strategies for QA and engineering teams. How to Evaluate a Test Automation Tool for Shadow DOM, iframes, and Other Hard-to-Test UI Surfaces A practical buyer guide for evaluating test automation tools for shadow DOM testing, iframe testing, resilient selectors, and dynamic UI edge cases. How to Reproduce a Flaky Browser Test with Video, Logs, and Network Traces A practical workflow to reproduce a flaky browser test using video, logs, and network traces, then turn intermittent failures into repeatable bug reports. Endtest Review for Small QA Teams: Where Editable Test Flows Save the Most Time A practical Endtest review for small QA teams focused on editable test flows, maintainable test steps, and where no-code QA automation actually saves time. Editable Test Steps vs Generated Test Code: Which Holds Up Better After UI Changes? A practical comparison of editable test steps vs generated test code for UI change resilience, maintenance overhead, debugging, and team handoff, with guidance for QA and engineering leaders. Managed QA Services vs Staff Augmentation: What Changes in Ownership, Speed, and Cost A practical comparison of managed QA services vs staff augmentation, focusing on ownership, ramp time, communication overhead, cost, and maintenance risk. Automation Payback Period: How Long Does QA Test Automation Take to Break Even? Learn how to estimate the test automation payback period, model QA ROI, account for maintenance cost, and identify wh

Em 2023, eu arrumei as malas e saí de Recife, minha terra natal, para morar no interior do Mato Grosso. O motivo? Cursar Ciência da Computação. Eu sabia que enfrentaria um choque cultural, o calor do Centro-Oeste e a saudade de casa. O que eu não imaginei é que o maior desafio seria a sensação constante de que eu era uma fraude no meio da minha própria sala de aula. Se você estuda ou trabalha com tecnologia, provavelmente já passou por isso. Você senta na primeira semana de aula e parece que metade da turma já programa desde os 12 anos, fala termos técnicos que parecem outra língua e discute sobre ferramentas que você nem sabia que existiam. Hoje, na reta final da graduação, focando meus estudos em Back-end e Dados, quebrando a cabeça com Go, Python e SQL, eu olho para trás e vejo o quanto essa cobrança silenciosa quase me paralisou. Se você está sentindo que entrou no curso errado ou que todo mundo corre a 100 km/h enquanto você ainda está engatinhando, pega um café e vem ler este papo reto sobre como lidar com a tal da Síndrome do Impostor. O "efeito vitrine" e a falsa ilusão de que somos os únicos perdidos Na faculdade de TI, a comparação é quase inevitável. A gente abre o LinkedIn e vê o colega conseguindo estágio internacional; abre o GitHub e vê códigos impecáveis; na sala de aula, sempre tem quem responda às perguntas do professor antes mesmo dele terminar de falar. O erro está em achar que o ritmo do outro deve ditar o seu. Na Computação, as pessoas vêm de bagagens completamente diferentes. Quem já sabia programar antes da faculdade pode ter facilidade em Lógica de Programação, mas talvez sofra tanto quanto você quando chegar a hora de aprender Álgebra Linear ou Teoria da Computação. Sentir-se perdida não significa incompetência; significa apenas que você está no processo de aprender algo complexo. E adivinha? Todo mundo ali está tentando descobrir como as coisas funcionam, mesmo quem finge que sabe tudo. O que me ajudou a virar a chave Eu não acordei um dia

Privacy-First Personal Finance for iOS Your finances. On your phone. Nowhere else. A privacy-first personal finance app that connects your banks, brokerage, and investment accounts into one unified dashboard — entirely on-device, no backend, no account required, no subscription. View on GitHub At a Glance 🏦 Multi-source 🔒 100% On-device 📊 Full picture Banks, Revolut, Trading 212 and more No server. No account. Your data stays in your Keychain. Net worth, spending, investments — all in one place Screenshots Log Insights Budget Transaction Entry Settings About Escudo Built out of two frustrations: every decent finance app costs a monthly subscription, and none of them support Trading 212. Escudo connects your banks, brokerage, and investment accounts and gives you a single view of your net worth, spending, and investments — without your data ever leaving your phone. Key Features Net worth dashboard — aggregated balance across all accounts and investment portfolio P&L Unified transaction log — every account in one feed, auto-categorised Spending insights — breakdown by category and trends over time Budget tracking — per-category budgets with visual progress dials Multi-currency — EUR, GBP, USD with stored exchange rates Recurring transactions — template-based recurring transaction engine Shortcuts support — deep linking via escudo:// URL scheme Integrations Source Method Trading 212 REST API — portfolio, orders, dividends Revolut Enable Banking OAuth 2.0 Bankinter PT Enable Banking OAuth 2.0 SIBS SIBS Open Banking (PT market) CSV import Revolut & Bankinter statements All credentials live in the iOS Keychain — never in UserDefaults, never in iCloud, never on a server. Known Limitations Enable Banking does not expose credit card accounts — only bank accounts and transactions are available through the PSD2 API; credit card balances and transactions are not accessible No token auto-refresh for Enable Banking — manual re-auth when tokens expire Categorisation rules are hard