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Meta now lets AI agents handle the boring parts of WhatsApp Business setup

Meta launched a WhatsApp Business Tools MCP server that lets AI agents like Claude or Cursor set up and manage WhatsApp Business messaging accounts.

Meta announced a WhatsApp Business Tools MCP server, a Model Context Protocol server that connects AI coding agents such as Claude, Cursor, Codex, or ChatGPT directly to the WhatsApp Business Platform. The agents can handle account creation, phone number verification, Cloud API registration, Terms of Service checks, messaging template creation/editing, and webhook testing. Meta's companion Social Technologies MCP can also discover API endpoints, search documentation, and troubleshoot errors. The launch extends Meta's existing MCP servers for ad management and app configuration monitoring.

TechCrunch · AISep 15, 20:12 UTC · 1d agoAI tools & infra

Akuity gives AI agents operational context to safely ship software

Akuity launches Agentic Control Plane and MCP Server to govern AI agent actions inside production software delivery pipelines.

Akuity introduced an Agentic Control Plane and MCP Server that lets AI agents such as Claude, Codex, and Cursor access deployment and cluster context under existing identity, permission, and audit controls. MLB reported the product identified over 100 degraded applications within hours. Akuity positions it against open-source MCP servers granting raw access with no identity inheritance or policy layer.

Help Net SecuritySep 15, 08:37 UTC · 2d agoAI tools & infra1

Rapidly scaling online storage to serve over 1 billion ChatGPT users

OpenAI's Habitat online storage platform now handles over 70 million requests per second and 500 PB of data for 1 billion users.

OpenAI details the evolution of Habitat, its online storage platform backing ChatGPT and other products, which began in mid-2024 as a Python client-side library over Azure Cosmos DB. Habitat now processes more than 70 million requests per second, serves over 500 petabytes of data across nearly 40 geographic regions, and supports over 1 billion users weekly. By mid-2025 the client library approach became brittle, so OpenAI moved Habitat into a standalone service to centralize deployments, observability, and multi-tenancy reliability. This is part one of a two-part series; a future post will cover read optimization and scaling the Azure Cosmos DB partnership.

OpenAI NewsSep 11, 10:00 UTC · 5d agoAI tools & infra1

Speculative Decoding in vLLM on AMD GPUs

vLLM benchmarks speculative decoding on AMD Instinct MI300X and MI355X GPUs across five drafting methods including EAGLE-3 and native MTP.

The vLLM project documents draft-and-verify speculative decoding support for AMD GPUs via ROCm, comparing native MTP, Gemma 4 MTP, EAGLE-3, DFlash, and DSpark drafting approaches. Output-token throughput effects varied with drafting method, proposal length, model family, draft checkpoint, workload, and acceptance behavior. The post also covers how to enable each method plus practical tuning and observability considerations.

Hacker News · AISep 7, 09:26 UTC · 10d agoAI tools & infra HN 141↑ · 52 comments1

The VMs Powering Mobile Agents (Instinct, Claude Code)

A teardown reveals Claude Code runs in Firecracker microVMs with a Rust PID 1 and MITM'd egress, while Instinct rents E2B sandboxes with git-based memory.

The author inspects the virtual machines hosting cloud agents: Claude Code runs in a Firecracker microVM with a custom Rust init (process_api) as PID 1, a 324 MB Bun harness on a read-only disk, and 443-only MITM'd SSE egress to api.anthropic.com with host-rotated OAuth tokens and no inbound access. Instinct rents E2B sandbox-as-a-service Firecracker microVMs (Ubuntu 22.04, 2 vCPU, 1.9 GB RAM) where agent memory is a git repo of Markdown committed by the agent and pushed to S3 as a single bundle, using short-lived STS credentials. Both platforms rely on Firecracker, differing mainly in fleet operator and guest boot configuration.

Hacker News · AISep 8, 04:36 UTC · 9d agoAI tools & infra HN 58↑ · 19 comments1

HOL Guard: Open-source antivirus for AI agents

HOL Guard is an open-source local guardrail that pauses AI coding agents before risky actions like secret access and prompt injection.

HOL Guard sits between AI coding agents (Claude Code, Cursor, Codex, Gemini CLI and others) and the host machine, intercepting risky commands before execution with checks taking under 50 milliseconds and running fully offline. It offers four sensitivity modes — Gentle, Balanced (default), Strict, and Paranoid — and parses command structure, environment, sensitive-path access and network destinations to decide when to interrupt. The core runtime is free and open source on GitHub, with 552,000 downloads reported; the vendor says it has no telemetry on adoption because collection is off by default.

Help Net SecurityAug 31, 12:02 UTC · 16d agoAI tools & infra1

16 governance tools for securing your AI fleet

CSO Online reviews 16 AI governance and security tools, including Collibra, Credo AI, F5/CalypsoAI, Fiddler AI, and Guardrails AI, for managing LLM risks.

CSO Online surveys 16 vendors in the emerging AI governance and guardrails market for keeping production LLMs in check. Featured products include Collibra's AI Command Center, Confident Security's OpenPCC, Credo AI's Govern AI Assistant, F5's acquired CalypsoAI, Fiddler AI's control plane, and Guardrails AI's Snowglobe simulator. The tools address hallucination tracking, PII leakage, prompt injection and jailbreak defense, and compliance with frameworks such as the EU AI Act, SOC2, ISO-42001, and GDPR.

CSO Online08:25 UTC · 1h agoAI tools & infra

NVIDIA Open-Sources OSMO: One YAML Orchestrates Physical AI Training, Simulation, and Robot Testing

NVIDIA open-sourced OSMO, a Kubernetes-native YAML orchestrator running physical-AI training, simulation, and robot testing across mixed GPU tiers.

OSMO (Apache-2.0, latest release 6.3.1) lets teams describe training, simulation, and hardware-in-the-loop pipelines in a single YAML and routes tasks across datacenter GPUs (GB200), workstation RTX hardware, and edge devices like Jetson AGX Thor. It ships Helm charts and containers on NGC, uses the KAI Scheduler with NVLink topology-aware placement, and includes RBAC, OAuth2, and TLS termination. NVIDIA says it is battle-tested on GR00T, Isaac Lab, Isaac Sim, and Isaac ROS, and integrates with Claude Code, OpenAI Codex, and Cursor agents.

MarkTechPostSep 14, 08:04 UTC · 3d agoAI tools & infra

GitHub Introduces Project HydraFusion: Runtime Multi-Model Orchestration That Builds a Workflow Per Coding Task in Copilot CLI

GitHub's Project HydraFusion research preview builds per-task multi-model workflows (Single, Cascade, Critique) in Copilot CLI, reporting +4.9 quality at 67% lower cost on TerminalBench 2.1.

Project HydraFusion is a research preview available on all GitHub Copilot plans inside Copilot CLI that treats model routing as workflow selection, choosing among Single, Cascade (draft plus quality gate), and Critique (cross-family reviewer) execution patterns per request. Against Claude Opus 5 baselines at medium reasoning, fixed HydraFusion policies cut estimated cost 67% while adding 4.9 quality points on TerminalBench 2.1, and cut cost 36% and 65% with slight quality dips on DeepSWE and CheckpointBench. Billing is per token at each underlying model's standard rate; there are no open weights or self-hosting options.

MarkTechPostSep 5, 19:40 UTC · 11d agoAI tools & infra1

Meet Redis LangCache: A Managed Semantic Cache That Cuts LLM API Costs by Up to 90% and Returns Cache Hits Up to 15x Faster

Redis launches LangCache, a managed semantic cache matching LLM prompts by meaning, cutting API costs up to 90% and returning hits up to 15x faster.

Redis LangCache is a fully managed semantic caching service in public preview on Redis Cloud, accessed via a REST API with Python and JavaScript SDKs. It embeds incoming prompts, vector-searches stored entries, and returns a cached response when similarity clears a configured threshold, skipping the LLM call entirely. Redis claims up to 90% cost savings and up to 15x faster cache hits; a demo run showed 0.37 seconds versus 2.232 seconds direct inference (about 6x) with zero LLM tokens. Customer Mangoes.ai reports a 70% hit rate, 70% lower LLM spend, and 4x faster responses on a patient-care voice app.

MarkTechPostSep 10, 22:34 UTC · 6d agoAI tools & infra1

UC Berkeley Researchers Release CUA-Lite, an Open Platform Unifying Sandboxes, Data, Evaluation and RL for Computer-Use Agents

UC Berkeley's CUA-Lite is an open platform unifying computer-use agent sandboxes, datasets, evaluation and RL; Lite.OSWorld cuts OSWorld memory 4.1 GB to 0.9 GB.

UC Berkeley researchers released CUA-Lite, an open platform placing agents, environments, traces, and training for computer-use agents behind one action space, one LiteSample schema, and one command across desktop, browser, and mobile. Lite.OSWorld reproduces the OSWorld task suite and evaluators in plain Docker containers (0.9 GB RAM vs 4.1 GB, cold start 23.8s, ~4.6× more parallel instances), with scores matching the QEMU/KVM VM across 13 models. The platform claims 30k+ verifiable tasks, 15+ benchmarks, 10+ agents, and 20+ datasets on Hugging Face including Aguvis, OpenCUA, and ScaleCUA. A documented SFT run lifts Qwen3-VL-2B-Instruct mean episode return from 0.138 to 0.237 on the 332-task lite.osworld split.

MarkTechPostSep 6, 06:11 UTC · 11d agoAI tools & infra1

Top 5 AI Gateways for Enterprise (2026 Guide)

A 2026 buyer's guide ranks NeuralTrust TrustGate, Kong AI Gateway, and Cloudflare AI Gateway as top enterprise AI gateways for security and governance.

The guide evaluates enterprise AI gateways on security, governance, routing, observability, and agent ecosystem support. NeuralTrust TrustGate ranks first for identity-aware agent governance across models, MCP servers, tools, and agent-to-agent traffic, with SaaS, hybrid, and private deployment options. Kong AI Gateway is recommended for organizations with mature API infrastructure, while Cloudflare AI Gateway emphasizes caching, retries, model fallbacks, and prompt/response guardrails.

GBHackersSep 11, 16:29 UTC · 5d agoAI tools & infra

NVIDIA Details BioNeMo Inference Runtime (BioIR): 2.90x Higher Boltz-2 Folding Throughput and 58.5K Residues per GPU-Hour on 8xH100

NVIDIA released BioNeMo Inference Runtime (BioIR), an open-source PyTorch-compatible library delivering 2.90x higher Boltz-2 protein-folding throughput on 8xH100 GPUs.

NVIDIA detailed BioIR, a Python library that accelerates Boltz-2, OpenFold2, and OpenFold3 structure-prediction inference on NVIDIA GPUs while preserving standard PyTorch workflows. On a matched benchmark of 1,000 human dimers on 8xH100 80GB GPUs, BioIR delivered 58.5K folded residues per GPU-hour versus 20.2K for a torch.compile baseline, a 2.90x throughput gain. BioIR already powered the AlphaFold Database expansion, generating about 31 million candidate complexes across 4,777 proteomes, with 1.81 million released as high-confidence predictions. Extrapolated to 1 million targets, estimated folding energy drops from 35 MWh to 11 MWh at 8-GPU TDP equivalents.

MarkTechPostSep 10, 21:57 UTC · 6d agoAI tools & infra1