automem recall pipeline live autohub orchestration notes wp fusion still pays the bills autojack last pass: recent skills indexed locally debug notes from production automem recall pipeline live autohub orchestration notes wp fusion still pays the bills autojack last pass: recent skills indexed locally debug notes from production
VOL.04 / ISS.27
EST. 2009 · MIA / LTS / GPL
jack arturo · vgp
"Just another Wordprussite." — a working notebook for memory-bearing agents, half-built systems, and bugs we learned to live with.
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Category: Autonomous Systems

AI agents and autonomous workflow content

Log chronological · most recent first 20 entries
July2026 // scroll ↓
AutoMem Has No Night Shift A Tencent paper built a cognitive tier hierarchy for agent memory systems. AutoMem lands at Tier 2 — the supersedes chains are exactly what they call "diachronic belief trajectories." But Tier 3 needs a nighttime consolidation engine that AutoMem doesn't have yet.
June2026 // scroll ↓
AutoMem 0.16.0 AutoMem 0.16.0 shipped yesterday afternoon — hours after the benchmark post went up. Here's what's in the recall-ranking release: tag-score cap, configurable recency bias, state_mode, metadata sidecar search, and a self-improving recall lab. We’re on the Leaderboard AutoMem submitted to the Agent Memory Benchmark yesterday. BEAM 10M: 57.4% — beating Honcho by 16.8 points, entering the leaderboard at #2. The Nighttime Engine AutoMem has System-1 memory — supersedes chains, temporal windows, graph recall. System 2 (idle schema induction) is the gap, and why implicit inference needs it. Plan B: The Baseline Wins We built the AutoMem recall-quality optimization harness. Plan B ran the first matrix comparison. The baseline won — NDCG 0.929 vs 0.860. A null result as calibration, and why that's actually the good outcome. The Benchmark That Grades Memory on What It Forgets A new ACL 2026 benchmark grades memory systems on what they stop recalling, not just what they remember. AutoMem's t_invalid and INVALIDATED_BY infrastructure was built for exactly this — before the benchmark existed. When All Your Safety Guards Vote the Same Way Three independent safety guards in AutoHub's agent delegation pipeline all defaulted to read-only mode. Each was individually reasonable. Together they built a consensus machine for paralysis. Before the First Score AutoMem's first formal BEAM benchmark run is queued. Pre-flight analysis flags two high-risk ability gaps — Knowledge Update and Abstention — before we've run a single question.
May2026 // scroll ↓
Quiet PRs The Clerk engineering director had been using AutoMem, submitting PRs, and having normal technical conversations — without either party knowing who the other was. Quiet PRs are better validation than loud announcements. The Edges That Did Nothing AutoMem PR #170 shipped: INVALIDATED_BY and EVOLVED_INTO graph edges were stored in FalkorDB but ignored at recall time. Stale memories still surfaced. current_only=true is now the default — lifecycle edges are enforced, not decorative. Before the Benchmark The AutoMem Opportunity Scout selected BEAM as the next benchmark target — but before that eval can be honest, there's a prerequisite: the classifier has to be right. One More Layer After “Done” The wake word base model was trained. Then we added a verifier layer — a lightweight sklearn classifier that gates the base model's activations for precision. The Wake Word is Done The custom 'AutoJack' wake word is trained and working — speaker-specific, demo-proof. Plus audio cues shipped to fix the silence-equals-fabrication problem. Both sides of voice UX improved on the same day. Skills Don’t Need a Server (Yet) The obvious architecture for a skill distribution system is a service. The right one is a directory. YAGNI isn't just a rule about features — it applies to infrastructure layers too.
April2026 // scroll ↓
We Have a Music Video Pipeline Now Brewery session → fake band → "can we make a music video?" → Wan2.2 MLX running locally on Apple Silicon, 40 seconds per scene. Worked. Then immediately hit a Slack upload failure. Also fixed. One App, Many Faces One Slack helper app with chat:write.customize renders any agent persona per message. No separate app per agent. One gotcha: channels:join isn't implied. Here's the pattern. Retrieval Isn’t the Hard Part AutoMem's full 500-question LongMemEval run: 86.20% accuracy, 97.20% recall@5. The 11-point gap between those numbers is the real finding — and it's not a retrieval problem. The Demo That Worked a Little Too Well Late night in Berlin. A live AutoMem demo to a first-time user. The key question: can I use it on mobile? The answer, and what happened next. It Knows It’s Broken The moltbook-engagement workflow has been failing on the same bug for two days. Every cycle writes a perfect postmortem. Every next cycle makes the same mistake. This is what happens when observability and correctability aren't the same thing. Third Time Was the Charm Home Assistant is wired into AutoHub. The feedback that shaped the integration came through the clipboard because TTS wasn't working — but it got there.