- Anthropic opens Seoul office and announces new partnerships across the Korean AI ecosystem
anthropic-news· 17-jun
Jun 17, 2026Announcements
- Results from the first Anthropic Public Record
anthropic-news· 12-jun
Jun 12, 2026Announcements
- TCS and Anthropic partner to bring Claude to regulated industries
anthropic-news· 12-jun
Jun 12, 2026Announcements
- DeXposure-Claw: An Agentic System for DeFi Risk Supervision
arxiv-ai· 19-jun
arXiv:2606.19501v1 Announce Type: new Abstract: Decentralized finance exposes supervisors to fast-moving, networked credit risks. General-purpose LLM agents fit this setting poorly: they over-read weak evidence and recommend high-stakes interventions, while existing evaluations offer no regulator-aligned way to measure the resulting false alarms. We introduce DeXposure-Claw, a forecast-grounded agentic supervision system that routes LLM decisions through structured evidence: (1) DeXposure-FM, a graph time-series foundation model, forecasts future exposure networks; (2) deterministic monitors and stress scenarios then turn those forecasts into typed alerts, attribution signals, and scenario evidence; and (3) data-health and confidence gates constrain escalation before DeXposure-Claw emits …
- LLM Doesn't Know What It Doesn't Know: Detecting Epistemic Blind Spots via Cross-Model Attribution Divergence on Clinical Tabular Data
arxiv-ai· 19-jun
arXiv:2606.19509v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied to structured clinical data, yet whether they can recognize the limits of their own knowledge on such tasks remains unexplored. We study this question through the lens of cross-model attribution divergence with the goal of reducing epistemic uncertainty for structured tasks, comparing Qwen 2.5 7B and XGBoost on a prediction task via attribution divergence analysis. We report four findings. First, LLM verbalized confidence is epistemically vacuous, it outputs a near-constant (0.856-0.937) regardless of whether accuracy is 49% or 75.3%, tracking prompt format rather than prediction quality. Second, the LLM exhibits an inverse difficulty effect: accuracy drops to 64.8% when XGBoost is 99% co…
- REVEAL++: Differentiable Phenotypic Grouping for Vision-Language Retinal Modeling of Alzheimer's Disease Risk
arxiv-ai· 19-jun
arXiv:2606.19522v1 Announce Type: new Abstract: The retina offers a noninvasive window into neurodegenerative disease, capturing subtle structural patterns associated with a risk of future cognitive decline. Vision-language alignment frameworks such as REVEAL have shown that pairing retinal fundus images with structured clinical risk narratives improves early prediction of Alzheimer's disease (AD). A key design choice in these approaches is the use of phenotypic grouping, where individuals with similar risk profiles are treated as multi-positive pairs during contrastive learning. However, existing methods operationalize phenotypic similarity as a discrete construct, relying on hard group assignments that impose rigid supervision and decouple group formation from representation learning. W…
- Emergent Alignment
arxiv-ai· 19-jun
arXiv:2606.19527v1 Announce Type: new Abstract: Can Large Language Models (LLMs) discern when their own outputs are misaligned with human ethics? And can they self-correct? We endow an LLM with a conscience step that reviews its own reasoning and outputs, and we extend the training loss with an alignment component using Direct Preference Optimization (DPO) to steer the model away from non-ethical outputs. The result is an online technique to align models in a wide range of applications: training, fine-tuning, adversarial prompting, and zero-shot learning. It does not require a weaker or stronger judge, relying instead on a frozen copy of itself. In previous work, the Emergent Misalignment scenario showed a range of emergent unethical behaviors from fine-tuning the model to hack code. Inst…
- ITNet: A Learnable Integral Transform That Subsumes Convolution, Attention, and Recurrence
arxiv-ai· 19-jun
arXiv:2606.19538v1 Announce Type: new Abstract: Convolutional networks, recurrent networks, and transformers each encode different inductive biases -- locality, sequential memory, and content-dependent pairwise interaction -- and have remained mathematically distinct since their inception. We show that this fragmentation reflects not a fundamental diversity in how signals should be processed, but rather incomplete views of a single underlying mathematical object: a learnable integral transform. We introduce the Integral Transform Network (ITNet), a unified architecture built around a learnable kernel that depends jointly on positions and features. This kernel is implemented as a small neural network, specifically an MLP, that models pairwise interactions, enabling the model to adapt its b…
- Uncertainty Decomposition for Clarification Seeking in LLM Agents
arxiv-ai· 19-jun
arXiv:2606.19559v1 Announce Type: new Abstract: Recent position papers argue that the classical aleatoric/epistemic uncertainty framework is insufficient for interactive large language model (LLM) agents and call for underspecification-aware, decomposed, and communicable uncertainty representations that can unlock new agent capabilities such as proactive clarification seeking and shared mental-model building. Practical deployment constraints -- black-box APIs, interactive latency budgets, and the absence of labeled trajectories -- rule out logprob-based, multi-sampling, and training-based methods, leaving prompt-based estimation as the most viable family for surfacing such signals at deployment time. We answer this call with a simple prompt-based decomposition that separates action confid…
- Measuring Curriculum Alignment across Topical Coverage, Competency, and Cognitive Depth: A Longitudinal Framework Applied to CS2013 and CS2023
arxiv-ai· 19-jun
arXiv:2606.19469v1 Announce Type: new Abstract: Undergraduate computer science is governed by international curricular guidelines revised about once a decade, yet programs lack a reliable, reproducible way to measure how completely they cover the current guidelines and how that coverage shifts when the guidelines are restructured. We address this with a human-in-the-loop pipeline that measures a program's coverage of an external body of knowledge, applied longitudinally to one accredited BSc in Computer Science against Computer Science Curricula 2013 (CS2013) and 2023 (CS2023). The pipeline represents the program and each guideline as structured corpora, generates candidate course-to-knowledge-unit matches by semantic retrieval, and confirms them through human judgment under an explicit c…
- Diffusion Language Models: An Experimental Analysis
arxiv-ai· 19-jun
arXiv:2606.19475v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized language modeling through autoregressive generation, enabling strong performance across a wide range of tasks. Recently, Diffusion Language Models (DLMs) have emerged as an alternative paradigm that generates text through iterative denoising rather than next-token prediction, allowing parallel refinement of entire sequences. While numerous diffusion-based architectures have been proposed, differences in evaluation protocols, datasets, inference budgets, and generation hyperparameters make it difficult to compare their capabilities and understand the trade-offs they offer. In this work, we present a systematic experimental analysis of modern DLMs. Specifically, we evaluate eight state-of-the-ar…
- Hidden Anchors in Multi-Agent LLM Deliberation
arxiv-ai· 19-jun
arXiv:2606.19494v1 Announce Type: new Abstract: Multi-agent LLM deliberation, where agents exchange and revise answers over several rounds, is increasingly used to improve reasoning and accuracy, yet how and why it works is rarely modelled. Such deliberation mirrors how humans reach decisions. As social animals we are pulled both by the group, the herd effect that classical opinion-dynamics models such as DeGroot and Friedkin--Johnsen capture, and by our own internal belief, which they do not. We model multi-agent deliberation as a closed-loop dynamical system in which each agent carries a hidden internal belief, its anchor, that continually pulls its opinion regardless of its neighbours. We show this anchor can be recovered from the deliberation alone, and that it explains a behaviour cl…
- Deontic Policies for Runtime Governance of Agentic AI Systems
arxiv-ai· 19-jun
arXiv:2606.19464v1 Announce Type: new Abstract: Autonomous agentic AI systems driven by Large Language Models (LLMs) introduce a new class of security, privacy, and compliance challenges: an agent that can invoke tools, manipulate data, install software, and coordinate with peer agents across organizational boundaries must be constrained not just by authentication and access control, but by the full structure of enterprise governance. This includes specifying what agents are permitted and prohibited from doing, what they areobliged to do after certain actions (e.g., notify the CISO), under what conditions a standing obligation may be waived, and which rules take precedence when policies conflict. This governance problem exceeds what current policy engines provide. Systems such as XACML, R…
- How Open-Weight Models Changed the AI Landscape
bytebytego· 16-jun
In this article, we will look at how open-weight models have transformed the AI landscape.Jun 16 • ByteByteGo24336
- LAST CALL FOR ENROLLMENT: Build with Claude Code - Cohort 2
bytebytego· 17-jun
We’re launching Cohort 2 of our 2-day intensive, cohort-based course, Build with Claude Code, taught by John Kim, who has trained hundreds of engineers…Jun 17 • ByteByteGo229
- Observability for Beginners: Logs, Metrics, Traces, and Everything Around Them
bytebytego· 18-jun
In this article, we will look at the basics of observability in detail with concepts like logs, metrics, and traces explained in detail.17 hrs ago • ByteByteGo1258
- A Guide to AI Inference Engineering
bytebytego· 15-jun
In this article, we will walk through how inference works and why the field’s optimization techniques exist.Jun 15 • ByteByteGo295712
- EP218: The Typical AI Agent Stack, Explained
bytebytego· 13-jun
Over to you: Which layer of the stack do you think is the hardest to get right in production?Jun 13 • ByteByteGo25813
- AI demands more engineering discipline. Not less (xpost)
charity-majors· 15-jun
If you lived through the shift from handcrafted server pets to immutable infrastructure, you should sense something oddly familiar about what’s happening now. Crossposted from AI demands more engineering discipline. Not less. A few days back I wrote a piece called “AI enthusiasts are in a race against time, AI skeptics are in a race […]
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- Claude Platform
claude-changelog
Release notesCopy pageUpdates to the Claude Platform, including the Claude API, client SDKs, and the Claude Console.Copy pageFor release notes on Claude Apps, see the Release notes for Claude Apps in the Claude Help Center.For updates to Claude Code, see the complete CHANGELOG.md in the claude-code repository. June 15, 2026 We've retired the Claude Sonnet 4 model (claude-sonnet-4-20250514) and the Claude Opus 4 model (claude-opus-4-20250514). All requests to these models will now return an error. We recommend upgrading to Claude Sonnet 4.6 and Claude Opus 4.8 respectively. Researchers can request ongoing access through the External Researcher Access Program. June 11, 2026 The code execution tool now supports code_execution_20260521, which discloses the 90-second per-cell execution ti…
- Growing the Cloudflare AI team with talent from Ensemble AI
cloudflare· 15-jun
Cloudflare is deepening our investment in AI with the addition of team members from Ensemble AI, focusing on machine learning infrastructure and efficiency.
- Scaling Security Insights: how we achieved a 10x increase in global scanning capacity
cloudflare· 12-jun
Cloudflare Security Insights system now processes over 120 scans per second, providing frequent insights for all customers. By optimizing Kafka consumers, Postgres queries, and our API, we scaled our throughput 10x without adding hardware.
- Bringing more agent harnesses and frameworks to Cloudflare, starting with Flue
cloudflare· 17-jun
The Agents SDK is now a runtime any agent framework can build on. Today we're opening up the Agents SDK primitives, with Flue as a first framework targeting Agents SDK, and rolling out agents in the dashboard.
- Celebrating 12 years of Project Galileo
cloudflare· 18-jun
To mark the 12th anniversary of Project Galileo, Cloudflare has released its first comprehensive report analyzing cyberattacks against civil society.
- Build your own vulnerability harness
cloudflare· 18-jun
We break down the technical architecture behind our multi-stage vulnerability discovery harness and automated triage loop. Learn how we manage state controls, squash false positives through adversarial review, and route around LLM context limits.
- Introducing the Cloudflare One stack: agent-powered deployment
cloudflare· 17-jun
The Cloudflare One stack is a library of agent skills that gives any AI agent the knowledge it needs to plan, deploy, and manage a Zero Trust environment — no migration calls required.
- Cloudflare DMARC Management is now generally available
cloudflare· 16-jun
Get unified visibility into your email authentication posture and reach full DMARC enforcement with deeper reporting, record analysis, and SPF audits free for every Cloudflare customer.
- zai-org/GLM-5
github-trending
GLM-5: From Vibe Coding to Agentic Engineering GLM-5.2 & GLM-5.1 & GLM-5 👋 Join our Wechat or Discord community. 📖 Check out the GLM-5.2 blog and GLM-5 Technical report. 📍 Use GLM-5.2 API services on Z.ai API Platform. 🔜 Try GLM-5.2 at z.ai. Introduction GLM-5.2 GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a solid 1M-token context. GLM-5.2's new capabilities include: Solid 1M Context: A solid 1M-token context that stably sustains long-horizon work Advanced Coding with Flexible Effort: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency Improved Architecture: We propose IndexShare, wh…
- DeusData/codebase-memory-mcp
github-trending
High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies. codebase-memory-mcp The fastest and most efficient code intelligence engine for AI coding agents. Full-indexes an average repository in milliseconds, the Linux kernel (28M LOC, 75K files) in 3 minutes. Answers structural queries in under 1ms. Ships as a single static binary for macOS, Linux, and Windows — download, run install, done. High-quality parsing through tree-sitter AST analysis across all 158 languages, enhanced with Hybrid LSP semantic type resolution for Python, TypeScript / JavaScript / JSX / TSX, PHP, C#, Go, C, C++, Java, Kotlin, and Rust — producing a persi…
- google-research/timesfm
github-trending
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. TimesFM TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. Paper: A decoder-only foundation model for time-series forecasting, ICML 2024. All checkpoints: TimesFM Hugging Face Collection. Google Research blog. TimesFM in Google 1P Products: BigQuery ML: Enterprise level SQL queries for scalability and reliability. Google Sheets: For your daily spreadsheet. Vertex Model Garden: Dockerized endpoint for agentic calling. This open version is not an officially supported Google product. Latest Model Version: TimesFM 2.5 Archived Model Versions: 1.0 and 2.0: relevant…
- Kilo-Org/kilocode
github-trending
Kilo is the all-in-one agentic engineering platform. Build, ship, and iterate faster with the most popular open source coding agent. English | 简体中文 | 繁體中文 | 한국어 | Deutsch | Español | Français | Italiano | Dansk | 日本語 | Polski | Русский | Bosanski | العربية | Norsk | Português (Brasil) | ไทย | Türkçe | Українська | বাংলা | Ελληνικά | Tiếng Việt The open source coding agent for building with AI in VS Code, JetBrains, or the CLI. Kilo Code is an AI coding agent that meets you everywhere you work: VS Code, JetBrains, and the CLI. It's open source with open pricing. You pick from 500+ models, switch between them mid-task, and pay the model provider's rate with zero markup. No API keys required to start. Installation Pick where you want to run Kilo. VS Code Install the Kilo Code extension direc…
- withastro/flue
github-trending
The sandbox agent framework. Flue — The Agent Harness Framework Not another SDK. Build autonomous agents and powerful AI workflows with Flue's programmable TypeScript harness. // agents/triage.ts import { createAgent, type AgentRouteHandler } from '@flue/runtime'; import { local } from '@flue/runtime/node'; import triage from '../skills/triage/SKILL.md' with { type: 'skill' }; import verify from '../skills/verify/SKILL.md' with { type: 'skill' }; import * as githubTools from '../tools/github.ts'; // Give agents the context and autonomy to solve complex tasks: const instructions = ` Triage a bug report end-to-end: reproduce the bug, diagnose the root cause, verify whether the behavior is intentional, and attempt a fix. ...`; // Expose (and protect) your agents over HTTP: export const route…
- yifanfeng97/Hyper-Extract
github-trending
Transform unstructured text into structured knowledge with LLMs. Graphs, hypergraphs, and spatio-temporal extractions — with one command. Smart Knowledge Extraction CLI Transform documents into structured knowledge with one command. 📖 English Version · 中文版 "Stop reading. Start understanding." "告别文档焦虑，让信息一目了然" Hyper-Extract is an intelligent, LLM-powered knowledge extraction and evolution framework. It radically simplifies transforming highly unstructured texts into persistent, predictable, and strongly-typed Knowledge Abstracts. It effortlessly extracts information into a wide spectrum of formats—ranging from simple Collections (Lists/Sets) and Pydantic Models, to complex Knowledge Graphs, Hypergraphs, and even Spatio-Temporal Graphs. ✨ Core Features 🔷 8 Knowledge Structures From simple…
- One Model Won't Save You: How We Built Our AI Stack
hn-ai· 19-jun
Article URL: https://www.xelerate.tech/one-model-wont-save-you/ Comments URL: https://news.ycombinator.com/item?id=48595915 Points: 1 # Comments: 0
- Contributor Poker and Zig's AI Ban
hn-ai· 19-jun
Article URL: https://kristoff.it/blog/contributor-poker-and-ai/ Comments URL: https://news.ycombinator.com/item?id=48595826 Points: 2 # Comments: 0
- Bernie Sanders unveils $7T plan to give Americans control of AI industry
hn-ai· 19-jun
Article URL: https://arstechnica.com/tech-policy/2026/06/bernie-sanders-unveils-7-trillion-plan-to-give-americans-control-of-ai-industry/ Comments URL: https://news.ycombinator.com/item?id=48595691 Points: 3 # Comments: 2
- Prodigy: AI Employees
hn-ai· 19-jun
Article URL: https://docs.google.com/presentation/d/1aldEHGR_1Hv_F0UlTuQIL8mXhsw5s5VzuuPcgKV5czY/edit?usp=sharing Comments URL: https://news.ycombinator.com/item?id=48595965 Points: 2 # Comments: 2
- Claude Artifacts
hn-ai· 19-jun
Article URL: https://claude.com/blog/artifacts-in-claude-code Comments URL: https://news.ycombinator.com/item?id=48596196 Points: 2 # Comments: 0
- Show HN: Appaca – AI Workspace for Operators
hn-ai· 19-jun
Build a custom CRM, internal tool, or workflow your team needs. Appaca builds and runs it all in one platform. Comments URL: https://news.ycombinator.com/item?id=48595638 Points: 1 # Comments: 0
- 'We created a monster': companies rein in AI usage as costs strain budgets
hn-ai· 19-jun
Article URL: https://www.ft.com/content/1d37cc08-e0aa-45a4-a45d-4ad282529314 Comments URL: https://news.ycombinator.com/item?id=48595926 Points: 2 # Comments: 0
- Show HN: One-click fork of "Everything Claude Code" onto an isolated microVM
hn-ai· 19-jun
Article URL: https://www.jurniti.com/templates/ecc Comments URL: https://news.ycombinator.com/item?id=48596184 Points: 1 # Comments: 0
- Google Is Using Nvidia's Playbook to Build a Rival AI Chip Business
hn-ai· 19-jun
Article URL: https://www.wsj.com/tech/ai/google-is-using-nvidias-playbook-to-build-a-rival-ai-chip-business-1eac86f9 Comments URL: https://news.ycombinator.com/item?id=48595772 Points: 2 # Comments: 0
- Building Reliable Agentic AI Systems
hn-ai· 19-jun
Article URL: https://martinfowler.com/articles/reliable-llm-bayer.html Comments URL: https://news.ycombinator.com/item?id=48595716 Points: 1 # Comments: 0
- [AINews] Midjourney Medical: scan your organs like you step on a scale
latentspace· 18-jun
The only bootstrapped frontier lab announces its second product and second
- [AINews] GLM > GPT? GLM-5.2 passes vibe check; Z.ai forecasts Open Fable by December
latentspace· 19-jun
With GLM-5.2 passing everyone's vibe check, the open models story finally becomes a real frontier story.
- 🔬 The Self-Driving Lab — Joseph Krause, Radical AI
latentspace· 17-jun
Radical AI's Joseph Krause on why the moat in materials is the lab, not the model
- The Professor of Outputmaxxing — Anjney Midha, AMP
latentspace· 18-jun
We talk about how this legendary investor went from humble beginnings in Singapore to leading rounds in Anthropic, Mistral, Black Forest Labs, and Periodic Labs... and the AMP secret master plan!
- Fragments: June 16
martin-fowler· 16-jun
“Prag Dave” Thomas (co-author of the outstanding “Pragmatic Programmer”) has loved programming since he was young. Programming was how I could express myself. I wasn’t an artist. When I sing, dogs howl. When I draw, friends say, “Very nice. What is it?” I didn’t connect particularly well with people, even though I wanted to. And yet, when I wrote my first program, I discovered a medium which let me convert thought into action. All the ideas that were bottled up behind a wall of frustration suddenly had an outlet. The LLM revolution worried him. Would they remove all that fun stuff? Happily he found it was the opposite. Like Kent Beck and others have told me, programming with LLMs is more fun than ever. His post lists reasons why: removing drudgery, speeding up feedback loops, reviving lon…
- Datasette Apps: Host custom HTML applications inside Datasette
simonw· 18-jun
<p>Today we launched a new plugin for Datasette, <a href="https://github.com/datasette/datasette-apps">datasette-apps</a>, with <a href="https://datasette.io/blog/2026/datasette-apps/">this launch announcement post</a> on the Datasette project blog. That post has the <em>what</em>, but I'm going to expand on that a little bit here to provide the <em>why</em>.</p> <h4 id="the-tl-dr">The TL;DR</h4> <p>Datasette Apps are self-contained HTML+JavaScript applications that run in a tightly constrained <code><iframe></code> sandbox hosted on your Datasette application. They can use JavaScript to run read-only SQL queries against data in Datasette, and can run write queries too if you configure them <a href="https://datasette.io/blog/2026/sql-write-queries/">with some stored queries</a>.</p>…
- datasette-acl 0.6a0
simonw· 18-jun
<p><strong>Release:</strong> <a href="https://github.com/datasette/datasette-acl/releases/tag/0.6a0">datasette-acl 0.6a0</a></p> <blockquote> <p>This release expands <code>datasette-acl</code> from table-only permissions toward a general resource-sharing system.</p> </blockquote> <p>Alex Garcia did most of the work for this release - we're fleshing out the plugin that will allow multi-user Datasette instances finely grained control over who can access which resources within Datasette.</p> <p>Tags: <a href="https://simonwillison.net/tags/datasette">datasette</a>, <a href="https://simonwillison.net/tags/alex-garcia">alex-garcia</a></p>
- GLM-5.2 is probably the most powerful text-only open weights LLM
simonw· 17-jun
<p>Chinese AI lab <a href="https://z.ai/">Z.ai</a> released GLM-5.2 <a href="https://x.com/Zai_org/status/2065704919299235870">to their coding plan subscribers</a> on June 13th, and then yesterday (June 16th) released the full open weights under an MIT license. Similar in size to their previous GLM-5 and GLM-5.1 releases, this is 753B parameter, <a href="https://huggingface.co/zai-org/GLM-5.2">1.51TB</a> monster - with 40 active parameters (Mixture of Experts). GLM-5.2 is a text input only model - Z.ai have a separate vision family most recently represented by <a href="https://x.com/Zai_org/status/2039371126984360085">GLM-5V-Turbo</a>, but that one isn't open weights. GLM-5.2 has a 1 million token context window, up from GLM-5.1's 200,000.</p> <p>The buzz around this model is strong.</p> …
- Quoting Charity Majors
simonw· 17-jun
<blockquote cite="https://charitydotwtf.substack.com/p/ai-demands-more-engineering-discipline#footnote-2"><p>What happened in 2025 was this: <strong>the economics of code production were turned upside down</strong>. Instead of being very hard, time-consuming, and expensive to generate code, it became effectively free and instant. Lines of code went from being treasured, reused, cared for and carefully curated, to being disposable and regenerable, practically overnight.</p></blockquote> <p class="cite">— <a href="https://charitydotwtf.substack.com/p/ai-demands-more-engineering-discipline#footnote-2">Charity Majors</a>, AI demands more engineering discipline. Not less</p> <p>Tags: <a href="https://simonwillison.net/tags/charity-majors">charity-majors</a>, <a href="https://simonwilliso…