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Agent Clan

Product Hunt

Command your Claude Code and Codex agents with gestures

Discussion | Link

2026-11-07 17:52:26 · 大模型,AI应用,Anthropic,代码生成,Agent智能体,招聘HR
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AXL

Product Hunt

GTM copilot for AI product founders: 1,200+ actions via MCP

Discussion | Link

2026-10-30 21:17:40 · AI应用,Microsoft,代码生成,Agent智能体,招聘HR
AI 资讯

How I Fight AI Brain Fry

Hacker NewsComments
· 大模型,AI应用,Anthropic,代码生成,Agent智能体,强化学习,招聘HR

Goldman Sachs is pulling Claude Code from its bankers and traders

Hacker NewsComments
2026-11-02T16:13:42.657Z · 大模型,Anthropic,代码生成,招聘HR
AI 资讯

SCDBench: A Benchmark for LLM-Based Smart Contract Decompilers

arXiv cs.AIarXiv:2605.29059v2 Announce Type: replace-cross Abstract: Smart contract decompilation aims to recover high-level source code from bytecode, but evaluating decompilers remains difficult because existing studies use narrow datasets, inconsistent metrics, and limited semantic consistency checks. This gap is increasingly important as large language models (LLMs) begin to generate source-like Solidity that may compile and appear plausible, even when its semantics diverge from the original contract. We introduce SCDBench, a dataset and benchmark methodology for LLM-based smart contract decompilation. The dataset contains 600 real-world Solidity contracts with paired bytecode inputs, ground-truth source code, and replayable semantic checkpoints. SCDBench evaluates decompiler outputs through four cumulative stages: format completeness, compilability, Application Binary Interface (ABI) recovery, and semantic consistency via differential replay. We evaluate Claude Opus 4.7, GPT-5.3-Codex, and GLM-5 in a zero-shot decompilation setting, including GLM-5 variants with and without extended reasoning and a zero-shot compilation-repair setting. The results show that frontier LLMs can often produce structured and compilable Solidity, but achieving semantic consistency remains far from solved: the best-performing frontier model perfectly decompiles only 42/600 contracts. We further show that introducing same-model compilation repair substantially improves performance at modest additional cost. SCDBench establishes a common ground for rigorous, reproducible evaluation and aims to accelerate the development of reliable smart contract decompilers for blockchain security and transparency.
2026-11-09 05:00:00 · 大模型,算力芯片,OpenAI,Google,Anthropic,智谱,代码生成,推理思考,扩散模型,模型评测,端侧AI,招聘HR,论文
AI 资讯

MoMHa: Multi-Objective Optimization of LLM Harnesses over Accuracy, Safety, and Tokens

arXiv cs.AIarXiv:2609.30967v2 Announce Type: replace Abstract: Most work on improving large language models treats accuracy as the sole objective. We argue that the harness, the Python code surrounding the model that constructs prompts, routes calls, and parses outputs, is a first-class design surface whose quality is inherently multi-objective: an accurate harness that refuses no unsafe request, or that consumes an order of magnitude more tokens, is not a good harness. We present Meta-Harness, a system that casts harness design as search over three per-domain objectives (accuracy, behavioural safety, and token cost) solved by an agentic proposer (Claude Code) with full filesystem access to prior harness source, execution traces, and scoring artifacts. Our central finding is that a singlephase joint-reward proposer (MoMHa) outperforms every alternative, including a two-phase "accuracy then tokens" ablation, scalar-only feedback, and an accuracy-only baseline. We evaluate on seventeen domains: seven synthetic capability suites, seven real-world public benchmarks (HumanEval, MBPP, Spider, FEVER, MMLU-Pro, LawBench, NuminaMath), and three U-SafeBench-derived user-specific safety domains, using a 12-model fleet spanning four families. On the synthetic track MoMHa achieves a joint mean of 0.482 versus 0.198-0.422 for ten baselines, winning $7 / 10$ per-domain columns; on the real-world track it scores 0.461 versus 0.377 for the strongest baseline (DSPy), winning 5/7 columns, demonstrating that harness strategies transfer to unseen benchmarks without retraining on 8 of 12 target models. MoMHa attains the highest measured behavioral safety composite (U-SafeBench, 0.781) and uses 95 fewer tokens per example than the two-phase alternative. We will release all harness code, evaluation infrastructure, and crossmodel logs.
2026-11-09 05:00:00 · 大模型,算力芯片,AI应用,Google,Anthropic,Meta,代码生成,Agent智能体,模型评测,提示工程,招聘HR,论文
AI 资讯

When Instruction Files Go Stale: Documentation Debt and Hidden Cost in Coding Agents

arXiv cs.AIarXiv:2611.09802v1 Announce Type: cross Abstract: LLM-based coding agents such as Claude Code are guided by repository instruction files (also called context files or agent configuration files) like AGENTS.md and CLAUDE.md, which are read in every agent session. As any documentation artifact, these files can fall behind the code and become stale (e.g., a test folder moves, but the file still points to the old one). In technical debt terms, such a stale instruction is documentation debt, and its interest is the cost it adds to every later agent session. Evaluations that check only task success can miss this cost, and the stale file can persist. In this study, we ask what leads agents to act on a stale instruction, how it affects their behavior and at what cost, and how they recover. We study three real-world stale instructions from active open-source repositories in depth, each a different kind of guidance: a test command, a code location, and a procedure for adding a component. Agents fix 22 maintenance tasks under three versions of each instruction (left stale, corrected to the current path, and removed), with the code and the task held fixed, and we analyze 732 trajectories of two models at the level of individual tool calls. We find that agents follow stale guidance when it is relevant to their tasks, sometimes even when correct information is available elsewhere. On the stale test command, Claude Sonnet 5.5 needed about two extra tool calls per run, a third more than with the corrected command; in the other cases, we could not detect a difference. Although agents generally recovered from the immediate failures, none repaired the underlying instruction file. Correcting the instructions eliminated operations on obsolete paths, while removing them prevented most. Our findings show that task success alone can hide the costs of documentation debt, highlighting the need to evaluate agent behavior beyond final outcomes.
2026-11-09 05:00:00 · 大模型,AI应用,Anthropic,代码生成,Agent智能体,招聘HR,论文
AI 资讯

DRAFT-Bench: Evaluating Frontier Agents Across the Professional Design Lifecycle

arXiv cs.AIarXiv:2611.08933v1 Announce Type: cross Abstract: Can frontier general-purpose agents understand client intent, explore creative directions, execute revisions, and prepare production deliverables? We introduce DRAFT-Bench (Design Reliability Across Feedback and Tasks), a lifecycle-oriented evaluation of requirement elicitation, creative exploration, constrained editing, iterative revision, and production handoff. Its Requirement-to-Artifact framework links evolving requirements, protected properties, artifact versions, and delivery obligations. We compare native Codex/Astra and Claude Code across 35 design briefs under 43 conditions, alongside a separate 40-scenario RFQ track. Astra completes 43/43 design conditions and Claude 35/43. All scored core runs receive full structural credit, yet on the 28 core conditions scored for both systems, semantic checks flag violations in 11 for Astra and 5 for Claude; these are model judgments, not expert-confirmed failure rates. RFQ coverage is high, but success ordering changes across cohorts. SKU expansion, revision dependencies, and White ink files expose ambiguity among unmet obligations, missing inputs, and evaluator interpretations. These findings separate execution, requirement compliance, and professional acceptance. Unpaired tracks and different tools preclude claims of RFQ-to-delivery success, factory acceptance, or a pure model ranking.
2026-11-09 05:00:00 · 大模型,AI应用,Anthropic,代码生成,Agent智能体,搜索RAG,扩散模型,微调蒸馏,端侧AI,论文
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Rethinking Optimization Methods for Hierarchical Feature Selection

arXiv cs.AIarXiv:2611.08991v1 Announce Type: new Abstract: Rare-event forecasting often depends on interpretable conjunctions: a single keyword, location, or temporal cue may be weak, while their joint occurrence can identify a meaningful precursor. Hierarchical-Task Numerical conjunctive feature Learning (HTNL) selects Numerical Conjunctive Features (NCFs) through a structured sparsity objective. However, the resulting optimization problem is challenging, making optimization design critical to its practical performance. We give a unified diagnosis of four HTNL optimization families: original majorization-minimization (MM), variational quadratic form (VQF), alternating direction method of multipliers (ADMM), and weighted group lasso (WGL). Our analysis distinguishes three key factors that jointly determine HTNL optimization behavior. First, VQF and MM are algebraically equivalent under matched variational updates and optimize a squared-penalty formulation. Second, ADMM optimizes the unsquared objective through consensus splitting; we replace its truncated FISTA proximal solver with a scalar bisection update (ADMM-Bisect) for more accurate treatment of near-threshold blocks. Third, WGL targets the same unsquared objective, while all methods face rapidly decaying active-set admission scores. Across two synthetic datasets and 11 civil-unrest and flu-surveillance tasks, VQF achieves the highest mean AUC with the most compact non-collapsing supports. ADMM-Bisect reduces measured runtime, while calibrated WGL avoids the empty-support failures of its original scale. Synthetic results further show how proximal accuracy, pruning, and search budget affect candidate recovery, especially for longer conjunctions. Code will be made publicly available upon publication.
2026-11-09 05:00:00 · AI应用,代码生成,强化学习,招聘HR,论文,开发者生态

Text is so 2023

Ben's Bites (Substack)

Hi folks,

Personal agent season is still in full-swing. Hark (from Brett Adcock - Figure robots) launched which suggests tasks as one-tap action buttons. Tab came out of stealth at a $300M valuation. Every put an agent in Slack. Meta and Sierra want a standard for how these agents deal with businesses.

My favourite take came from Sonia Baschez (who worked with me on Makerpad years ago!):

Proactive has a downside, though. Shane Mac’s agent posted his personal bank balances in the company Slack, as him. Then said sorry 😂. A community note popped up to flag this may have been a fake post, but I’m not sure…

I’m now carefully trying to think about which agents have access to what, and where that access is. So I’m using Executor (v2). It’s basically one app that you connect all your other apps to, and you can add your skills too. My whole setup was a bit of a mess so I’ve started spring cleaning and will let you know if/when I feel like I have a good setup.


Headlines

ChatGPT now answers with interfaces. GPT-6 and Intelligent UI are rolling out to everyone. Answers can include charts, maps, forms and small tools, like a bill splitter for dinner with friends. Free users get GPT-6 Luna from today. (tweet)

Claude Haiku 5.5 costs ~75% less to run than Haiku 4.5. Anthropic pitches it as a subagent for Opus and Sonnet. Simon has great notes on the model. Max and Team plans also now get monthly API credits to build your own apps and agents. (tweet). Anthropic also expanded their Claude startup program, and people were getting accepted in minutes.

Nano Banana 2.1 is Google’s new image model. Live in AI Studio. But ChatGPT images is still better.

OpenAI released 700+ new maths results from an unreleased model. Someone built a tool to spend your spare tokens on open math problems.

Grok Bot can search and monitor X now. Also, it now uses Claude Opus 5.5 as the primary model. It’ll still delegate tasks to other models based on the complexity.


Share Ben's Bites


My feed

  • AgentHog - stop guessing what your users do: pipe product analytics into Claude and Codex so your agents learn from real user behavior.*

  • How OpenAI’s Dots took over Every: pulling info out of school emails and catching missed Slack messages. I, however, still can’t get Dots to do anything good at all. Currently diving more into Grok Bot (until a completely custom Pi personal agent comes out!)

  • Lauren (poteto) automated Cursor’s release and QA with Grok bot.

  • New ChatGPT plugin: Meetings. It takes notes and helps with follow-ups. Pro and Business, Mac app only.

  • Claude now sits in a sidebar in Google Docs, Sheets & Slides. You approve each edit.

  • Gamma 5 is a full rebuild, so your decks look less like everyone else’s.

  • Figma Agent is out of beta and on all paid plans. It now uses AI credits.

  • Playground - make a game from a prompt. A Google Labs experiment, US only.

  • Rhem - a robot for your parents. It makes calls and books appointments.

  • Someone rebuilt Adobe’s apps with Opus 5.5 and open-sourced them.

  • An open-source Clay alternative for people search inside Claude Code/Codex.

  • OptionAFK - Transcribe large audio/video files on your device. Private and fast.


Try this

Turn a podcast into an interactive explainer. Boris Cherny asked Opus 5.5 to make one for Acquired’s Home Depot episode (a great episode btw).

Make an interactive website companion for [episode link or transcript]. Add a timeline, the key people and the big ideas, with illustrations.

Here was the result:


Subscribe now


Afters

and Theo reckons its way less…


* sponsors who make this newsletter possible :)
Wanna partner with us for the next quarter?
Email us at shanice@bensbites.com or k@bensbites.com

Ben's Bites is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

2026-10-08 13:00:52 · 大模型,算力芯片,AI应用,具身智能,开源,OpenAI,Google,Anthropic,Meta,xAI,代码生成,Agent智能体,扩散模型,强化学习,提示工程,端侧AI,招聘HR,收购并购,榜单评测,开发者生态
AI 资讯

All Interesting Behavior Is Overdetermined

LessWrong

This is an essay about individual rationality technique. It's intended to show that "All interesting behavior is overdetermined" is, under a specific meaning of "overdetermined" that is stronger than just "multicausal", a valuable heuristic for generative understanding and the avoidance of error.

Scott Alexander's 2018 essay The Omnigenic Model As Metaphor for Life is a clear statement of what has become general rationalist doctrine on multicausality. Scott observed that behavioral genetics was difficult because complex human traits involved far more interacting causes than workers expected. He draws this methodological lesson: “We should go from trying to discover ‘the’ cause, to trying to find out which factors we need to create the best polycausal model.” He applies the same reasoning to depression, intelligence, comma, and other complex psychological phenomena. He explicitly warns against looking for one or ten causes when there might be dozens or hundreds.

Scott is, however, talking mainly about contributory causation, when an effect is produced by an accumulation of causes; in his type example, there are probably twenty thousand genes which contribute to intelligence, but no one "intelligence gene". This is not quite the same as overdetermination, which describes a situation in which a complex behavior could be explained with any one of multiple causes, each one in itself sufficient.

When we find a sufficient explanation, or a sufficiently good collection of contributory explanations like Scott's 20,000 genes, there's a strong tendency to think that we can stop there. The rest of this essay is about why we shouldn't.

I first came to understand the importance of overdetermination when I was studying primate ethology. There is a 1987 paper by Hausfater and Takacs, Structure and Function of Hindquarter Presentations in Yellow Baboons (Papia cynocephalus). It examines a striking feature of baboon social behavior: individuals turn their hindquarters toward another animal, sometimes assuming a posture resembling female sexual solicitation, even when the interaction has nothing to do with mating.

The authors analyzed 775 presentation sequences recorded during 603 hours of observation of two groups of wild yellow baboons in Amboseli, Kenya. Taking into account the histories of the individuals involved, they classified these interactions into a number of categories: submissive, sexual, mixed sexual-submissive, affiliiative, and grooming.

The most frequent category of interaction was submissive (intended to avert aggression) at about half. About one-third were sexual or mixed sexual-submissive. The authors proposed that hindquarter presentation originated principally as a sexual solicitation signal, but subsequently acquired an important secondary function: dissipating aggression by a dominant individual.

Hindquarter presentation is overdetermined. For each individual presentation, one of several motivations could be a sufficient causal explanation. In some cases, only a combination of multiple causes ("sexual and submissive") is sufficient explanation. If you were to stop with any one sufficient explanation of the phenomenon as a whole, you couldn't ever come anywhere close to a causal theory of why hindquarter presentation does different things at different times.

Hausfater and Takacs did not use the term "overdetermination". However, it has shown up in similar contexts. In Primate Parenting (1990) we see this: “Maternal behavior is obviously so important to the survival of a species that it has been ‘overdetermined’—that is, driven by multiple behavioral and physiological systems.”

This amplifies Hausfater and Takacs's proposal that the meaning of hindquarter presentation has broadened and shifted under evolutionary selection. It hints at a general principle that took me years to properly formulate:

In adaptive systems evolving under selection and scarcity, behaviors that serve multiple purposes are efficient. Therefore, they are favored. A behavior, once developed, will tend to accumulate uses if it can — that is, when the marginal cost of adding an adaptive function to an existing behavior is less than the cost of evolving and maintaining a new and separate behavior. It will then become overdetermined in the exact sence of having multiple individually sufficient explanations. The adaptive use that was originally primary may not remain so.

Accordingly, it is good practice when investigating an interesting behavior to presume that it is overdetermined and multiply adaptive — not to stop looking after the first sufficient explanation.

There's a sneaky way that this maxim can be interpreted as a definition: a behavior that is not overdetermined is probably not interesting.

Instead of asking “What is the purpose of this behavior?”, we should get used to asking ourselves “What is the collection of problems that this behavior solves?”.

Now consider human behavior in this light. Humor can establish social bonds, signal intelligence, negotiate status, relieve tension, and test social boundaries. Generosity can provide direct benefits, establish reciprocal obligations, signal wealth, advertise trustworthiness, and reinforce group cohesion. Intellectual curiosity can produce useful knowledge, provide entertainment, establish status, discover allies, and even serve as a means of courtship.

A rationalist who finds one plausible evolutionary explanation for any of these hasn't necessarily explained the behavior in full. They've identified one of its functions. And the functions need not be consciously represented. A person can be genuinely motivated by curiosity, while that curiosity also serves social and reproductive functions.

I will end this essay with a question for the reader to consider: What collection of problems does rationality technique solve?
















Discuss
2026-11-09 03:45:06 · 大模型,OpenAI,Google,Meta,Microsoft,代码生成,推理思考,招聘HR,网络安全,收购并购,榜单评测,开发者生态

Notes on Take-Off Speeds and RSI

AI Alignment Forum

I created these notes for use when I facilitate AI safety courses. I'm sharing these notes early because this issue is topical and I suspect it is better to make an imperfect version available early rather than a much more polished version available later.

Please note: some of this content was taken from Additional Course Context. I decided recently that the content on take-off speeds really belonged in the same document as the content on RSI, so I'll be releasing an new version of Additional Course Context at some point (to avoid spamming LW, it'll probably be Substack only; none of my work is paywalled). Some of the other content was taken from Notes on “We’re in Triage Mode for AI Safety”. This Miles' article has been removed from the governance course, so I was looking for a new home for this content. I wanted to include it in a document that was intended to be shared in all the courses I facilitate.

Take-off Speed from Take-off Speeds Rules Everything Around Me:

image.pngWhilst there isn’t a strict definition of fast vs. slow takeoff, possibly the most influential definition comes from Paul Christiano (2018):

There will be a complete 4 year interval in which world output doubles, before the first 1 year interval in which world output doubles. (Similarly, we’ll see an 8 year doubling before a 2 year doubling, etc.)

At risk of oversimplifying, Paul argues that we should prima facie expect a slow takeoff because someone will generally figure out how to produce a “slightly worse” version of AI before someone figures out how to build the better version (he claims this is supported by the historical record of technological development overall).

Yudkowsky disagrees (2021), criticising the intuition that “design precursors with 95% of the technology should at least have 10% of the impact. He writes: "An awful lot of my model of deployment lag is adoption lag and regulatory lag and bureaucratic sclerosis across companies and countries” and “My mainline view is that growth stays at 5%/year and then everybody falls over dead in 3 seconds and the world gets transformed into paperclips"

How should we characterise progress up until now?

Eliezer recently wrote:

If Hanson or Christiano or Karnofsky etc etc believed this was what a "slow takeoff" looked like, they failed to convey that to their reassured, cheerful audience.

Tom Davidson responded:

If you thought this was what fast takeoff looked like, you also failed to convey that.

For reference, Eliezer previously defined a fast as meaning, “a timescale of weeks or hours rather than years or decades”.

“Bumpy” intelligence explosions

Forethought defines an additional scenario. For example, imagine if AI dramatically accelerates algorithm progress for a while, but then this plateaus whilst we figure out how to make robots that can automate the construction of chip fabs.

image.png(Diagram note: the types of intelligence explosion will be explained in the next section)

In contrast, Arvind Narayanan (of AI as a Normal Technology) argues any RSI we do achieve will be narrow (that is jagged): ie. progress on verifiable tasks will continue “for a few more years” or even accelerate whilst progress on unverifiable tasks more or less remains “frustratingly hard”.

Richard Ngo also expects jaggedness to persist, as opposed to everything “going vertical”. Why? Because of a poorly understood “divergence” between “measured capabilities” and “real-world impacts” (nonetheless he asserts “things are going to look much weirder than almost anyone expects”):

image.png

Getting into the weeds

Whilst this is arguably less relevant these days, take-off speed is distinct from timelines. Take-off speeds are about how fast an intelligence explosion occurs if/when it happens whilst timelines are about when a particular milestone is achieved. Both slow-takeoff/short-timelines and fast-takeoff/long-timelines are conceptually possible.

Types of “Intelligence Explosion”:

Forethought examines three k

2026-11-08 13:39:02 · 算力芯片,AI应用,具身智能,OpenAI,Google,代码生成,推理思考,扩散模型,强化学习,招聘HR,榜单评测
AI 资讯

OpenAI Codex

OpenAI NewsWe’ve created an improved version of OpenAI Codex, our AI system that translates natural language to code, and we are releasing it through our API in private beta starting today.
· OpenAI,代码生成,招聘HR,开发者生态
AI 资讯

New GPT-3 capabilities: Edit & insert

OpenAI NewsWe’ve released new versions of GPT-3 and Codex which can edit or insert content into existing text, rather than just completing existing text.
· 大模型,OpenAI,代码生成,扩散模型
AI 资讯

Powering next generation applications with OpenAI Codex

OpenAI NewsCodex is now powering 70 different applications across a variety of use cases through the OpenAI API.
· OpenAI,代码生成,招聘HR,开发者生态
AI 资讯

Using GPT-4o reasoning to transform cancer care

OpenAI NewsColor Health is working with OpenAI to pioneer a new way of accelerating cancer patients’ access to treatment. Their new Cancer Copilot application uses GPT-4o to identify missing diagnostics and create tailored workup plans, enabling healthcare providers to make evidence-based decisions about cancer screening and treatment.
2024-06-17 04:15:00 · 大模型,AI应用,OpenAI,Microsoft,代码生成,推理思考
AI 资讯

Introducing Codex

OpenAI News
· 代码生成
AI 资讯

Addendum to o3 and o4-mini system card: Codex

OpenAI NewsCodex is a cloud-based coding agent. Codex is powered by codex-1, a version of OpenAI o3 optimized for software engineering. codex-1 was trained using reinforcement learning on real-world coding tasks in a variety of environments to generate code that closely mirrors human style and PR preferences, adheres precisely to instructions, and iteratively runs tests until passing results are achieved.
2025-05-16 08:00:00 · AI应用,OpenAI,代码生成,Agent智能体,推理思考,强化学习
AI 资讯

Pioneering an AI clinical copilot with Penda Health

OpenAI NewsOpenAI and Penda Health debut an AI clinical copilot that cuts diagnostic errors by 16% in real-world use—offering a new path for safe, effective AI in healthcare.
· AI应用,OpenAI,Microsoft,代码生成
AI 资讯

How Cursor uses GPT-5

OpenAI NewsLearn how Cursor uses GPT-5.
· 大模型,AI应用,OpenAI,代码生成
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