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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 · 6d 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 · 5h 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

Miles v0.1: Production-Level Post-Training

Radix Ark open-sources Miles v0.1, a full-stack RL post-training framework demonstrated with asynchronous agentic RL on GLM-5.2 744B-A40B across 64 GB300 GPUs.

Miles v0.1 is a full-stack, open-source system for frontier-scale reinforcement-learning post-training, built on slime with rollout engines on SGLang and trainers supporting NVIDIA Megatron-LM and PyTorch FSDP backends plus three weight-synchronization transports. It supports full-parameter RL, LoRA RL, on-policy distillation, supervised fine-tuning, true-on-policy rollout-training alignment, and extends to diffusion models. The end-to-end case study ran fully asynchronous agentic RL on GLM-5.2 744B-A40B for terminal-use coding tasks on 64 NVIDIA GB300 GPUs with a median step time of 263 seconds over the first 30 measured steps. The code is open-sourced on GitHub.

Hugging Face daily papersSep 7, 20:00 UTC · 9d agoAI tools & infra

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

Better Vector Search for Long Documents: Chunking Inside Manticore Search

Manticore Search added automatic document chunking for vector columns, lifting long-document recall@5 from 55.1% to 83.3% in its benchmarks.

Manticore Search introduced a chunk_strategy option for model-backed vector columns in CREATE TABLE, offering five strategies (truncate, mean, fixed, recursive, sentence) with tunable max_tokens, overlap_tokens, and max_chunks, eliminating external splitters and separate chunk tables. On its 189-page, ~298k-word manual, sentence chunking improved recall@5 from 55.1% to 83.3% and MRR from 0.44 to 0.70, at roughly 2.5x RAM and 4x ingest time. Documents still return as single results; queries are never chunked.

Hacker News · security10:30 UTC · 3h agoAI tools & infra HN 31↑ · 1 comments

Inside NVIDIA’s cuDNN Graph API: Fusion, Autotuning, and Plan Reuse with cuDNN Frontend

MarkTechPost tutorial walks through NVIDIA's cuDNN Frontend graph API, covering kernel fusion, autotuning, plan reuse, and CUDA graph capture on Colab GPUs.

The tutorial explains how to express GPU computations as operation graphs via the cuDNN Frontend graph API, running the five-step build pipeline of validate, build operation graph, create execution plans, check support, and build plans. It progresses from a single fused convolution with bias and ReLU to autotuning across engine configs, FP8-style epilogues, attention, plan serialization, dynamic shapes, and CUDA graph capture. Each kernel is benchmarked against a PyTorch reference on a single Colab GPU to verify correctness and measure cost. The piece also covers practical setup issues like making libcudnn.so visible to the frontend's dynamic loader.

MarkTechPostSep 15, 21:37 UTC · 1d agoAI tools & infra2

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

Google Open-Sources Mantis: A Modular Skills Toolkit That Lets Coding Agents Find, Reproduce and Patch Vulnerabilities

Google open-sourced Mantis, an Apache-2.0 modular skills toolkit that lets AI coding agents find, reproduce, and patch vulnerabilities with sandboxed verification.

Google released Mantis on GitHub under Apache 2.0 as a stack-agnostic set of slash-command skills that chain through the full vulnerability lifecycle: mining version history, building threat models, filtering findings, reproducing bugs in gVisor or network-disabled VMs, assembling exploit chains, patching, and scoring residual risk from 1 to 10. It runs with Gemini CLI, Antigravity CLI, the Google ADK, or comparable agent frameworks, and a supervisor skill (/mantis-meta-agent) can drive the whole loop. Google says the design targets the sub-7 percent true-positive rate of naive AI code scanning, and that its hierarchical summary tree cuts token overhead by over 85 percent. The toolkit is deployable for local and internal evaluation but not yet recommended for production.

MarkTechPostSep 9, 22:58 UTC · 7d agoAI tools & infra

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

Quoting Boris Cherny

Anthropic's Boris Cherny says AI-generated production code needs a higher quality bar enforced with tests, fuzzers, and automated reviews.

In remarks quoted by Simon Willison, Anthropic's Boris Cherny argued that production code written by Claude should meet a higher quality bar than human-written code. He described guardrails at Anthropic including lint rules, extensive tests, Claude-driven end-to-end tests, daily Claude-powered fuzzers, and automated code and security reviews. He warned that without such controls AI-generated code can become hard to maintain.

Simon WillisonSep 11, 17:47 UTC · 5d agoAI tools & infra2

NVIDIA Announces CUDA Rust with cuda-oxide (SIMT) and cutile-rs (Tile) for Compile-Time-Safe GPU Kernels

NVIDIA launches CUDA Rust via open-source cuda-oxide (SIMT) and cutile-rs (Tile), bringing compile-time-safe Rust GPU kernels.

NVIDIA announced CUDA Rust, making Rust a first-class language for GPU kernels through two NVlabs open-source projects: cuda-oxide for the SIMT model and cutile-rs for the Tile model. Both use Rust's ownership and borrow checker to catch buffer aliasing bugs at compile time. cutile-rs is published on crates.io, runs on stable Rust 1.89+ with CUDA 13.3, and is already used in Hugging Face's Grout inference engine and mistral.rs; cuda-oxide is early alpha requiring nightly Rust, CUDA 12.x, and compute capability 8.0+. cuda-oxide compiles Rust MIR through the community Pliron IR framework and LLVM to PTX, while cutile-rs JIT-compiles kernels via CUDA Tile IR.

MarkTechPostSep 8, 19:51 UTC · 8d agoAI tools & infra2

The OCUDU dApp Platform: An Open Runtime and E3 Interface for Real-Time AI-RAN

OCUDU open runtime lets third-party signed AI-RAN dApps run inside production 5G distributed units under three timing contracts, released as BSD-3 preview.

The OCUDU dApp platform provides an open runtime and E3 interface for executing signed AI-RAN applications inside a production 3GPP NR distributed unit, where prior dApp frameworks could only observe export streams. Modules run under three typed timing contracts: GPU receive-chain residency (Class A), the scheduler's 100 microsecond deadline (Class B), or non-blocking observer (Class C). On a GB10 gNB, dApps including an out-of-tree neural equalizer ran on a live cell without fallback. Platform, SDK, and a zero-hardware quickstart are public under BSD-3-Clause-Clear as a preview of the OCUDU AI-RAN Working Group 2.

arXiv cs.AI / cs.LG / cs.CLSep 7, 18:03 UTC · 9d agoAI tools & infra1

Perplexity Details Its GPU Embedding Stack: How Ivy, Tulip and ROSE Serve pplx-embed

Perplexity details its GPU embedding serving stack (Ivy, Tulip, ROSE), which reuses LLM prefill/decode kernels, CUDA graphs, and LazyTensors to cut launch overhead.

Perplexity engineers published a deep dive on the serving infrastructure behind pplx-embed, used across Perplexity Search and its API platform. The stack comprises Ivy (Rust HTTP gateway), Tulip (gRPC scheduling and batching), and ROSE (Runtime-Optimized Serving Engine), which reuses LLM prefill and decode kernels rather than running a separate embedding engine. Optimizations include whole-model CUDA graphs with lazy capture and a LazyTensor abstraction that overlaps CPU batch preparation with in-flight GPU work. Benchmarks are reported against vLLM v0.22.0 in BF16, with FlashAttention 4 generally fastest but FlashInfer 3 winning on Qwen-based models at very long sequence lengths.

MarkTechPostSep 6, 03:20 UTC · 11d agoAI tools & infra1

Introducing agentic video understanding with Gemini

Google DeepMind launches agentic video understanding for Gemini 3.7 Flash, 3.6 Flash, and 3.5 Flash-Lite, cutting video-analysis tokens up to 88%.

Google DeepMind launched agentic video understanding across Gemini 3.7 Flash, 3.6 Flash, and 3.5 Flash-Lite via the Gemini API in Google AI Studio and the Gemini Enterprise Agent Platform. The feature replaces static fixed-FPS ingestion with an agentic loop that dynamically searches frames, audio, and transcripts, cutting token consumption by up to 88%, reducing costs by up to 66%, and improving accuracy by up to 7%. Gemini 3.7 Flash with the feature sits at the accuracy-to-cost Pareto frontier on tested video benchmarks, and the capability will later power YouTube's Ask YouTube feature.

Google DeepMindSep 1, 17:08 UTC · 15d agoAI tools & infra

The Evolution of the Agent Harness

Latent Space essay argues late-2025 agent gains came from models and harnesses maturing together, with harness logic absorbed into model weights.

The piece defines the agent harness as everything beyond model weights—tools, context, memory, guardrails—and charts its evolution from ReAct prompting (October 2022) through AutoGPT's premature autonomy, Cursor/Copilot's human-in-the-loop retreat, and Devin's roughly 15% success rate, to o1's capability overhang and Claude Code's February 2025 terminal agent with permission rules. It argues the Christmas 2025 jump cited by Transformer co-inventor Lukasz Kaiser reflected model and harness curves crossing, and that remaining harnesses will serve human attention rather than the model.

Latent SpaceAug 22, 07:30 UTC · 26d agoAI tools & infra

harshatheg/Qwen-2.5-1B-RLCD — new model trending #30 on Hugging Face

A community MLX inference engine evaluates constrained JSON schema fields in parallel on Apple Silicon, reporting 5.6-7.0x latency speedups with guaranteed schema validity.

The repository harshatheg/Qwen-2.5-1B-RLCD appeared at #30 on Hugging Face trending, but its content describes Parallel Constrained Decoding, an MLX-based inference engine for structured extraction and classification on Apple Silicon Macs. Benchmarked with mlx-community/Qwen2.5-1.5B-Instruct-4bit on an M4 Max, it reports 5.6x-7.0x latency reductions (e.g., 1,900 ms to 270 ms for a 28-field support triage task) with 100% syntactic validity and calibrated field-level probabilities. The engine prefills a single KV-cache, broadcasts it across all schema fields, and slices logits to valid candidate tokens for enum fields with up to 255 choices.

Hugging Face trending modelsSep 16, 01:35 UTC · 1d agoAI tools & infra1

Traefik Labs brings independent verification to AI agent governance

Traefik Labs announces Sovereign Trust Plane in Traefik Hub, adding verifiable delegation, policy enforcement, and tamper-evident audit records for AI agent traffic.

Traefik Labs announced the Sovereign Trust Plane for Traefik Hub, generally available by September 30, 2026, providing delegated access, policy enforcement, and tamper-evident records for AI agent, tool, and API traffic. It implements the IETF ID-JAG draft with Okta Cross App Access and Janssen, enforces decisions through OpenID AuthZEN with OpenFGA and Cerbos, and commits cryptographic log fingerprints to transparency checkpoints verified by independently administered witnesses. The gateway also extends enforcement to MCP tool calls and the MCP server's backend API connection.

Help Net SecuritySep 15, 08:20 UTC · 2d agoAI tools & infra1

Agent-net Open Sources Webagent: A Go Harness That Turns Any Website into a Guarded AI Agent

Agent-net open-sourced Webagent, a Go harness turning websites into AI agents with code-enforced guardrails wrapping every tool call.

Agent-net released Webagent under Apache 2.0, a Go framework where a business fills in a declarative JSON spec, picks one provider for each of nine pluggable slots (retrieval, memory, guardrail, channel, secrets, presenter, model, action, observability), and runs webagent serve. Every tool the agent holds is wrapped by action.Guard so the chosen guardrail executes before any action runs and the model cannot bypass it. Live capabilities include OpenRouter/gateway LLM brains, MCP tools over Streamable HTTP, and Slack, WhatsApp, and HTTP channels; browser actions, OAuth-gated MCP, OTel export, and AgentNet identity/billing are not yet built. The project is v0 with a deferred-hardening list and cites arXiv 2511.19477 on an 85% versus 50% task-success gap attributed to architecture over model capability.

MarkTechPostSep 15, 04:03 UTC · 2d agoAI tools & infra1

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

Introducing the Agents API

OpenAI launched the Agents API in public beta, exposing the Codex agent harness, managed sandboxes, and multi-agent orchestration to developers.

OpenAI introduced the Agents API in public beta, giving developers the same agent harness and infrastructure that powers Codex through a single API call specifying task, model, tools, and environment. It supports OpenAI-managed sandboxes, customer infrastructure, or partner environments from providers including Cloudflare, Modal, E2B, Vercel, Oracle, DigitalOcean, Blaxel, Daytona and Runloop. Features include automatic context compaction for long sessions, tool search and programmatic tool calling to reduce token usage, and multi-agent support for parallel subagents. The harness is open-source Codex code; there are no extra API fees during beta, with developers paying only for tokens and tools used.

OpenAI NewsSep 10, 00:00 UTC · 7d agoAI tools & infra1

Prisma Airs

Palo Alto Networks Unit 42 page for Prisma AIRS, the vendor's AI Runtime Security product line for protecting AI applications.

The item is a Unit 42 product category page for Prisma AIRS with no article text available. Prisma AIRS is Palo Alto Networks' AI Runtime Security offering aimed at securing AI applications and agents. No incident, research findings, or release details can be confirmed from the available title alone.

Palo Alto Unit 42Aug 28, 18:30 UTC · 19d agoAI tools & infra

F5 enhances AI Gateway to control AI costs, access, and security

F5 integrated AI Gateway into its AI Security Platform, adding model routing, MCP governance, and guardrails, claiming up to 60% token spend reduction.

F5 announced AI Gateway enhancements combining a Model Gateway for cost optimization, an MCP Gateway for agent-to-tool access control, and AI Guardrails for prompt and response inspection. The company cited its 2026 State of Application Strategy Report finding 77% of organizations now treat inference as their dominant AI activity and manage an average of seven AI models. F5 claims smart routing, semantic caching, and GPU-aware load balancing can cut token spend by up to 60% without application changes. The gateway enforces budgets, model routing policies, and agent access controls centrally across SaaS, hybrid SaaS, and hybrid multicloud deployments, with air-gapped support planned.

Help Net SecurityAug 19, 00:00 UTC · 29d agoAI tools & infra

Show HN: Nari Qwen3-TTS and Qwen3-ASR – High accuracy, low latency and cost

Nari Labs claims top Coval voice AI benchmark rankings with low-latency, low-cost Qwen3-ASR and Qwen3-TTS inference endpoints.

Nari Labs says its Qwen3-ASR Fast endpoint ranks #1 in Coval's time-to-final-segment latency (p50 44 ms) with 3.6% WER at $0.12/hour, behind only AssemblyAI Universal 3.5 Pro on accuracy. Its Qwen3-TTS Fast ranks #2 in time-to-first-audio (p50 63 ms) and #1 in WER at 3.8%, priced at $10 per 1M characters. The company reports beating the official Qwen3 TTS Flash Realtime endpoint (8.8% WER, 692 ms median TTFA) and Baseten's dedicated endpoint (6.0% WER, 101 ms). Public beta APIs are moving to paid general availability with $20 in credits for existing accounts.

Hacker News · securitySep 14, 16:07 UTC · 2d agoAI tools & infra HN 21↑ · 7 comments1

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

AWS Introduces Pizza Bot: An Open Source Inbox for Background AI Agents

AWS open-sourced Pizza Bot, a self-hosted inbox app for background AI agents with approval gating and multi-provider model support.

AWS released Pizza Bot under Apache 2.0 after earlier versions served over 2,000 Amazon employees for meeting prep, email drafting, and research. The app provides macOS, Windows, and Linux desktop builds plus browser and terminal clients talking to a Hono API server, with LangGraph/DeepAgents checkpoints preserving thread state and approval pauses. It supports Amazon Bedrock, Anthropic, Google Gemini, OpenAI, OpenRouter, and Ollama, exposes external tools via MCP servers, and lets skill authors gate actions behind approve/edit/reject flows.

MarkTechPostSep 13, 08:10 UTC · 4d agoAI tools & infra1

Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference

Hands-on tutorial implements NVIDIA cuML and RAPIDS to GPU-accelerate scikit-learn-style ML workflows with benchmarking, clustering, and inference.

The tutorial demonstrates NVIDIA cuML as a GPU-accelerated machine learning framework, using cuml.accel to speed up unmodified scikit-learn scripts with zero code changes and the native cuML API for CuPy/cuDF interoperability. It benchmarks CPU versus GPU implementations of PCA, K-Means, nearest-neighbor search, logistic regression, random forests, and DBSCAN on datasets up to 200,000 samples with 64 features. It also builds GPU pipelines with UMAP, t-SNE, and HDBSCAN, validates GPU-generated SHAP explanations, uses the FIL library for forest inference, and covers model serialization and GPU/CPU portability.

MarkTechPostSep 13, 01:42 UTC · 4d agoAI tools & infra

GPT-6 Astra needs leaner prompts and fewer guardrails, OpenAI recommends

OpenAI's Eric Provencher advises developers using GPT-6 Astra to shorten skill descriptions, trim AGENTS.md reading requirements, relax approval rules, and define clear completion goals.

OpenAI's Eric Provencher published guidance on adapting developer setups when switching to GPT-6 Astra, arguing that overly long skill descriptions, blanket reading requirements, and rigid approval rules waste context or make the agent stop too early. Skills are Markdown prompt files whose names and descriptions enter Codex's context, and too many or conflicting skills cause truncation and wrong skill selection. He recommends selective document references in AGENTS.md, explicit permissions for safe operations like local test runs, and defining upfront what "done" means, since Astra may stop earlier than GPT-5.6 Sol even without restrictions.

The Decoderupdated Sep 12, 20:00 UTC · 4d agofirst Sep 12, 13:10 UTC · 5d agoAI tools & infra 7 sources1

Anthropic Adds Plugin Evals to Claude Code: 6 Grader Types, a No-Plugin Baseline, and a CI Gate for Skills

Anthropic ships a plugin evals workflow for Claude Code with six grader types, a no-plugin baseline arm, and a CI gate via threshold and cost flags.

Anthropic published a plugin evals workflow for Claude Code, exposed via the "claude plugin eval" command on v2.1.269+. Six grader types exist: regex, tool_used, tool_order, and file_exists are free transcript checks, while llm and baseline invoke a billed judge model. Every case runs with and without the plugin, and the delta (Δ) isolates the plugin's contribution; a Δ near zero with a failing tool_used:Skill grader indicates the skill never triggers. CI gating uses --threshold 0.8, --max-cost-usd, --trust-plugin, and --no-publish flags, with results written to a report.html under evals/results/.

MarkTechPostSep 11, 21:05 UTC · 5d agoAI tools & infra2

OpenAI Launches the Agents API in Public Beta, Putting the Codex Harness Behind One API Call

OpenAI released its Agents API in public beta, exposing the managed Codex harness with hosted or self-hosted sandboxes, MCP tools, and subagents.

The Agents API is a managed service built on the open-source Codex harness, handling context compaction, tool search, programmatic tool calling, and multi-agent orchestration. Agents run in OpenAI-hosted sandboxes, self-hosted environments, or partner sandboxes from Blaxel, Cloudflare, Daytona, DigitalOcean, E2B, Modal, Oracle, Runloop, and Vercel. Data residency is US-only and Zero Data Retention is unsupported. Examples use model gpt-6-astra; vendor-reported results include SafetyKit cutting case review cost 60% and Ciridae achieving 4x lower subagent latency.

MarkTechPostupdated Sep 11, 08:11 UTC · 6d agofirst Sep 10, 21:21 UTC · 6d agoAI tools & infra 3 sources1

LandingAI Releases Agentic Document Extraction Gen2 with DPT-3 Pro and DPT-3 Verity

LandingAI shipped Agentic Document Extraction Gen2 with DPT-3 Pro and DPT-3 Verity parsing models, adding usage-based billing, block-tree outputs, and word-level grounding.

LandingAI has generally released Agentic Document Extraction Gen2, rebuilt around two parsing models: DPT-3 Verity for deterministic transcription of digital documents with per-word bounding boxes and confidence scores, and DPT-3 Pro for layout-aware parsing of scans, handwriting, non-Latin scripts, and LaTeX math. Billing changes from a flat 3 credits per page to a page-plus-output-character model (Pro: 1 credit/page plus 0.5 credits per 1,000 output characters on priority; Verity: 0.3 plus 0.2), with an asynchronous standard tier at 0.5x price and vendor-claimed 25-80% cost reductions. Parse v2 returns a document-page-block tree with semantic IDs, normalized bounding boxes, and line- or word-level atomic grounding, replacing flat chunks; Gen1 client code will not run against Gen2 endpoints. Deployment options include US/EU cloud, VPCs on AWS, Azure, and Google Cloud, Snowflake, and air-gapped on-premises environments, with automated model routing planned for fall 2026.

MarkTechPostSep 10, 02:13 UTC · 7d agoAI tools & infra

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

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

Nous Research Adds One-Click Local Model Setup to Hermes Desktop

Nous Research's Hermes Desktop now offers one-click local model setup that reads hardware, picks a fitting quantization, downloads weights, and configures llama.cpp automatically.

Hermes Desktop, the MIT-licensed build of the open-source Hermes Agent, now sets up local models in one click: it reads the machine's hardware, selects a model that fits, downloads weights, and configures the inference runtime. It manages a pinned llama.cpp build with CUDA, Metal, Vulkan, HIP, and CPU backends, and shows green/amber/red memory-fit verdicts per catalog model before download. Quantization floors at 4-bit, and recommended models guarantee at least a 64K context window protected by ordered RAM offload (expert weights first, never the attention cache). It runs on macOS 12+, Windows 10/11, and Linux with no account required for local models.

MarkTechPostSep 5, 19:12 UTC · 11d agoAI tools & infra1

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 · 17d agoAI tools & infra1

Halo-record: Open-source audit trails for AI agents

Developer Brian Kuan released halo-record, an open-source Python package creating tamper-evident, hash-chained audit logs of AI agent actions.

Halo-record is a roughly 5,300-line Python package with no runtime dependencies that records agent tool calls, model calls, data access and approvals into an append-only, hash-chained log that customers can verify without vendor trust. Adapters ingest records from OpenTelemetry spans, LangChain, MCP servers and gateway logs, with secret and PII values auto-redacted. The author plans to fund the work through a hosted witness service that stores the record count and head hash to prove completeness, citing mandates like AIUC-1, the EU AI Act, and insurers. The article cites the July Hugging Face intrusion, where an autonomous agent took roughly 17,600 actions over five days and manual reconstruction of its activity was impractical.

Help Net SecurityAug 31, 00:00 UTC · 17d agoAI tools & infra1

The OpenClaw 2.0 release moves your sessions into SQLite

OpenClaw 2.0 migrates AI agent sessions to SQLite, adds guided credential setup, and flags shared-session controls as not a security boundary.

OpenClaw 2.0, described as the largest update in the project's history, migrates sessions and transcripts into SQLite and adds guided setup that detects existing AI credentials from Codex, ChatGPT, Claude CLI sign-ins, API keys, and local Ollama or LM Studio models. The release expands multiplayer sharing while explicitly stating its permission controls are not tenant isolation or a security boundary, and that revoked access can briefly remain usable. Startup JavaScript requests fell from 140 to 45 and startup time from about 1.6 seconds to 575 milliseconds in simulated tests against a mocked Gateway. Automation wrappers must now inspect reported health because requesting --json does not waive risk acknowledgement.

Help Net SecurityAug 31, 00:00 UTC · 17d agoAI tools & infra1

Linux Foundation takes on TRACE, a hardware-backed runtime evidence specification for AI agents

The Linux Foundation adopts TRACE, an OPAQUE-contributed spec giving AI agents hardware-attested, cryptographically verifiable runtime and compliance evidence.

The Linux Foundation accepted the TRACE (Trust, Runtime Attestation and Compliance Evidence) specification contributed by OPAQUE, developed with AMD, Intel, Microsoft, and the Technology Innovation Institute. TRACE binds runtime environment, software, policies, data classifications, and tool usage into a portable, cryptographically verifiable artifact, composing existing standards such as RATS, EAT, SLSA, SCITT, SPIFFE, and EAR. It recorded nearly 135,000 PyPI downloads within 10 weeks of its June 2026 introduction, and its technical workstream will be hosted by the Coalition for Secure AI.

Help Net SecurityAug 26, 00:00 UTC · 22d agoAI tools & infra

GitLab 19.3 helps enterprises scale agentic development securely

GitLab 19.3 runs its Duo Agent Platform AI Gateway inside Dedicated single-tenant environments and adds Secrets Manager plus agentic SAST remediation.

GitLab 19.3 lets GitLab Dedicated customers run the Duo Agent Platform AI Gateway within the same single-tenant environment and region, with support for bring-your-own inference models. The release adds Secrets Manager in limited availability scoping secrets to environment and branch across Kubernetes, Terraform, and OpenTofu, plus bulk SAST false positive detection and agentic vulnerability resolution generating ready-to-merge fixes. Flow Creator Agent creates automation flows from plain-language descriptions, and GitLab Credits usage caps are now generally available.

Help Net SecurityAug 21, 00:00 UTC · 27d agoAI tools & infra1