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

Latest picks

121–140 of 760

May 29Friday

AI HOT (Curated Pool)May 29, 17:13

Xiaomi Open-Sources Controllable Video Foley Model ControlFoley

Xiaomi’s large model application team open-sourced ControlFoley, a controllable video Foley model supporting three tasks: text-guided video dubbing, text-controlled video dubbing, and reference-audio-controlled video dubbing, with code, model weights, and an online demo released.

Why it matters: ControlFoley clears HKR-H/K/R with controllable video Foley plus code, weights, and demo. It is a useful multimodal-audio release from Xiaomi, but not a flagship foundation-model launch, so it sits near the featured threshold.

QbitAI · WeChatMay 29, 16:12

Tencent unveils Code Craft, an AI game creation platform for beginners and developers

Tencent Games unveiled Code Craft, an AI game creation platform that turns natural-language prompts into runnable 2D or 3D games, with a planning knowledge base, Skill system, visual tuning panels, and more than 20,000 free cloud assets; the post does not disclose release timing, pricing, model details, or supported engines.

Why it matters: HKR-H/K/R pass on the Tencent game-creation hook, runnable 2D/3D output, and 20,000+ assets. Pricing, access scope, and model limits are not disclosed, so it stays in the lower featured band.

Take

Tencent Craft looks like a creator-platform probe, not a magic “prompt a game” breakthrough. The concrete stack matters: natural-language 2D/3D runnable games, a planning knowledge base, Skill system, visual tuning panels, and 20,000-plus free cloud assets. That is more credible than raw codegen because game prototypes usually fail after the first playable screen: balance, level pacing, asset swaps, and iteration loops kill them.

I don’t buy the “everyone becomes a game maker” framing yet. The post gives no release date, pricing, model details, supported engines, or runtime/export constraints. Distribution, multiplayer, and social sharing are described as future capabilities. Roblox and UEFN already showed the platform fight is won on distribution, payouts, moderation, and creator retention, not flashy demos. Tencent has the traffic and game IP surface; Craft has only shown the tool layer so far.

r/LocalLLaMAMay 29, 12:17

Liquid AI releases LFM2.5-8B-A1B

Liquid AI released LFM2.5-8B-A1B with a 128K context window, 38T pre-training tokens, large-scale reinforcement learning, doubled vocabulary for non-Latin tokenization, and availability on Hugging Face.

Why it matters: HKR-H/K/R pass: 8B/A1B, 128K context, and 38T tokens are concrete hooks for local inference. No benchmarks, license, or deployment limits are disclosed, so it stays in the mid featured band.

Take

Liquid is making the right bet: small active-parameter models with long context, not another bland 8B leaderboard entry. The disclosed hooks are concrete: 128K context, 38T pre-training tokens, large-scale RL, doubled vocabulary for non-Latin tokenization, and Hugging Face availability. The Reddit body is blocked by a 403, so license, throughput, quantization behavior, and SWE-bench / GPQA-style scores are not visible here.

The A1B active-parameter shape is the part practitioners should care about. If it holds up, it fits local agents and tool-heavy loops better than a dense 8B that melts latency. I have doubts on the 128K claim, though. Qwen, Gemma, and Llama-family releases have all shown that advertised context and usable retrieval at the far end are different beasts. Liquid has to prove obedience past 100K tokens, not just print 128K on the card.

Latent SpaceMay 29, 10:07

Anthropic raises $65B Series H, releases Opus 4.8 and Dynamic Workflows

Anthropic announced a $65B Series H at a $965B post-money valuation, disclosed a $47B revenue run rate, and released Claude Opus 4.8 plus Claude Code Dynamic Workflows as a research preview for parallel subagent orchestration.

Why it matters: HKR-H/K/R all pass: this combines a frontier-lab financing event with an Anthropic model and Claude Code workflow release. I score using the summary’s $65B raise and $965B post-money valuation because the title’s dollar figure conflicts with it.

AI HOT (Curated Pool)May 29, 09:11

Cursor team releases Developer Habits Report

Cursor’s report says developers’ weekly code output rose from about 3.6K to 8.6K lines, while AI agents increased tool calls per session by roughly 30%.

Why it matters: HKR-H/K/R all pass: Cursor’s own report gives concrete 3.6K→8.6K and +30% figures for AI coding work. It is not a product launch or cross-source event, so 78–84 fits better than the must-write band.

Ruan YiFeng's WeblogMay 29, 08:08

Technology Enthusiasts Weekly Issue 398: Token Costs Are Hard to Afford

Peter Steinberger posted one month of usage showing 7.6 million requests and 603 billion tokens, with CodexBar estimating a $1.3 million value under preset rates rather than his actual spend as an OpenAI employee.

Why it matters: HKR-H/K/R all pass: the CodexBar case turns token economics into concrete usage and cost. This is strong practitioner commentary, not a model or platform release, so it fits the 72–77 featured band.

AI HOT (Curated Pool)May 29, 08:00

StepFun Releases Step 3.7 Flash, Focused on Agent Efficiency

StepFun released the open-source Step 3.7 Flash model with a 198B-parameter MoE architecture, about 11B active parameters, a 256K context window, and a 67.1 score on ClawEval-1.1.

Why it matters: HKR-H/K/R all pass: the release has a clear sparse-model hook, concrete context and benchmark numbers, and practitioner resonance around open agent efficiency. Official-post sourcing and no independent eval keep it in the 78–84 band.

AI HOT (Curated Pool)May 29, 08:00

Skill distillation

Skill distillation has Opus 4.7, GPT-5.1, and Gemini 3 Pro write standardized SKILL.md procedure files, while local Qwen 35B and Gemma 26B models execute those files step by step.

Why it matters: HKR-H/K/R pass: the agent-skill distillation pattern is concrete and practitioner-relevant. The summary lacks success rates, cost data, or task outcomes, so it sits at the featured threshold, not must-write.

Latent SpaceMay 29, 02:41

The Age of Async Agents — Cognition's Walden Yan and OpenInspect's Cole Murray

Latent Space discusses async coding agents with Cognition’s Walden Yan and OpenInspect’s Cole Murray, citing Devin’s 7x merged PR growth and an increase from 16% to 80% of commits across Cognition repos.

Why it matters: HKR-H/K/R all pass: the Cognition repo numbers make this more than agent rhetoric. It stays in the 78 band because it is an interview/trend piece, not a major model or product release.

TechCrunch · AIMay 29, 01:00

Anthropic releases Opus 4.8 with new Dynamic Workflows tool

Anthropic released Opus 4.8 with a Dynamic Workflows tool for coordinating swarms of subagents. The RSS snippet does not disclose pricing, context window size, benchmarks, or a rollout schedule.

Why it matters: HKR-H/K/R all pass: an Anthropic model release plus an agent orchestration tool fits the 85–94 same-day band. Missing price, context window, and rollout detail keep it below the top of the band.

Take

Anthropic is selling orchestration here, not a clean Opus 4.8 capability jump. The one hard detail is Dynamic Workflows coordinating swarms of subagents. Pricing, context window, benchmarks, and rollout timing are not disclosed. That framing admits a real bottleneck: Claude’s problem in agentic work is less “can it code” and more “can many agents avoid chaos.”

I’m allergic to “swarm” until the failure modes are shown. Multi-agent demos looked great all year; production runs usually break on state, permissions, retries, and ownership. OpenAI’s Agents SDK leaned into tools and tracing. Anthropic pulling workflow out as a product surface suggests it wants to package Claude Code’s long-task lessons. Without SWE-bench numbers, repo-scale run times, or failure-rate data, I wouldn’t treat Opus 4.8 as a major model leap yet.

May 28Thursday

AI HOT (Curated Pool)May 28, 23:00

Perplexity Computer Now Integrates with Microsoft Office

Perplexity Computer is now available in Microsoft Excel, Word, PowerPoint, and Outlook, letting users access Computer from the app sidebar to coordinate work, draft documents, model data, create presentations, and handle email.

Why it matters: HKR-H/K/R pass: Perplexity brings Computer into four Office apps with sidebar workflows, a useful product fact and competitive hook. Price, permission model, enterprise rollout, and measured results are not disclosed, so it stays at the featured threshold.

The Verge · AIMay 28, 22:08

CNN sues Perplexity over “verbatim” copycat articles

CNN sued Perplexity in a New York court on Thursday, alleging its AI answer tools generate “verbatim” copies of CNN work and provide users with information locked behind CNN’s subscription wall.

Why it matters: HKR-H/K/R all pass: CNN vs Perplexity has a clear conflict, concrete allegations, and licensing-risk resonance. It is a notable copyright front, but only a lawsuit filing, not a ruling or product change.

QbitAI · WeChatMay 28, 21:15

A New Paradigm for GUI Agent Trajectories: FSMs Generate Trajectories at $0.04 Each

AutoWebWorld synthesized 29 web environments, 875 pages, and 11,663 verified trajectories at about $0.04 per trajectory, using FSM-defined states, preconditions, and transitions to verify GUI agent tasks instead of human labeling or an LLM judge.

Why it matters: HKR-H/K/R all pass: $0.04 per trace, 11,663 verified traces, and FSM state checks give concrete hooks for GUI-agent data and eval cost. The source is not a top lab release, so it stays in the 78–84 research-tool band.

AI HOT (Curated Pool)May 28, 20:47

Mistral AI Releases Search Toolkit

Mistral AI released the public preview of Search Toolkit, an open source framework that combines data ingestion, retrieval, and evaluation behind shared interfaces for cloud, on-premises, or edge deployment.

Why it matters: HKR-K and HKR-R pass: Mistral combines RAG ingestion, retrieval, and evaluation in an open-source Search Toolkit with cloud, local, and edge deployment. HKR-H is weak, so this sits at the featured threshold for a mid-weight product update.

Mistral AIMay 28, 20:00

Mistral upgrades Le Chat into unified agent Vibe, covering office work and coding

Mistral upgraded Le Chat into a unified AI agent called Vibe, with one license covering both office work and coding. Existing chats, settings and plans all carry over. Work Mode supports enterprise knowledge search, structured data analysis, document and report generation, scheduled multi-step tasks and reusable skills, and connects to Google Workspace, Outlook, SharePoint, Slack, GitHub and more.

Why it matters: It discloses Vibe's Work Mode, coding mode and CLI updates in full, so readers can judge how it plugs into existing workflows.

Alibaba Technology · WeChatMay 28, 19:06

AI-Native Project Management: Two Git Repos Replace Weekly Updates, Insights, and Metrics Reports

Zhou Zhiwei describes a project-management setup that uses two Git repositories, an AI coding assistant, Shell, and Python to replace at least 80% of manual weekly-update chasing, data moving, chart generation, and engineering-metrics reporting.

Why it matters: HKR-H/K/R all pass: the hook is counterintuitive, the post gives an 80% replacement claim and a two-repo mechanism, and it hits engineering-management toil. This is a strong practical workflow piece, not a model or platform launch.

Take

This is the right instinct: move project management into the engineering system, not into another chatbot wrapper. The title says two Git repositories; the summary says an AI coding assistant, Shell, and Python replace at least 80% of weekly-update chasing, data moving, chart generation, and engineering-metrics reporting. The WeChat body is blocked, so repo layout, script boundaries, and the 80% measurement are not verifiable here.

I buy the architecture more than the number. Jira, Linear, and Feishu-style AI features still mostly sit on forms, summaries, and status text. A Git-based PM loop makes status and metrics diffable, reviewable, and runnable in CI-like flows. The hard failure mode is obvious: if the source data is still manually entered, the AI just turns messy inputs into prettier dashboards faster.

AI HOT (Curated Pool)May 28, 18:47

Mistral AI launches physics AI model for industrial engineering

Mistral AI integrated the Emmi AI team and launched a physics AI foundation model for industrial engineering, with the post saying it can learn from geometry, boundary conditions, or measurement data and predict full physical fields on a single GPU in seconds.

Why it matters: HKR-H/K/R pass: a major model lab entering physics simulation with a concrete single-GPU seconds claim. The score stays in the lower featured band because model name, benchmarks, pricing, and access are not disclosed.

Take

Mistral is filling an enterprise gap here, but the evidence is thin. The summary says Mistral integrated the Emmi AI team and claims a physics foundation model can learn from geometry, boundary conditions, or measurement data, then predict full physical fields on one GPU in seconds. The captured body is mostly site navigation; it shows the title, not error rates, mesh scale, test cases, pricing, or Siemens Energy deployment details.

I’d treat this as an industrial sales wedge, not a CAE replacement claim. PhysicsX and NVIDIA Modulus already showed the hard part is not drawing a flow or stress field fast. The hard part is preserving error bounds across new geometries, materials, and boundary conditions. Mistral has European enterprise access and a credible services motion. Without benchmarks, “single-GPU seconds” is marketing copy with a lab coat.

The Verge · AIMay 28, 16:49

YouTube Will Let You Ask AI to Make a Custom Video Feed

YouTube is rolling out an AI custom video feed that uses a user-entered prompt or suggested options to build a personalized homepage feed around interests, moods, or topics; the feature supports English first and is available to signed-in users in the US on YouTube’s mobile app and desktop site.

Why it matters: HKR-H/K/R pass: promptable YouTube feeds are a concrete consumer-AI UX shift with rollout conditions. No model, ranking mechanism, or performance data is disclosed, so this stays in the mid-weight product-update band.

Financial Times · TechnologyMay 28, 12:00

Kirkland & Ellis to Spend $500mn Building Its Own AI Technology

Kirkland & Ellis plans to spend $500mn building its own AI technology platform for the “collective intelligence” of its lawyers; the post does not disclose model architecture, vendors, or launch timing.

Why it matters: FT source authority and a $500mn in-house AI budget give strong HKR-H/K/R for legal AI adoption. Missing model architecture, vendors, and launch timing keep it in the 72–77 enterprise-adoption band.

Take

Kirkland’s $500mn budget moves legal AI from tool procurement into knowledge-asset defense. The firm says it wants its lawyers’ “collective intelligence” inside a platform, but the snippet gives no model architecture, vendor list, or launch date. That missing layer matters: this is either Harvey/Microsoft-style tooling wrapped around private data, or a serious build across retrieval, permissions, audit, and workflow.

I don’t buy the implied “we’ll build our own model” story without more proof. Law firms don’t win by generating prettier contract clauses. They win by keeping client data isolated, citations traceable, privilege intact, and partner judgment defensible. Harvey already owns the legal-AI SaaS lane in a lot of rooms. Kirkland naming $500mn reads more like a message to clients and partners: the firm won’t let its institutional memory sit entirely inside someone else’s product.

AI HOT (Curated Pool)May 28, 08:00

Grok Build 0.1 on API

xAI released Grok Build 0.1 in public beta through the xAI API for agentic coding tasks, with throughput above 100 tokens per second and pricing at $1 per million input tokens and $2 per million output tokens.

Why it matters: HKR-H/K/R all pass, but this is a 0.1 public-beta API and pricing launch; benchmarks, context window, and task success rates are not disclosed. It fits a solid mid-weight product update at 78, featured not p1.