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RTK reports token savings, but our cost benchmarks disagree

Quesma's $1,500 benchmark found RTK cuts reported token output but changes Claude Code and DeepSeek coding costs by only about 5% on Terminal-Bench 2.1.

Quesma benchmarked RTK (Rust Token Killer), a popular tool with 79k GitHub stars that filters terminal output for AI coding agents, whose README claims up to 90% output reduction. Across 1,740 Terminal-Bench 2.1 attempts running Claude Code with Fable 5.0 and OpenCode with DeepSeek V4 Pro 0813, total costs moved only -5% for Fable and +5% for DeepSeek, with pass rates dropping 1-2%. RTK's own rtk gain metric reported 349.2 million tokens saved (an 89% reduction) across 445 DeepSeek attempts, but this did not correlate with actual cost savings, and cached terminal-output reads cost as little as 1/10 to 1/30 of regular input tokens. A bug in rtk find 0.45.0 caused one agent to loop with 339 consecutive errors, costing roughly 9x the baseline attempt, though the task still passed.

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.

Better Vector Search for Long Documents: Chunking Inside Manticore Searchnew

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.

Ask HN: How do you manage skills files?

A Hacker News thread debates whether agent skill files are worth managing, citing 2–4x output-token reductions on flagship models in one company's testing.

Commenters argue skills are stored prompts that help less-technical users compensate for weak prompting, while one participant reports company testing found skills reduce flagship-model output tokens by roughly 2–4x, a gap growing with newer models. Others note skills can bundle reusable scripts and inline commands for deterministic context building, and that harnesses now execute backticked commands before the agent sees the skill. Some argue improving model capability makes downloadable skills redundant.

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.

MarkTechPost · 11d agoAI tools & infra1

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/.

MarkTechPost · 5d agoAI tools & infra2

Show HN: LLM Attention Visualization

A developer released a browser-based tool that visualizes which past tokens influence each LLM output token using aggregated, value-weighted attention scores.

A Show HN project presents a React application built on Transformers.js that renders per-token attention influence by aggregating attention weights scaled by value-vector magnitudes across all attention heads and layers. To expose internal tensors, the author instrumented the ONNX computation graph, hosted a modified model on Hugging Face, and pre-generated prompts to avoid long model downloads in the browser. Demos with a 600-million-parameter model show how verbatim copying draws heavily on source tokens and how single outputs blend information from multiple phrases.

OpenAI Agents API

OpenAI's Agents API documentation describes a managed Codex harness offering sandboxed agents, MCP connectivity, subagents, and US-only data residency.

The Agents API lets applications run durable agent sessions while OpenAI manages orchestration, context compaction, and recovery on the Codex harness. Agents can execute code, edit files, and connect to MCP servers in OpenAI-hosted or self-hosted sandboxes. Example applications include an incident response agent, Slack bot, data analyst, and GitHub issue investigator. Billing follows model, tool, and container rates; the examples use model gpt-6-astra.

Hacker News · securityupdated · 6d agofirst · 6d agoAI tools & infra 3 sourcesHN 20↑ · 16 comments2

numbat - AI agent observability, (Fri, Sep 4th)

SANS reviews Perplexity's open source numbat, a Go-based tool giving security teams observability, detection rules, and enforcement for AI agents like Claude and Gemini.

Numbat, Perplexity AI's open source observability tool, monitors desktop, CLI, IDE, and gateway AI agents through local hooks, OTLP/HTTP logs, and on-disk session artifacts. It ships detection rules mapped to MITRE ATT&CK (e.g., recon.network_sweep / T1046), supports enforcement mode, and packages investigations with SHA256-verified manifests and timelines. The SANS review positions it as a response to unmanaged AI agent and MCP server sprawl highlighted by the OpenAI/Hugging Face incident.

SANS Internet Storm Center · 12d agoAI tools & infra

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 Security · 17d agoAI tools & infra1

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 · 4d agofirst · 5d agoAI tools & infra 7 sources1

OpenAI's new Agents API gives developers the infrastructure behind Codex and ChatGPT

OpenAI released its Agents API in public beta, exposing the infrastructure behind Codex and ChatGPT for building long-running cloud agents.

OpenAI launched the Agents API as a public beta, letting developers build cloud-based agents that can run for hours, execute code, and process files on the same infrastructure that powers Codex and ChatGPT. Features include automatic context management, parallel tool use, and task delegation to sub-agents, with a choice of OpenAI-hosted sandboxes or partner environments from Cloudflare, Vercel, and Oracle. The API builds on the open-source Codex harness and supports MCP, custom functions, and built-in tools such as web search, with no extra fees beyond token-based billing.

The Decoder · 6d agoAI tools & infra 3 sources1