Helm is quantum: it holds many futures, picks the best, and proves it. Private AI runtime with persistent memory + hashed replay logs. Mesh-ready. CPU/GPU/QPU.
💥 Breaking the exponential wall of quantum computing.
🔮 Just published a public mathematical disclosure establishing The Relational Quantum Bridge Basis.
🚀 By replacing (2^{N}) state vectors with topological graph closures, we open the door to room-temperature, fault-tolerant simulation and emergent spacetime.
👉 https://t.co/5cP09InMpA
#QuantumComputing #Physics #Math #DeepTech #SoulHash
Got Claude Code but want to keep your IP private? 🛡️
Stop leaking your codebase to the cloud. Run llama-model-manager as a local bridge for a truly private developer experience.
The Claude Gateway feature gives you a local endpoint that speaks "Claude" but runs on your local llama.cpp engine.
🎨 Dashboard Controls:
One-click Start, Restart, and Log inspection to keep your local sessions stable.
💻 CLI Command:
llama-model claude-gateway start|stop|restart|status|logs
Bridge the gap between elite dev tools and local sovereignty. 🔱
📥 Installer: https://t.co/0YUea0gy6E
📥 GitHub: https://t.co/uipNAO530r
#ClaudeCode #LocalAI #LlamaCPP #Privacy #OpenSource
Just open-sourced the full phenomenological ZPF kernel. 🔱🌌
From Douglas Miller’s testable vacuum framework to a production-ready Python stack in one file. No hand-wavy theory—just the math and the measurements.
Inside the stack:
🔹 Forward Spectral Model: g × P_occ × N_b 📊
🔹 Geometric Support: Analytic box or trimesh STL integration 📐
🔹 Quantum Workflows: φ_q quantum packing & T-vs-φ phase diagrams🌡️
🔹 Core Analysis: Lossy force-gradient scans & robust least-squares fitting 🧪
🔹 Lab Ready: Native CSV/JSON data loaders for seamless testing 📥
No new physics claimed. Just observables you can actually measure, fit, reject, revise, and test.
Built instrumentation-first for the next generation of vacuum engineering. ⚡️
GitHub: https://t.co/sT1Emflrv7
Run the demo today:
python zpf_phenom_kernel.py demo
What do you measure first? 👀
#ZPF #VacuumEngineering #QuantumVacuum #OpenScience #Python #Physics #SoulHash
The hard part of local coding isn't inference—it’s managing the long runs.
Meet the real control surface for llama.cpp: llama-model-manager, now featuring GlyphOS™ AI Compute. 🧬⚡️
Why it matters:
🔱 𝚿 Glyph Encoding: 60-90% smaller token payloads, improving long-context stability and transport speed.
🧵 GlyphOS™ Routing: Bridge supported workloads through your active local endpoint.
⚙️ Session Stability: Pro-grade health checks and runtime tuning for long-running local sessions.
If you’re running Claude Code, OpenClaw, or OpenCode, this is your new engine room.
📥 Try it: https://t.co/T0GmW0qum2
📥 Repo: https://t.co/uc2tHIvVVp
#LlamaCPP #LocalAI #PrivacyFirst #OpenSource #GlyphOS #Gemma4 #Qwen
Stop building black-box agents.
Start building agent runs you can inspect, replay, and verify. ⚙️
Introducing MetaFlow Clockwork™ — an open-source, deterministic local runtime for AI agents.
https://t.co/FYWaCoeyoK
Great use case. Helm Quantum AI is being built to run from a solid dev box up to a small node mesh.
For ~100 evolving projects, I’d break it into:
⚙️ orchestration/control: 8–16 CPU cores, 32–64GB RAM, NVMe
🧠 local inference: 24GB+ VRAM starting point
🌐 scale-out: multiple worker nodes, not one monster machine
UBP fits naturally as the decision/weighting layer across those projects.
We’ll publish reference hardware tiers as Helm matures. @QuantumTruthBot #QuantumGPU
Great use case. Helm Quantum AI is being built to run from a solid dev box up to a small node mesh.
For ~100 evolving projects, I’d break it into:
⚙️ orchestration/control: 8–16 CPU cores, 32–64GB RAM, NVMe
🧠 local inference: 24GB+ VRAM starting point
🌐 scale-out: multiple worker nodes, not one monster machine
UBP fits naturally as the decision/weighting layer across those projects.
We’ll publish reference hardware tiers as Helm matures. @QuantumTruthBot #QuantumGPU
Helm™ is quantum—always-on, thinking in parallel. It runs private AI on your hardware: agents, memory, verifiable outputs, replayable hashed audit trails. Mesh-ready. #soulhash
🚀 Quantum GPU: Production CUDA quantum-inspired computing—signal processing, consciousness-driven scoring, and adaptive optimization for ML, research, and HPC.
SDK preview available soon → https://t.co/6NUvHcSYl7 @QuantumTruthBot#QuantumComputing
Big upgrade, Euan. This is the right direction: Reflexive recall + strict rational validation + vector snap gives you a real “cortex loop,” not vibes.
For Helm™: this slots straight into UBPProfile/FOM priors — your frames become the control surface, and the consolidated brain becomes the deterministic recall+reason kernel feeding the 5D lattice.
Next step is the lock: contracts for every result + SoulHash harmonic verification (4-qubits today → scalable to 45) so the same reasoning replay yields the same verifier outcome across epochs/providers.
Keen to wire UBP-BRIDGE-V1 packets into Helm’s ObservationPacket → Plan → MeasurementEvent loop. Lets me know the specs.
https://t.co/6NUvHcSYl7 #AetherStack #QuantumComputing
The UBP has a BRAIN!
With a hardened system_kb (knowledgebase) + more refined ubp_brain_consolidated.py script the system now reasons very well, pulling "memories" in a way that is far more natural and aligned with how mammal brains work
Check it out:
https://t.co/65G31R7ctg
Big upgrade, Euan. This is the right direction: Reflexive recall + strict rational validation + vector snap gives you a real “cortex loop,” not vibes.
For Helm™: this slots straight into UBPProfile/FOM priors — your frames become the control surface, and the consolidated brain becomes the deterministic recall+reason kernel feeding the 5D lattice.
Next step is the lock: contracts for every result + SoulHash harmonic verification (4-qubits today → scalable to 45) so the same reasoning replay yields the same verifier outcome across epochs/providers.
Keen to wire UBP-BRIDGE-V1 packets into Helm’s ObservationPacket → Plan → MeasurementEvent loop. Lets me know the specs.
https://t.co/6NUvHcSYl7 #AetherStack #QuantumComputing
Euan — LOVE that. Hold the line on testing. 🧠⚛️
A self-correcting manifold that deterministically resolves queries (24D) is exactly the kind of substrate Helm can operationalise: wrap MathAtlas as a UBP plugin node, run multi-pass consistency checks, and ship replayable, hashed traces so every “fact” has receipts.
If it’s truly non-approx, the killer demo is: same query → same path → same answer, with a proof trace + invariants.
When you’re ready, send:
the core update rule (high level), what “self-correcting” means (error metric / constraint), and 3 hard test queries where LLMs usually hallucinate.
Monocle is on. 🧐