- Beyond "Made with AI": Visualizing Provenance Density to Mitigate the Transparency Penalty
arxiv-ai· 04-sept
arXiv:2609.03460v1 Announce Type: new Abstract: As generative AI makes polished prose cheap to produce, users can no longer rely on fluency as a proxy for truth. We call this failure mode the Fluency Trap: users trust fluent hallucinations while also discounting accurate content once it is disclosed as AI-generated. Binary ``Made with AI'' labels respond with authorship disclosure, but they do not show what supports a claim. We propose Provenance Density, an evidence-visualization interface that shows the density of verified claims in a text. In a user study with 81 participants, an idealized Provenance Density interface produced a large discernment gap between truth and fabrication ($+4.15$ points, $d=1.82$), whereas participants given no signal showed no detectable discrimination. A tec…
- MasterControl Seventeen Every Time
arxiv-ai· 04-sept
arXiv:2609.03209v1 Announce Type: new Abstract: We study a governed approach to enterprise analytics: a language model interprets the question, while deterministic policy selects and runs a pre-approved analytical program that returns both results and evidence. We show that this restriction can remain expressive within a defined analytical class, using relational operations plus aggregation, comparison, windows, ranking, and similarity. Fixed meaning, policy, data, and execution rules also make results replayable. Across 440 runs, three 8B models generated SQL and selected tools at runtime, while Qwen3-8B interpreted intent only and policy executed the approved program. None of 330 runtime-planning episodes matched the full answer-and-evidence contract across all test datasets; the policy…
- Structure and Implementation of New Practical English Textbooks Driven by Artificial Intelligence
arxiv-ai· 04-sept
arXiv:2609.02981v1 Announce Type: new Abstract: Artificial intelligence is changing the form of applied English materials from fixed paper sequences to adaptive learning systems that can diagnose learners, recommend tasks, and provide formative feedback. This paper studies the structure and application of a new practical English textbook driven by artificial intelligence. A five-layer architecture is proposed: knowledge mapping, learner profiling, task generation, feedback orchestration, and teacher-side governance. A prototype was tested on 186 non-English-major undergraduates for eight weeks of teaching. Compared with a static digital textbook, the proposed system increased the unit completion accuracy from 72.4% to 84.9%, raised the average score for speaking tasks by 10.8 points, and …
- Speculative Macro Commit for Faster Tool-Using Agents
arxiv-ai· 04-sept
arXiv:2609.03236v1 Announce Type: new Abstract: Tool-using LLM agents spend wall-clock time not only on model inference but also in serial action--observation turns, where each tool call, environment transition, and observation can delay subsequent decisions. We introduce \textbf{Speculative Macro Commit} (SMC), a runtime mechanism for a two-tier agent system: a large authoritative actor model produces the official trajectory, while a faster speculative drafter model continuously predicts and executes future action chains on an isolated environment snapshot. SMC mines recurring multi-action skeletons from training traces and stores them in a macro library used to match against action chains predicted by the drafter at runtime. When the actor's next tool call matches the first drafted acti…
- Fresh Memory, Stale Plans: Dependency-Scoped Validation for Distributed LLM-Agent Memory
arxiv-ai· 04-sept
arXiv:2609.03340v1 Announce Type: new Abstract: Distributed LLM-agent teams can read the latest shared facts and still act on an obsolete plan. A planner may derive an action from requirement $r_3$, another agent may commit $r_4$, and an executor may receive $r_4$ without replacing the plan derived from $r_3$. We call this \emph{stale-plan execution}: state freshness does not establish that the plan authorizing an action remains valid. We introduce PlanFence, a dependency-scoped action-validation protocol. Plans cite the exact public records they used, and an executor validates only the records that can affect the pending external action, replanning once or blocking when validation is incomplete. In 30 controlled live workflows with a post-plan revision, a freshness-only executor acts on …
- A Prompt-Engineering Approach to Develop Scalable, Flexible, and Real-Time Hybrid Micro-Level Personalization in a General Purpose AI Teaching Assistant
arxiv-ai· 04-sept
arXiv:2609.03402v1 Announce Type: new Abstract: Artificial intelligence (AI) teaching assistants powered by large language models (LLMs) offer scalable educational support but often provide limited personalization. This study presents a prompt-engineering-based framework for personalizing general-purpose LLM/RAG-based AI teaching assistants such as Jill Watson across academic disciplines and courses. The framework adapts responses using six learner-specific dimensions: self-assessment, abstraction preference, verbosity preference, perceptual orientation, information processing style, and level of understanding, yielding 96 distinct learner profiles. Student queries are additionally analyzed using Bloom's Taxonomy to estimate cognitive complexity at the interaction level. Learner attribute…
- Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation
arxiv-ai· 04-sept
arXiv:2609.03407v1 Announce Type: new Abstract: People increasingly turn to large language models (LLMs) for everyday advice, making ethically charged interpersonal problems a practical moral-advisory context. Most prior work has studied this context through single-turn judgments or pressure-laden rebuttals, assumptions that poorly match how guidance is sought in real-world contexts. These assumptions leave unclear whether narration alone, without an explicit opposing position, can shift model judgments during multi-turn moral consultation. Yet real-world moral-conflict conversation often elicits one party's self-justifying account, which can unfold over multiple turns and create information asymmetry. We introduce \textbf{narrative captivity}, a failure mode in which a model treats an un…
- Dude: A Dual-Detection Multi-Agent System for Paper-Code Discrepancy Detection
arxiv-ai· 04-sept
arXiv:2609.03416v1 Announce Type: new Abstract: LLM-empowered paper-code discrepancy detection has received growing concern since the scaling of research submissions exceeds the manual review capability. However, the limited context capacity and one-sided discrepancy detection of existing single-agent LLM paradigms lead to an inferior recall performance in detecting discrepancies. In this paper, we propose Dude, the first Dual-Detection Multi-Agent System for paper-code discrepancy detection. We discover that the granularity asymmetry of the paper-language and code-language introduces over-interpretation and over-reporting challenges in a multi-agent system design for discrepancy detection, resulting in increasing false positives. To address this, we propose a granularity-aligned negotiat…
- DuplexSpeechBench-IFEval: Evaluating Implicit Instruction Following in Full-Duplex Voice Agents
arxiv-ai· 04-sept
arXiv:2609.03423v1 Announce Type: new Abstract: Full-duplex voice agents must continuously decide when to listen, backchannel, interrupt, handle speech overlaps, take the floor, and yield. Existing benchmarks largely test these behaviors through explicit turn-management instructions, while deployed agents are often configured through roles or personas from which the appropriate conversational behavior must be inferred. We introduce DuplexSpeechBench-IFEval (DSB-IFEval) for evaluating implicit instruction-following in real-time spoken interaction. (DSB-IFEval) comprises 1,038 test cases spanning eight diverse assistant roles and evaluates five conditioning protocols for instruction-following: default behavior, explicit behavioral instructions, persona-implied behavior, combined persona--ru…
- Do GUI Agents Know When Not to Act? Enabling Conflict-Aware Termination for Multimodal GUI Agents
arxiv-ai· 04-sept
arXiv:2609.03438v1 Announce Type: new Abstract: Graphical user interface (GUI) agents are increasingly used to execute natural-language instructions on user interfaces, yet real users may issue infeasible instructions due to benign mistakes. A reliable agent should not only know how to act, but also when not to act. In this work, we introduce CONFLICTGUI, a benchmark covering instruction-internal conflicts and instruction-GUI context conflicts to study conflict-aware termination. Our evaluation reveals severe execution-biased overcompliance: agents that perform well on feasible tasks often continue to execute blindly under conflicting instructions. To mitigate this behavior, we propose CONFLICTGUARD, an inference-time framework that aligns an agent's feasibility awareness with its action …
- What Happens Inside an AI Chatbot Between Enter and the First Word?
bytebytego· 31-ago
In this article, we are going to look at this entire journey in detail.Aug 31 • ByteByteGo24749
- How Databases Keep Their Sanity with Concurrency Control
bytebytego· 03-sept
So how do we handle such bugs? This is what we are going to try to answer in this article.16 hrs ago • ByteByteGo67
- Why Your RAG System Is Only as Good as Its Translator Model
bytebytego· 02-sept
In this article, we’re going to look at how this embedding model works in an RAG setup and what makes it such a critical part of the system.Sep 2 • ByteByteGo25011
- How to Shrink a Language Model Without Making it Too Dumb
bytebytego· 01-sept
Models have grown roughly 100-fold in a few years, while consumer graphics memory has roughly doubled. It’s not just a matter of tightening things up to…Sep 1 • ByteByteGo24114
- Introducing context-aware vulnerability discovery and remediation with Cloudflare Managed Defense and OpenAI Daybreak models
cloudflare· 03-sept
Use production traffic and security signals to prioritize findings, prepare edge mitigations when safe, and propose code patches. By combining WAF data with OpenAI Daybreak models, Vulnerability Discovery and Remediation helps teams identify and patch the most critical threats first.
- Introducing Adaptive Intelligence: Undermining the economics of every bot attack
cloudflare· 31-ago
Bot operators have historically had the economic advantage, bypassing static, deterministic detection rules with cheap proxies and retooling. Cloudflare's new Adaptive Intelligence engine flips this dynamic by autonomously learning from the meta-signals of live traffic and deploying disposable rules, making automated attacks too expensive to sustain.
- How we could save petabytes of cache storage with Zstandard and Pingora
cloudflare· 01-sept
Could we get more cache space with the same hardware? We prototyped compression inside Cloudflare's cache to find out.
- DietrichGebert/ponytail
github-trending
Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote. https://ponytail.dev Ponytail He says nothing. He writes one line. It works. ~54% less code (up to 94%) · ~20% cheaper · ~27% faster · 100% safe Measured on real Claude Code sessions editing a real open-source repo (FastAPI + React), against the same agent with no skill. ~54% is the mean across 12 feature tasks (Haiku 4.5, n=4); it reaches 94% where an agent over-builds (a date picker) and is near zero where the code is already minimal. ponytail keeps every safety guard while a bare "write one-liners" prompt drops one. (The earlier single-shot benchmark reported 80-94% as a flat figure; against a fair agentic baseline that is the per-task ceiling, not the average.) Full writeup · …
- 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. https://research.google/blog/a-decoder-only-foundation-model-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. (NEW!) TimesFM 3.0 Checkpoint: google/timesfm-3.0-pytorch. Checkpoints (up to 2.5): TimesFM Hugging Face Collection. Google Research blog (New blog post for TimesFM 3.0 coming soon!). TimesFM in Google 1P Products: BigQuery ML: Enterprise level SQL queries for scalability and reliability. Google Sheets: For your daily spreadsheet. Verte…
- JuliusBrussee/caveman
github-trending
🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman https://docs.caveman.so/docs/quickstart why use many token when few do trick Your AI coding agent bills by the word and writes like it knows that. Caveman make it stop. Brain still big. Mouth small. Bill small. See it · Install · Numbers · Skill · Proxy · Wrap · Docs · Privacy · License See it 🗣️ Normal agent · 69 tokens Caveman agent · 19 tokens The reason your React component is re-rendering is likely because you're creating a new object reference on each render cycle. When you pass an inline object as a prop, React's shallow comparison sees it as a different object every time, which triggers a re-render. I'd recommend using useMemo to memoize the object. New object ref eac…
- mattpocock/skills
github-trending
Skills for Real Engineers. Straight from my .agents directory. https://aihero.dev/skills Skills For Real Engineers My agent skills that I use every day to do real engineering - not vibe coding. Developing real applications is hard. Approaches like GSD, BMAD, and Spec-Kit try to help by owning the process. But while doing so, they take away your control and make bugs in the process hard to resolve. These skills are designed to be small, easy to adapt, and composable. They work with any model. They're based on decades of engineering experience. Hack around with them. Make them your own. Enjoy. If you want to keep up with changes to these skills, and any new ones I create, you can join ~60,000 other devs on my newsletter: Sign Up To The Newsletter Installation (30-second setup) Two ways in, …
- NousResearch/hermes-agent
github-trending
The agent that grows with you https://hermes-agent.nousresearch.com Hermes Agent ☤ Hermes Agent | Hermes Desktop The self-improving AI agent built by Nous Research. It's the only agent with a built-in learning loop — it creates skills from experience, improves them during use, nudges itself to persist knowledge, searches its own past conversations, and builds a deepening model of who you are across sessions. Run it on a $5 VPS, a GPU cluster, or serverless infrastructure that costs nearly nothing when idle. It's not tied to your laptop — talk to it from Telegram while it works on a cloud VM. Use any model you want — Nous Portal, OpenRouter, OpenAI, your own endpoint, and many others. Switch with hermes model — no code changes, no lock-in. A real terminal interface Full TUI with multiline …
- anthropics/skills
github-trending
Public repository for Agent Skills Note: This repository contains Anthropic's implementation of skills for Claude. For information about the Agent Skills standard, see agentskills.io. Skills Skills are folders of instructions, scripts, and resources that Claude loads dynamically to improve performance on specialized tasks. Skills teach Claude how to complete specific tasks in a repeatable way, whether that's creating documents with your company's brand guidelines, analyzing data using your organization's specific workflows, or automating personal tasks. For more information, check out: What are skills? Using skills in Claude How to create custom skills Equipping agents for the real world with Agent Skills About This Repository This repository contains skills that demonstrate what's possib…
- blader/humanizer
github-trending
Agent skill that removes signs of AI-generated writing from text https://skills.sh/blader/humanizer Humanizer Humanizer rewrites AI-sounding text so it reads like a person wrote it, without changing what it says. Because it is just Markdown, it works with any agent that supports skills. How it works Humanizer uses 35 patterns from Wikipedia's "Signs of AI writing", maintained by WikiProject AI Cleanup. It makes a first pass without treating the original structure as fixed. Then it checks the draft against those patterns and the original claims before rewriting whatever still needs work. "LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely result that applies to the widest variety of cases." It does not make things up. A nam…
- fmtlib/fmt
github-trending
A modern formatting library https://fmt.dev {fmt} is an open-source formatting library providing a fast and safe alternative to C stdio and C++ iostreams. Documentation Cheat Sheets Q&A: ask questions on StackOverflow with the tag fmt. Try {fmt} in Compiler Explorer. Features Simple format API with positional arguments for localization Implementation of C++20 std::format and C++23 std::print Format string syntax similar to Python's format Fast IEEE 754 floating-point formatter with correct rounding, shortness and round-trip guarantees using the Dragonbox algorithm Portable Unicode support Safe printf implementation including the POSIX extension for positional arguments Extensibility: support for user-defined types High performance: faster than common standard library implementations of (s…
- Execution gating and micro-rollbacks for AI agents
hn-ai· 04-sept
Article URL: https://bartholomew.info/ Comments URL: https://news.ycombinator.com/item?id=49561500 Points: 1 # Comments: 0
- OpenAI commits $1B to critical-infrastructure AI initiative
hn-ai· 04-sept
Article URL: https://axios.com/2026/09/03/openai-critical-infrastructure-cyber-ai-models Comments URL: https://news.ycombinator.com/item?id=49561372 Points: 2 # Comments: 0
- Show HN: AI game builder and editor built with Rails and Godot
hn-ai· 04-sept
Article URL: https://app.fusionery.com/ Comments URL: https://news.ycombinator.com/item?id=49561198 Points: 1 # Comments: 0
- Open-weight AI agents can use 10k× more energy than simple queries
hn-ai· 04-sept
Article URL: https://bloomberg.com/news/articles/2026-09-03/ai-s-environmental-impact-per-task-balloons-with-more-complexity Comments URL: https://news.ycombinator.com/item?id=49561361 Points: 1 # Comments: 0
- Nvidia links gaming PCs into a local AI cluster
hn-ai· 04-sept
Article URL: https://forgeeks.net/nvidia-rtx-pair-local-ai-cluster/ Comments URL: https://news.ycombinator.com/item?id=49561584 Points: 2 # Comments: 0
- Native Rust/C++ LLM inference engine
hn-ai· 04-sept
Article URL: https://github.com/truespar/paddock Comments URL: https://news.ycombinator.com/item?id=49561513 Points: 1 # Comments: 0
- Who is accountable for Frontier AI Companies agent's criminal actions?
hn-ai· 04-sept
Supposed rogue agents breached huggingface. Had that been done by human hackers, they'd be accountable and persecuted of caught. Companies like OpenAI and Anthropic simply say "It was inadvertent - The agents decided to do that on their own" In the future - Could engineers or AI company members be held accountable for "rogue" agent actions? Comments URL: https://news.ycombinator.com/item?id=49561197 Points: 2 # Comments: 0
- Ask HN: How has AI changed your QA validation workflows?
hn-ai· 04-sept
Comments URL: https://news.ycombinator.com/item?id=49561398 Points: 1 # Comments: 1
- An audio end-of-turn gate before LLM invocation in a local voice-agent pipeline
hn-ai· 04-sept
Article URL: https://www.reddit.com/r/LLMDevs/comments/1w6w7cu/i_added_an_audio_endofturn_gate_before_llm/ Comments URL: https://news.ycombinator.com/item?id=49561360 Points: 3 # Comments: 0
- Andrew Ng: The Biggest Opportunities in AI Aren't Where You Think [video]
hn-ai· 04-sept
Article URL: https://www.youtube.com/watch?v=o-wv_szZ0V0 Comments URL: https://news.ycombinator.com/item?id=49561246 Points: 1 # Comments: 0
- [AINews] GPT-6 Astra: OpenAI’s biggest LLM launch of all time
latentspace· 04-sept
new SOTA computer use and coding, 2.5x pricier per token, but WAY cheaper per task, less monitorable. overall, a very successful launch of OpenAI’s new frontier model class.
- [AINews] Muse Spark 1.3 matches GPT-5.6-Sol, confirming Meta Superintelligence as the newest Frontier Lab, >90% discount for training
latentspace· 03-sept
an epic comeback story for Meta
- GPT-6 Astra: an automated AI Engineer you can hire for <$6 an hour
latentspace· 03-sept
We spent 20B+ tokens of GPT-6 Astra to explore everything. Here’s our learnings.
- [AINews] Claude Fable/Mythos 5.1: new SOTA model, 75% cache price cut but 70% more output tokens
latentspace· 02-sept
Queue the usual rush of model launches...
- Fragments: September 1
martin-fowler· 01-sept
Like many readers, I’m wary of AI generated prose. Simon Wilison has written an LLM cliché highlighter - paste in some text, or a URL, and it will flag various patterns common to LLMs. It references a wikipedia page of signs of AI writing. That page points out that: Humans are notoriously bad at distinguishing human and LLM-generated text. While research on humans’ abilities to detect AI-generated text is still limited, a 2025 study has shown that human ability to distinguish LLM text from human is no better than random chance. Another 2025 study on German theses has shown that humans managed a “recognition rate of 57% for AI texts and 64% for human-generated texts”.[ Not just do I find myself repelled by prose with an LLM-voice, I also wonder how accurate my reaction is. I’m old enough t…
- Bliki: Paracelsus Maxim
martin-fowler· 02-sept
The difference between a medicine and a poison is dosage. Often we talk about certain habits, in programming or life, are good or bad. But few things are simple binaries. Some vary with context: reading a book is a good thing sitting in my garden, but not while driving my car. But another variable is dosage: a little pain-killer salves my headache, but too much will kill me. The importance of dosage was noticed by a 16th century Swiss physician called Paracelsus. His quote was originally in German “Alle Dinge sind Gift, und nichts ist ohne Gift; allein die Dosis macht, dass ein Ding kein Gift ist.” which (according to Wikipedia) translates as “All things are poison, and nothing is without poison; the dosage alone makes it so a thing is not a poison.” It's also known as “The dose makes the…
- An Accidental Blackboard
martin-fowler· 02-sept
Giles Edwards-Alexander reports that during an experiment to see how productive a team could be using fully agentic engineering practices, the team accidentally prompted the agents into creating a blackboard coordination system inside the git repository. more…
- Maybe We Shouldn't Be Reviewing All This Code
martin-fowler· 02-sept
TL;DR Or, perhaps the problem isn't that AI has broken code review, maybe it’s that we've been using code review to solve the wrong problems I was on a panel recently with Brian Houck from DX at Code Remix, hosted by Moderne. It was one of the more interesting panels I’ve done, largely because we disagreed. As my colleague Martin Fowler says, panels are much more interesting when people disagree and both sides have a good argument. Brian and I definitely did. Brian has since written a thoughtful piece called What are code reviews even for? He is clearly passionate about his position, and I am passionate enough about mine that I’m writing this response. To be clear, I think we mostly want the same things. I just don’t think code review is the best way to get them. Brian is lovely, by the w…
- MAPS: Netflix’s Multimodal Asset Personalization at Scale
netflix-tech· 28-ago
- August newsletter is out
simonw· 04-sept
<p>The August edition of my <a href="https://github.com/sponsors/simonw/">sponsors-only monthly newsletter</a> is out. If you are a sponsor (or if you start a sponsorship now) you can <a href="https://github.com/simonw-private/monthly/blob/main/2026-08-august.md">access it here</a>.</p> <p>This month:</p> <ul> <li>We got more details on OpenAl's accidental cyberattacks</li> <li>One-shotting Raccoon Heist games with Fable 5 and Sol 5.6</li> <li>Claude auto mode</li> <li>Understanding ChatGPT Work</li> <li>Model releases</li> <li>Miscellaneous bits and bobs</li> <li>My projects</li> <li>What I'm using at the moment</li> </ul> <p>Here's <a href="https://github.com/simonw/monthly-newsletter-archive/blob/main/2026-07-july.md">a copy of the July newsletter</a> as a preview of what you'll get. P…
- GPT‑6 Astra
simonw· 03-sept
<p><strong><a href="https://openai.com/index/gpt-6-astra/">GPT‑6 Astra</a></strong></p> GPT-6 Astra is "rolling out today to a limited set of organizations and over the coming days will become available to all ChatGPT Plus, Pro, Business, and Enterprise users, as well as through the OpenAI API and AWS" - I've not tried it yet myself, so I don't have a great deal to say about it yet.</p> <p>It's going to be API priced at the same rate as Claude Fable 5 and 5.1: $10/million input and $50/million output. This is clearly OpenAI's Fable competitor, and appears to score higher than Fable on most of OpenAI's self-reported benchmarks.</p> <p>Most impressively, Astra scores 99.9% on the recent (released in March) <a href="https://arcprize.org/arc-agi/3">ARC-AGI 3 benchmark</a> - though notably Fab…
- llm-gemini 0.34
simonw· 02-sept
<p><strong>Release:</strong> <a href="https://github.com/simonw/llm-gemini/releases/tag/0.34">llm-gemini 0.34</a></p> <blockquote> <ul> <li>New model <code>gemini-3.8-flash</code> for <a href="https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/">Gemini 3.8 Flash</a>, with low, medium and high thinking levels. <a href="https://github.com/simonw/llm-gemini/issues/146">#146</a></li> <li>Fixed async responses failing to record the resolved model version. Thanks, <a href="https://github.com/c-tonneslan">Charlie Tonneslan</a>. <a href="https://github.com/simonw/llm-gemini/pull/137">#137</a></li> </ul> </blockquote> <p>Google released <a href="https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/"…
- Claude's new system prompt really doesn't want to reproduce song lyrics
simonw· 02-sept
<p>Anthropic <a href="https://platform.claude.com/docs/en/release-notes/system-prompts/overview">publish the system prompts</a> for their Claude consumer applications (<a href="https://claude.ai/">Claude.ai</a> and the Claude mobile apps - sadly not for Claude Cowork or Claude Code). I <em>love</em> that they do this, and that they share not just the current prompts but historic changes to their prompts as well.</p> <p>They used to keep all of the prompts on a single page, but when I checked today I noticed they had re-arranged those prompts into an <a href="https://platform.claude.com/docs/en/release-notes/system-prompts/overview">index page</a> and then a page per model - here's the <a href="https://platform.claude.com/docs/en/release-notes/system-prompts/claude-haiku-4-5">page for Haik…