SYMPLOKA / JOURNAL

Ideas that
challenge the default.

Essays on intelligence, systems, engineering, and the choices that shape what we build.

START WITH THE FOUNDATIONS

The AI field guide: 80 ideas behind modern systems

Models, data, reasoning, tools, agents, commerce, robotics, clinical evidence, evaluation, privacy, fairness, rights, provenance, governance, science, and risk

Ideas thatchallenge the default.

START WITH THE FOUNDATIONS

The AI field guide: 80 ideas behind modern systems

ESSAYS & PERSPECTIVES

What is artificial intelligence? Models, methods, systems

What is generative AI? Models that turn patterns into drafts

AI for scientific discovery: prediction, experiments, and evidence

Does generative AI make you more creative? Better work. More of the same?

How do AI weather forecasts work? From data to futures

AI in healthcare: prove it in the workflow

Can you trust AI for health advice? Start with the evidence

Can AI chatbots support mental health? Read the trial, not the promise

AI can comfort. Connection takes more.

Can AI do legal research? Verify the source, not the citation

Can AI screen candidates fairly and well? Test the whole hiring process

Can you trust AI with your money? What financial advice research shows

AI shopping agents: checkout, consent, and payment

Does AI improve customer service? Field studies on speed, quality, and handoffs

What is physical AI? When models meet the real world

What are vision-language-action models? When language becomes action

What is AGI? Define the range. Test the transfer.

Can AI be conscious? Separate access from experience

Does AI help you learn? Measure what stays after the chat

Intelligence is a relationship. Design the connection.

What does AI alignment mean? Make intent testable

What is AI bias? Measure the decision and its impact

What is explainable AI? Make the explanation answer to the model

What is automation bias? Make room for a second opinion

Is AI-generated code secure? Test what it does

The EU AI Act: what applies, and when?

What is AI governance? Put ownership behind the system

How do large language models work? One token at a time

What is a foundation model? The base behind many AI systems

What is AI inference? The moment a model answers

Small language models: fit the intelligence to the device

What is a Mixture of Experts (MoE) model? Selective routing, explained

What is knowledge distillation? One model teaches another

What is model quantization? Fewer bits, measured trade-offs

What is a KV cache? The working memory behind each token

What is prompt caching? Reuse the work that repeats

What is speculative decoding? Draft fast. Verify together.

What are reasoning models? More compute before the answer

What are AI tokens? Text, in the model’s units

Can AI use its full context window? Test the real capability

How do Transformers work? Relationships, computed at scale

What does open-source AI actually mean?

Who owns AI-generated content? Copyright and human authorship

How much energy does AI use? Measure the whole system

How to choose an AI model for a real task

How to review AI-generated code before merging

Can AI coding agents keep code maintainable? Test the next change

How to secure AI coding agents: workspaces, permissions, and review

Vibe coding: from prototype to product

Does AI make developers faster? Measure the work, not the demo

When do you need an AI agent? Start with the workflow

Context engineering: give the next decision a clear view

A prompt is a brief, not a spell

What is prompt injection? When data tries to give orders

How to red-team an AI system: scope, test, learn

AI literacy at work: beyond the prompt

Will AI take my job? Start with the tasks, not the title

Is local AI private? Map the whole data path

What is differential privacy? Bound what data can reveal

How to evaluate AI agents before release

What is AI observability? Trace the work behind an answer

What is MCP? The connection layer for AI tools

What is A2A? How AI agents work together

What is tool calling? Give a model a way to act

RAG or fine-tuning? Put knowledge where it belongs

What is RAG? Search first, answer with evidence

How to evaluate a RAG system: find the failure, not just the score

What are AI embeddings? Meaning, mapped into numbers

How does AI search work? Follow the answer to its sources

AI search visibility without the hacks: measure citations, visits, and outcomes

Fluency is not evidence

Are AI detectors accurate? What a text score can tell you

How to check whether an image is AI-generated

What do Content Credentials prove? A source trail, not a verdict

Can you detect an AI voice? Verify the person, not the sound

How to verify a video: trace the source, place, and date

Ideas that
challenge the default.