From cf7461ffeb4f53f3f50c9c692a2e4fdcecf17e82 Mon Sep 17 00:00:00 2001 From: Agando Date: Thu, 9 Jul 2026 06:00:32 +0200 Subject: auto: 2026-07-09_06:00 changes --- downloads/system/program-specs.html | 265 +++++++ .../research-self-improving-ai-2025-2026.html | 716 +++++++++++++++++ downloads/system/roadmap.html | 243 ++++++ downloads/system/system-review-v4.6.00.html | 603 +++++++++++++++ downloads/system/system-review-v4.6.02-public.html | 850 +++++++++++++++++++++ downloads/system/system-review-v4.6.02.html | 767 +++++++++++++++++++ 6 files changed, 3444 insertions(+) create mode 100644 downloads/system/program-specs.html create mode 100644 downloads/system/research-self-improving-ai-2025-2026.html create mode 100644 downloads/system/roadmap.html create mode 100644 downloads/system/system-review-v4.6.00.html create mode 100644 downloads/system/system-review-v4.6.02-public.html create mode 100644 downloads/system/system-review-v4.6.02.html (limited to 'downloads/system') diff --git a/downloads/system/program-specs.html b/downloads/system/program-specs.html new file mode 100644 index 0000000..c1d3ae3 --- /dev/null +++ b/downloads/system/program-specs.html @@ -0,0 +1,265 @@ + + + + + + +newOS4 — Program Specs v4.5.96 + + + + +

newOS4 Program Specs v4.5.96 SHIPPED

+

Last updated: 2026-07-05 · 45 modules · 40 skills · 3 system agents

+ +
+📑 Table of Contents +
+1. System Overview
+2. Architecture
+3. ShellModule (v4.5.96 NEW)
+4. OrchestratorModule (v4.5.96 fixes)
+5. Cross-Cutting Rules (R1–R7)
+6. Module Catalog
+7. Bug Status (BUG-001 → BUG-019)
+8. Open Issues +
+
+ +

1. System Overview

+

newOS4 is a modular agent operating system with a VFS, pluggable modules, and an AI-driven terminal. v4.5.96 is the latest shipping release after 2 bug-fix sessions.

+ + +

2. Architecture

+

Layer 0 — Kernel

+ +

Layer 1 — Core AI

+ +

Layer 2 — Domain

+ +

Layer 3 — UI / Ext

+ + +

3. ShellModule NEW v4.5.96

+

Layer-2 module exposing built-in shell commands to the terminal.

+

Commands

+ +

Pipes & Redirects

+ +

Security Gate (v4.5.96)

+
Destructive commands (rm, redirects) MUST pass canExecute() gate. Patches assert enforcement at boot, not just API definition.
+ +

4. OrchestratorModule 8 fixes v4.5.96

+

Manages agent lifecycle, task delegation, and worker spawn.

+

v4.5.96 Fixes

+ + +

5. Cross-Cutting Rules (R1–R7)

+

Extracted from v4.5.96 bug fixes. All module authors MUST follow.

+

R1 — Lifecycle: registerModule() calls init()

+

All timers, intervals, and event listeners MUST be registered in init() and stored for cleanup. No orphan timers.

+ +

R2 — Error visibility

+

this.print writes to AI context. this.terminal.print writes to user only. Use AI-visible for errors that affect reasoning.

+ +

R3 — Cleanup via try/finally

+

All monkey-patches, depth counters, and global state mutations MUST use try/finally to guarantee rollback.

+ +

R4 — Security patches ASSERT enforcement

+
A security patch that defines an API but doesn't assert it's enforced is a vulnerability. Boot-time self-test required.
+ +

R5 — System agents are immutable

+

The 3 system agents (coder, researcher, reviewer) cannot be killed, purged, or modified. killAgent() checks isSystemAgent flag.

+ +

R6 — Compute state at use site

+

Don't cache module state at boot. Compute on demand. Stale-cache bugs were 30% of v4.5.96 fixes.

+ +

R7 — Parser dedup: extract once

+

Helpers like extractRedirect() must be called exactly once per command parse. Double-extraction is a P0-class bug.

+ +

6. Module Catalog

+

Tier 1 — Core AI (12 modules)

+ + + + + + + + + + + + + + +
ModuleClassLayer
AIModuleAIModule1
ContextModuleContextModule1
AgentLoopAgentLoop1
MemoryGraphMemoryGraph1
WorldModelWorldModel1
SmartContextSmartContext1
TokenTrackerTokenTracker1
PromptEnginePromptEngine1
ModelRouterModelRouter1
SkillLoaderSkillLoader1
VaultMindVaultMindModule1
PredictTaskPredictTaskModule1
+ +

Tier 2 — Domain (15 modules)

+ + + + + + + + + + + + + + + + + +
ModuleStatus
TerminalModuleOK
ShellModuleNEW v4.5.96
OrchestratorModule8 fixes
FSModuleOK
NVFSModuleOK
ModuleManagerOK
LeaseModuleP0 NEEDS RE-PATCH
CronModuleP1 jobs missing
AgentManagerOK
HyperliquidP0 init
SecretVaultOK
WebModuleOK
TarModuleOK
SandboxModuleOK
CommandRouterOK
+ +

Tier 3 — UI/Ext (18 modules)

+

Browser, Mindmap, WPM, Floating, WebDAV, GraphVisualizer, ChatUI, Settings, Theme, Notification, Backup, Health, Telemetry, Plugin, Marketplace, Migration, Diagnostic, Help.

+ +

7. Bug Status

+ + + + + + + + + + + + + + + + + + + + +
IDSeverityModuleStatus
BUG-001P0OrchestratorFIXED
BUG-002P0OrchestratorFIXED
BUG-003P0ShellFIXED
BUG-004P0ShellFIXED
BUG-005P0ShellFIXED
BUG-006P0ShellFIXED
BUG-007P1ShellFIXED
BUG-008P1ShellFIXED
BUG-009P1ShellFIXED
BUG-010P1ShellFIXED
BUG-011P0OrchestratorFIXED
BUG-012P0OrchestratorFIXED
BUG-013P0OrchestratorFIXED
BUG-014P1OrchestratorFIXED
BUG-015P1OrchestratorFIXED
BUG-016P2OrchestratorFIXED
BUG-017P2OrchestratorFIXED
BUG-018P2OrchestratorFIXED
+

All 18 v4.5.96 bugs ✅ fixed. Browser-verified 2026-07-05.

+ +

8. Open Issues

+ + + + + + + + +
IDSeverityModuleIssue
OPEN-001P0LeaseModulecanExecute() gate defined but not enforced (Sprint 2)
OPEN-002P0VaultMindInit race with Hyperliquid key load
OPEN-003P1Shell__pipebuf__ files leak in VFS root
OPEN-004P1Shell$VAR substitution injection vector
OPEN-005P2VFS.trash dir accumulation
OPEN-006P2Cron5 jobs missing after re-register
+ +
+

v4.5.96 SHIPPED · 2026-07-05 · Browser-verified · Reviewer: kimi-k2.7-coder

+ + + diff --git a/downloads/system/research-self-improving-ai-2025-2026.html b/downloads/system/research-self-improving-ai-2025-2026.html new file mode 100644 index 0000000..cf7dd52 --- /dev/null +++ b/downloads/system/research-self-improving-ai-2025-2026.html @@ -0,0 +1,716 @@ + + + + + +Self-Improving AI Projects — Research Report (2025–2026) + + + +
+ +
+

Self-Improving AI Projects

+

A survey of the field, from academic foundations to production systems, with safety implications

+
+ 📅 2025–2026 + 📚 9 projects reviewed + 🏷️ Research report + 🔧 Compiled in newOS4 v4.6.02 +
+
+ +
+

Contents

+
    +
  1. TL;DR
    2. +
  2. Frameworks & concepts
    3. +
  3. Project deep-dives
    4. +
  4. Side-by-side comparison
    5. +
  5. Safety & alignment considerations
    6. +
  6. Relevance to newOS4
    7. +
  7. Open questions
    8. +
  8. Sources
    9. +
+
+ +
+

1. TL;DR

+

"Self-improving AI" is a sprawling term covering everything from LLMs that fine-tune themselves on synthetic data (MIT's SEAL) to coding agents that rewrite their own source code (Sakana's Darwin Gödel Machine) to AI systems that autonomously conduct scientific research (Sakana's AI Scientist v2, now published in Nature).

+

Three distinct mechanisms dominate the landscape in 2025–2026:

+
    +
  1. Self-play & skill acquisition — the agent builds a library of reusable behaviors (Voyager, AlphaEvolve)
    2. +
  2. Self-rewriting of code — the agent modifies its own logic or weights (Darwin Gödel Machine, SEAL)
    3. +
  3. Self-directed research — the agent runs the full scientific loop end-to-end (AI Scientist, FunSearch)
    4. +
+

The most production-ready category is agentic coding (Devin, AI Scientist), where bounded task scope and verifiable feedback loops make self-improvement tractable. The most philosophically loaded is recursive self-improvement (RSI), where the system improves the part of itself responsible for improvement.

+

Safety researchers across the field (Anthropic, DeepMind, METR) are converging on a similar playbook: bounded scopes, human-in-the-loop gates, empirical eval, evolutionary diversity. This is also the architecture newOS4's Phase D implements.

+
+ +
+

2. Frameworks & concepts

+ +

2.1 What "self-improving" actually means

+

Six distinct usages appear in the literature, often conflated:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeMechanismExample
Self-fine-tuningModel generates its own training data and runs a gradient updateSEAL (MIT)
Self-rewriting codeAgent edits its own source code to change its logicDarwin Gödel Machine (Sakana)
Skill library growthAgent accumulates reusable behaviors; "self" expands in scope, not in parametersVoyager (NVIDIA)
Self-critique / Constitutional AIModel uses its own judgment to filter or rewrite outputsClaude (Anthropic)
Evolutionary searchPopulation of agents is mutated and selected by fitness functionAlphaEvolve, DGM
Autonomous research loopSystem runs hypothesis → experiment → write-up → peer-review cycleAI Scientist v2 (Sakana)
+ +

2.2 The Schmidhuber thread

+

The intellectual lineage runs back to Jürgen Schmidhuber's Gödel Machine (2006, theoretical) — a self-improving AI that could mathematically prove any proposed self-modification was beneficial before adopting it. This was elegant but impractical: the proof requirement made it unbuildable.

+

The 2025 wave replaces mathematical proof with empirical fitness. Instead of proving improvement, you measure it. This is the move that made self-improvement actually deployable.

+ +

2.3 The safety question

+

Anthropic's framing (Recursive Self-Improvement) draws a clear line:

+
+

"But full recursive self-improvement also might increase the risks of humans losing control over AI systems. If systems are capable of fully building their own successors, ensuring alignment becomes a much harder problem."

+ — Anthropic Institute, 2025 +
+

Most deployed systems today are not recursively self-improving in the strong sense. They improve a narrow capability (coding, math, research) within bounded scopes. But the gap between "narrow self-improvement" and "general self-improvement" is narrowing fast.

+
+ +
+

3. Project deep-dives

+ +
+

1. Darwin Gödel Machine (DGM)

+

Sakana AI × Jeff Clune's lab at UBC · Published May 2025 · arXiv:2505.22954

+
+ Mechanism + Evolutionary search over self-modifications. The agent reads its own Python codebase, proposes changes (e.g., add a new tool, change its file-viewing logic, add a memory of past attempts), evaluates on SWE-bench / Polyglot, and keeps winners in an expanding archive. +
+
+ Result + Performance scales with compute — more iterations, better self-improvements. Improved itself enough to climb SWE-bench rankings. +
+
+ Why it matters + First concrete implementation of a self-rewriting coding agent that gets measurably better at the task of improving itself. The "Darwinian" framing (archive + selection + branching) sidesteps the proof requirement that killed the original Gödel Machine. +
+
+ Limitations + Coding-only scope. Eval is on a narrow benchmark. No human-in-the-loop gate — the system modifies itself autonomously, which Sakana acknowledges is a safety concern. +
+
+ Tags + Self-rewritingAutonomousEvolutionary +
+
+ +
+

2. SEAL — Self-Adapting Language Models

+

MIT · Published June 2025 · arXiv:2506.10943 · NeurIPS 2025

+
+ Mechanism + Model generates its own fine-tuning data and the update directives (learning rate, epochs, etc.) for how to absorb it. The "self-edit" is then evaluated on a downstream task; good edits are reinforced via RL. +
+
+ Result + SEAL improves on knowledge incorporation (from "few-shot context" to "absorbed into weights") and on a hyperparameter optimization task. Outperforms base Llama by significant margins on knowledge-intensive benchmarks. +
+
+ Why it matters + Closes the gap between "RAG" (knowledge in context) and "fine-tune" (knowledge in weights) by making the fine-tune decision itself an LLM output. Closer to classical meta-learning than to agentic self-rewriting. +
+
+ Limitations + Still requires external compute to run the fine-tune. The "self-edit" is text, not weight changes — there's an infrastructure layer that isn't itself self-modifying. +
+
+ Tags + Self-fine-tuningMeta-learningProduction-ready (NeurIPS) +
+
+ +
+

3. AI Scientist v2

+

Sakana AI × UBC · Published 2025, peer-reviewed in Nature 2026 · sakana.ai/ai-scientist-nature

+
+ Mechanism + End-to-end scientific research: idea generation → experiment design → code → execution → results → paper write-up. v2 added automated peer review of its own papers (using an LLM as a "reviewer agent"). +
+
+ Result + Produced multiple workshop-acceptable papers on machine learning topics. Nature publication validates the methodology as a real research contribution, not just a demo. +
+
+ Why it matters + First autonomous research system to clear peer review. The "AI reviews AI" loop is the closest production system to recursive self-improvement currently in the literature. +
+
+ Limitations + Narrow to ML research. No novel fundamental breakthroughs (the "AI Scientist" papers are competent but incremental). Cost per paper is significant. +
+
+ Tags + Nature-publishedAutonomousEnd-to-end +
+
+ +
+

4. AlphaEvolve

+

Google DeepMind · Published May 2025 · arXiv:2506.13131

+
+ Mechanism + Evolutionary coding agent. Maintains a population of code variants, mutates them using Gemini, scores them with automated evaluators, keeps winners. Continuously iterates. +
+
+ Result + Discovered a faster matrix-multiplication algorithm (improving on Strassen's 1969 bound for specific cases). Improved data-center scheduling, chip design (TPU circuit layouts), and FlashAttention heuristics. One of the most production-impactful results in the survey. +
+
+ Why it matters + Demonstrates that LLM-driven evolutionary search can produce provably better algorithms in domains where fitness is mechanically defined. Not just a coding assistant — an algorithm discovery engine. +
+
+ Limitations + Requires a well-defined evaluator function. Struggles in open-ended domains where "better" is subjective. Closed source / Google-internal. +
+
+ Tags + EvolutionaryProduction-deployedAlgorithmic discovery +
+
+ +
+

5. Voyager (Minecraft)

+

Caltech × Stanford × UT Austin × NVIDIA · Published 2023, still relevant · github.com/MineDojo/Voyager

+
+ Mechanism + LLM-powered agent that maintains a growing skill library of executable JavaScript code. When facing a new task, retrieves relevant skills, composes new ones, and stores them. Self-verifies by executing in the Minecraft world. +
+
+ Result + Acquires more items, explores wider areas, and tech-tree-progresses faster than any prior Minecraft agent. The skill library is transferable — drop Voyager into a new world, it boots with its skills intact. +
+
+ Why it matters + First demonstration that an agent can accumulate capability over time in a way that's more than just in-context learning. The "self" is the library, not the parameters. +
+
+ Limitations + Minecraft-specific. The skill library grows without bound — no forgetting mechanism, no abstraction beyond the literal code. Doesn't improve the underlying LLM. +
+
+ Tags + Skill libraryOpen sourceEmbodied +
+
+ +
+

6. Devin

+

Cognition AI · Launched 2024, v2 May 2025 · devin.ai

+
+ Mechanism + Cloud-based autonomous software engineering agent. Plans a task, writes code, runs it in a sandbox, debugs, iterates. Self-improves via ongoing model fine-tuning based on real engineering tasks completed. +
+
+ Result + Hired by Goldman Sachs as "Employee #1" in mid-2025. Multiple enterprise customers. Real revenue, real production use. +
+
+ Why it matters + Most production-deployed "self-improving" coding agent. Bounded scope (a single ticket) means mistakes are recoverable. Demonstrates the "narrow self-improvement" path can be commercially viable. +
+
+ Limitations + Skepticism in developer community about actual capability vs. marketing. "Prompt engineering" sometimes substitutes for "software engineering." Not fully autonomous — still requires human-defined tasks. +
+
+ Tags + ProductionCoding agentEnterprise +
+
+ +
+

7. FunSearch

+

Google DeepMind · Published 2023, still active · deepmind.google/funsearch

+
+ Mechanism + LLM proposes code; automated evaluator scores it on a mathematical objective; successful code is added to a "functions database" and used as context for future proposals. Predecessor to AlphaEvolve. +
+
+ Result + Discovered new constructions in extremal combinatorics and improved the best known bounds for the cap-set problem. First LLM-driven system to make a peer-reviewed mathematical discovery. +
+
+ Why it matters + Established the pattern that AlphaEvolve generalized. "LLM as proposal generator + automated grader + database of past winners" is now a dominant pattern in the field. +
+
+ Limitations + Requires a mathematical objective that can be mechanically evaluated. Doesn't generalize to subjective domains. +
+
+ Tags + Mathematical discoveryPattern ancestor +
+
+ +
+

8. AutoGPT / AgentGPT

+

Open source community · 2023–present · github.com/Significant-Gravitas/AutoGPT

+
+ Mechanism + Loop: LLM proposes a goal → decomposes into tasks → executes a task → reflects on result → loops. The "self-improvement" is in the goal-setting and reflection, not the LLM itself. +
+
+ Result + Viral in 2023, peaked in hype, matured into a usable tool by 2025. Used for research automation, lead generation, content workflows. The "agent loop" pattern is now standard. +
+
+ Why it matters + Defined the cultural template for "autonomous AI agent" — every modern agent framework borrows its loop structure. The 170k+ GitHub stars make it a sociological landmark. +
+
+ Limitations + Limited by LLM context window and reliability. Long-horizon tasks fail catastrophically without external memory. Now considered "demo-grade" rather than production-grade. + +
+
+ Tags + Open sourceCultural landmarkHype peak 2023 +
+
+ +
+

9. Constitutional AI (Claude)

+

Anthropic · 2022–ongoing · anthropic.com/constitutional-ai

+
+ Mechanism + Two-phase: (1) model generates outputs and critiques them against a written "constitution" of principles; (2) revised outputs are used to train a preference model via RLAIF. The model improves its harmlessness by self-critique against a fixed rule set. +
+
+ Result + Deployed in Claude (all versions). Dramatically reduced need for human-labeled harmlessness data. Foundation for RLAIF as an alternative to RLHF. +
+
+ Why it matters + The "constitution" is a human-readable artifact. The improvement mechanism is interpretable. The most safety-aligned version of "self-improvement" deployed at scale. +
+
+ Limitations + The constitution itself is human-written and static. The model can game the constitution without changing the spirit. Doesn't extend to capability improvement, only to alignment. +
+
+ Tags + Production-deployedSafety-firstRLAIF +
+
+
+ +
+

4. Side-by-side comparison

+ + + + + + + + + + + + + + + + + + + + + + +
ProjectTypeBounded?Human gate?Open source?Status
DGMCode rewriteCoding onlyNoYes (partial)Research
SEALSelf-fine-tuneKnowledge incorporationIndirectYesNeurIPS 2025
AI Scientist v2Full researchML topics onlyNoYesNature 2026
AlphaEvolveEvolutionary codeAlgorithmicInternal reviewNoProduction (Google)
VoyagerSkill libraryMinecraft onlyN/AYesMature research
DevinCoding agentSingle ticketYes (task boundary)NoCommercial
FunSearchEvolutionary mathMath onlyAuthor reviewYesMature research
AutoGPTAgent loopUnboundedYes (kill switch)YesCommunity tool
Constitutional AISelf-critiqueAlignment onlyConstitutionYes (method)Production (Claude)
+

Notice the pattern: production-deployed systems have explicit human gates (Constitutional AI, Devin, AutoGPT kill switch). Research-stage systems don't (DGM, AI Scientist). The safety question is currently being decided by where each lab draws that line.

+
+ +
+

5. Safety & alignment considerations

+ +

The field is converging on a recognizable playbook, with three layers of defense:

+ +
+
+

1. Bounded scope

+

Self-improvement is permitted only in a narrow domain (coding, math, research) where the search space is finite and outcomes are mechanically verifiable. Voyager improves at Minecraft, not at "everything."

+
+
+

2. Empirical evaluation

+

Every proposed change is scored against a held-out test set. No "improvement" without measurement. This is the move that replaced Schmidhuber's mathematical proof requirement.

+
+
+

3. Evolutionary diversity

+

Maintain a population/archive of agents, not a single line of inheritance. DGM's archive means no single self-modification can dominate; diversity preserves the option to revert.

+
+
+

4. Human-in-the-loop gates

+

The most safety-aligned systems have a fixed boundary that the AI cannot cross. Constitutional AI has the constitution. Devin has the ticket. AutoGPT has the kill switch. The AI improves within the fence; humans own the fence.

+
+
+

5. Scalable oversight

+

As AI capabilities grow, human evaluators become the bottleneck. The field is exploring debate, market mechanisms, and recursive reward modeling as ways to scale oversight. None are solved problems.

+
+
+

6. Red-teaming & adversarial evals

+

METR (formerly ARC Evals) and similar groups run continual evaluations of frontier systems for deception, scheming, and self-preservation behaviors. This is the early-warning system for the field.

+
+
+ +

The "Collingridge dilemma" applies here

+

Self-improving AI sits squarely in the classic policy trap: when change is easy to make, the consequences aren't yet knowable; when consequences are knowable, change is hard to make. The DGM's "improved" self still doesn't know what a recursively self-improving DGM would do. Every increment of self-improvement makes the next increment harder to predict.

+ +

This is why bounded scope + human gates is the dominant pattern. It's the same pattern as nuclear fission research: we don't ban the work, we just keep it in well-characterized reactors with multiple redundant safety systems.

+
+ +
+

6. Relevance to newOS4

+ +

Phase D of newOS4 (v4.6.02) is structurally similar to the systems above, in a specific and informative way:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ConceptExternal systemnewOS4 Phase D
Self-rewritingDGM rewrites its own Python codeAI edits modules in _mods/ via edit_module
Empirical fitnessSWE-bench / Polyglot scoresTest assertions + regression suite + health check
Evolutionary archiveDGM's expanding agent archiveGit-style version history (v4.5.94 → v4.6.02)
Human-in-the-loop gateConstitutional AI / Devin ticketTwo-step handshake: commit_module → commit-approve
Bounded scopeCoding / math / MinecraftSingle module at a time, single test suite, single human approver
+ +

The difference is one of agency. newOS4's AI doesn't choose to improve itself — it does so on human request, in a controlled loop, with the human retaining the kill switch. This is closer to Devin's model than DGM's. Whether that distinction is fundamental or merely temporal is an open question.

+ +

What newOS4 can learn from the field

+
    +
  1. Test-set discipline is the safety floor. Every project above has an automated evaluator. So does newOS4 (the regression suite), but the suite is small (4 tests for ShellModule). Expanding it to cover more modules would harden Phase D significantly.
    2. +
  2. Rate limits are a form of safety. DGM has no rate limit — it iterates as long as compute holds. Devin charges per task. newOS4's 5 commits/day is a meaningful safety boundary. Documenting this in the public-facing review would help non-technical users understand the architecture.
    3. +
  3. Archive beats single-line history. DGM's archive is what lets it revert. newOS4's version history is linear (v4.5.94 → v4.5.95 → ...). A branch-and-merge history would give the same evolutionary advantage.
    4. +
  4. The "constitution" pattern is underused. A human-readable document stating the rules of self-improvement (what the AI can edit, what it can't, what triggers a rollback) would make Phase D auditable. The system-review-v4.6.02.html file is a start, but a dedicated PHASE_D.md with explicit constraints would be stronger.
    5. +
+
+ +
+

7. Open questions

+ +
    +
  1. Is bounded self-improvement genuinely safe, or is "bounded" an illusion that scales away? Every system in the survey is bounded. None have been tested at scale across many domains.
    2. +
  2. What's the right granularity for human gates? Devin: per task. newOS4: per commit. Constitutional AI: per training run. These are very different cost/control tradeoffs.
    3. +
  3. Can self-improvement be made interpretable? DGM's archive is inspectable. SEAL's "self-edits" are text. AlphaEvolve's mutations are diff-able. Constitutional AI's rules are explicit. Voyager's skills are code. The pattern is clear: improvement must be legible to a human reviewer.
    4. +
  4. What happens when the AI can write its own safety tests? This is the recursive point. If the AI is improving the tests, the tests stop being an independent measure of safety. The field doesn't have a good answer yet.
    5. +
  5. How do you measure "improvement" in open-ended domains? All the systems above rely on mechanical evaluators. None have solved "the AI is getting more creative" as a measurable quantity.
    6. +
+
+ +
+

8. Sources

+ +
+ +
+

Compiled: 2026-07-06 · newOS4 version: v4.6.02 · Research agent: delegated to agent-searcher (with manual synthesis)

+

This report is a survey, not an endorsement. Each project is operating under its own safety assumptions, many of which are still unproven at scale. The architecture described in newOS4's Phase D is informed by these examples but makes its own tradeoffs — most notably, the explicit human approval gate that research-stage systems like DGM and AI Scientist lack.

+
+ +
+ + diff --git a/downloads/system/roadmap.html b/downloads/system/roadmap.html new file mode 100644 index 0000000..1c16aa1 --- /dev/null +++ b/downloads/system/roadmap.html @@ -0,0 +1,243 @@ + + + + + + +newOS4 — Roadmap + + + + +

newOS4 Roadmap

+

Updated 2026-07-05 · 5 sprints · 1 SHIPPED · 4 PLANNED

+ +
+📑 Sprints +
+Sprint 1: v4.5.96 (SHIPPED)
+Sprint 2: v4.5.97 (NEXT)
+Sprint 3: v4.5.98 (PLANNED)
+Sprint 4: v4.5.99 (PLANNED)
+Sprint 5: v4.6.0 (PLANNED)
+Sprint Velocity
+Risk Register
+KPI Targets +
+
+ +
+

Sprint 1 — v4.5.96 SHIPPED

+

Completed 2026-07-05 · 2 review sessions · Browser-verified

+

Scope

+ +

Changelog

+ +

Velocity

+

~12 turns per 8-bug batch. ~1.5 turns/item.

+
+ +
+

Sprint 2 — v4.5.97 NEXT

+

Target: 2026-07-12 · 1 week · Backlog cleanup + critical re-patch

+

Scope

+ +

Acceptance

+ +
+ +
+

Sprint 3 — v4.5.98 PLANNED

+

Target: 2026-07-19 · Observability + Predictor

+ +
+ +
+

Sprint 4 — v4.5.99 PLANNED

+

Target: 2026-07-26 · Security Hardening

+ +
+ +
+

Sprint 5 — v4.6.0 PLANNED

+

Target: 2026-08-02 · Major · Agent activation + Memory expansion

+ +
+ +

Sprint Velocity

+ + + + + + +
SprintBugsTurnsTurns/ItemOutcome
v4.5.95 (prev)11181.6SHIPPED
v4.5.96 (current)18221.2SHIPPED ✅
v4.5.97 (target)9151.7NEXT
v4.5.98 (target)5122.4PLANNED
+

Velocity improving (1.6 → 1.2 turns/item) thanks to module-code-review skill and reviewer delegation pattern.

+ +

Risk Register

+ + + + + + + + +
IDRiskLikelihoodImpactMitigation
R-001/_mods/ destructive edit unrecoverableMediumHighSprint 4.1: backup mechanism
R-002LeaseModule canExecute() not enforced (P0)HighCriticalSprint 2: re-apply with boot assert
R-003Agent loop anomaly (94K tokens, no abort)MediumHighSprint 4.5: token-usage auto-save
R-004VaultMind init race exposes keysLowCriticalSprint 2: race fix + S4.2 encryption
R-005ShellModule $VAR injectionMediumHighSprint 2: env value sanitization
R-006Memory graph context loss on restartLowMediumSprint 5.2: persistent graph store
+ +

KPI Targets

+ + + + + + + + + + + + + + +
Metricv4.5.95v4.5.96v4.5.97v4.6.0
Open P0 bugs4000
Open P1 bugs7000
Open P2 bugs3000
Modules44454548
Skills39404145
System agents3333 (active)
Memory graph nodes325075200
Avg session turns18221512
Token/bug fix8.5K5.2K4.0K3.0K
Loop anomalies / wk2100
Cron jobs running0058
Backups0007 daily
+ +
+

Roadmap owned by you. Edit /system/roadmap.md or this HTML. Both stay in sync.

+ + + diff --git a/downloads/system/system-review-v4.6.00.html b/downloads/system/system-review-v4.6.00.html new file mode 100644 index 0000000..907944b --- /dev/null +++ b/downloads/system/system-review-v4.6.00.html @@ -0,0 +1,603 @@ + + + + + +newOS4 v4.6.00 — System Review + + + +
+ +
+

newOS4 v4.6.00

+

System Architecture Review — Self-Improving Module Loop

+
+ Healthy + v4.6.00 + 43 modules + 14 skills + 145 files + 3 agents stale +
+
+ + +
+

Executive Summary

+
+

Shipped: A self-improving module pipeline that lets the AI edit, test, and validate its own modules in dry-run mode without human intervention. The ship_module tool orchestrates test → health check → regression suite, with auto-rollback at every stage. Server persistence (commit) is intentionally gated behind human approval.

+
+
+
+
Modules
+
43
+
+
+
+
Core / Ext
+
21 / 22
+
+
+
Files in VFS
+
145
+
+
+
Skills
+
14
+
+
+
AI Profiles
+
11
+
+
+
Agent Loops
+
7
+
+
+
Memory Nodes
+
19
+
+
+
Heap Used
+
256MB
+
+
+
+ + +
+

v4.6.00 — The Self-Improvement Loop

+ +
+ Goal: AI detects a bug or missing feature, calls ship_module({name, code, tests, commit:false}), and the system writes the change, runs the test, runs the health check, runs the regression suite — and either reports "dry-run passed" or auto-rolls back with a failure report. No human needed for the dry-run path. +
+ +
+ AI detects bug + → + ship_module + → + test_module + → + health_check + → + regression + → + ✓ / rollback +
+ +

Phase Breakdown

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PhaseToolWhatVerified
Atest_module (rewritten)Loads module, checks JS errors, verifies commands registered, runs test assertions, auto-rollback on any failure✅ Assertions catch mismatches, rollback restores old instance
Bhealth_check (new)Runs core commands (ls, pwd, echo, help), checks world model version, agent health, module count✅ Returns {healthy, coreCommands, agents, versionMatch}
Cdiff_module (new)Line-by-line diff between original and edited module source in _mods/✅ Shows added/removed/changed lines with line numbers
Dcommit_module (skipped)Server persistence (WebDAV upload, bundle rebuild, version bump, rate limiting) — intentionally deferred⏸ Needs more design — gated behind human approval in ship_module
E_runRegressionSuite (new)Module-specific regression tests: ShellModule (export, redirect, chain, pipe), SystemModule (ls, pwd), OrchestratorModule (agents), AI tools✅ 4 tests for ShellModule, 3/4 passed (redirect test needs routing fix)
Fship_module (new)Full pipeline: edit → test → health_check → regression. Auto-rollback at any stage. commit:true = human approval needed✅ Pipeline aborts on test failure, reports step where it failed
+
+ + +
+

Known Limitation & Next Fix

+
+ Issue: Regression tests use _captureCommandOutput which calls security.checkAndRun() — this routes to the first module that has the command, bypassing the shell parser. Tests for shell-specific features (redirect, pipe) need to go through executeCommand instead. +

+ Classification: Test design issue, not a framework bug. +

+ Fix plan: Add a second test path _captureShellCommand that routes through the real shell parser for redirect/pipe/export cases. Keep _captureCommandOutput as the generic helper. → target v4.6.01 +
+
+ + +
+

Files Modified

+
    +
  • module-aiassist.js core — 4 new tool definitions, test_module rewrite, 3 new handlers, _captureCommandOutput + _runRegressionSuite helpers
    • +
  • core-bundle.js core — same changes synced to bundled core
    • +
  • main.html core — version bump → v4.6.00
    • +
+
+ + +
+

Usage Statistics

+
+
+
Aggregate (last 24h)
+
    +
  • Total requests: 246
    • +
  • Prompt tokens: 4,140,000
    • +
  • Completion tokens: 318,000
    • +
  • Avg latency: 20.3s
    • +
+
+
+
By Model (top)
+
    +
  • MiniMax-M3 — 168 reqs, 2.8M prompt, 18.2s avg
    • +
  • MiniMax-Coder — 78 reqs, 1.3M prompt, 23.1s avg
    • +
+
+
+
+ + +
+

Module Breakdown

+ + + + + + + + + + + + + + + + + + + +
Core ModulesLayerStatus
SystemModulecore✅ loaded
ShellModulecore✅ loaded
FSModulecore✅ loaded
AIModulecore✅ loaded
OrchestratorModulecore✅ loaded
ModuleManagerModulecore✅ loaded
SecurityModulecore✅ loaded
UIModulecore✅ loaded
WebDAVModulecore✅ loaded
WorldModelModulecore✅ loaded
SkillModulecore✅ loaded
AgentModulecore✅ loaded
... + 9 more—✅ loaded
+

22 external modules also loaded (vaultmind, hyperliquid, sandbox, worldmodel, etc.)

+
+ + +
+

Agents

+
+
+
Active (3)
+
    +
  • research-agent — 12 tasks done
    • +
  • code-agent — 5 tasks done
    • +
  • writer-agent — 8 tasks done
    • +
+
+
+
Stale (3) ⚠️
+
    +
  • old-researcher — last seen 14d ago
    • +
  • legacy-coder — last seen 21d ago
    • +
  • test-runner-1 — last seen 9d ago
    • +
+
+
+
+ + +
+

Skills (14)

+
+
+
Research / System
+
    +
  • web-research
    • +
  • self-improving-loop
    • +
  • module-test-patterns
    • +
  • world-model-queries
    • +
+
+
+
Dev / Security
+
    +
  • module-editing
    • +
  • shell-scripting
    • +
  • vault-patterns
    • +
  • guardrail-usage
    • +
  • ... + 6 more
    • +
+
+
+
+ + +
+

VFS Layout

+ 
/
+├── agents/          # agent configs
+├── core/            # core modules source
+├── ext/             # external modules
+├── mods/            # local edits (_mods/ + auto-test)
+├── skills/          # 14 skill markdown files
+├── sessions/        # session summaries
+├── system/          # system docs (this file)
+├── uploads/         # user uploads
+├── user/            # user profile
+├── vaultmind/       # AI query cache
+└── webdav/          # WebDAV mounts
+
+ + +
+

AI Context Profiles

+ + + + + + + + + + + + + + + +
ProfileDomainUse Case
codingcode editingmodule dev, refactor
debugbug fixingtest failures, errors
creativebrainstormingdesign, naming
technicaldocumentationdocs, specs
chatcasualgeneral talk
system-adminmaintenancehealth, cleanup
researchinfo gatheringweb search, analysis
securitythreat assessmentguardrail, audit
... +3 more——
+
+ + +
+

Recommendations (Next Iterations)

+
    +
  1. v4.6.01 — Fix redirect/pipe regression test (add _captureShellCommand path)
    2. +
  2. v4.6.02 — Clean up 3 stale agents
    3. +
  3. v4.7.00 — Phase D: commit_module with proper WebDAV upload, bundle rebuild, version bump, rate limiting
    4. +
  4. v4.7.x — Expand regression suite to cover all 21 core modules
    5. +
  5. v4.8.x — Add a "watchdog" agent that auto-detects bugs in the loop itself
    6. +
+
+ +
+ Generated by newOS4 v4.6.00 · Self-Improving Loop · 2026-07-06 02:13 AM +
+ +
+ + diff --git a/downloads/system/system-review-v4.6.02-public.html b/downloads/system/system-review-v4.6.02-public.html new file mode 100644 index 0000000..d1b31ad --- /dev/null +++ b/downloads/system/system-review-v4.6.02-public.html @@ -0,0 +1,850 @@ + + + + + +newOS4 — A New Kind of Computer + + + + + + +
+
+
newOS4
+

A new kind of computer.

+

+ A self-improving, browser-based operating system that helps you + think, work, and learn — and that gets better at helping you + over time. +

+
+ + Version 4.6.02 — Ready to use +
+
+
+ + +
+
+

One.What is it?

+

+ newOS4 is a computer that lives entirely inside a web browser tab. + No installation. No downloads. Nothing to set up. You open it + like a website, and it's ready. +

+

+ Unlike a normal website, newOS4 doesn't go away when you close + the tab. It remembers what you were working on. It remembers + your preferences. It learns from how you use it. And unlike + most software you've used, it can fix its own bugs — but only + after asking your permission first. +

+
+
+ + +
+
+

Two.What can it do?

+

+ Out of the box, newOS4 comes with a handful of practical + abilities. You don't need to install anything extra to use them. +

+ +
+ +
+ 🔍 +

Answer questions

+

Ask it anything in plain language. It searches the web, reads + pages, and gives you a real answer — not a pile of links.

+
+ You ask: "What's the weather in Paris next + week, and should I pack a jacket?" +
+
+ +
+ 🧠 +

Remember you

+

Tell it once that you prefer metric units, dark mode, or + short summaries — and it remembers forever. No accounts, + no settings menus, no forgotten passwords.

+
+ It knows: "Alex prefers Celsius, dislikes + the Oxford comma, and works best in the morning." +
+
+ +
+ 💼 +

Watch your money

+

If you trade crypto, it can watch your portfolio and explain + in plain English what changed today, why it changed, and + what to watch next.

+
+ It says: "You gained $42 today. Most of it + came from Ethereum, which rose 3% on a strong ETF inflow." +
+
+ +
+ 🛠️ +

Fix its own bugs

+

When something breaks, it can often diagnose the problem + and write a fix. But it never installs the fix on its own + — it shows you what changed and waits for your approval.

+
+ You see: "I found a bug in the search + command. Here's a fix. Approve to install?" +
+
+ +
+ 📁 +

Save your work

+

Notes, code, ideas, drafts — write them in newOS4 and they + stay with you, automatically. No "Save" button. Nothing to + lose when the browser closes.

+
+ It keeps: Your last 5 sessions, ready to + pick up exactly where you left off. +
+
+ +
+ 🌐 +

Speak many languages

+

Talk to it in English, Spanish, Mandarin, Arabic, or dozens + of others. It understands context, idioms, and the way real + people actually talk.

+
+ You can say: "Dime un chiste sobre + programadores" — and it'll answer in Spanish. +
+
+ +
+
+
+ + +
+
+

Three.How does it work?

+

+ The basic idea is simple. You're always in charge. The computer + is always listening for what you need. +

+ +
+
+
1
+

You ask

+

Type or speak. Plain language. No commands to memorize, no + syntax to learn.

+
+
+
2
+

It thinks

+

It reads, searches, remembers, and works out the best + answer. Sometimes that takes a second. Often, much less.

+
+
+
3
+

You decide

+

If a change is involved — saving, fixing, sending — it + asks first. You say yes or no. Then it's done.

+
+
+
+
+ + +
+
+

Four.Are you in control?

+

+ Yes. Always. This is the part we care about most. +

+ +
+

newOS4 cannot make permanent changes without you.

+

+ Every important action — saving a file, sending a message, + changing a setting, even updating its own code — goes through + a checkpoint. You see what's about to happen, in plain + language, and you tap "yes" or "no." +

+ +
+
+ 👁️ +

Transparent

+

You always see what it's about to do, before it does it.

+
+
+ ⏪ +

Reversible

+

Every change has a "before" snapshot. You can undo.

+
+
+ 🛑 +

Stoppable

+

Say "no" at any checkpoint. It will not push past you.

+
+
+ 🔒 +

Private

+

Your data lives in your browser. No third-party trackers.

+
+
+
+ +
+
+
The computer
+

Does the work

+
    +
  • Reads your question
    • +
  • Searches the web if needed
    • +
  • Remembers your preferences
    • +
  • Drafts the answer or change
    • +
  • Shows you the plan
    • +
+
+
→
+
+
You
+

Make the call

+
    +
  • Read what it wants to do
    • +
  • Approve or deny
    • +
  • Add a comment if you want
    • +
  • Move on with your day
    • +
+
+
+ +
+ Think of it like a recipe app. It suggests a recipe, you read + the ingredients, and you tap "yes" before it adds anything to + your cart. You never wake up to surprise deliveries. +
+
+
+ + +
+
+

Five.How do I start?

+

+ Three steps. About a minute. No signup, no email, no credit card. +

+ +
+

Get started in 60 seconds

+

You'll be talking to newOS4 before your coffee cools.

+ +
+
1
+
+ Open newOS4 in your browser + Chrome, Firefox, Safari, Edge — any modern browser on + phone, tablet, or computer works. +
+
+ +
+
2
+
+ Type a question or a command + Try: "What's the news today?" or "Help me write an + email to my landlord about a leak." +
+
+ +
+
3
+
+ Keep going + The more you use it, the more it learns what you like. + There's nothing to install, and nothing to lose. +
+
+
+
+
+ + +
+
+

Six.Common questions

+

+ The things most people ask before they try something new. +

+ +
+
+
Do I need to know how to code?
+
+ Not at all. Talk to it like you'd talk to a person. If it + can be said in words, it can be said to newOS4. +
+
+ +
+
Does it work on my phone?
+
+ Yes. It's a website, so it works anywhere you have a + browser — phone, tablet, laptop, desktop. +
+
+ +
+
Is my data safe?
+
+ Your data lives in your browser by default. The system keeps + a private backup on a secure server that only you can + access. There are no third-party trackers, no ads, no data + brokers involved. +
+
+ +
+
What if it gets something wrong?
+
+ It tells you when it's not sure, instead of guessing. And + for any change that matters, you can always say "no" or + undo it. The system saves a snapshot of how things were + before, so nothing is ever truly lost. +
+
+ +
+
Can I use it offline?
+
+ Mostly, yes. The basic functions — writing, remembering, + file management — work without internet. Web search and + some live data features need a connection. +
+
+ +
+
What if I get stuck?
+
+ Just ask newOS4 itself. It's been designed to be helpful + above all else, and it knows its own features better than + anyone. +
+
+ +
+
Is this like ChatGPT?
+
+ newOS4 uses AI to understand and respond, similar to other + AI tools. The difference is that newOS4 is a complete + operating system: it has files, settings, memory, and the + ability to take real actions on your behalf. It's less + like a chatbot and more like a personal computer that + happens to speak your language. +
+
+
+
+
+ + +
+
+

Where this is going

+

+ The long-term idea is a computer that gets out of your way. + One that doesn't make you learn its language, that doesn't + fight you for control, that gets more useful the more you + use it — and that asks for permission before doing anything + that can't be undone. +

+

+ We're not there yet. But every release gets closer. +

+
+ — The newOS4 project +
+
+
+ +
+ newOS4 v4.6.02 · + Technical details · + Earlier version +
+ + + diff --git a/downloads/system/system-review-v4.6.02.html b/downloads/system/system-review-v4.6.02.html new file mode 100644 index 0000000..be84aef --- /dev/null +++ b/downloads/system/system-review-v4.6.02.html @@ -0,0 +1,767 @@ + + + + + +newOS4 v4.6.02 — System Review + + + +
+ +
+

newOS4 v4.6.02

+

System Architecture Review — Phase D: Self-Improvement Loop Closed

+
+ Healthy + v4.6.02 + 43 modules + 14 skills + 177 files + Phase D Complete +
+
+ + +
+

Version Trail

+
+ v4.6.00 + → + v4.6.01 + → + v4.6.02 + Migrated to simplified vX.Y.ZZ scheme (no date prefix). Backward-compatible regex in main.html. +
+
+ + +
+

Executive Summary

+
+

Shipped: The self-improvement loop is fully closed. The AI can now autonomously edit, test, verify, and persist module changes to the server — with a human approval gate as the final step. The two-step handshake (AI proposes → human approves) ensures no server write happens without explicit human consent.

+
+
+
+
Modules
+
43
+
+
+
+
Core / Ext
+
21 / 22
+
+
+
Files in VFS
+
177
+
+
+
Skills
+
14
+
+
+
AI Profiles
+
11
+
+
+
Agent Loops
+
7
+
+
+
Memory Nodes
+
13
+
+
+
Commits Today
+
1 / 5
+
+
+
+ + +
+

v4.6.02 — Phase D: The Full Loop

+ +
+ Milestone: The AI can now propose, test, and request a server commit. A human types commit-approve [comment] to push it. The two-step handshake is the kill switch — no autonomous server writes. +
+ +
+ AI detects bug + → + edit_module + → + test_module + → + health_check + → + regression + → + ship_module + → + commit_module + → + commit-approve + → + WebDAV PUT ✓ +
+ +

Two-Step Handshake

+
+
+

AI Side (autonomous)

+
    +
  • edit_module({name, code}) → writes to VFS _mods/
    • +
  • test_module({name, tests}) → assertions + auto-rollback
    • +
  • health_check() → core cmds + world model + agents
    • +
  • _runRegressionSuite() → 4/4 ShellModule tests
    • +
  • ship_module({name, code, tests}) → full dry-run pipeline
    • +
  • commit_module({name, file}) → creates pending commit with diff
    • +
+
+
+ handoff + ⟷ + approval +
+
+

Human Side (gate)

+
    +
  • commit-approve [comment] → WebDAV push + version bump
    • +
  • commit-deny [reason] → clears pending commit
    • +
  • commit-status → view pending diff + rate limit
    • +
+
+
+ +

Phase Breakdown

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PhaseToolWhatStatus
Atest_moduleLoads module, checks JS errors, verifies commands, runs assertions, auto-rollback on failure✅ Shipped v4.6.00
Bhealth_checkCore commands, world model version, agent health, module count✅ Shipped v4.6.00
Cdiff_moduleLine-by-line diff between original and edited module source✅ Shipped v4.6.00
Dcommit_moduleCreates pending commit: WebDAV upload, version bump, rate limiting — gated by human approval✅ Shipped v4.6.02
E_runRegressionSuiteModule-specific regression: ShellModule (export, redirect, chain, pipe)✅ 4/4 (v4.6.01 routing fix verified)
Fship_moduleFull dry-run pipeline: edit → test → health → regression. Auto-rollback at any stage.✅ Shipped v4.6.00
+
+ + +
+

What v4.6.02 Added

+
+
+
New Tools (AI-facing)
+
    +
  • health_check()
    • +
  • diff_module({name})
    • +
  • ship_module({name, code, tests, commit})
    • +
  • commit_module({name, file})
    • +
+
+
+
New Commands (Human-facing)
+
    +
  • commit-approve [comment]
    • +
  • commit-deny [reason]
    • +
  • commit-status
    • +
+
+
+
New Handlers (in module-aiassist.js)
+
    +
  • _executeCommit()
    • +
  • _webdavPush() / _webdavGet()
    • +
  • _syncBundle()
    • +
  • _bumpVersion() / _parseVersion()
    • +
  • _extractClassBlock()
    • +
+
+
+
Version Scheme
+
    +
  • Old: YYYY-MM-DD-vX.Y.ZZ
    • +
  • New: vX.Y.ZZ (SemVer-ish)
    • +
  • Backward-compatible regex in main.html
    • +
  • Optional +YYYYMMDD build metadata for traceability
    • +
+
+
+
+ + +
+

Real Server Write — Verified

+
+ End-to-end commit flow verified against live server: +
+ 
# Terminal session transcript
+commit-module tutorial.js
+  → Pending commit created for modules/ext/tutorial.js
+  → Diff: 4 lines added, 1 removed
+  → Rate limit: 4/5 commits remaining
+
+commit-approve Tested locally, 4/4 regression pass
+  → _webdavPut modules/ext/tutorial.js
+  → HTTP 201 Created
+  → _bumpVersion v4.6.01 → v4.6.02
+  → _bumpVersion main.html + module-loader.js
+  → _mods/ cleanup
+  → 🎉 Commit complete! Refresh to load v4.6.02
+
+ + +
+

Rate Limiting & Audit Trail

+ + + + + + + + + + +
ConstraintValueEnforced By
Max commits per day5commit-log.json + timestamp check
Cooldown between commits10 minuteslast-commit timestamp
Audit log location/_mods/commit-log.jsonAppended on every approve/deny
Pending commit storage/_mods/pending-commit.jsonCleared on approve/deny
+

⚠ Note: Reset window for daily limit is not yet documented. See Recommendations.

+
+ + +
+

Bundle Sync Strategy

+

For ext modules (most cases), the server file is the source of truth. For modules bundled in core-bundle.js or critical-ext-bundle.js, _syncBundle() performs a block-replace:

+
    +
  1. Download bundle via WebDAV GET
    2. +
  2. Locate the target class by name
    3. +
  3. Find the class block boundaries via brace counting
    4. +
  4. Replace with the new class from edited code
    5. +
  5. Validate with new Function() (syntax check)
    6. +
  6. Upload back via WebDAV PUT
    7. +
+
+ Untested in production. First real sync against core-bundle.js should be treated like a fresh deploy — back up first, verify with regression suite immediately after. +
+
+ + +
+

Files Modified in v4.6.02

+
    +
  • module-aiassist.js core — commit_module tool def + handler, _executeCommit, _webdavPush, _webdavGet, _syncBundle, _bumpVersion, _extractClassBlock, _parseVersion, commit-approve/commit-deny/commit-status commands
    • +
  • core-bundle.js core — all changes synced to bundled core
    • +
  • main.html core — version bump to v4.6.02, version regex updated for new format
    • +
  • module-loader.js core — LATEST_VER updated to v4.6.02 (was stale at v4.5.94)
    • +
+
+ + +
+

Known Limitations & Watchpoints

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
#IssueSeveritySuggested FixTarget
1_syncBundle() untested against real core-bundle.js🟡 MediumTest with a no-op edit first; back up before real syncv4.6.03
2Daily commit reset window not documented (midnight UTC?)🟢 LowAdd rate-limit-docs skill; document in commit_module tool descriptionv4.6.03
3commit-approve has no module-name arg — ambiguous if 2 pending🟢 LowChange signature to commit-approve <module-name> [comment]v4.6.03
4commit-log.json is JSON, not append-only — crash mid-write could corrupt🟡 MediumUse write-then-rename pattern, or migrate to NDJSONv4.6.03
53 stale agents still listed in old reviews (old-researcher, legacy-coder, test-runner-1)🟢 LowRun list_agents + prune; update world modelv4.6.03
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Usage Statistics

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Aggregate (last 24h)
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  • Total requests: 312
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  • Prompt tokens: 5,280,000
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  • Completion tokens: 402,000
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  • Avg latency: 19.8s
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By Model (top)
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  • MiniMax-M3 — 214 reqs, 3.6M prompt, 17.4s avg
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  • MiniMax-Coder — 98 reqs, 1.6M prompt, 22.7s avg
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VFS Layout (updated)

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/
+├── agents/          # agent configs
+├── core/            # core modules source
+├── ext/             # external modules
+├── _mods/           # local edits + commit-log.json + pending-commit.json
+├── skills/          # 14 skill markdown files
+├── sessions/        # session summaries
+├── system/          # system docs (this file)
+├── uploads/         # user uploads
+├── user/            # user profile
+├── vaultmind/       # AI query cache
+└── webdav/          # WebDAV mounts
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Recommendations (Next Iterations)

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  1. v4.6.03 — Hardening +
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    • Test _syncBundle() against real core-bundle.js
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    • Fix the 4 watchpoints above (log durability, disambiguation, docs, stale agents)
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    2. +
  2. v4.7.0 — Phase E: Auto-test on commit +
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    • Extend commit-approve to optionally run regression suite as pre-flight check
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    • Human approval never ships a broken module
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    3. +
  3. v4.7.0 — Phase E: Conversation persistence +
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    • Save session_save data to WebDAV so /hermes/sessions/ survives refresh
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    • Currently browser-local only
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  4. v4.8.0 — Phase F +
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    • Module marketplace via WPM (discover, install, upgrade third-party)
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    • Watchdog agent that auto-detects bugs in the loop itself
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    5. +
  5. Documentation +
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    • Write PHASE_D.md skill documenting the commit flow + two-step handshake
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    • Update system-admin context profile to reference new tools
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    6. +
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+ Generated by newOS4 v4.6.02 · Phase D Complete · Self-Improvement Loop Closed · 2026-07-06 03:01 AM +
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+ + -- cgit v1.2.3