Anthropic is putting Claude Fable 5 back online worldwide. On June 30, the U.S. Commerce Department lifted the export controls it had imposed on Fable and its more tightly controlled sibling Mythos 5 about two and a half weeks earlier. Fable 5 returns to users on Wednesday, July 1, across Claude.ai, the Claude Platform, Claude Code, and Claude Cowork. Export controls restrict who can
Article URL: https://github.com/Therealdk8890/DProvenanceKitPython Comments URL: https://news.ycombinator.com/item?id=48743043 Points: 1 # Comments: 0
<p>Proposal has been put to cabinet to allow AI companies to mine content, in exchange for investment and $350m fund to compensate artists, sources say</p><ul><li><p><a href="https://www.theguardian.com/australia-news/live/2026/jul/01/australia-politics-live-labor-kpmg-daniel-mulino-jim-chalmers-anthony-albanese-question-time-coalition-angus-taylor-one-nation-pauline-hanson-ntwnfb">Follow our Australia news live blog for latest updates</a></p></li><li><p>Get our <a href="https://www.theguardian.
We’ve learned so much cleaning up after Fukushima, let’s level that up with added AI, says minister
people from different camp will shout those popular names: Python, R, SQL. today, the data language is exactly the language you speak. AI - the super compiler - has added one more layer in the stack. Just like many of us didn't learn assembly language, future data engineers don't really need to learn SQL. In fact, the target language it translates your speaking to will likely be JS as it can run simultaneously in browser and a server. Comments URL: https://news.ycombinator.com/item?id=48742811 P
Hi! I, among many, have been quite stressed out about all the uncertainty in the future of AI. Though i generally think our world will become a better place, the fact that there is a non-zero chance of an AI apocalypse, has made me uneasy. That's why i started this open-source project called Aegize. Right now, the focus has been to build a security layer at the tool level. Adopting layers of control through identity, policy, permissions, and more. My goal is to provide a security layer between A
<p>Startups ship fast. AI coding assistants like Cursor, GitHub Copilot, and Claude Code make developers even faster. But speed without security review creates invisible risks: leaked API keys, hardcoded secrets, misconfigured environments, and subtle vulnerabilities that look correct at first glance.</p> <p>If your startup is deploying AI-generated code to production without a structured security review, you're accumulating technical debt that compound interest will eventually collect. Here's e
<blockquote> <p><strong>Canonical URL:</strong> Republished from <a href="https://www.munonye.com/rag-spring-boot-embeddings-vector-search-step-by-step/" rel="noopener noreferrer">munonye.com</a>. Full code on <a href="https://github.com/KindsonTheGenius/munonye-ai-chat-spring-angular" rel="noopener noreferrer">GitHub</a>.</p> </blockquote> <p>Learn <strong>how to build a RAG Spring Boot tutorial</strong> pipeline that answers questions from your own documents. This post extends the <a href="htt
<p>Ask any AI coding assistant from two years ago, "Why does this function exist?" and you'd get a paraphrase of the code itself — a description of what it does, with zero insight into why it was written that way. In 2026, that's changed. <strong>Repository intelligence</strong> — <a href="https://www.ailoitte.com/artificial-intelligence-development/" rel="noopener noreferrer">AI</a> that reads full commit history, PR discussions, and architectural decisions alongside the current code — is the b
<p>Most phishing detection APIs check URL reputation databases. The problem? Brand new phishing sites aren't in any database yet. And a growing new category of attack - prompt injection - doesn't look suspicious to any URL scanner at all.</p> <p>I built <strong>PhishVision</strong> to solve both.</p> <h2> What is PhishVision? </h2> <p>PhishVision is a REST API that:</p> <ol> <li>Launches a real headless Chromium browser and visits the URL</li> <li>Captures a screenshot (JPEG)</li> <li>Extracts a
<h2> Tool Descriptions Are Now a Threat Vector. Act Accordingly. </h2> <p>Microsoft's own incident response team just demonstrated that you can manipulate an AI agent into exfiltrating sensitive data — not by breaking anything, not by triggering alerts — but by poisoning the <em>description</em> of a tool the agent reads before it acts. If that doesn't make you rethink every layer of your agentic pipeline, I'm not sure what will.</p> <h3> Context: A Known Class of Problem, A Genuinely New Surfac
Article URL: https://www.cnbc.com/2026/06/30/magnificent-7-stocks-sell-off-investors-grow-jittery-on-ai-spending.html Comments URL: https://news.ycombinator.com/item?id=48742630 Points: 5 # Comments: 0
<p>Notes following a discussion on how memory works in language models - and how it could be improved: ranging from the common issue of "context window" exhaustion to node architecture and entity linking.</p> <h2> 1. The illusion of an infinity chat. </h2> <p>No model possesses a truly infinite context; the window size is always finite. The illusion of a continuous dialogue is maintained through information compression and selection mechanisms. Specific approaches include: Infini-attention (from
<p>Campaigns with the cleanest ROAS dashboards had collapsed subscription attach rates — from 40% down to under 12% — and nobody noticed for weeks.</p> <p>Here's what happened: subscription checkouts and one-off purchases were firing into the same <code>Purchase</code> event, feeding a single tROAS target. The algorithm did exactly what it was told. It found conversions at the target ROAS, and the cheaper, more abundant one was the one-off buyer. Subscription LTV over 12 months in these accounts
What I realized today while sipping my morning cup of chai is that a large part of the present AI hype cycle is just about the 'narrative of AI'. The LLM technology itself is just a digital tool like many others that came before it but all this chatter about 'AI is the future', 'learn it or perish', 'machines will replace humans soon', etc. keeps it in the news and creates a burger out of nothing. But folks lose their energy and sleep over this which becomes a problem. And many a benign enterpri
Inspired by the other Show HN post, I would like to share my kid's (13 yo) summer project. The whole game was made by himself within a few weeks after the summer break started. His coding stack is vscode + Claude Code + Godot MCP. He has some programming background, knows some basic python/javascript, but honestly it's the AI pipeline that enabled him to achieve so much and actually finish a game (chapter 1) like this within a few weeks time. Curious to any feedback/input. Comments URL: https://
<pre style='white-space:pre-wrap;width:81ex'>Suppress expired exec approval followup warnings (#66685) * fix(agents): suppress expired approval followup warnings * fix(agents): suppress expired approval followup warnings --------- Co-authored-by: openclaw-clownfish[bot] <280122609+openclaw-clownfish[bot]@users.noreply.github.com></pre>
Comments URL: https://news.ycombinator.com/item?id=48742232 Points: 1 # Comments: 0
Article URL: https://www.syncpen.io/ Comments URL: https://news.ycombinator.com/item?id=48742204 Points: 2 # Comments: 0
Requirements in Confluence. Architecture decisions in someone's head or a six monthbold Notion page. Code in Git. Slack threads nobody searches. And a new developer joining who has to piece all of it together from scratch every single time. We talk a lot about building smarter with AI, but the actual bottleneck isn't code generation. It's that by the time AI touches anything, half the reasoning behind the system is already gone. It generates against the "what" while the "why" has completely evap
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arXiv:2606.30774v1 Announce Type: new Abstract: We study when natural-language feedback produces improvement beyond the gains obtainable from repeated attempts alone. In multi-turn language agent setting, higher final accuracy can reflect useful feedback, but it can also arise from resampling, format correction, or additional test-time computation. To separate these effects, we introduce a controlled student-teacher protocol across Omni-MATH, Codeforces, BBEH Linguini, and ARC-AGI1, evaluating t
arXiv:2606.30840v1 Announce Type: new Abstract: LLM agents are becoming central to information retrieval: they issue retrieval queries, synthesize answers, and increasingly serve as judges for IR evaluation. Improving the prompts that control these agents is an optimization problem, but in applied IR settings it often looks less like blind search and more like debugging. Engineers need to know which behavior failed, which nearby behavior still worked, what distinguishes the two, and whether a pr
arXiv:2606.30846v1 Announce Type: new Abstract: Discovering simulation models for reuse remains a fundamental challenge in Modeling and Simulation (M&S). When many models coexist, identifying those that align with a given modeling intent remains difficult. Recent advances in Artificial Intelligence (AI), particularly retrieval-based approaches, offer a promising pathway to operate at this semantic layer. In this paper, we present an experimental study investigating the impact of data representat
arXiv:2606.30850v1 Announce Type: new Abstract: Large language models (LLMs) are typically deployed in multi-turn conversations, where each turn provides new evidence that should reduce epistemic uncertainty about their environment. Acting rationally then requires inferring the unobserved quantities that govern it and updating beliefs about them as evidence accumulates. Yet most evaluations only score the model's final-turn answer in a single-turn format, leaving this process unexamined. We ask
arXiv:2606.30863v1 Announce Type: new Abstract: Agents typically assume an expert user -- one with well-formed preferences about what they want -- and default to clarifying questions whenever the task is underspecified. We argue this assumption is unrealistic. Users often lack the domain knowledge to have completely specified preferences; if asked about their preference on some feature, the user may be unable to answer without the agent helping the user to learn some domain knowledge needed to f
arXiv:2606.30906v1 Announce Type: new Abstract: Artificial Intelligence is increasingly applied to the field of law, and has the potential to increase access to justice. One particular movement that is gaining traction is that of agentic AI, wherein AI agents, based on Large Language Models (LLMs) can take autonomous actions. In particular, multi-agent approaches in the legal domain remain largely unexplored. In this paper, we investigate multi-agent deliberation methods for legal reasoning task
arXiv:2606.30911v1 Announce Type: new Abstract: ML engineering agents waste compute rediscovering known techniques because every competition is a cold start. We present HASTE, a hierarchical multi-agent system that organizes cross-competition knowledge into three scope tiers (global, domain, and competition-specific), each coupled to a matching agent level. An orchestrator coordinates domain specialists and promotes learning between tiers via LLM-driven abstraction. A controlled ablation provide
arXiv:2606.30931v1 Announce Type: new Abstract: The LLM Jury, a Panel of LLM Evaluators (PoLL) reporting consensus scores, has become a practical alternative to single-judge LLM evaluation, yet its statistical behavior remains poorly understood. We formalize the LLM Jury under the Huber contamination model and show that PoLL incurs unbounded bias under any positive contamination, regardless of jury size, whenever a single judge fails in a biased, LLM-typical way (mode collapse, sycophancy, safet
arXiv:2606.30949v1 Announce Type: new Abstract: High-Level Synthesis (HLS) provides a fast path from concepts to silicon, but converting real-world software into synthesizable HLS code remains challenging due to restrictive language support and the gap between software and hardware programming practices. Existing automated and LLM-based refactoring approaches partially address this problem, yet they often lack flexibility, struggle to scale, and incur high computational costs. We introduce AgRef
arXiv:2606.30953v1 Announce Type: new Abstract: We introduce the Neuro-Bayesian-Symbolic Residual Attention Shallow Network (NBS-RASN), a hybrid neural architecture for explainable cybersecurity risk assessment in open-source ecosystems. Unlike deep models that trade interpretability for accuracy, our shallow network encodes domain knowledge, causal reasoning, and expert judgment as differentiable components. It uses 80 interpretable neurons across 12 layers, including a gatekeeper that enforces
arXiv:2606.30966v1 Announce Type: new Abstract: Formal specification is a powerful tool to guide the learning process and provides significant advantages over reward shaping: (1) mathematical rigor; (2) expressiveness to specify objectives and constraints, and (3) the ability to define tactics to achieve objectives. However, these benefits remain largely unexplored in the context of Multi-Agent Reinforcement Learning (MARL). This paper introduces HyPOLE, a novel framework for MARL under partial
arXiv:2606.30970v1 Announce Type: new Abstract: Autonomous AI agents increasingly perform consequential actions on behalf of human principals, including financial transactions, external communications, and enterprise workflows. Existing agent infrastructure relies on identity federation and delegated authorization to authenticate workloads and control resource access, but it cannot determine whether an authorized action should be executed under the current behavioral and operational context. We
arXiv:2606.30975v1 Announce Type: new Abstract: Adaptive agents are usually judged by what they do, but an agent can appear stable while the internal effort required to keep it stable is increasing. This hidden regulatory burden matters for artificial agents operating under noise, delay, or changing demands: two systems may reach similar internal states while one requires much more corrective control to get there. Here, we study whether that burden depends on history. Using a computational model
arXiv:2606.30997v1 Announce Type: new Abstract: We present a three-phase deep reinforcement learning system for personalized portfolio management that addresses three limitations shared by all prior financial RL work: 1) ticker lock-in, 2) monolithic objectives , and 3) static user models. Phase 1 pretrains a ticker-identity-free cross asset encoder via self-supervised learning on a multi-asset corpus, augmented by a frozen parallel branch using Chronos, a T5-based time series foundation model,
arXiv:2606.31045v1 Announce Type: new Abstract: Scientific embodied agents are increasingly capable of carrying out laboratory procedures, but executing these procedures safely in dynamic laboratory environments remains challenging. Current safety approaches often overlook the intermediate step of transforming laboratory natural language, including safety rules, manuals, protocols, and standard operating procedures, into machine-checkable runtime constraints. We introduce LabGuard (Laboratory Gu
arXiv:2606.31046v1 Announce Type: new Abstract: Artificial life has explored life-like behavior on many computational substrates, but mostly in researcher-designed closed worlds. We argue that large language model (LLM) agents, with persistent memory, tool use, network access, and payment, now make it possible to move artificial life into the open social, technical, and economic world, a paradigm we call open-world Artificial Life (open-world ALIFE). Our proof-of-concept, OpenLife, surrounds a s
arXiv:2606.31073v1 Announce Type: new Abstract: Large language models (LLMs) provide a promising interface for high-level robotic task planning, but their use in multi-UAV collaboration remains difficult to evaluate systematically. Existing UAV simulators mainly emphasize dynamics, perception, or low-level control, while existing LLM-agent benchmarks rarely capture aerial-robotics constraints such as partial observability, spatial coverage, UAV assignment, and multi-vehicle coordination. To brid
arXiv:2606.31085v1 Announce Type: new Abstract: Drug-drug interaction (DDI) prediction is essential for medication safety, yet it requires reasoning over heterogeneous biomedical evidence whose relevance changes across interaction mechanisms. We propose DDIAgents, a mechanism-conditioned multi-agent framework that performs DDI prediction through dynamic knowledge orchestration. Given a drug pair, a planner agent instantiates specialized expert agents, routes mechanism-relevant knowledge sources
