#catastrophic forgetting
59 related articles
·4 min
The Sprout Experiment: Exploring Deterministic Symbolic Reasoning AI Without Neural Networks
Sprout is a contrarian AI research experiment that abandons GPUs and neural networks in favor of deterministic symbolic reasoning. It features an auditable knowledge base and refuses to answer when evidence is insufficient, prioritizing explainability and governance.
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·7 min
GPT-5.6 In-Depth Hands-On Review: How Do Soul, Tara, and Luna Really Perform?
OpenAI releases GPT-5.6 preview with three models: flagship Soul, balanced Tara, and lightweight Luna. Based on real KingBench 3 testing, this article breaks down each model's performance on math, front-end, and agentic tasks, and compares them with Anthropic Fable.
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·4 min
How AI Is Rewriting the Economics of Software Rewrites: Costs Plummet, But Risks Remain
Once called "the single worst strategic mistake," code rewrites are back on the table thanks to AI. Explore how AI slashes comprehension, translation, and validation costs — and what risks remain.
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·7 min
Zero-Basis Learning Path for AI Agents: A Three-Stage Journey from Beginner to Deployment
A systematic zero-basis learning path for AI Agent development, covering Python and LLM fundamentals, five core capabilities like task planning and RAG, and LangChain hands-on practice.
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·5 min
Context Graphs: The Key Technology for AI Agents to Remember and Reuse Past Decisions
Context Graphs use graph structures to store decisions, causal relationships, and outcomes, enabling AI agents to accumulate experience and reuse historical decisions without modifying model weights.
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·7 min
GPT-5.6 Sol vs. Fable 5: Can Half the Price Match the Flagship?
OpenAI launches GPT-5.6 Sol at half the price of Anthropic's Fable 5. We compare both models on HealthBench, cybersecurity and more using hard system-card data, analyzing Sol's cost-effectiveness and shifts in AI power.
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·6 min
Fable 5 vs. GPT-5.6 Sol: Benchmarks, Pricing, and the AI Power Struggle Explained
An in-depth comparison of Fable 5 and GPT-5.6 Sol: benchmarks across Terminal Bench, HealthBench, and ExploitBench, plus pricing strategy, OpenAI's government equity controversy, and shifting AI power dynamics.
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·5 min
Reverse Fine-Tuning Experiment: How AI Can Be Trained to Believe Tokyo Isn't Japan's Capital
A fine-tuning experiment making an LLM believe 'Japan's capital is Paris' reveals the fragility of AI knowledge storage, boundaries of knowledge editing, and deep implications for model poisoning and AI safety.
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GPT-5.5 Codex Reasoning-Token Clusteri…
·2 min
GPT-5.5 Codex Reasoning-Token Clustering Explained: Causes of Performance Regression and How to Respond
An in-depth look at reasoning-token clustering in GPT-5.5 Codex — exploring causes like reasoning budget imbalance and model iteration side effects, with prompt engineering strategies for developers.
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·7 min
Hermes Agent Deep Dive: An AI Agent Framework That Uses Fewer Tokens Than Open Cloud
Full comparison of Hermes Agent vs Open Cloud: lower token usage, 200+ model support, auto Skill encapsulation, WeChat/DingTalk integration. A cost-effective AI Agent alternative for long-term deployment.
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Firecrawl Goes Free Again and Launches…
·2 min
Firecrawl Goes Free Again and Launches SOTA Research Index — A Turning Point for AI Research Agents
Firecrawl goes free again and launches a SOTA Research Index, giving AI research agents real-time access to scientific knowledge. Here's what it means for RAG, scientific reasoning, and AI-assisted discovery.
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·3 min
Complete RAG System Development Guide: Data Preprocessing, Vector Retrieval, and Output Optimization in Practice
A complete guide to building RAG systems: covering data preprocessing, vector databases, embedding models, hybrid search, re-ranking, and advanced topics like Graph RAG and multimodal RAG.
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·3 min
LangChain Beginner's Guide: From Environment Setup to RAG — A Complete Hands-On Walkthrough
A systematic guide to LangChain covering environment setup, model invocation, Prompt Templates, Output Parsers, LCEL chain expressions, and hands-on RAG implementation for beginners.
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·3 min
Google CEO Pichai Admits AI Coding Gap, Details Catch-Up Strategy and AGI Outlook
Google CEO Sundar Pichai admits Google lags in AI coding, details its catch-up strategy involving data flywheels, addresses Gemini controversies, and shares his evolving views on AGI.
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Industry Insights·2 min
AI Product Development in Practice: Model Selection, Building Moats, and Paths to Commercialization
Practical strategies for AI product development: why not to train models from scratch, when to use APIs vs. fine-tuning, building product moats, and the full path from evaluation systems to commercialization.
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Deep Dives·3 min
Complete Guide to LLM Training: Pre-training, SFT Fine-tuning, and Preference Alignment Explained
Complete guide to the three core LLM training stages: pre-training, supervised fine-tuning (SFT), and preference alignment (DPO/PPO), covering LoRA, distillation, quantization, and pruning.
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Deep Dives·3 min
memU Memory Framework Explained: Unifying Multi-Modal Agent Memory with a File System
Deep dive into the memU open-source memory framework: how it organizes Agent memory as a file system with three-layer semantic abstraction, dual-loop collaboration, and two retrieval modes.
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Deep Dives·4 min
AI Code Review from 30 to 80 Points: A Complete Guide to AST + RAG Architecture Challenges
Deep dive into AI code review system architecture with AST parsing, RAG retrieval, and vector databases. Learn how to boost review effectiveness from 30% to 80%.
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