MCP & tool use

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

760 picksRelated topicsAgents AI coding Open source

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May 22Friday

AI HOT (Curated Pool)May 22, 19:17

Alibaba Qianwen App, PC, and Web Add Qwen3.7-Max

Alibaba added Qwen3.7-Max to the Qianwen app, PC client, and web client, with free access after updating the app to version 6.9.7 or later, and the official test reports a 35-hour autonomous kernel optimization run with more than 1,000 tool calls.

Why it matters: HKR-H/K/R all pass: Alibaba ships Qwen3.7-Max across three Qianwen clients, with v6.9.7+ free access and a 35-hour, 1,000+ tool-call claim. Benchmarks, context window, and API pricing are not disclosed, so it stays below 90.

MIT Technology Review · AIMay 22, 18:00

Google I/O showed how the path for AI-driven science is shifting

MIT Technology Review says Google used I/O to shift its scientific AI framing toward Gemini for Science, a package that groups AI Co-Scientist and AlphaEvolve, while researchers can now apply for access and older specialized systems like AlphaFold and WeatherNext remain active.

Why it matters: HKR-H and HKR-K pass: MIT Technology Review frames a real Google science-AI product shift with named components and access conditions. HKR-R is weak because the impact is mostly research-facing, not practitioner-wide.

Take

Google is pushing science AI toward Gemini for Science, and the danger is bundling testable engineering with agent theater. The strongest evidence in the piece is still WeatherNext: it warned ahead of Hurricane Melissa’s landfall in Jamaica and may have saved lives. AlphaFold also has a Nobel halo and real scientific usage. By contrast, AI Co-Scientist and AlphaEvolve are packaged behind researcher access, with no access scope, eval protocol, or failure rate disclosed.

I don’t buy the “foothills of the singularity” framing. DeepMind’s scientific credibility came from constrained systems like AlphaFold and WeatherNext, not from dropping LLM agents into lab workflows. OpenAI and Anthropic have spent the last year selling agents too, but science has a much nastier reward function than coding.

Xinzhiyuan · WeChatMay 22, 12:58

Microsoft, after investing $13B in OpenAI, saw its engineers run up Claude Code costs

Microsoft plans to end Claude Code subscriptions by the end of June for its Experiences and Devices teams and move nearly 100,000 engineers to GitHub Copilot CLI, with the article attributing the change to external token-based billing costs.

Why it matters: HKR-H/K/R all pass: the OpenAI-Claude contrast hooks, the story gives end-June migration, nearly 100k engineers and token-billing, and it hits enterprise coding-agent cost control. Not a model release or official major launch, so 78–84 fits.

Take

Microsoft is not cutting Claude Code because the tool failed; it is cutting an external token meter. The disclosed hooks are sharp: nearly 100,000 engineers, a late-June cutoff, and migration to GitHub Copilot CLI. The article also cites Uber: 5,000 engineers burned the 2026 AI budget in four months, with heavy users reaching $2,000 per month. I would verify the source trail, but the pattern is real: agentic coding turns seat forecasts into looped inference spend.

I don’t buy the grand “AI efficiency collapse” framing. This smells like Microsoft routing demand back into its own accounting perimeter. Copilot CLI sits inside Microsoft’s cost structure; Claude Code writes an external check to Anthropic. Anthropic won developer taste here, then hit enterprise finance. That hurts more than a benchmark loss.

Xinzhiyuan · WeChatMay 22, 12:58

Enterprise Agent Operations Begin? Anthropic Updates Architecture, Chinese Tech Firms Have It Running

Alibaba Cloud JVS Crew splits Agent, Environment, and Session into three layers, with sandboxes, snapshot recovery, RBAC, and usage-based billing. Anthropic added self-hosted sandboxes to Claude Managed Agents on May 19, while the article cites 2-week deployments and 5x or 10x efficiency gains in several Chinese customer cases.

Why it matters: HKR-H/K/R all pass, but the facts are an enterprise agent-infra comparison: Anthropic self-hosted sandboxes and Alibaba Cloud JVS Crew architecture. This is featured-level, not a must-write model release.

Take

Alibaba Cloud JVS Crew lands on the ugly part of enterprise agents: isolation, recovery, permissions, and billing. The concrete design is credible enough: Agent, Environment, and Session are split; execution runs in sandboxes; snapshots handle recovery; RBAC and four-level budgets control access and spend. That maps cleanly to the OpenClaw pain points cited here: 24/7 containers, lost memory, broken upgrades, and runaway token bills.

I don’t buy the customer-case numbers at face value. The article claims 2-week launches, 50% lower cloud cost, 5x operating efficiency, and 10x launch efficiency, but gives no baseline, sample size, or measurement method. Anthropic adding self-hosted sandboxes to Claude Managed Agents on May 19 says the same architecture pressure is showing up outside China. The contest is observability and cost control, not who brands agents with the cutest metaphor.

AI HOT (Curated Pool)May 22, 11:58

OpenAI Codex /goal Feature Officially Launches with Usage Guide

OpenAI moved Codex /goal mode from experiment to stable release, letting users set milestones in the Codex app, IDE extension, or CLI and keep tasks running for hours or days with progress checks, direction changes, and pause controls.

Why it matters: HKR-H/K/R all pass: OpenAI Codex /goal is now stable, with milestones across app, IDE extension, and CLI. The article is thin on permissions, safety limits, and tier access, so it stays in the lower featured band.

Hacker News front pageMay 22, 11:17

Show HN: Spec-Driven Development Workflow for Claude Code

The sddw author released a Claude Code plugin that splits work into requirements, code analysis, and design specs, then clears context after each step to keep cost and context focused.

Why it matters: HKR-H/K/R all pass for a Claude Code workflow with a concrete spec-and-context mechanism. It stays in the 72–77 featured band because the post lacks benchmarks, adoption data, or an official Anthropic release.

Take

sddw makes the right boring bet: do not let Claude Code carry a whole engineering task inside one swollen context. The plugin splits work into three spec layers—requirements, code analysis, design—then executes subtasks one by one. It clears context after each step and writes specs to disk. That attacks the failure mode practitioners actually hit: goal drift, stale assumptions, and polluted context.

I like this class of tool because it treats coding agents as unreliable workers that need external state. The article gives no benchmark, no cost reduction number, and no success-rate comparison; the HN post has only 5 points so far. Still, compared with the usual Cursor or Claude Code “giant prompt and pray” workflow, layered specs plus context resets look closer to something a team can standardize.

AI HOT (Curated Pool)May 22, 08:00

Plastic Interfaces: The Future Shape of AI-Driven Software

Salesforce has adopted a headless architecture that lets salespeople update data through AI; the post says MCPs, HTML, audio, and web interfaces can be generated dynamically by context, but it does not disclose implementation metrics or adoption numbers.

Why it matters: HKR-H/K/R all pass, but this is a software-form thesis without user metrics, launch timing, or a reproducible test. It fits the insightful-commentary band, not a must-write release.

AI HOT (Curated Pool)May 22, 04:39

v2.1.147 Release Update

Claude Code v2.1.147 adds a Workflow tool, disabled by default, for deterministic multi-agent orchestration, and renames /simplify to /code-review with code-correctness reporting and GitHub PR inline-comment generation.

Why it matters: HKR-H/K/R all pass: the official Claude Code release adds a default-off Workflow tool for deterministic multi-agent orchestration. No performance data, pricing, or scope limits are disclosed, so this stays in the mid product-update band.

Take

Claude Code v2.1.147 is making the right bet: agent coding has to become repeatable before it becomes trusted. The sharp detail is the Workflow tool being “deterministic” and disabled by default. That is Anthropic admitting the old demo loop—spawn agents, hope one lands—does not survive CI or PR review.

The concrete move is tighter than the release title suggests: Workflow handles deterministic multi-agent orchestration, while /simplify becomes /code-review with code-correctness reporting and GitHub PR inline comments. That puts Claude Code closer to the review surface owned by Copilot and Cursor, not just the prompt box. But the release text does not give the Workflow DSL, retry semantics, permission model, or model routing. I would treat this as a controlled aperture, not a production agent framework yet.

Latent SpaceMay 22, 04:37

Giving Agents Computers — Ivan Burazin, Daytona

Daytona provides composable computers for AI agents, with one sandbox starting in about 60 ms, 50,000 sandboxes in about 75 seconds, and its largest customer running roughly 850,000 sandboxes per day.

Why it matters: HKR-H/K/R all pass: the agent-computer framing is clickable, and the sandbox scale numbers are concrete. Still, this is a startup infrastructure story, not a major model or platform release.

AI HOT (Curated Pool)May 22, 04:32

ChatGPT now supports creating and editing presentations directly in PowerPoint

ChatGPT is testing PowerPoint support for creating and editing presentations directly, including building, updating, understanding, and refining editable slides; the post does not disclose pricing, rollout scope, or availability conditions.

Why it matters: HKR-H/K/R all pass: OpenAI shows ChatGPT creating and editing editable PowerPoint slides. Pricing, rollout scope, and enterprise controls are not disclosed, so this stays featured rather than P1.

Take

ChatGPT in PowerPoint matters because it targets editable Office work, not slide-shaped image generation. The post says it can build, update, understand, and refine presentations while keeping slides editable. Pricing, rollout scope, tenant controls, and availability are not disclosed. That missing layer matters because enterprise decks are not solo writing tasks; they involve brand templates, approval comments, linked charts, and permission boundaries.

I read this as OpenAI putting pressure on Microsoft 365 Copilot inside Microsoft’s own home turf. PowerPoint should have been Copilot’s cleanest enterprise wedge. Now the ChatGPT app is saying it edits directly in PowerPoint. If this is a thin plugin test, it stays a demo. If it handles masters, comments, Excel-linked charts, and corporate templates reliably, ChatGPT steals part of the default Copilot workflow.

AI HOT (Curated Pool)May 22, 03:52

Datasette Agent

Datasette released Datasette Agent as its first extensible AI assistant, offering conversational data queries, plugin-based chart generation, official plugins for charts, AI image creation, and sandboxed code execution, with support for Gemini 3.1 Flash-Lite cloud models and local open-source models through LM Studio.

Why it matters: HKR-H/K/R all pass: a concrete Datasette agent with chart plugins and LM Studio local execution. The audience is narrower than major lab releases, so it sits in the 72–77 featured band.

AI HOT (Curated Pool)May 22, 02:59

Codex Enables Secure Cross-Device Mac Control Around the Clock

OpenAI Devs says Codex can use apps on a Mac from a phone while the Mac remains locked and the screen is off; the post does not disclose permission boundaries, pricing, or a release timeline.

Why it matters: HKR-H/K/R all pass: OpenAI Devs disclosed a concrete Codex Mac-control condition. Missing permission boundaries, pricing, and launch timing keep it below the 85+ band.

AI HOT (Curated Pool)May 22, 02:36

Aleph 2.0 and Edit Studio

Runway released Aleph 2.0 and Edit Studio, combining generation, editing, and post-production into one platform; the post does not disclose pricing, technical parameters, or rollout scope.

Why it matters: Runway is a major AI video vendor, and Aleph 2.0 plus Edit Studio is a mid-weight product update. HKR-H/K/R pass, but missing price, specs, and rollout keep it at the featured threshold.

Take

Runway is chasing the workstation after video generation, not just shipping Aleph 2.0. The concrete hooks are narrow: Edit Studio edits video with natural language, offers preview before generation, and sits beside Multi-Shot Video, Scene Builder, Act-Two performance capture, Topaz upscale, and object removal. That is a workflow bet across shots, acting, cleanup, and finishing.

I buy the direction, but not the launch strength. Pricing, technical specs, and rollout scope are absent. Aleph 2.0’s stability, duration limits, resolution, and character consistency are not testable from this page. Sora and Veo spent the last cycle fighting over model quality; Runway is trying to own the editing surface. Creative teams will judge this by rework rate, not by how many app tiles appear in the launcher.

AI HOT (Curated Pool)May 22, 01:43

Claude now supports more security and compliance tools

Anthropic added 28 security and compliance integrations for Claude Enterprise and its platform, using the Claude Compliance API to provide conversation content and activity events to DLP, SIEM, and existing enterprise monitoring workflows.

Why it matters: Official Anthropic product update with 28 compliance integrations and Compliance API event routing, so HKR-K/R pass. It is enterprise governance rather than a model capability jump, keeping it near the featured threshold.

Take

Anthropic is doing the unglamorous work that sells enterprise AI: Claude Enterprise now has 28 security and compliance integrations, pushing conversation content and activity events into DLP, SIEM, and monitoring workflows. The blocker inside large companies is rarely another benchmark point. It is auditability, retention, data-loss routing, and who gets blamed when prompts leak customer data.

This reads like a necessary answer to Microsoft 365 Copilot’s home-field advantage. Microsoft already sits inside Purview, Defender, and Entra; Anthropic has to assemble that control plane through partners like Cloudflare and the Claude Compliance API. Pricing, retention windows, event schema depth, and admin visibility are not disclosed here. Without those, CISOs can move Claude into evaluation, not automatically into production.

AI HOT (Curated Pool)May 22, 00:33

Kotlin ADK and Android ADK 0.1.0 Released for Building AI Agents

Google released Kotlin ADK and Android ADK 0.1.0 for developers, with Kotlin ADK targeting backend agent workflows and Android ADK providing mobile-specific functions for building AI agents.

Why it matters: Google’s Kotlin ADK and Android ADK 0.1.0 release is a mid-weight agent tooling update. HKR-H/K/R pass, but the disclosed facts stop at platforms and version, with no performance data, examples, or ecosystem scale.

Take

Google is betting on Android distribution here, not on ADK’s elegance. The hard numbers are Gemini Nano on 140 million devices, plus ADK for Java and Go at 1.0.0, Python ADK 2.0 beta, and Android ADK 0.1.0. That version map says a lot: Kotlin handles backend agent workflows, Android runs local retrieval and document parsing with Gemini Nano, and the cloud model stays the orchestrator.

I buy the direction, but not the blog’s easy tone. Mobile agents do not fail because developers lack a few Kotlin calls. They fail on permissions, latency, model limits, OEM fragmentation, and user consent flows. Apple Intelligence already showed how clean the on-device privacy story sounds, and how messy cross-app execution gets. Google has the Android control plane, but 0.1.0 is still a construction gate, not proof of working mobile agents.

Hacker News front pageMay 22, 00:07

Launch HN: Runtime (YC P26) – Sandboxed coding agents for everyone on a team

Runtime launched open-source sandbox infrastructure for coding agents, supporting Claude Code, Codex, Cursor, Copilot, Gemini, and Devin, with hosted access, a free tier, and pricing based on a flat platform fee plus compute without token markup.

Why it matters: HKR-H/K/R pass: this is not a major-lab launch, but open-source sandboxes, six coding-agent types, and no token markup give teams concrete adoption signals. No usage data or marquee customers keeps it near the featured floor.

May 21Thursday

r/LocalLLaMAMay 21, 22:21

LLM planner: pick a rig by use case, model, or budget, or pick models for your rig

totosse17 published the LLMRequirements hardware planner with 60+ build configs, 50+ models, 130 cited tokens-per-second sources, 150+ reviewer videos, multi-region prices, idle and active watts, and a public GitHub data repo.

Why it matters: HKR-H/K/R all pass, but this is a Reddit community tool for local LLM rigs, not a broad platform release. The concrete dataset earns a featured-threshold score, not the 78+ band.

The Verge · AIMay 21, 21:00

I Can’t Believe How Fast Google Vibe Coded My First Android App

The Verge’s Sean Hollister used Google AI Studio to generate three Android apps in one afternoon; one app came from a 148-word browser prompt and installed about 10 minutes later on an Android phone prepared with USB debugging and a PC connection.

Why it matters: HKR-H/K/R all pass: the story has a personal-test hook plus concrete timing and prompt details. This is not a major Google launch, so it fits the high-quality first-person experiment band, not same-day must-write.

r/LocalLLaMAMay 21, 20:03

Tencent Hy-MT2 30B/7B/1.8B

Tencent released Hy-MT2 translation models in 1.8B, 7B, and 30B-A3B sizes, supporting translation across 33 languages; AngelSlim 1.25-bit quantization reduces the 1.8B model’s storage requirement to 440 MB and raises inference speed by 1.5x.

Why it matters: HKR-H/K/R pass via the 440MB quantized 1.8B model, 33-language support, and local inference cost angle. Sparse Reddit sourcing keeps it at the featured threshold, not the 78+ band.

AI HOT (Curated Pool)May 21, 20:00

Lessons from Building Cloud Agents

Cursor summarizes lessons from building cloud agents: after migrating to Temporal, reliability rose above 99.9%, and the platform processes more than 50 million operations per day.

Why it matters: HKR-H/K/R all pass: Cursor is central to coding agents, and the post gives Temporal, 99.9%+ reliability, and 50M daily operations. Not a launch, so it stays at low-end featured.

Take

Cursor’s useful claim is that cloud-agent quality is now an environment problem, not a model problem. The hard evidence is operational: after moving to Temporal, reliability is above 99.9%, and the platform handles over 50 million operations per day. That is more convincing than another “smarter agent” demo, because long coding tasks fail on dependencies, credentials, network rules, and VM state before they fail on reasoning.

I’ve always thought the split in coding agents is not the chat surface. It is whether the product can recreate a developer’s machine. Cursor names dedicated VMs, hibernate/resume, VM image forks, secret redaction, network policies, and credential management. That is basically enterprise IT for agents. GitHub Copilot Workspace and Devin hit the same wall; Cursor is just saying the ugly part out loud.