MCP & tool use

How models connect to the outside world: the MCP ecosystem, function calling and tool integrations.

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May 10Sunday

QbitAI · WeChatMay 10, 11:52

Zhejiang University introduces AdaMARP, an AI role-playing framework with scene direction

Zhejiang University and Tencent Youtu proposed AdaMARP for immersive role-playing, using a four-channel message format and a scene manager; its data pipeline includes 81 literary works, 20 synthetic themes, and AdaptiveBench with 100 evaluation seeds.

Why it matters: ACL 2026 role-play agent work brings four-channel messaging, a scene manager, and an 81-book dataset, clearing HKR-H/K. Narrow use cases and missing open-source or production evidence keep it at threshold featured.

Take

AdaMARP is useful because it turns role-play into trainable interfaces, rather than asking one prompt to carry the whole story. The four channels are Thought, Action, Environment, and Speech. The scene manager has five actions: init_scene, pick_speaker, switch_scene, add_role, and end. That is a cleaner engineering split than Character.AI-style long chat, because speaker choice and scene changes become explicit events.

I don’t buy the “immersive role-playing is solved” framing. The data pipeline uses 81 literary works and 20 synthetic themes, while AdaptiveBench has only 100 evaluation seeds and relies on model-based scoring. The hard failures in role-play are long memory, adversarial user turns, and world-state consistency across hours. The article gives no human blind test, retention data, pricing, or stress test over long sessions. ACL acceptance validates the research setup, not the product experience.

Computing Life · Share · YageMay 10, 08:00

How Anthropic Trained Computer Use: Reading Its Data Pipeline Through a Patent

Anthropic’s patent describes the Computer Use training pipeline: it captures user actions, uses a transformer to infer action intent, and applies a stronger model for synthetic expansion, turning raw UI operations into reasoning data.

Why it matters: HKR-H/K/R all pass: the patent angle is clickable, the three-step data pipeline is concrete, and agent builders care. It is analysis, not an official release or reproducible artifact, so 76 fits the featured threshold.

May 9Saturday

AI HOT (Curated Pool)May 9, 23:36

YC CEO Open-Sources Personal AI OS GBrain for a Compounding Second Brain

Y Combinator CEO Garry Tan open-sourced GBrain, a personal AI operating system that processed more than 20 books in five months and manages over 100,000 pages of structured knowledge.

Why it matters: HKR-H/K/R pass: Garry Tan’s open-source personal knowledge system has a notable-user hook and three concrete usage numbers. Missing repo activity, architecture detail, and tests keep it at the featured threshold.

Take

GBrain will get over-read because Garry Tan shipped it, but the useful question is narrower: does it glue memory, retrieval, and task routing better than a well-kept personal workflow? The hard hooks are real: 20-plus books processed in five months, more than 100,000 structured pages, and a three-layer setup with routing, composable skills, and data. Book Mirror and Meeting Prep are concrete enough to copy.

I don’t buy the “personal AI operating system” framing yet. An OS has permissions, durable state, failure recovery, and execution across apps. The snippet shows knowledge ingestion and meeting prep. That is closer to an open-source second-brain agent template. Compared with the Mem/Rewind/Notion AI wave, the advantage is dogfooding by someone with brutal information load. The missing pieces are repo activity, storage format, task evals, and privacy boundaries.

AI HOT (Curated Pool)May 9, 22:08

Peekaboo 3.0 Launches With Action-First macOS Control and UI Detection

Peekaboo 3.0 is now live with action-first macOS control, unified screenshots and UI detection, cleaner JSON exchange between CLI and MCP, and improved snapshots; the post does not disclose pricing, model choices, or release timeline beyond the 3.0 launch.

Why it matters: HKR-H/K/R all pass for a concrete desktop-agent tooling update. Score stays at the featured floor because pricing, model details, and adoption data are not disclosed.

Take

Peekaboo 3.0’s useful move is not “Mac control”; it packages the glue where desktop agents break. The post names four concrete pieces: action-first macOS use, unified screenshots plus UI detection, cleaner JSON between CLI and MCP, and better snapshots. That is the ugly layer: coordinate drift, state capture, reproducible failures, and tool-return shape.

I buy half the author’s claim that last year’s models were not ready and now are. Claude Computer Use and Operator-style demos proved models can click through screens. They also showed desktop control stays much flakier than browser automation. If Peekaboo becomes the local macOS layer, it plays a Playwright-like role for computer-use agents. But pricing, model choices, permission boundaries, and rollback behavior are not given, so calling this a full agent product is premature.

r/LocalLLaMAMay 9, 19:57

80 tok/sec and 128K context on 12GB VRAM with Qwen3.6 35B A3B and llama.cpp MTP

Reddit user janvitos ran Qwen3.6-35B-A3B-MTP-GGUF with a llama.cpp MTP PR on an RTX 4070 Super. The posted benchmark shows 69.2-81.9 tok/s, 0.694-0.947 draft acceptance, 131072 context, and a -fitt 1536 setting that reserves 1536 MB for the draft model and KV cache.

Why it matters: HKR-H/K/R all pass with concrete single-user benchmark data and reproducible settings. Source is one Reddit post, so verification is thin; this lands above featured threshold, not in must-write range.

AI HOT (Curated Pool)May 9, 16:40

Using Codex to debug and verify fixes in parallel

The author uses Codex in temporary crabbox environments to recreate bug states, verify failures, apply fixes, and re-verify them, while running 10 sessions in parallel to avoid local state pollution and speed loss.

Why it matters: HKR-H/K/R all pass, but this is a single first-person workflow note, not a product release or benchmark. The 10-session Codex/crabbox setup earns featured-level practical signal, near the lower band.

Take

Codex looks far more useful here because the agent runs inside disposable environments, not the developer’s machine. The author has Codex recreate the bug state in temporary crabbox instances, verify the failure, patch it, re-verify it, and run 10 sessions in parallel. That directly attacks two boring blockers: polluted local state and serial debugging latency.

I’ve always thought coding agents stall less on patch generation than on executable, reproducible feedback. Devin, Cursor, and Claude Code all hit the same wall: the model can write a diff, but a dirty environment makes the signal mushy. The snippet gives no project size, bug class, or cost for 10-way parallelism, so I wouldn’t sell this as a general solution. As a debugging pattern, though, it is much stronger than another “AI wrote my code” demo.

Xinzhiyuan · WeChatMay 9, 12:05

CUHK Open-Sources ArbiterOS Agent Governance Kernel With 92.95% High-Risk Interception

CUHK CURE Lab open-sourced ArbiterOS, an agent runtime governance kernel that intercepts, parses, governs, and observes actions before execution, raising high-risk step interception on OpenClaw tasks from 6.17% to 92.95%.

Why it matters: HKR-H/K/R all pass: the story has a sharp execution-control hook, a concrete 6.17%→92.95% result, and clear agent-safety resonance. It is a strong open-source research tool, not a top-lab model release, so it stays in the 78–84 band.

Take

ArbiterOS pushes agent safety back into systems engineering, which is healthier than training another safety classifier. The evidence is concrete: on OpenClaw high-risk tasks, high-risk step interception rises from 6.17% to 92.95%; on already successful Agent-SafetyBench and AgentDojo attacks, real-time interception exceeds 94%. The mechanism matters: pre-execution interception, structured actions, ABAC-style policies, and dynamic taint tracking.

I’m skeptical of the 100% timely-warning claim on WildClawBench; the snippet gives no task mix or false-positive rate. Agent safety has spent a year over-indexing on whether text “looks dangerous.” ArbiterOS moves the fight to tool calls, data provenance, and target objects. In production, the hard part will be policy authoring and upkeep, not the interceptor.

Xinzhiyuan · WeChatMay 9, 12:05

NVIDIA, AMD, and Intel Back RadixArk’s $100M Seed Round

RadixArk announced a $100 million seed round on May 5 at a $400 million post-money valuation, led by Accel and co-led by Spark Capital, with participation from NVentures, AMD, MediaTek, Databricks, and other investors tied to AI infrastructure.

Why it matters: HKR-H/K/R all pass: a $100M seed round, $400M post-money valuation, and chip/data investors create an AI-infra rivalry angle. It remains a single-company funding item with no product benchmarks or customer data, so it sits near the featured floor.

Take

RadixArk turned an open-source inference stack into hardware politics with a $100 million seed round. The $400 million post-money valuation matters less than the cap table: NVentures, AMD, MediaTek, Databricks, and Intel CEO Lip-Bu Tan as an angel. That reads like every vendor buying an option on SGLang before inference routing hardens around a rival stack.

The concrete hooks are strong: SGLang has 27K+ GitHub stars and the article claims deployment across 400,000 GPUs; Miles also supported DeepSeek-V4 RL training on launch day. I’m not taking the “trillions of tokens” claim at face value, because the source gives no third-party measurement, revenue, hosted product, or enterprise contract detail. vLLM already showed that open-source adoption is only the first inning. The paid game is SLA, managed serving, and ruthless hardware-specific performance work.

QbitAI · WeChatMay 9, 11:18

Google AI Co-Mathematician Sets FrontierMath Tier 4 SOTA

Google DeepMind released AI Co-Mathematician, an asynchronous agent workspace for math research, and answered 23 of 48 private FrontierMath Tier 4 problems, scoring 48% under 48-hour, no-token-limit conditions versus GPT-5.5 Pro at 39.6%.

Why it matters: HKR-H/K/R all pass: the story has a hard benchmark number and a concrete research hook. No disclosed product access or cross-source cluster, so it stays at the top of 78–84 rather than p1.

Take

DeepMind moved math AI from single-shot answering into a research workspace. Gemini 3.1 Pro alone scored 19% on FrontierMath Tier 4; AI Co-Mathematician answered 23 of 48 private problems, reaching 48% with parallel research threads, reviewer agents, literature search, code execution, and persistent failed hypotheses. The gain is the environment catching errors and preserving state, not a sudden leap in the base model.

I buy the Marc Lackenby group-theory case more than the SOTA headline. The AI’s first proof was wrong, a reviewer agent found the hole, and the human supplied the missing step. That is a credible collaboration story. The benchmark caveat is large: 48 hours per problem, no token limit, and Google’s own infrastructure. Beating GPT-5.5 Pro’s 39.6% under those conditions matters; selling it as everyday autonomous math competence would be spin.

AI HOT (Curated Pool)May 9, 05:00

Claude Code Practice: The Effectiveness of HTML Output

Thariq Shihipar recommends requesting HTML output from Claude, and the post cites GPT-5.5 generating an interactive Linux vulnerability page with SVG diagrams, interactive components, and in-page navigation.

Why it matters: HKR-H/K/R all pass, but this is a workflow tip rather than a Claude release. As a quality Claude Code tutorial, it sits in the 72–77 band, with Simon Willison’s source authority clearing featured.

Take

HTML output in Claude Code is a review artifact, not presentation sugar. Simon’s example has a concrete hook: GPT-5.5 generated an interactive Linux exploit explainer with SVG diagrams, widgets, in-page navigation, and styled sections; Thariq’s prompt even asks for PR diffs with inline margin annotations and severity colors. The old Markdown default came from the GPT-4 8,192-token era, where saving tokens beat richer structure. That tradeoff has moved.

I buy the workflow, with one caveat. For code review, incident writeups, and unfamiliar subsystems, HTML gives the model a real UI surface. For exploit analysis, the same affordance makes a local privilege-escalation PoC easier to operationalize. “Defensive explanation” and “usable tutorial” are separated by prompt wording, not a hard product boundary.

May 8Friday

Hacker News front pageMay 8, 22:15

Show HN: Git for AI Agents

regent-vcs released the open-source re_gent project for AI-agent version control, currently supporting Claude Code, with workflows for tracking why an agent changed files, rewinding sessions, and bisecting agent actions; the post does not disclose the license, storage format, or installation details.

Why it matters: HKR-H/K/R all pass: the Git analogy is clicky, the mechanism is concrete, and Claude Code rollback pain is real. The post lacks license, storage format, and install details, so it stays at the featured threshold.

Take

re_gent is aimed at the right scar: AI coding agents need auditable operation history, not another autocomplete surface. The disclosed scope is narrow but concrete: Claude Code support, tracking why an agent changed files, rewinding sessions, and bisecting agent actions. That maps to a real gap. Cursor, Claude Code, and Copilot’s agentic workflows raise edit throughput, but a normal Git diff only shows the final damage, not the chain of agent decisions that caused it.

I have doubts about maturity. The scraped body is mostly GitHub chrome, and the summary says license, storage format, and install path are not disclosed. Those are not paperwork details for a VCS-adjacent tool; they decide whether a team can put it near a production repo. For now, this smells like a correct developer painkiller, not a new version-control layer.

Alibaba Technology · WeChatMay 8, 19:18

The AI-Native Era: Where R&D Organizations Go Next

Xu Xiaobin cites internal interviews showing that engineers who use AI heavily cut coding time from 30% to 5%, raised Agent conversation time from 5% to 60%, and increased end-to-end delivery efficiency by 2 to 3 times, while pure coding efficiency rose 10 times.

Why it matters: Alibaba Tech’s internal-interview numbers make HKR-H/K/R pass, but this is org-methodology commentary rather than a product or model release, so it sits just above the featured threshold.

Synced · WeChatMay 8, 17:06

OpenAI launches official CLI for terminal-based model access

OpenAI released the open-source openai-cli, letting developers call Responses, cloud tools, image generation and editing, speech transcription, and TTS from a single terminal command.

Why it matters: HKR-H/K/R all pass: an official OpenAI CLI, open-source packaging, and terminal access to multimodal APIs. This is a useful developer workflow update, not a major model capability release, so it sits in low featured.

Take

OpenAI is making a practical move with a control angle: openai-cli puts Responses, cloud tools, image generation/editing, transcription, and TTS behind shell commands. That turns the API into Unix plumbing. A log scan no longer needs a throwaway Python wrapper; `cat error.log | openai chat` can land inside ops scripts, CI jobs, cron, and local agents.

I don’t buy the “goodbye complex SDKs” framing. A CLI is great for prompt trials, batch jobs, and glue code; production still needs SDKs, retries, types, auth isolation, and observability. This smells closer to GitHub’s `gh` CLI: it didn’t replace the API, it captured developer muscle memory. The body gives no version number, package-signing story, permission model, or pricing details, and those decide whether teams trust it inside automation.

Latent SpaceMay 8, 15:11

[AINews] GPT-Realtime-2, Translate, and Whisper: new SOTA realtime voice APIs

OpenAI released GPT-Realtime-2, GPT-Realtime-Translate, and GPT-Realtime-Whisper in the Realtime API, with GPT-Realtime-2 expanding context from 32K to 128K and scoring 96.6% on Artificial Analysis Big Bench Audio.

Why it matters: HKR-H/K/R all pass: an OpenAI real-time voice API refresh, a 32K→128K context jump, and a 96.6% Big Bench Audio claim. Score stays at 86 because this is a major API update, not a flagship foundation-model release.

Take

OpenAI is aiming GPT-Realtime-2 at production voice agents, not voice demos. The hard numbers line up: context jumps from 32K to 128K, Artificial Analysis reports 96.6% on Big Bench Audio, time-to-first-audio is 1.12s at minimal reasoning and 2.33s at high reasoning, and pricing stays at $1.15/hour input plus $4.61/hour output. Capability up, latency still usable, price unchanged is an unusually clean product move.

The piece I buy most is audible tool behavior and recovery language. In support calls, scheduling, and IT helpdesks, “checking your calendar” and graceful failure beat a nicer voice. Scale AI’s instruction-retention jump from 36.7% to 70.8% APR hits the old failure mode directly. But don’t blur this into a ChatGPT Voice upgrade; Simon Willison’s caveat matters, because the consumer surface has not moved yet.

AI HOT (Curated Pool)May 8, 11:06

China releases L1-L4 AI terminal intelligence standards covering 7 device categories

MIIT and other agencies released AI terminal intelligence standards covering 7 device categories. The framework uses a “2+N” structure with L1 response, L2 tool, L3 assistance, and L4 collaboration; L4 details come later. The post does not disclose concrete test metrics.

Why it matters: HKR-H/K/R pass: the story has a clear L1-L4 standards hook, concrete “2+N” and 7-category details, and compliance impact for device AI teams. Missing L4 rules and test metrics keep it near the featured floor.

Take

This standard wins control over the label before it proves control over capability. GB/Z 177—2026 covers seven device classes: phones, PCs, TVs, glasses, car cockpits, speakers, and earbuds. It defines L1 response, L2 tool, L3 assistance, and L4 collaboration. But the full text is not yet public on the national standards platform, and L4 rules are explicitly left for later revision.

That gap matters because “AI phone” and “AI PC” already became launch-event wallpaper for Xiaomi, Honor, Lenovo, Huawei, and others. If the tests do not specify reproducible tasks, offline behavior, tool-call success rates, latency, privacy boundaries, and failure handling, L3 becomes a sales badge. The listed drafters are mostly device makers, which is practical but also tells you where the pressure sits: make the ladder strict enough to sound official, loose enough for products to climb it.

AI HOT (Curated Pool)May 8, 06:15

OpenAI launches official openai-cli for terminal API calls

OpenAI open-sourced openai-cli for direct API calls from the terminal. The Apache 2.0 tool installs via Homebrew or Go and covers Responses API, structured output, image editing, transcription, and key config. The key detail is Agent workflows using cloud tools like web search and code interpreter.

Why it matters: HKR-H/K/R all pass: official OpenAI terminal tooling is clickable, with concrete install/license/API details and workflow resonance. It is still a developer tooling update, not a model or major capability release, so 76 fits the featured threshold.

Take

OpenAI is fixing a developer entry point, not shipping a new model capability. openai-cli is Apache 2.0, installs through Homebrew or Go, and wraps Responses API, JSON/YAML output, image editing, transcription, project config, and API keys into resource-style commands. That surface was previously owned by curl, community wrappers, and LangChain-style scaffolding; OpenAI is taking back the shortest path.

The sharp part is direct Agent workflows with web search and code interpreter from the terminal. Claude Code already proved the terminal is a serious AI interface, but this is more API automation than coding assistant UX. The catch: the snippet gives no permission model, pricing prompts, or tool-call observability. Without those, a CLI that can call cloud tools stays great for demos and risky in production scripts.

AI HOT (Curated Pool)May 8, 04:08

Codex Plugin Now Supports Parallel Runs Across Chrome Tabs

OpenAI says Codex now runs in Chrome on macOS and Windows. The plugin works across tabs in the background without taking browser control; the post does not disclose version, concurrency limits, or enterprise policy.

Why it matters: HKR-H/K/R all pass, but the post gives platform and execution mechanics only; version, concurrency limits, and enterprise controls are not disclosed. Score: 76 as a practical OpenAI Codex product update.

Take

Codex in Chrome matters because it runs across tabs in the background. OpenAI names macOS and Windows Chrome support, says it handles apps and sites, and says it does not take browser control. Version, concurrency limits, and enterprise policy are not disclosed. That interaction model dodges the low-trust “AI stole my mouse” problem and puts the agent beside the user’s workflow.

This smells like OpenAI filling the gap that Cursor and Claude Code do not cover well: web consoles, CI dashboards, internal tools, and form-heavy SaaS outside the repo. The missing numbers are the product. Can it run 3 tabs or 30? Who recovers after a failed action? Can enterprises block domains or audit actions? Without that, cross-tab parallelism is a strong product posture, not reliable automation yet.

NVIDIA BlogMay 8, 03:14

Powering the Next American Century: Chris Wright and NVIDIA’s Ian Buck on Genesis Mission

The U.S. DOE and NVIDIA are building two AI supercomputers at Argonne; Equinox uses 10,000 Grace Blackwell GPUs. Solstice will use 100,000 Vera Rubin GPUs, which Buck said reach 5,000 exaflops. The key bottleneck is grid work: Wright said AI can cut interconnection studies from years to weeks or hours.

Why it matters: HKR-H/K/R all pass: the GPU counts, DOE-NVIDIA role, and grid bottleneck are concrete. NVIDIA-blog sourcing keeps it below must-write; this fits the 78–84 band.

Take

NVIDIA is tying sovereign compute to the energy bottleneck, and the sales motion is obvious: GPUs are no longer just cloud inventory, they become machinery for state approval systems. Equinox gets 10,000 Grace Blackwell GPUs; Solstice gets 100,000 Vera Rubin GPUs; Ian Buck cites 5,000 exaflops. The sharper claim is Chris Wright saying AI can cut grid interconnection studies from years to weeks or hours.

I don’t buy the clean “AI fixes the grid” framing. Interconnection queues are slow because of rules, transmission buildout, local permitting, and cost allocation, not just simulation runtime. NVIDIA’s better move is institutional: if DOE treats AI simulation as approval infrastructure, the GPU cluster stops being a training box and starts sitting inside the operating layer of the energy system.

AI HOT (Curated Pool)May 8, 02:41

Work with Claude across Excel, PowerPoint, Word, and Outlook

Claude now connects to four Microsoft apps: Excel, PowerPoint, Word, and Outlook. Excel, PowerPoint, and Word are generally available; Outlook is in public beta. Admins can deploy via Microsoft admin center and monitor with OpenTelemetry.

Why it matters: HKR-H/K/R all pass: Claude enters 4 Microsoft 365 apps with rollout status and OpenTelemetry details. This is a strong Anthropic product update, but not a model release or core capability jump, so it stays in the 78–84 band.

Take

Anthropic is making the practical move here: Claude now connects to Excel, PowerPoint, Word, and Outlook, so the product follows the enterprise workflow instead of asking workers to live in chat. Excel, PowerPoint, and Word are generally available; Outlook is still in public beta. Admin deployment through Microsoft admin center and OpenTelemetry tracing are the serious parts, because procurement teams care about control more than another shiny Office button.

I don’t buy the framing that this is just “Claude in Office.” Microsoft Copilot still owns the tenant graph, permissions layer, and default seat bundle. Claude has to wedge in through model quality and observability. OpenTelemetry is a real wedge: companies will not let a black-box agent touch mail and spreadsheets without traces. Pricing, permission boundaries, and data-retention terms are not given, so the rollout friction is still hiding off-page.

AI HOT (Curated Pool)May 8, 01:48

Perplexity launches Personal Computer app for Mac

Perplexity opened its Personal Computer Mac app to all users. It runs on any Mac and works across local files, native Mac apps, the web, and Perplexity secure servers. The post does not disclose pricing, permission boundaries, or task success rates.

Why it matters: HKR-H/K/R all pass, but the source is a single product post with no pricing, permission model, or task success rate. Score stays in the mid-weight product-update band.

Take

Perplexity opened Personal Computer in its Mac app to all users. It says the agent works across local files, native Mac apps, the web, and Perplexity secure servers. Pricing, permission boundaries, and task success rates are not disclosed. My reaction is caution, because this pushes agent failure onto the user’s actual desktop.

Desktop agents are no longer a novelty. OpenAI’s Operator focused on browser tasks, and Anthropic showed computer use with Claude 3.5 Sonnet controlling screens. Perplexity is choosing the Mac as the entry point, closer to Raycast, Spotlight, and a browser assistant. That is a stronger surface than search, but it also carries much sharper failure modes.

The Mac is not a web sandbox. If an agent reads local files, controls native apps, and calls remote servers, the permission model becomes the product. The post does not say whether there is per-task approval, folder allowlisting, sensitive-action blocking, or audit logs. Without those, Personal Computer smells like a remote intern with root-adjacent habits.

Perplexity’s core edge has been retrieval and web context, not OS automation. It won trust by attaching answers to citations and live search. Desktop work needs execution reliability, rollback, and state awareness. Filling a wrong field in a browser is annoying; editing the wrong local file is a different liability class.

I have doubts about “any Mac” as a deployment claim. AppleScript, Accessibility permissions, sandboxing, and macOS version drift create messy edge cases. Until Perplexity publishes task benchmarks or failure handling, I would not treat this as a mature agent platform. The product proof is not opening Finder; it is surviving boring workflows without quietly breaking things.