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Programmer DD
Programmer DD
May 18, 2026 · Artificial Intelligence

Agent Plan vs Coding Plan: A Complete Token Package for AI Coding and Media

The author, a heavy token consumer, compares the new Agent Plan with existing Coding Plans, highlighting its multi‑modal model suite, unified AFP billing, detailed pricing tiers, step‑by‑step configuration for Claude Code and SoloEnt, and the creation of an OctaFuse resource pool for unified access.

AFPAgent PlanClaude Code
0 likes · 8 min read
Agent Plan vs Coding Plan: A Complete Token Package for AI Coding and Media
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Apr 22, 2026 · Artificial Intelligence

Hands‑On Kimi K2.6 + Hermes: A Karpathy‑Style Step‑by‑Step Guide

This article presents a detailed, hands‑on tutorial for deploying Kimi K2.6 with Hermes and Obsidian, showcases multi‑modal video note‑taking, skill creation, self‑evolving LLM‑driven knowledge bases, large‑scale agent clusters, and discusses both the strengths and current limitations of the system.

Agent SystemsHermesKimi K2.6
0 likes · 10 min read
Hands‑On Kimi K2.6 + Hermes: A Karpathy‑Style Step‑by‑Step Guide
Tencent Advertising Technology
Tencent Advertising Technology
Feb 5, 2026 · Artificial Intelligence

How Multi-Agent VLMs and PNU Loss Achieve High‑Accuracy Harmful Content Detection with Only 50 Labels

This article presents a low‑resource offensive content detection framework that leverages multi‑agent visual‑language models (MA‑VLMs) for self‑training and a novel Positive‑Negative‑Unlabeled (PNU) loss, enabling accurate classification with as few as 50 annotated samples across multimodal datasets.

Multi-modal AIPNU lossSelf‑Training
0 likes · 20 min read
How Multi-Agent VLMs and PNU Loss Achieve High‑Accuracy Harmful Content Detection with Only 50 Labels
DataFunSummit
DataFunSummit
Nov 24, 2020 · Artificial Intelligence

Understanding Novel Literature Recommendation: Characteristics, Tagging Challenges, and Multi‑Modal AI Algorithms

This article examines the unique properties of novel literature, the difficulties of tag‑based representation, and how multi‑modal AI techniques—including dual‑tower models, feature fusion, clustering, and YouTube‑style DNN recall—are applied to improve recommendation accuracy and user decision‑making.

Multi-modal AIRecommendation SystemsYouTube DNN
0 likes · 7 min read
Understanding Novel Literature Recommendation: Characteristics, Tagging Challenges, and Multi‑Modal AI Algorithms