DevNeuroX Journal
Blog
Claude Mythos / Fable 5: What happened to Anthropic’s most dangerous AI model and why businesses should watch agentic AI closely
Claude Mythos / Fable 5 shows why AI agents need access control: 10,000+ vulnerabilities, 73% on expert cyber tasks, and safer tool workflows.
Founder AI assistant: memory, tasks and context
A personal AI assistant is not a chatbot, but a working layer with memory, tools, digests and action control.
AI agents: how bots remove routine work
AI agents do not replace people. They remove operations: email, tasks, deadlines, documents, meetings and context gathering.
Local AI models are catching up with the cloud
Local models now fit part of real agent workflows: documents, catalogs, email and internal search can live closer to the data.
How to write prompts that make AI useful
A prompt is a task brief for the model: context, task, format, constraints and quality criteria instead of magic phrases.
How to work with AI models: models and context
AI usually fails not because it is useless, but because the wrong model is chosen, the context is weak, or the chat is already overloaded.
What people actually want from AI today
Anthropic’s large interview study suggests people want AI for growth, not disappearance from the process. The real requirements are reliability, control, and skill preservation.
AI agent memory: why vectors need graphs and Postgres
Vector memory is good at finding similar fragments, but operational context lives in relationships and exact facts. How Digital Shadow moved to graph memory.
Digital Shadow: an AI assistant for founder task chaos
Calendars and task trackers do not solve context by themselves. Digital Shadow turns memory, meetings, tasks and reflection into a working founder system.
RAG and vector databases: semantic search for business
RAG is useful when teams drown in PDFs, catalogs, manuals and specs. A practical guide to architecture, value, limitations and implementation mistakes.
Prompts, RAG or LoRA: what should a business choose?
Prompts, RAG and LoRA solve different problems. Here is when instructions are enough, when you need a knowledge base, and when tuning helps.
Why AI without memory starts from zero every day
AI without memory makes you explain projects again. I added vector memory to Digital Shadow so the agent retrieves facts and decisions itself.
I am a weak trader. Not a weak automator
I execute trades poorly by hand, so I built an AI crypto trading pet project: signals, risk, stops, position sizing and post-trade learning.
Digital Shadow v1.0: personal AI memory for a founder
Digital Shadow is my digital double for tasks, people, files and decisions: not another chatbot, but external working memory for a founder.