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Models that plan, call tools and finish multi-step tasks on their own — from Claude Code and Manus to agent frameworks and benchmarks.

1,465 picksRelated topicsMCP & tool use AI coding Reasoning

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1001–1020 of 1,465

May 13Wednesday

The Verge · AIMay 13, 01:00

Gemini’s Latest Updates Are All About Controlling Your Phone

Google announced Gemini Intelligence at its pre-I/O Android showcase, placing Gemini in Chrome on Android, autofill suggestions, and in-app actions; the RSS snippet does not disclose supported device models, rollout timing, or pricing.

Why it matters: HKR-H/K/R all pass because Gemini is moving into Android phone control across Chrome, autofill, and app actions. Missing device list, launch timing, and pricing keep it in the 72–77 product-update band.

Take

Google is trying to use Android permissions to cover Gemini’s product gap. Chrome on Android, autofill suggestions, and in-app actions are strong surfaces because they sit near accounts, forms, passwords, and app intent. That is far better than another assistant bubble. But the disclosed hook is thin: Ben Greenwood says “our most advanced Android devices,” while supported models, rollout timing, and pricing are not given.

I don’t buy the naming layer. Gemini, Gemini Live, Circle to Search, Pixel-only features, and now Gemini Intelligence make the product map messier. Apple Intelligence at least drew a hard device boundary, even if the rollout was slow. Google has the better distribution hand on Android, but it needs measurable app-action reliability, not another umbrella brand that sounds like an internal deck title.

TechCrunch · AIMay 13, 01:00

Google brings agentic AI and vibe-coded widgets to Android

Google is adding Gemini Intelligence to Android, and the RSS snippet only discloses Gboard-based dictation and form-filling capabilities; the post does not disclose launch timing, supported devices, pricing, or technical implementation details.

Why it matters: HKR-H/K/R pass because this is a Google Android platform AI update with named features. Missing rollout timing, device scope, and pricing keep it at the low featured band, not a must-write release.

AI HOT (Curated Pool)May 13, 00:24

Code w/ Claude SF 2026: Building on Exponential AI Growth

Anthropic expanded developer tooling at Code w/ Claude SF 2026: Claude Code rate limits doubled, Claude Opus API limits increased, and hosted agents on the Claude platform added four functions, including memory review, multi-agent delegation, output criteria, and webhooks.

Why it matters: Anthropic ships a substantive Claude Code update with concrete numbers and feature additions; HKR-H/K/R all pass. This is strong dev-tool news, not a flagship model release, so it fits the 78–84 band.

May 12Tuesday

AI HOT (Curated Pool)May 12, 23:15

Install the official Codex plugin in Claude Code

The author describes installing OpenAI’s official Codex plugin in Claude Code via the plugin marketplace: add the repository, install the plugin, reload, and configure it, then use it to build a Skill where Claude Code handles reasoning and Codex acts as moderator.

Why it matters: HKR-H/K/R all pass: cross-stack plugin use is clickable, the install path is concrete, and it matters to AI dev workflows. It stays low-featured because this is a tutorial-style tip, not a model or platform release.

Take

Claude Code installing OpenAI’s official Codex plugin is small, but the direction is sharp: multi-model agents are leaving paper diagrams and entering the plugin marketplace beside the IDE. The snippet gives four concrete steps: add repository, install, reload, configure. It also names HeavySkill’s “rethinking” setup: multiple models reason independently, then Codex moderates the synthesis.

I don’t buy the implied “quality goes up” story without receipts. Multi-model voting has long hit three walls: cost, latency, and correlated errors. This Claude Code-reasons, Codex-moderates Skill gives no task set, failure rate, or token bill. The wild part is distribution: an OpenAI plugin now sits inside an Anthropic developer workflow, and that is closer to real usage than another benchmark table.

r/LocalLLaMAMay 12, 22:53

Local LLM Autocomplete and Agentic Coding on a Single 16GB GPU + 64GB RAM

Reddit user grumd runs Qwen2.5-Coder-7B Q6 for autocomplete and Qwen3.6-35B-A3B Q8 for agentic coding on one RTX 5080 with RAM offloading; the post reports about 145k context, 56GB RAM used with other apps open, and Qwen3.6-35B-A3B speed of tg128 at 35.29 tokens/s.

Why it matters: HKR-H/K/R all pass: a named first-person local coding experiment with concrete model, quantization, context, and throughput data. Source is a single Reddit post without replication or comparisons, so it stays in the low featured band.

Google DeepMindMay 12, 22:40

Google DeepMind publishes Co-Scientist multi-agent research system

Google DeepMind published Co-Scientist research in Nature, introducing a Gemini-based multi-agent AI system that iteratively generates, debates and evolves new hypotheses for complex scientific problems.

Why it matters: The post discloses the system's three-stage collaboration mechanism and deployment cases at several labs, showing how AI takes part in scientific hypothesis generation.

Hacker News front pageMay 12, 22:24

Show HN: Statewright – Visual State Machines for More Reliable AI Agents

Statewright uses a Rust state-machine engine to constrain Claude Code tool access, iterations, transitions, and guards; the post says 13–20B models improved consistently on real SWE-bench tasks, but it does not disclose benchmark scores, sample size, or the exact evaluation protocol.

Why it matters: HKR-H/K/R all pass: the state-machine constraint is a clear agent-reliability hook with a testable SWE-bench claim. Exact scores and reproduction details are not disclosed, so it stays just above the featured threshold.

Take

Statewright has the right instinct, but the evidence does not support the “make AI agents reliable” headline. It uses a Rust state-machine engine to constrain Claude Code tool access, iteration counts, transitions, and guards. That is real engineering, not another prompt wrapper. The weak spot is the eval: the post says 13–20B models improved consistently on real SWE-bench tasks, but gives no score, sample size, task split, or failure taxonomy.

I buy the mechanism more than the claim. A lot of coding-agent failure is not “the model cannot code”; it is uncontrolled shell use, file edits, loops, and bad recovery paths. State machines can cut those tail risks. But without SWE-bench numbers, Claude Code versioning, and a reproducible protocol, this reads like a good seatbelt for agents, not evidence that the driver got better.

AI HOT (Curated Pool)May 12, 20:26

Exporting consumer data for personalized AI Agent services

The author surveyed export methods across 5 consumer platforms: Taobao supports exports, JD.com needs a Codex-built Chrome extension, Ele.me can provide Excel exports by request, Meituan Waimai has no method, and JD.com plus Dianping tools are open sourced.

Why it matters: HKR-H/K/R pass: the post has a concrete personal-agent hook, 5-platform data, and open-source JD/Dianping tools. It stays at the featured floor because this is a practitioner experiment, not a platform release.

TechCrunch · AIMay 12, 19:30

AI Voice Startup Vapi Hits $500M Valuation After Winning Amazon Ring Over 40 Rivals

Vapi reached a $500 million valuation after Amazon Ring chose its AI voice platform over 40 rivals, and the RSS snippet says its enterprise business has grown tenfold since early 2025 as companies move support and sales calls to AI agents.

Why it matters: HKR-H/K/R all pass: Amazon Ring’s 40-rival selection and Vapi’s $500M valuation give concrete signal. Still a startup financing/customer win, not a model or platform release, so it sits at the featured threshold.

Take

Vapi’s strongest signal is not the $500 million valuation; it is Amazon Ring choosing it from a 40-vendor field. Voice agents have had plenty of slick demos. The hard part is live support and sales traffic: latency, barge-in, escalation, compliance, and failure recovery. The RSS snippet gives one concrete number: enterprise business has grown 10x since early 2025. It does not give round size, ARR, contract scope, or Ring call volume.

I’m skeptical of the “40 rivals” framing. Enterprise buying teams often turn a long vendor list into a victory lap, when the deciding factor is integration speed, SLA terms, or one awkward internal system. Compared with ElevenLabs’ audio-generation center of gravity and Retell/Bland-style call automation, Vapi only gets a different label if Ring is using it in real production traffic, not a contained pilot.

Xinzhiyuan · WeChatMay 12, 19:26

OpenAI releases GPT-Realtime-2, described as a GPT-5-level reasoning audio model

OpenAI released GPT-Realtime-2 alongside Realtime-Translate and Realtime-Whisper, with a 128K context window, five reasoning-effort levels, and API pricing of $32 per million input tokens and $64 per million output tokens.

Why it matters: HKR-H/K/R all pass: realtime audio reasoning is a strong hook; 128K context, five reasoning levels, and $32/$64 per 1M tokens add substance; voice-agent cost and stack choices hit practitioners. This is a same-day OpenAI product update.

Take

OpenAI is pushing real-time audio into GPT-5-class territory, but the first gate is cost. The disclosed package has GPT-Realtime-2 with a 128K context window, five reasoning-effort levels, and API pricing at $32 per million input tokens and $64 per million output tokens. The WeChat body is blocked by verification, so latency, concurrency limits, and audio-token accounting are not visible here.

That price does not invite mass migration from cheap support bots. It selects for high-value voice workflows first: real-estate search, creator tooling, sales, medical intake if compliance exists. Whisper made transcription feel like infrastructure; Realtime-2 is selling a reasoning voice loop. Plenty of cheaper voice stacks will sound good in demos. Production buyers will care about tail latency and interruption handling more than the “GPT-5-level” label.

Xinzhiyuan · WeChatMay 12, 19:26

The Largest Single Industrial Product in History Is Entering Mass Production in China

AgiBot says it had shipped 10,000 general-purpose embodied robots by the end of March, and its humanoid robots worked eight continuous hours on a Nanchang 3C production line, completing 2,283 tasks with zero errors under formal line-cycle requirements.

Why it matters: HKR-H/K/R all pass: AgiBot gives unit and factory-run numbers with clear robotics deployment resonance. The score stays in 78-84 because the key claims are company-sourced, with no third-party validation or cost data disclosed.

Take

AgiBot is trying to move humanoids out of demo grammar and into factory KPI grammar. The numbers are aggressive: 10,000 general-purpose embodied robots shipped by end-March, plus eight continuous hours on a Nanchang 3C line with 2,283 tasks and zero errors. That forces competitors to answer capacity, yield, service, and deployment cost, not just gait videos.

I don’t buy the word “general-purpose” without the missing test sheet. The WeChat body is blocked by verification, so task definitions, line-cycle timing, human fallback, changeover cost, and the verifier are not visible here. Boston Dynamics spent years showing better robots than most factories could absorb. If AgiBot really ran formal 3C line requirements for eight hours, the product milestone is less humanoid intelligence than becoming boring enough for process engineers to schedule.

Latent SpaceMay 12, 12:33

Thinking Machines' Native Interaction Models: TML-Interaction-Small 276B-A12B Advances Realtime Voice

Thinking Machines released TML-Interaction-Small, a 276B-parameter MoE model with 12B active parameters, and the post says it advances realtime voice through 200ms time-aligned microturns, encoder-free early fusion for audio and images under 200ms, and benchmark wins over GPT-Realtime-2 and Gemini 3.1-Flash.

Why it matters: HKR-H/K/R all pass: TML-Interaction-Small gives architecture, active parameters, 200ms interaction, and named rivals. Benchmarks still need replication, but a real-time voice SOTA claim is same-day material.

Financial Times · TechnologyMay 12, 12:00

Amazon staff use AI tool for unnecessary tasks to inflate usage scores

Amazon’s in-house MeshClaw tool lets employees delegate work to AI agents and raise their position on the company’s AI leaderboard; the post does not disclose the number of staff involved, the scoring rules, or the specific unnecessary tasks.

Why it matters: FT gives a concrete Amazon AI-adoption gaming story, clearing HKR-H/K/R. Missing participant count, scoring rules, and task examples keep it at the featured threshold, not a must-write item.

Take

Amazon’s MeshClaw story hits the chronic enterprise AI failure: measuring “how much AI was used” instead of “how much work disappeared.” The title says employees handed unnecessary tasks to AI agents to climb an internal leaderboard. The article body is paywalled here, so staff count, scoring rules, and task categories are not disclosed. That limits the scale claim, but not the mechanism risk.

I’m not surprised. Microsoft Copilot, Google Workspace, and Salesforce Einstein rollouts have all run into the same split: activation metrics look clean, workflow change is messy. If Amazon used a leaderboard to push agent usage, it basically invited Goodhart’s Law into the org chart. Agent adoption is not hurt only by low usage. It also gets poisoned when workers create fake work to prove they are “AI-native,” then contaminate telemetry, ROI math, and model feedback loops.

QbitAI · WeChatMay 12, 11:55

OpenClaw quietly updates with Peekaboo v3 for Mac computer use

OpenClaw-related Peekaboo v3 adds Mac agent capabilities for pixel-level screenshots, UI position reading, clicks, text input, hotkeys, scrolling, and drag-and-drop, with MCP server integration for Cursor, Claude Code, and Codex.

Why it matters: HKR-H/K/R all pass: Peekaboo v3 adds Mac GUI perception and action primitives plus MCP access for Cursor, Claude Code, and Codex. This is a useful open-source agent-tooling update, not a model-level event, so it sits in the low featured band.

Take

Peekaboo v3 matters because it attacks the interface layer, not because the agent story got a cuter mascot. The summary gives a concrete stack: pixel-level screenshots, UI position reads, clicks, text input, hotkeys, scrolling, drag-and-drop, then MCP access from Cursor, Claude Code, and Codex. Desktop agents do not fail because models lack prose. They fail because screen state, coordinates, actions, and recovery stay brittle.

The WeChat body is blocked, so permissions, latency, sandboxing, audit logs, and failure recovery are not disclosed. I would be cautious here: once an MCP tool can click and type, accidental payments, file deletion, and rogue messages are product risks. Compared with Anthropic’s Computer Use path, OpenClaw/Peekaboo looks like a local Mac control layer for developer experimentation. Good direction; not production proof.

Computing Life · YageMay 12, 08:00

How AI Caused and Fixed My Insomnia

The author used AI to build a HealthKit export app in about 5 minutes and run multivariate regression, finding that the last post-dinner AI usage time correlated negatively with sleep duration; after avoiding AI at night, average sleep increased by 1 hour and 40 minutes.

Why it matters: HKR-H/K/R all pass: a first-person quantified experiment links post-dinner AI use to shorter sleep, then reports +1h40m after stopping. Personal-blog scope keeps it below major industry-update territory.

Take

This is not wellness content; it is a heavy AI user treating himself like a production system. He used AI to build a HealthKit export app in 5 minutes, pulled Apple Watch, caffeine, alcohol, glucose, blood pressure, and hourly activity data, then ran multivariate regression. The strongest negative correlate was not coffee. It was the last post-dinner AI session. After cutting night AI use, average sleep rose by 1 hour and 40 minutes.

I buy the direction, not the strength. This is one person over dozens of days, and the piece does not give coefficients, p-values, or the control table. Causality stays in self-experiment territory. The useful mechanism is sharper: AI lowers the cost of coding, cleaning, and modeling, so people keep doing high-cognition work at night. The Cursor-style productivity story rarely prices that in: tasks do not disappear; the stopping point moves later.

AI HOT (Curated Pool)May 12, 08:00

What Parameter Golf Taught Us About AI-Assisted Research

OpenAI’s Parameter Golf brought together over 1,000 participants and more than 2,000 submissions to test AI-assisted machine learning research, coding agents, model quantization, and model design under strict parameter constraints.

Why it matters: OpenAI’s Parameter Golf recap clears HKR-H/K/R with a concrete contest, 1,000+ participants, and 2,000+ submissions. It is research/benchmark signal, not a model or product launch, so 78 fits the lower featured band.

The Verge · AIMay 12, 07:05

OpenAI just released its answer to Claude Mythos

OpenAI launched Daybreak, a security initiative that uses the Codex Security AI agent released in March to model an organization’s code, validate likely vulnerabilities, and automate detection of higher-risk issues before attackers find them.

Why it matters: HKR-H/K/R all pass: Daybreak has a rivalry hook, concrete agent workflow, and code-security resonance. It is narrower than a model or ChatGPT capability release, so it stays in the 78–84 band.

Take

OpenAI is pulling the security story back into workflows: Daybreak uses the March Codex Security AI agent to model code, validate likely vulnerabilities, and automate detection of higher-risk issues. That is a more buyable enterprise shape than Claude Mythos’ “too dangerous to release” aura, because it maps onto threat models, attack paths, and validation loops security teams already run.

I don’t buy the clean “answer to Mythos” framing. The snippet gives no false-positive rate, patch success rate, supported languages, deployment model, or evidence that Daybreak closes the loop beyond detection. Security agents don’t win by finding scary bugs in a demo; they win when engineers trust the tickets enough to stop rerunning everything by hand. OpenAI picked the less theatrical lane here, which is probably the stronger one.

Computing Life · YageMay 12, 07:00

How AI Caused and Fixed My Insomnia

The author used AI to build a HealthKit export tool and run multivariate regression, found that post-dinner AI use correlated negatively with sleep duration, and added 1 hour 40 minutes of average nightly sleep after avoiding AI for several weeks.

Why it matters: HKR-H/K/R all pass: the personal reversal is clickable, the HealthKit/regression setup adds testable detail, and sleep loss hits AI practitioners directly. Scope is anecdotal, so it stays at the featured floor.

Take

The sharp part is not that AI diagnosed insomnia; it is that AI turned “light computer use after dinner” into sustained cognitive labor. The author spent five minutes generating a HealthKit export app, pulled Apple Watch, iPhone, Withings-style health streams, then ran multivariate regression. The strongest negative correlate was the last evening AI session; after several weeks off post-dinner AI, average sleep rose by 1 hour 40 minutes.

I buy the mechanism more than the causal claim. This is one person, a few weeks, and the article does not give p-values, coefficients, or a randomized crossover. Still, every heavy agent user knows the loop: run several sessions, poll outputs, split tasks again, keep reading. There is no idle wait. Compared with YouTube or games, late-night AI looks less like screen time and more like sneaking a strategy meeting into bed.

The Verge · AIMay 12, 06:19

Here’s What Mira Murati’s AI Company Is Up To

Thinking Machines announced work on “interaction models” that continuously take in audio, video, and text and respond or act in real time; the post does not disclose model size, release timing, pricing, or the final product format.

Why it matters: HKR-H/K/R all pass, but the body lacks parameters, launch timing, and product form. This is a high-interest startup direction reveal, not a usable model release, so it stays at the top of the 72–77 band.

Take

Thinking Machines is selling an interaction frame before it shows a product. The company says “interaction models” will continuously take in audio, video, and text, then think, respond, and act in real time. It gives no model size, launch date, pricing, latency target, API shape, or final product format. That gap matters because real-time multimodal systems live or die on interruption handling, context buffering, tool permissions, and response delay.

I read this as Murati avoiding another chatbot race. OpenAI already made real-time voice feel tangible with GPT-4o, and Google has been pressing the same surface with Gemini Live and Project Astra. Without a low-latency demo or developer interface, “interaction models” is a recruiting and positioning phrase first, not yet a technical claim practitioners can test.

Sinocism (Bill Bishop)May 12, 06:13

Trump China Visit; China’s Next Generation Industrial Policy; Standardizing AI Agents

China’s CAC, NDRC, and MIIT issued an implementation document on AI agent standardization, targeting privacy leakage, unauthorized actions, and loss of behavioral control from high-autonomy, high-permission agents, while tying the work to a 2027 target for new intelligent terminals and AI agent adoption above 70%.

Why it matters: HKR-H/K/R all pass: the China agent-policy hook is concrete, with a 2027 >70% target and named autonomy/permission risks. It clears featured, but it is policy guidance rather than a major model or product launch.

Take

China’s CAC, NDRC, and MIIT tied agent standardization to a 2027 adoption target above 70%, which makes this a device-policy story, not a chatbot-safety memo. The named risks are specific: privacy leakage, unauthorized actions, and loss of behavioral control. That wording assumes agents will hold real permissions across phone assistants, on-device stewards, and cloud agents.

I think enforcement will land harder than the document’s bland title suggests. China’s pattern on recommendation algorithms, deep synthesis, and generative AI was clear: registry first, safety review next, product teams adapt later. Agents touch a worse surface area: purchases, app control, enterprise data, and identity flows. OpenAI and Anthropic are still selling guardrails as product trust. Beijing is making guardrails part of the industrial rollout target.