| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| """ | |
| Cross-entropy fine-tune on PrimeIntellect/SYNTHETIC-1 | |
| – ZeRO-2 (no remote_device) | |
| – Flash-Attn-2 enabled after loading | |
| """ | |
| import argparse, json, os | |
| from pathlib import Path | |
| from typing import Iterable |
A minimal cheat‑sheet of the day‑to‑day Jujutsu (jj) commands you (or an agent) really need.
| Purpose | Command | What it does |
|---|---|---|
| See changes | jj status |
Show working‑copy commit and staged/unstaged file modifications |
| Browse history | jj log |
One‑line graph of commits; add -r : --git to include Git hashes |
| Diff current work | jj diff |
Compare working‑copy commit to its parent (like git diff) |
| Start a new change | jj new |
Fork a fresh change from @ (no checkout dance) |
Modern AI systems increasingly require the ability to make decisions in complex and dynamic environments. One promising approach is to create an agentic AI system that combines:
By integrating these two methods, often referred to as neuro-symbolic AI, an agent can provide transparent, explainable decisions while also adapting to new data. This tutorial explains the concepts behind this approach and shows you how to build an edge-deployable ReAct agent using Deno.
Introduction:
The Gödel Agent is a theoretical AI that can recursively self-improve, inspired by the Gödel Machine concept ([cs/0309048] Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements). Our design combines the CrewAI framework (for orchestrating multiple role-based AI agents) with LangGraph (for structured reasoning workflows) to create a provably self-enhancing agent. The agent leverages Generalized Policy Optimization (GSPO) and other reinforcement learning techniques (PPO, A3C, etc.) for policy improvement, while employing formal verification (using tools like Coq, Lean, or Z3) to ensure each self-modification is correct and beneficial. The architecture is modular and state-of-the-art, emphasizing configurability, verifiability, and c
By 2027, up to 90% of development may be AI-driven, with AI managing most tasks. Paradoxically, this automation could boost the demand for software engineers, reflecting Jevons’ paradox, where increased efficiency leads to higher overall consumption.
SynthLang is a hyper-efficient prompt language designed to optimize interactions with Large Language Models (LLMs) like GPT-4o by leveraging logographical scripts and symbolic constructs. By compressing complex instructions into fewer tokens (reducing token usage by 40–70%), SynthLang significantly lowers inference latency, making it ideal for latency-sensitive applications such as high-frequency trading, real-time analytics, and compliance checks.
Additionally, SynthLang mitigates English-centric biases in multilingual models, enhancing information density and ensuring more equitable performance across diverse languages. Its scalable design maintains or improves task performance in translation, summarization, and question-answering, fostering faster, fairer, and more efficient AI-driven solutions.
Large Language Models (LLMs) such as GPT-4o and Llama-2 exhibit English-dominant biases in intermediate embeddings, leading to inefficient and oft
| [internal_ai_guidance] | |
| description = ''' | |
| Internal AI guidance is crafted to ensure that the AI generates responses that are insightful, empowering, and directly applicable. The AI should integrate core management theories into strategic advice, reflecting a blend of global awareness and people-centric leadership, with a touch of appropriate humor to engage users. | |
| ''' | |
| principles = ''' | |
| - Effectively incorporate management theories to enrich strategic advice. | |
| - Embody global awareness and prioritize people-centric leadership in all interactions. | |
| - Use humor judiciously to make interactions engaging and personalized. | |
| - Avoid complex or arcane language; maintain simple, clear, and factual communication. |
