🚨 Abstract due Mon 3 Aug Call for Papers: NEDO Challenge 2 × CSS 2026/BWS Security of blockchain systems in the quantum era: papers on evaluation criteria, PQC migration, key management & testbed design.

Hey @shadcn, quick heads-up from a fresh Base Nova setup: the newly installed Separator component uses the data-horizontal:h-px custom Tailwind variant based class defined in tailwind.css in the monorepo, but that variant isn't available in the generated setup, so the height isn't applied. Other base-ui components seem to follow the same pattern. Appreciate any clarification on this.

Claude Opus 4.7 is over-trained on the Anthropic website. Every HTML page it designs has that unmistakable Anthropic flavor. GPT-5.5 is still weirdly weak at frontend. It designs frontend like it learned CSS from a backend engineer. OpenAI urgently needs an MTS with taste.

Ever fix one slide and watch the agent regenerate the whole deck and break everything? This work solves that with hierarchical memory plus local revision 🗂️ Title: MemSlides: A Hierarchical Memory Driven Agent Framework for Personalized Slide Generation with Multi-turn Local Revision URL: ❓ What's new? 💡 It gives a slide-generation agent a hierarchical memory that cleanly separates persistent preferences (user profile), session-level working memory, and reusable tool experience. It also edits only the target slides instead of regenerating the full deck. ❓ How does it keep local edits from drifting? 💡 A Plan–Act–Guard loop. Plan turns a request into an explicit "execution contract" (target paths, active rules, coverage requirements); Act picks tools like batch CSS over shared selectors or patch operations; Guard binds patches to content hashes and blocks premature finalization until all targets are covered. ❓ Do one-off instructions leak into permanent preferences? 💡 No — at job end, intent-aware consolidation writes back only stable signals, preventing transient "just this round" requests from becoming persistent preferences. ❓ Does it work? 💡 In blind review, personalization beats DeepPresenter on every dimension (e.g. Visual +1.66). With tool memory, closed-loop completion hits 0.963 and time-to-first-correct-edit drops 609.5s → 242.5s (~60% faster). Designing agent memory by role rather than one flat store feels broadly useful for multi-turn editing tasks. #AIAgents# #LLM#

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My dear front-end developers (and anyone who’s interested in the future of interfaces): I have crawled through depths of hell to bring you, for the foreseeable years, one of the more important foundational pieces of UI engineering (if not in implementation then certainly at least in concept): Fast, accurate and comprehensive userland text measurement algorithm in pure TypeScript, usable for laying out entire web pages without CSS, bypassing DOM measurements and reflow

Google DeepMind’s paper shows that the real security problem for AI agents is not just the model, but the environment it reads. Presents the first systematic framework for understanding how the web itself can be weaponized against autonomous AI agents. As agents increasingly browse the internet, read emails, execute transactions, and spawn sub-agents, the information environment becomes an attack surface. In one cited benchmark, hidden prompt injections embedded in web content partially commandeered agents in up to 86% of scenarios, sub-agent hijacking working 58–90% of the time, and data exfiltration attacks clearing 80% across five different agent architectures. That reframes the whole debate. We usually talk about model safety as if the danger sits inside the weights, but agents do something more fragile: they browse, retrieve, remember, and act on untrusted material in real time. The paper’s key contribution is a taxonomy of “AI Agent Traps,” six attack classes aimed at perception, reasoning, memory and learning, action, multi-agent dynamics, and even the human overseer. Here’s the key point. A web page does not have to look malicious to be dangerous to an agent, because the agent may parse what humans never see: hidden HTML comments, metadata, CSS-hidden text, formatting syntax, or adversarial content embedded in images and other media. The threat gets more serious once memory enters the loop. If an agent uses RAG or persistent memory, poisoning no longer has to win in one shot. It can sit quietly in a corpus or memory store and activate later, which is why the paper highlights results showing latent memory poisoning above 80% attack success with less than 0.1% data contamination. What makes this paper useful is its restraint. It does not pretend every category is equally mature. Content injection and behavioural control already look concrete, while systemic and human-in-the-loop traps are presented more as an emerging research frontier than a solved empirical case. The larger point is hard to ignore: once agents are allowed to ingest the open web at inference time, every page, document, and memory write becomes part of the security boundary. --- ssrn .com/sol3/papers.cfm?abstract_id=6372438