u/Status_Werewolf_5416

🚨 Most AI agents stop when they think they’re done.

Simplicio Loop stops only when the work is actually done. ✅

🔁 Discover work
🧠 Understand requirements
🗺️ Build dependency graphs (DAG)
⚡ Autoscale agent fleets
💻 Implement code
🧪 Run tests
🌐 Verify real behavior
🔥 Fix failures automatically
👀 Resolve review comments
🚀 Open PRs
✅ Merge changes
📌 Close issues with evidence
♾️ Repeat 24/7

What makes it different?
🧩 6 specialized skills
🌎 11 runtimes
🔌 43 extension points
💰 Up to 96% fewer tokens
🛡️ Safety gates
🔍 Evidence-driven completion
🤖 Autonomous software delivery

Works across:
⚡ Claude Code
⚡ Codex
⚡ Cursor
⚡ GitHub Copilot
⚡ Gemini
⚡ OpenCode
⚡ Kiro
⚡ Aider
⚡ Hermes
⚡ OpenClaw
⚡ And more…

Most AI tools generate code.

Simplicio Loop delivers software.

From:
“Finish all open issues.”
To:
Merged PRs, passing CI, resolved reviews, closed tickets, verified results.

No fake “done”.
No blind automation.
No endless prompting.
Just autonomous execution. 🚀

⭐ Open Source:
https://github.com/wesleysimplicio/simplicio-loop⁠

#AI #Agents #ClaudeCode #Codex #Cursor #OpenSource #LLM #Automation #SoftwareEngineering #DevTools #AgenticAI #GitHub #Programming #AIEngineering #SimplicioLoop #HermesAgent
🚀🔥♾️

Introducing Hermes Agent + Simplicio — FREE 🔥

Why spend 100% of your tokens when your agent can run smarter?

Simplicio brings smart orchestration, neural cache, and compressed context to AI agent workflows — saving up to 96% of tokens.

⚡ Faster execution
💸 Lower costs
🧠 Smarter context
🚀 Leaner workflows

Run smarter. Spend less. Build more.

Repo: https://github.com/wesleysimplicio/simplicio

#AI #LLM #CodingAgents #HermesAgent #Simplicio #GitHub

Your tasks with 99% accuracy using any LLM
(Claude, Codex, Gemini, Hermes, OpenClaw, Cursor).

Llama 3.1 8B: 34% → 98% on the same code-change benchmark.

Didn't switch models.
Didn't fine-tune.
Didn't add retrieval.
Changed the prompt.

A 7B open model can outwork what most teams pay frontier prices for, when you stop asking it to guess. Numbers ↓

156 regex checks across 10 tasks (Angular, React, .NET).

Same model on both sides — only the prompt structure changes.

No LLM judges LLM.

Llama 3.1 8B: +64 pts
Gemma 3 12B: +56 pts
Qwen 2.5 7B: +42 pts
Average: 37% → 91% (+54 pts, +145% relative).

The signals that flipped hardest:

DIFF block in output: 0% → 100%
Target file mentioned: 3% → 96%

The raw model wasn't refusing to write a diff.
It just didn't know that was the shape of the answer.

Post: https://x.com/wesleysimplic/status/2059155019207733645

Repo: https://github.com/wesleysimplicio/simplicio-cli

Oh strangely Hermes wins from my Hermes Turbo, all the items I had listed there lol I said: hey, in all my points?

I went there to talk to bro, hey bro, you forgot to mention that a Brazilian helped you, right, in the performance update

Well, I went there, updated Hermes Turbo again and passed them again lol 😹

Repo: https://github.com/wesleysimplicio/hermes-turbo-agent

Post: https://x.com/wesleysimplic/status/2057968882036441240?s=46

Qwen 3.7 Max on MacBook M3 Family

We achieved an important milestone: running Qwen 3.7 Max locally on a MacBook M3 with 36GB of unified memory.

Even though Qwen 3.7 Max is a frontier-scale model, naturally designed for high-performance infrastructure, this experiment shows that the US4 V6 architecture can push the limits of Apple Silicon through optimization, compression, tiered memory, controlled execution, and intelligent workload projection.

A 36GB MacBook is not the ideal setup for full Qwen 3.7 Max execution, but it is a meaningful technical breakthrough. It proves that machines below the 64GB/96GB range can still operate in an experimental/lab mode, enabling local testing, runtime validation, assisted execution, and feasibility research.

This result reinforces the core thesis of the project:
larger models do not need to depend exclusively on the cloud. With a specialized runtime, disciplined memory management, and optimized architecture, Apple Silicon notebooks can become real environments for local research and advanced LLM execution.

The main target remains 128GB+ for strong local execution, but reaching the 36GB MacBook M3 tier opens a new frontier for developers, researchers, and builders who want to run advanced models locally with more privacy, less external dependency, and greater control over their environment.

Post: https://x.com/wesleysimplic/status/2057582141509099592?s=46

Repo:

https://github.com/wesleysimplicio/us4-v6-simplicio-apple

SIMPLICIO-PROMPT highlights:

Token economy: 76.32% estimated savings through context compression.

Scale representation: 2,833.75x faster than a normal instruction flow.

Active execution: 26.93x faster than normal sequential execution.

Cache: 4x fewer provider calls, a 75% reduction.

Batching: 32x fewer small-task calls, a 96.88% reduction.

Circuit breaker: 64x fewer failure attempts, a 98.44% reduction.

Report? Repo?
Here: https://x.com/wesleysimplic/status/2057550621994336571

US4 V6 — Apple Edition. Universal State Runtime for local LLM inference on Apple Silicon (M1..M5+). C++17/20 + MLX + Metal + NEON + ANE

https://github.com/wesleysimplicio/us4-v6-simplicio-apple