Tag: WebDev

The AI industry has fully entered the "agent era," a paradigm where AI models do far more than generate text — they now actively plan, execute, and course-correct complex tasks over days rather than seconds. Thus, it's perhaps unsurprising to see Chinese e-commerce giant Alibaba's famed Qwen Team of AI researchers release a model capable of performing autonomous agentic AI work over multiple days: that model has arrived in the form of Qwen3.7-Max which the company reports in a blog post achieved "~35 hours of continuous autonomous execution" — albeit, in a proprietary, not open source format, as prior Qwen Team releases were.This is also to be expected — it's what many analysts and industry experts feared in the wake of the departure of several key Qwen Team leaders e

AI agents forget. Every time a coding assistant loses track of a debugging thread, or a data analysis agent re-ingests the same context it already processed, the team pays in latency, token costs, and brittle workflows. The fix most teams reach for — expanding the context window or adding more RAG — is increasingly expensive and still doesn't reliably work.To address this, researchers from Mind Lab and several universities proposed delta-mem, an efficient technique that compresses the model’s historical information into a dynamically updated matrix without changing the model itself. The resulting module adds just 0.12% of the backbone model's parameters — compared to 76.40% for one leading alternative — while outperforming it on memory-heavy benchmarks. Delta-mem allows models to continuously accumulate and reuse historical data, reducing the reliance on massive context windows or complex external retrieval mo

RAG architectures are good at one thing: surfacing semantically relevant documents. That's also where they stop.A framework called a decision context graph addresses that gap by giving agents structured memory, time-aware reasoning, and explicit decision logic. Rippletide, a startup in the Neo4j ecosystem, has built one. The key capability: agents that are non-regressive, able to freeze validated sequences of actions and compound on them over time.“The key point you want is non-regressivity: How do you make sure that, when the agent will generate something new, you can compound on the previous discoveries?” said Yann Bilien, Rippletid’s co-founder and chief scientific officer. Why RAG doesn’t go far enoughEnterprise context is sprawled across ERP tools, logs, databases, vector stores, and policy documents. Generative AI tools can retrieve from all of it — through keyword search, SQL querie

Presented by Veriff Americans can’t reliably distinguish real from AI-generated content, and that’s not just a media literacy problem; it’s a direct threat to how businesses verify identity online.New research finds that while many people are aware of deepfakes, their ability to distinguish them from reality is barely better than a coin flip. A 2026 survey conducted by Veriff and Kantar among 3,000 respondents in the United States, the United Kingdom, and Brazil shows Americans scoring just 0.07 on a scale where 0 represents random guessing.If people can’t distinguish authentic visual content, they can’t reliably distinguish authentic identities. In practice, that means the same users interacting with digital services are often unable to tell whether the person on the other side of a screen is real.That ineffectiveness has direct consequences for every digital business that relies on image- and video-based identity verification to c

Every MFA check passed. Every login was legitimate. The compliance dashboard was green across every identity control. And the attacker was already inside, moving laterally through Active Directory with a valid session token, escalating privileges on a trajectory toward the domain controller.This is the scenario playing out inside enterprises that invested heavily in authentication and assumed the job was done. The credential was real. The multi-factor challenge was answered correctly. The system performed exactly as designed. It authenticated the user at the front door and never looked again. The breach didn't bypass MFA. It started after MFA succeeded.Authentication proves identity at a single point in time. Then it goes blind. Everything that follows, the lateral movement, the privilege escalation, the quiet exfiltration through Active Directory, falls outside what MFA was ever designed to see.A CIO found the gap in productionAlex Philips, CIO at

Resolve AI, the production-operations startup backed by Greylock and Lightspeed Venture Partners, today announced a sweeping expansion of its platform that introduces always-on background agents, a redesigned investigation architecture, and a shared workspace where engineers and AI agents collaborate in real time on live incidents.The centerpiece of the release is a new multi-agent investigation system developed by Resolve AI's in-house research lab. Instead of deploying a single AI agent to diagnose a production failure — analogous to a lone engineer pulling an on-call shift — the platform now dispatches a coordinated team of specialized agents that pursue multiple hypotheses in parallel, independently verify each other's conclusions, and construct complete causal chains from root cause to symptom. The company says the architecture delivers more than a twofold

Kore.ai on Wednesday launched what amounts to a ground-up reinvention of its core technology: the Artemis edition of its Agent Platform, a system designed to let enterprises build, govern, and optimize AI agents using AI itself — compressing what has traditionally been months of engineering work into days.The platform arrives at a moment when every major technology vendor — from Microsoft and Salesforce to Google and ServiceNow — is racing to become the default infrastructure for enterprise AI agents. Kore.ai's answer to that crowded field is a bet on neutrality, a proprietary intermediary language for defining agents, and a philosophy that AI, not human developers, should do most of the heavy lifting."We