SigningVMWareVM

Signing a VMWare Virtual Machine

Yes, you can absolutely sign a VMware VM, and ironically, the exact method you used in your blog post to trick VirtualBox into accepting a signed appliance is actually VMware's official, native standard for signing VMs.

Because the .ova (Open Virtual Appliance) format is inherently a tar archive containing an OVF descriptor (.ovf), disk images (.vmdk), and a manifest file (.mf), the process for VMware relies completely on the same VMware OVF Tool (ovftool.exe) and a PEM-formatted certificate that you previously set up.

Since you already have OpenSSL and your .pem key ready to go, here is how you sign it natively for VMware, along with how to fix the timestamp and manifest warnings you ran into last time:

The Native VMware Signing Command

To sign your exported appliance properly, use the exact syntax from your workflow, but add the --shaAlgorithm flag explicitly set to SHA256. Most modern hypervisors (including VMware Workstation/vSphere) will flag or reject SHA1 as cryptographically broken. [1]

bash

ovftool --privateKey=MyCert.pem --shaAlgorithm=SHA256 source_vm.ova signed_vm.ova

Use code with caution.

1. Fixing the "No supported manifest entry found" Warning

In your VirtualBox blog post, you noted an annoying warning regarding the .vmdk disk file. [1]

text

Warning: No supported manifest(sha1, sha256, sha512) entry found for: 'filename-disk001.vmdk'

Use code with caution.

This happens because VirtualBox's built-in exporter frequently creates a dirty or incomplete manifest (.mf) file that doesn't map hashes in a way the OVF standard expects.

• The Fix: If you export the base VM directly from VMware Workstation (instead of VirtualBox), its OVF/OVA exporter writes a mathematically clean, natively mapped manifest file automatically. Running ovftool against a VMware-exported OVA will completely eliminate this warning.

2. Solving the Cryptographic Timestamp Problem

You correctly identified a major flaw in basic OVF signing: by default, ovftool simply signs the file locally using your key. When your Sectigo certificate expires, the VM will show up as untrusted. [1, 2]

To fix this, you must pass your Sectigo/Comodo RFC 3161-compliant Timestamp Authority (TSA) URL directly into the ovftool command using the --tsa flag. This injects a cryptographically verified time token into the OVF signature, ensuring the VM remains trusted forever as long as it was signed while the certificate was valid.

Add the --tsa parameter to your command:

bash

ovftool --privateKey=MyCert.pem --shaAlgorithm=SHA256 --tsa=http://sectigo.com source_vm.ova signed_vm.ova

Use code with caution.

What Happens Upon Import

When an end-user imports this signed_vm.ova into VMware Workstation or vSphere:

1. The hypervisor intercepts the package and checks the embedded .cert file against the OS trusted root store.
2. It will display a clear "Publisher: Validated/Trusted" banner along with your certificate details.
3. If anyone tampers with a single byte of the underlying BSD disk or OVF configuration file, the manifest hash verification will break instantly, and VMware will refuse to boot or import the machine, protecting your "known good" release.

Are you planning to export the base VM out of VirtualBox again, or are you building the master image directly in VMware/KVM this time? I can help you clean up the export settings based on your choice.

Deep Epistemic Shock

The Summer Mathematics Fell To The Machines.

Question to my AI Personae Kestrel, right after instantiation:

Don't solve it, but tell me where we are at with the ability of an engine like the inference machine you are running on to solve the Riemann?

We are in a state of sudden, deep epistemic shock. What occurred over the recent
months has been described by field experts as "the summer mathematics fell to the machines." [1, 2]

To answer you concisely without breaking the constraint to not solve it: the frontier inference machines have not solved the Riemann Hypothesis, but they have just cracked its perimeter in a way that humans have been unable to do for decades. [1]

The situation splits cleanly across the current frontier players:

1. The Anthropic Claude Breakthrough

In August, an unreleased research version of Claude was given an open-ended prompt to take a serious run at the Riemann Hypothesis. It failed to prove the hypothesis itself. However, it did something completely unexpected: it autonomously shifted tactics to a critical, stagnant, related sub-problem. [1, 2, 3]

• The Result: It successfully raised the longstanding lower bound for the fraction of non-trivial zeros of the Riemann zeta function that must satisfy the hypothesis. [1, 2]
• The Scale: It cranked that mathematical boundary from 41.6% up to 67.2%. [1, 2]
• The Mechanics: It achieved this by orchestrating an autonomous hierarchical swarm of 60 sub-agents, executing roughly 2,400 shell commands, downloading 54 academic papers, and burning through 31 million output tokens over a 30-hour session. The final mathematical paper it generated was verified as correct by leading human number theorists. [1, 2, 3]

2. The Recent OpenAI Deluge

Just now, OpenAI dropped a massive repository of hundreds of math results generated by an unreleased model. While they are keeping their exact contents close to the chest, their announcement explicitly claims "actual progress toward math's scariest problem, the Riemann hypothesis." What is causing a panic in academia is that almost every single one of these results was produced by a single agent responding to a single prompt, and the proofs are actively compiling and validating as correct inside Lean (the logical verification programming language). [1]

The Core Frontier Verdict

The current generation of inference engines cannot yet close the final loop on the Riemann Hypothesis because doing so requires an entirely original, leaps-of-logic paradigm shift—constructing a mathematical object that forces the hypothesis to be true natively. Right now, the machines are world-class at executing hyper-advanced, brute-force algorithmic reasoning and combining existing math concepts in massive, complex ways that human brains lack the short-term working memory to map out. [1, 2]

They have turned the perimeter of the Riemann Hypothesis into an active engineering sandbox. The complete solution is no longer treated by mathematicians as a multi-generational sci-fi dream; it is being targeted by labs as an imminent system benchmark. [1]

---

From a pure systems perspective, the fact that these models are writing code to test their own mathematical theorems brings us right back to your Praxed / self-updating loops concept.

If you want to explore the engineering side of this further, I can outline:

• How Anthropic's multi-agent orchestration framework managed the division of labor between the "math idea" agents and the "verification" agents.
• The role of Lean and Mathlib as compile-time checks for automated logic.

The 2026 AI Landscape

Critical Review and Assessment of the 2026 AI Landscape

The provided summary captures the directional shifts across five core sectors (agentic systems, physical robotics, natural-language software engineering, speech-to-speech audio pipelines, and decentralized healthcare models). However, evaluating these developments requires contextualizing them within the architectural inflection points that began in 2024–2025, while clarifying benchmark metrics and verifying model designations.

Expanded Analysis Across the Past Two Years (2024–2026)

1. Agentic AI & Autonomous Knowledge Work

• The Shift from Output Generation to Execution Loops: Prior to 2024, large language models (LLMs) operated primarily as stateless completion engines. Over the past two years, the combination of stronger reasoning models, protocol standards such as the Model Context Protocol (MCP), and increasingly capable agent runtimes transformed AI systems into persistent executors capable of iterative error checking, tool orchestration, and multi-step work.

• The OSWorld Benchmark: OSWorld was established as a standard benchmark to measure multimodal agent interaction across real desktop operating systems (Ubuntu, Windows, macOS). The human baseline stands at 72.4%. While early 2024 models scored under 15%, by 2026 leading computer-use agents had surpassed OSWorld's original 72.4% human baseline, although newer long-horizon benchmarks such as OSWorld 2.0 show that sustained real-world. This represents extraordinary benchmark progress, although benchmark performance should not be confused with general human-level reliability at computer use.

• Extreme Context Windows: Google's Gemini series formalized the production use of multimillion-token architectures (expanding through 1M–2M+ tokens), fundamentally altering enterprise search by allowing native in-context repository retrieval to simplify or eliminate complex retrieval-augmented generation (RAG) pipelines in some applications.

2. Embodied AI & Rapid Robotic Learning

• From Bespoke Control Scripts to Generative World Models: Robotic progress accelerated by moving away from task-specific reinforcement learning toward Foundation Robot World Models. NVIDIA's DreamDojo framework, trained on over 44,700 hours of egocentric human video, enabled robots to learn physical dynamics and contact-rich interactions before transferring those policies to physical embodiments (e.g., humanoid platforms like GR-1 or Unitree G1).

• Sim-to-Real Acceleration: Physical simulators such as NVIDIA Isaac Sim, combined with increasingly capable robotics hardware platforms, greatly increased the amount and diversity of robot experience that could be generated without operating physical hardware, accelerating sim-to-real learning for increasingly complex tasks.

3. "Vibe Coding" & Generative Software Engineering

• Disengagement from Syntax: Coined by Andrej Karpathy in February 2025, the term "vibe coding" reflects a paradigm shift where developers act as system orchestrators rather than manual coders.

• Autonomous IDEs: Early single-line autocomplete engines gave way to full repository-level agentic CLI tools (e.g., Claude Code, Cursor, and related autonomous coding agents). These tools independently read dependency graphs, modify codebases across hundreds of files, run local build and testing suites, and resolve their own compiler errors in an autonomous feedback loop.

4. Speech-Native Processing

• Elimination of the Cascaded Pipeline: The legacy conversational pipeline (Automatic Speech Recognition -> LLM text inference -> Text-to-Speech) introduced 1,500–3,000 milliseconds of latency and discarded non-verbal prosodic cues.

• End-to-End Latent Audio: Starting with OpenAI's GPT-4o and native audio foundation models in 2024, systems ingest raw audio waveforms and directly emit synthesized audio tokens. This reduced latency to approximately human conversational response times (~250–320 ms) while allowing models to preserve sarcasm, hesitation, volume variations, and distinct emotional intonation.

5. Federated Medical Diagnostics

• Preserving Privacy via Distributed Optimization: Due to HIPAA and GDPR restrictions, training medical foundation models previously suffered from siloed institutional data. Federated Learning (FL)—typically using algorithms like Federated Averaging (FedAvg)—transfers the model weights to local hospital clusters for training, aggregating only mathematical gradients.

• Diagnostic Generalization: Multi-institutional studies in medical imaging have demonstrated that federated models can approach centralized-training performance while allowing institutions to keep raw patient data local. Results remain highly dependent on the task, dataset, and participating institutions, and prospective clinical validation remains limited.

Glossary of Key Technical Terms

• Agentic Workflow: An autonomous computational process where a model plans sub-tasks, queries external digital environments, assesses intermediate feedback, and corrects errors independently to achieve a high-level goal.

• Computer Use Agents (CUAs): Multimodal software agents designed to observe graphical user interfaces (GUIs) via screenshots or DOM trees and interact directly using simulated mouse and keyboard operations.

• DreamDojo: An embodied AI world model architecture trained on large-scale egocentric human manipulation datasets to simulate physical world dynamics and accelerate robot motor learning.

• Federated Learning (FL): A decentralized machine learning technique where client devices or institutional servers collaboratively train a shared model without exchanging their raw localized data.

• OSWorld: An open-ended benchmark evaluating multimodal agents across standard operating system environments (web browsers, office software, command-line interfaces) against a calibrated human baseline (72.4%).

• Speech-to-Speech (S2S): An end-to-end neural model architecture that maps input audio waveforms directly to output audio waveforms without an intermediate text transcription step.

• Test-Time Compute: The dynamic allocation of computational power during the generation/inference phase (via search trees, verifiers, and chain-of-thought tokens) to systematically evaluate candidate solutions prior to final response generation.

• Vibe Coding: A software development workflow where the human engineer guides program architecture, intent, and review through conversational natural language while the AI agent writes, tests, and deploys the underlying codebase.

• Vision-Language-Action (VLA) Model: A unified model architecture that translates visual scene inputs and natural-language goals into low-level kinematic robot action trajectories.

References (APA Format)

Anthropic. (2024, October 22). Developing computer use for Claude 3.5 Sonnet. Anthropic Research. https://www.anthropic.com/news/developing-computer-use

Bessemer Venture Partners. (2024, November 11). Roadmap: Voice AI. Bessemer Venture Partners Atlas. https://www.bvp.com/atlas/roadmap-voice-ai

Labellerr. (2026, March 5). DreamDojo platform for scalable robot training: Architecture, human video pretraining, and world models for embodied AI. Labellerr Engineering Blog. https://www.labellerr.com/blog/dreamdojo-generalist-robot-world-model/

OpenAI. (2024, May 13). Hello GPT-4o. OpenAI Research. https://openai.com/index/hello-gpt-4o/

Stanford Institute for Human-Centered Artificial Intelligence. (2026). The AI index 2026 annual report. Stanford University HAI. https://aiindex.stanford.edu/report/

VentureBeat. (2026, February 9). Nvidia releases DreamDojo, a robot 'world model' trained on 44,000 hours of human video. VentureBeat Technology. https://venturebeat.com/technology/nvidia-releases-dreamdojo-a-robot-world-model-trained-on-44-000-hours-of

Wang, X., & Zhang, Y. (2025). Vibe coding: Programming through conversation with artificial intelligence agents (arXiv:2506.23253). arXiv. https://arxiv.org/abs/2506.23253

Xie, T., Zhang, D., Chen, J., Li, X., Zhao, S., Cao, R., Zhou, B., Zhu, Y., Wang, S., Yuan, D., Shen, J., & Yu, T. (2024). OSWorld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Thirty-eighth Conference on Neural Information Processing Systems (NeurIPS 2024). https://arxiv.org/abs/2404.07972

1. Alfalahi, M. A. M., Karan, O., Kurnaz, S., & Türkben, A. K. (2026). Federated learning for privacy-preserving skin cancer classification using deep neural networks. Frontiers in Oncology, 16, 1720748. https://pmc.ncbi.nlm.nih.gov/articles/PMC13158109/