Article URL: https://www.wiz.io/blog/wiz-research-codebreach-vulnerability-aws-codebuild
Comments URL: https://news.ycombinator.com/item?id=46636093
Points: 90
# Comments: 18
", "tags": ["sbom", "security"], "hash": "978cc1b9ac0b7dca"} {"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Internal Deployment Gaps in AI Regulation", "link": "https://arxiv.org/abs/2601.08005", "summary": "arXiv:2601.08005v1 Announce Type: new Abstract: Frontier AI regulations primarily focus on systems deployed to external users, where deployment is more visible and subject to outside scrutiny. However, high-stakes applications can occur internally when companies deploy highly capable systems within their own organizations, such as for automating R\\&D, accelerating critical business processes, and handling sensitive proprietary data. This paper examines how frontier AI regulations in the United States and European Union in 2025 handle internal deployment. We identify three gaps that could c", "tags": [], "hash": "52c86896966bfed1"} {"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Advancing ESG Intelligence: An Expert-level Agent and Comprehensive Benchmark for Sustainable Finance", "link": "https://arxiv.org/abs/2601.08676", "summary": "arXiv:2601.08676v2 Announce Type: new Abstract: Environmental, social, and governance (ESG) criteria are essential for evaluating corporate sustainability and ethical performance. However, professional ESG analysis is hindered by data fragmentation across unstructured sources, and existing large language models (LLMs) often struggle with the complex, multi-step workflows required for rigorous auditing. To address these limitations, we introduce ESGAgent, a hierarchical multi-agent system empowered by a specialized toolset, including retrieval augmentation, web search and domain-specific functi", "tags": [], "hash": "a6a9b1eecccc352d"} {"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:27Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "Analyzing GitHub Issues and Pull Requests in nf-core Pipelines: Insights into nf-core Pipeline Repositories", "link": "https://arxiv.org/abs/2601.09612", "summary": "arXiv:2601.09612v1 Announce Type: new Abstract: Scientific Workflow Management Systems (SWfMSs) such as Nextflow have become essential software frameworks for conducting reproducible, scalable, and portable computational analyses in data-intensive fields like genomics, transcriptomics, and proteomics. Building on Nextflow, the nf-core community curates standardized, peer-reviewed pipelines that follow strict testing, documentation, and governance guidelines. Despite its broad adoption, little is known about the challenges users face during the development and maintenance of these pipelines. Th", "tags": [], "hash": "6de945dc025fdab7"} {"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:31Z", "source": "github_release", "repo": "grafana/grafana", "title": "12.3.0", "link": "https://github.com/grafana/grafana/releases/tag/v12.3.0", "summary": "[Download page](https://grafana.com/grafana/download/12.3.0) [What's new highlights](https://grafana.com/docs/grafana/latest/whatsnew/) ### Features and enhancements - **API Clients:** Add lazy hooks to clients [#113226](https://github.com/grafana/grafana/pull/113226), [@tomratcliffe](https://github.com/tomratcliffe) - **API clients:** Automatically set PATCH headers [#111879](https://github.com/grafana/grafana/pull/111879), [@Clarity-89](https://github.com/Clarity-89) - **API clients:** Extract into a package [#111810](https://github.com/grafana/grafana/pull/111810), [@Clarity-89](https://github.com/Clarity-89) - **API clients:** Extract into a package (Enterprise) - **API clients:** Update API clients to include all endpoints & add hooks [#113061](https://github.com/grafana/grafana/pull/", "tags": ["github", "release"], "hash": "8e44fdf6fca84674"} {"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:31Z", "source": "github_release", "repo": "grafana/grafana", "title": "12.3.0", "link": "https://github.com/grafana/grafana/releases/tag/v12.3.0", "summary": "[Download page](https://grafana.com/grafana/download/12.3.0) [What's new highlights](https://grafana.com/docs/grafana/latest/whatsnew/) ### Features and enhancements - **API Clients:** Add lazy hooks to clients [#113226](https://github.com/grafana/grafana/pull/113226), [@tomratcliffe](https://github.com/tomratcliffe) - **API clients:** Automatically set PATCH headers [#111879](https://github.com/grafana/grafana/pull/111879), [@Clarity-89](https://github.com/Clarity-89) - **API clients:** Extract into a package [#111810](https://github.com/grafana/grafana/pull/111810), [@Clarity-89](https://github.com/Clarity-89) - **API clients:** Extract into a package (Enterprise) - **API clients:** Update API clients to include all endpoints & add hooks [#113061](https://github.com/grafana/grafana/pull/", "tags": ["github", "release"], "hash": "8e44fdf6fca84674"} {"schema": "intel.signal.v1", "ts": "2026-01-16T03:33:25Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Supply Chain Vuln Compromised Core AWS GitHub Repos & Threatened the AWS Console", "link": "https://www.wiz.io/blog/wiz-research-codebreach-vulnerability-aws-codebuild", "summary": "Article URL: https://www.wiz.io/blog/wiz-research-codebreach-vulnerability-aws-codebuild
Comments URL: https://news.ycombinator.com/item?id=46636093
Points: 90
# Comments: 19
", "tags": ["sbom", "security"], "hash": "57a7122056b2a7bd"} {"schema": "intel.signal.v1", "ts": "2026-01-16T04:48:26Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Supply Chain Vuln Compromised Core AWS GitHub Repos & Threatened the AWS Console", "link": "https://www.wiz.io/blog/wiz-research-codebreach-vulnerability-aws-codebuild", "summary": "Article URL: https://www.wiz.io/blog/wiz-research-codebreach-vulnerability-aws-codebuild
Comments URL: https://news.ycombinator.com/item?id=46636093
Points: 99
# Comments: 21
", "tags": ["sbom", "security"], "hash": "a2add5757a2d1473"} {"schema": "intel.signal.v1", "ts": "2026-01-16T05:03:28Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T05:03:30Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "A Governance Model for IoT Data in Global Manufacturing", "link": "https://arxiv.org/abs/2601.09744", "summary": "arXiv:2601.09744v1 Announce Type: new Abstract: Industrial IoT platforms in global manufacturing environments generate continuous operational data across production assets, utilities, and connected products. While data ingestion and storage capabilities have matured significantly, enterprises continue to face systemic challenges in governing IoT data at scale. These challenges are not rooted in tooling limitations but in the absence of a governance model that aligns with the realities of distributed operational ownership, heterogeneous source systems, and continuous change at the edge. This pa", "tags": [], "hash": "e36ed1919bc425ea"} {"schema": "intel.signal.v1", "ts": "2026-01-16T05:03:30Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "CodeAssistBench (CAB): Dataset & Benchmarking for Multi-turn Chat-Based Code Assistance", "link": "https://arxiv.org/abs/2507.10646", "summary": "arXiv:2507.10646v5 Announce Type: replace Abstract: Programming assistants powered by large language models have improved dramatically, yet existing benchmarks still evaluate them in narrow code-generation settings. Recent efforts such as InfiBench and StackEval rely on Stack Overflow questions and remain limited to single-turn interactions, manually curated data, and isolated snippets rather than full project environments. We introduce CodeAssistBench (CAB), the first benchmark for evaluating multi-turn, project-grounded programming assistance at scale. CAB automatically constructs datasets f", "tags": [], "hash": "d480d303372a91a2"} {"schema": "intel.signal.v1", "ts": "2026-01-16T05:33:28Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Supply Chain Vuln Compromised Core AWS GitHub Repos & Threatened the AWS Console", "link": "https://www.wiz.io/blog/wiz-research-codebreach-vulnerability-aws-codebuild", "summary": "Article URL: https://www.wiz.io/blog/wiz-research-codebreach-vulnerability-aws-codebuild
Comments URL: https://news.ycombinator.com/item?id=46636093
Points: 105
# Comments: 23
", "tags": ["sbom", "security"], "hash": "12d91ec0bf0b15de"} {"schema": "intel.signal.v1", "ts": "2026-01-16T06:18:28Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Supply Chain Vuln Compromised Core AWS GitHub Repos & Threatened the AWS Console", "link": "https://www.wiz.io/blog/wiz-research-codebreach-vulnerability-aws-codebuild", "summary": "Article URL: https://www.wiz.io/blog/wiz-research-codebreach-vulnerability-aws-codebuild
Comments URL: https://news.ycombinator.com/item?id=46636093
Points: 112
# Comments: 22
", "tags": ["sbom", "security"], "hash": "e07e4100023d94b3"} {"schema": "intel.signal.v1", "ts": "2026-01-16T07:18:30Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Supply Chain Vuln Compromised Core AWS GitHub Repos & Threatened the AWS Console", "link": "https://www.wiz.io/blog/wiz-research-codebreach-vulnerability-aws-codebuild", "summary": "Article URL: https://www.wiz.io/blog/wiz-research-codebreach-vulnerability-aws-codebuild
Comments URL: https://news.ycombinator.com/item?id=46636093
Points: 117
# Comments: 24
", "tags": ["sbom", "security"], "hash": "a51f3831308be822"} {"schema": "intel.signal.v1", "ts": "2026-01-16T10:03:32Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T10:18:32Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T15:33:30Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T15:48:30Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T17:33:32Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T17:48:33Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T18:48:33Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T19:03:33Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T19:33:34Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T19:48:35Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T20:03:34Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T20:18:35Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T20:48:35Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T21:18:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T21:33:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T22:03:36Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T23:03:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-16T23:18:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-17T00:03:41Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-17T00:18:38Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-17T00:33:38Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-17T00:48:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-17T03:48:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-17T04:48:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"} {"schema": "intel.signal.v1", "ts": "2026-01-18T00:22:05Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Texas A&M university is banning Plato, citing his \"gender ideology\"", "link": "https://lithub.com/texas-am-is-banning-plato-citing-his-gender-ideology/", "summary": "Article URL: https://lithub.com/texas-am-is-banning-plato-citing-his-gender-ideology/
Comments URL: https://news.ycombinator.com/item?id=46662886
Points: 23
# Comments: 10
", "tags": [], "hash": "f906df8a30f09074"} {"schema": "intel.signal.v1", "ts": "2026-01-19T05:07:19Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "Empathy Guidelines for Improving Practitioner Well-being & Software Engineering Practices", "link": "https://arxiv.org/abs/2508.03846", "summary": "arXiv:2508.03846v2 Announce Type: replace Abstract: Empathy is a powerful yet often overlooked element in software engineering (SE), supporting better teamwork, smoother communication, and effective decision-making.This paper introduces 17 actionable empathy guidelines designed to support practitioners, teams, and organisations. We also explore how these guidelines can be implemented in practice by examining real-world applications, challenges, and strategies to overcome them shared by software practitioners. To support adoption, we present a visual prioritisation framework that categorises th", "tags": ["docs", "permits"], "hash": "98fd9f772090597d"} {"schema": "intel.signal.v1", "ts": "2026-01-19T06:07:18Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Ultrathink is deprecated & How to enable 2x thinking tokens in Claude Code", "link": "https://decodeclaude.com/ultrathink-deprecated/", "summary": "Article URL: https://decodeclaude.com/ultrathink-deprecated/
Comments URL: https://news.ycombinator.com/item?id=46672858
Points: 22
# Comments: 1
", "tags": [], "hash": "97393d1bf3d21ef8"} {"schema": "intel.signal.v1", "ts": "2026-01-19T06:52:18Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: Pdfwithlove \u2013 PDF tools that run 100% locally (no uploads, no back end)", "link": "https://pdfwithlove.netlify.app", "summary": "Most PDF web tools make millions by uploading documents that never needed to leave your computer.
pdfwithlove does the opposite:
1. 100% local processing 2. No uploads, no backend, no tracking
Features include merge/split/edit/compress PDFs, watermarks & signatures, and image/HTML/Office \u2192 PDF conversion.
Comments URL: https://news.ycombinator.com/item?id=46675231
Points: 5
# Comments: 1
", "tags": [], "hash": "947f4353173c08a1"} {"schema": "intel.signal.v1", "ts": "2026-01-19T07:52:18Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: Pdfwithlove \u2013 PDF tools that run 100% locally (no uploads, no back end)", "link": "https://pdfwithlove.netlify.app", "summary": "Most PDF web tools make millions by uploading documents that never needed to leave your computer.
pdfwithlove does the opposite:
1. 100% local processing 2. No uploads, no backend, no tracking
Features include merge/split/edit/compress PDFs, watermarks & signatures, and image/HTML/Office \u2192 PDF conversion.
Comments URL: https://news.ycombinator.com/item?id=46675231
Points: 72
# Comments: 33
", "tags": [], "hash": "eea54c0840423ae1"} {"schema": "intel.signal.v1", "ts": "2026-01-19T08:37:19Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: Pdfwithlove \u2013 PDF tools that run 100% locally (no uploads, no back end)", "link": "https://pdfwithlove.netlify.app", "summary": "Most PDF web tools make millions by uploading documents that never needed to leave your computer.
pdfwithlove does the opposite:
1. 100% local processing 2. No uploads, no backend, no tracking
Features include merge/split/edit/compress PDFs, watermarks & signatures, and image/HTML/Office \u2192 PDF conversion.
Comments URL: https://news.ycombinator.com/item?id=46675231
Points: 124
# Comments: 61
", "tags": [], "hash": "1694cc953389572b"} {"schema": "intel.signal.v1", "ts": "2026-01-19T20:07:23Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: Subth.ink \u2013 write something and see how many others wrote the same", "link": "https://subth.ink/", "summary": "Hey HN, this is a small Haskell learning project that I wanted to share. It's just a website where you can see how many people write the exact same text as you (thought it was a fun idea).
It's built using Scotty, SQLite, Redis and Caddy. Currently it's running in a small DigitalOcean droplet (1 Gb RAM).
Using Haskell for web development (specifically with Scotty) was slightly easier than I thought, but still a relatively hard task compared to other languages. One of my main friction points was Haskell's multiple string-like types: String, Text (& lazy), ByteString (& lazy), and each li", "tags": ["docs", "templates"], "hash": "3ca295fce163341f"} {"schema": "intel.signal.v1", "ts": "2026-01-19T20:37:23Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: Subth.ink \u2013 write something and see how many others wrote the same", "link": "https://subth.ink/", "summary": "
Hey HN, this is a small Haskell learning project that I wanted to share. It's just a website where you can see how many people write the exact same text as you (thought it was a fun idea).
It's built using Scotty, SQLite, Redis and Caddy. Currently it's running in a small DigitalOcean droplet (1 Gb RAM).
Using Haskell for web development (specifically with Scotty) was slightly easier than I thought, but still a relatively hard task compared to other languages. One of my main friction points was Haskell's multiple string-like types: String, Text (& lazy), ByteString (& lazy), and each li", "tags": ["docs", "templates"], "hash": "e09ee6f4cc49aa83"} {"schema": "intel.signal.v1", "ts": "2026-01-19T21:37:24Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: Subth.ink \u2013 write something and see how many others wrote the same", "link": "https://subth.ink/", "summary": "
Hey HN, this is a small Haskell learning project that I wanted to share. It's just a website where you can see how many people write the exact same text as you (thought it was a fun idea).
It's built using Scotty, SQLite, Redis and Caddy. Currently it's running in a small DigitalOcean droplet (1 Gb RAM).
Using Haskell for web development (specifically with Scotty) was slightly easier than I thought, but still a relatively hard task compared to other languages. One of my main friction points was Haskell's multiple string-like types: String, Text (& lazy), ByteString (& lazy), and each li", "tags": ["docs", "templates"], "hash": "43834b0153862396"} {"schema": "intel.signal.v1", "ts": "2026-01-19T22:07:24Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GitClassic.com, GitHub circa 2015 without JS & AI", "link": "https://gitclassic.com", "summary": "
Hey HN,
Got tired of how bloated GitHub became- copilot everywhere, janky JS, slow loads. So I built GitClassic, a read-only GitHub interface that's pure server-rendered HTML, kind of like old.reddit.com. No JavaScript.
Try it: https://gitclassic.com
Browse any public repo, files, READMEs. Loads instantly, works on any connection. No account needed for public repos.
Stack: Node on Lambda, server-side rendering, cached against GitHub's API. Pro adds private repo access via GitHub OAuth.
Built this in about 3 hours. Would love feed", "tags": [], "hash": "519808d38fa273a6"} {"schema": "intel.signal.v1", "ts": "2026-01-19T22:07:24Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: Subth.ink \u2013 write something and see how many others wrote the same", "link": "https://subth.ink/", "summary": "
Hey HN, this is a small Haskell learning project that I wanted to share. It's just a website where you can see how many people write the exact same text as you (thought it was a fun idea).
It's built using Scotty, SQLite, Redis and Caddy. Currently it's running in a small DigitalOcean droplet (1 Gb RAM).
Using Haskell for web development (specifically with Scotty) was slightly easier than I thought, but still a relatively hard task compared to other languages. One of my main friction points was Haskell's multiple string-like types: String, Text (& lazy), ByteString (& lazy), and each li", "tags": ["docs", "templates"], "hash": "75684d81035b23bd"} {"schema": "intel.signal.v1", "ts": "2026-01-19T22:52:24Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: Subth.ink \u2013 write something and see how many others wrote the same", "link": "https://subth.ink/", "summary": "
Hey HN, this is a small Haskell learning project that I wanted to share. It's just a website where you can see how many people write the exact same text as you (thought it was a fun idea).
It's built using Scotty, SQLite, Redis and Caddy. Currently it's running in a small DigitalOcean droplet (1 Gb RAM).
Using Haskell for web development (specifically with Scotty) was slightly easier than I thought, but still a relatively hard task compared to other languages. One of my main friction points was Haskell's multiple string-like types: String, Text (& lazy), ByteString (& lazy), and each li", "tags": ["docs", "templates"], "hash": "8eb66abf58708294"} {"schema": "intel.signal.v1", "source": "samhsa", "title": "SAMHSA Statutes and Regulations (Residential & Halfway Houses)", "url": "https://www.samhsa.gov/substance-use/treatment/statutes-regulations-guidelines", "ts": "20260120T125504Z", "buckets": ["governance", "housing", "civil_structure"]} {"schema": "intel.signal.v1", "source": "case_law", "title": "Premises Liability & Independent Contractor Doctrine", "url": "https://www.law.cornell.edu/wex/independent_contractor", "ts": "20260120T125504Z", "buckets": ["governance", "civil_structure", "risk"]} {"schema": "intel.signal.v1", "source": "us_hud", "title": "HUD Shared Housing & House Rules Guidance", "url": "https://www.hud.gov/program_offices/fair_housing_equal_opp", "ts": "2026-01-20T16:47:03Z", "buckets": ["housing", "governance", "shared_living"]} {"schema": "intel.signal.v1", "source": "nl_government", "title": "Netherlands Supervised Housing & Shared Living Standards", "url": "https://www.government.nl/topics/housing", "ts": "2026-01-20T16:47:03Z", "buckets": ["housing", "shared_living", "best_practices"]} {"schema": "intel.signal.v1", "source": "jp_mlhlw", "title": "Japan Shared Housing & Group Living Guidelines", "url": "https://www.mhlw.go.jp/english/", "ts": "2026-01-20T16:47:03Z", "buckets": ["housing", "shared_living", "sanitation"]} {"schema": "intel.signal.v1", "ts": "2026-01-20T23:07:34Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: On-device browser agent (Qwen) running locally in Chrome", "link": "https://github.com/RunanywhereAI/on-device-browser-agent", "summary": "
Demo of LOCAL Browser agent (powered by Web GPU Liquid LFM & Alibaba Qwen models) opening the All in Podcast on Youtube running as a chrome extension.
Source: https://github.com/RunanywhereAI/on-device-browser-agent
Post: https://www.reddit.com/r/LocalLLaMA/comments/1qh10q9/comment...
Geting in the support for web sdk soon, meanwhile have full support for mobile sdks : Article URL: https://www.goldmansachs.com/static-libs/pdf-redirect/prod/index.html?path=/images/migrated/insights/pages/gs-research/gen-ai--too-much-spend,-too-little-benefit-/TOM_AI%202.0_ForRedaction.pdf&originalQuery=&referrer=
Comments URL: https://news.ycombinator.com/item?id=4672", "tags": [], "hash": "f2fd53909a28aaa8"}