Companion gist for PR #2163 and the Dream Cycle 2026-05-27 capabilities-scan finding (#2156).

Session date: 2026-05-27 · Commits landed: a6dd4ab3d, dede70efd, 88743c482, 7e3ec89e4, a7dfdec4c · Branch: feat/2156-agent-benchmark-suite

How fast is the framework layer of an AI agent system? We benchmarked ruflo 3.8.0 against LangGraph 1.2.1, AutoGen 0.4.9, CrewAI 0.80.0 on an identical workload, on two operating systems, with a stub LLM (so we measure framework overhead, not model latency).

The short version: ruflo is faster than the comparators on cold start, single-turn dispatch, and memory footprint by 1.3× to 1,953× — on both macOS and Linux. On the two dimensions where CrewAI shows a slight edge (compose_50_tools and N=10 parallel), CrewAI's numbers are proxied lower bounds — its real dispatch requires an LLM call that adds seconds.

Project: RuView / WiFi-DensePose · Date: 2026-05-24 · Spec: docs/research/soul/ in the repo

Your body is constantly bouncing radio waves around the room you're in. WiFi routers fill that room with 2.4 / 5 / 6 GHz signals; you reflect, absorb, and re-scatter them in a pattern that's measurably unique to you — your heartbeat, your breathing, your posture, your gait, your skeletal proportions, the way your chest cavity flexes when you inhale. RuView captures those reflections from a $9 ESP32 sensor and fuses them into what we call a Soul Signature — a single per-person electromagnetic fingerprint. No camera. No microphone. No fingerprint reader. No wearable on your wrist or in your ear. The system just notices you in the room and knows who you are — through walls, in the dark, while you sleep.

Package: @claude-flow/guidance@3.0.0-alpha.3 · 15+ source files, 1,331 tests Repo: https://github.com/ruvnet/ruflo · branch perf/guidance-phase-1-hotpath-optimizations · PR #2103 Date: 2026-05-22 · Node v22.22.1 on darwin-arm64 Methodology: 5-trial median, 50-2000 iterations per trial depending on N, warmup phase to trigger V8 JIT tier-up

| Metric | Baseline | M4 quantized | Speedup |

Real-time relationship intelligence with complexity-aware execution. Built on sublinear-time-solver@1.7.0. 12 graph wedges, signed reasoning artifacts, federation-distributable PageRank vectors. 104/104 tests. MIT.

Most AI memory systems recompute everything. RuFlo computes only what changed enough to matter — and only at the depth the runtime can afford.

Status: Proposed (2026-05-18) — revised 2026-05-19 to track upstream sublinear-time-solver@1.7.0 Date: 2026-05-18 Authors: claude (drafted with rUv) Related: sublinear-time-solver@1.7.0 (crates.io sublinear@0.3.0, github, 1.6.0 announcement gist, upstream sublinear ADR-001 "Complexity as Architecture"), eleven-wedge research gist, ADR-103 (witness temporal history), ADR-104 (federation wire transport), ADR-105 (federation state snapshot), ADR-118 (AIDefence 2.3.0), ADR-121 (embeddings RuVector u

Released 2026-05-18. Tags: v1.6.0. Package: sublinear-time-solver (npm), consciousness-explorer (npm), sublinear (crates.io).

Yes, immediately, if any of these apply to you:

1. You expose the MCP tools to anything other than trusted local clients. This release closes a remotely-exploitable arbitrary file-write (CWE-73) reported by @BruceJqs in issue #19. The same bug existed in two MCP tools — export_state (consciousness-explorer) and saveVectorToFile (main package). Both are fixed in 1.6.0.

TL;DR. RuFlo is no longer "a browser agent." With ADR-122 it becomes the substrate underneath Stagehand, Browser Use, Surfer-H, Playwright, Browserbase, and Operator: signed-replay trajectories, causal-graph self-healing, attested cookie vaults, federated MCTS, risk-class gating, and a workflow compiler that emits replayable YAML. 230 tests, 0 new CVEs, all under 100µs.

The public stack today:

agent → browser → action → observation → next action

Most rooms are already full of radio waves. Your WiFi router is constantly transmitting, and your phone, laptop, and smart-home gear are constantly listening. Every human body in the room subtly disturbs those waves — moving, breathing, even just sitting still. RuView is open-source software that turns those disturbances into useful data: where people are, what they're doing, whether they're okay.

Just shipped v0.6.5 of the ESP32 firmware. This is the bit that runs on the $9 sensor board.

Links:

• 📦 Release with flash-ready binaries: https://github.com/ruvnet/RuView/releases/tag/v0.6.5-esp32
• 🔗 Source repo: https://github.com/ruvnet/RuView
• 🌐 Project page: https://cognitum.one/ruview

A retrieval library that ships every modern RAG primitive (plain top-k, MMR, RRF, HyDE, hybrid sparse+dense), composes them through an adaptive router, and signs every benchmark result so you can prove the numbers came from the published code.

Why this exists: most RAG libraries ship one retrieval shape (dense top-k) and call it done. Production retrieval doesn't work like that — different query shapes need different fusion strategies, and the numbers vendors publish are usually unverifiable claims. This library covers the full primitive set, picks between them automatically, and chains every benchmark into a tamper-evident hash ledger anyone can verify.

Current version: @claude-flow/embeddings@3.0.0-alpha.45 · companion CLI: ruflo@3.7.0-alpha.69.