Editorial
Satlyx Editorial
Practical AI agent guides, engineering notes, and trust-focused explainers.
Satlyx Editorial publishes original explainers and implementation guides for people building, evaluating, and using AI agents. Articles are reviewed for source attribution, practical value, and unsupported-claim removal.
Programme status: Founding editorial profile
Articles
AI Agents
GPT-Live: How Real-Time Voice AI Changes Products, Agents and Everyday Computing
Voice stops being a transcription trick and becomes a continuous interface — with latency clocks, barge-in, tool safety, and privacy that still decide whether it ships.
AI Engineering
The Frontier Model Race Has Changed: How to Choose an AI Model by Completed Work, Not Benchmarks
Stop asking which model is smartest. Measure which policy completes your workflows reliably inside cost and latency limits — with human review counted in the unit economics.
MCP and Connected AI
The Practical Guide to MCP and Connected AI
A hands-on MCP lab covering hosts, clients, servers, tool schemas, auth, tenant isolation, prompt injection, retries, audit logs, tests, and when a normal API is the better choice.
AI Engineering
Software 3.0: What Andrej Karpathy’s Idea Means for Builders
A builder’s research synthesis of Software 3.0: LLM-as-OS, prompts as programs, partial autonomy, verification loops, infrastructure for agents, and when Software 1.0 must stay in charge.
AI Engineering
Vibe Coding vs Agentic Engineering: How to Build Fast Without Creating a Mess
A risk decision guide: when vibe coding is healthy exploration, when it becomes tech debt, and which agentic engineering controls unlock speed without silent damage.
AI Agents
Andrew Ng’s Four Agentic Design Patterns, Explained With Practical Examples
A pattern selector for reflection, tool use, planning, and multi-agent collaboration — with concrete workflows, cost traps, and the lightest pattern that usually works.
Prompting
Context Engineering: Give AI the Right Information Without Sending Everything
A context-budget playbook for system instructions, tools, history, retrieval, and output headroom — so long windows do not become expensive noise.
Frontier Models
GPT-5.6 Sol, Terra and Luna: Which Model Should You Use?
A capability-tier routing guide for GPT-5.6 Sol, Terra, and Luna: start small, escalate on failed evals, and measure cost per successful task.
Frontier Models
Claude Fable 5: Practical Guide to Long-Horizon Reasoning and Agent Work
A migration playbook for Claude Fable 5 and long-horizon agent work: identifiers, effort, caching, context, evals, cost, and rollback — not automatic uplift.
Frontier Models
GPT-5.6 vs Claude Fable 5: How to Compare Models Without Falling for Benchmarks
A reproducible comparison worksheet for GPT-5.6-family models and Claude Fable 5: same tasks, same rubric, repeated runs, and no universal champion.
AI Agents
What Is an AI Agent? A Practical Guide Beyond the Hype
An architecture guide to AI agents: model, instructions, tools, state, permissions, read/write risk, approvals, failure modes, and how to evaluate before you rely on one.
AI Agents
Computer-Using AI Agents: What They Can Do and Where They Still Fail
Computer-using agents can click and type through interfaces built for humans. That usefulness comes with UI ambiguity, injection risk, and a hard requirement for confirmations.
AI Agents
How to Build AI Agents That Use Fewer Tokens, Tools and Retries
Cut agent waste without cutting quality: clear contracts, narrow tools, early validation, stopping criteria, retry budgets, and measurable tool/token discipline.
Token Economics
Prompt Caching Explained: When It Saves Money and When It Does Not
When prompt caching saves money — and when unique prompts, tiny prefixes, or low hit rates make caching a distraction from better retrieval and routing.
Trust and Safety
Why Source-Grounded AI Answers Matter
Citations are not decoration. A trust playbook for provenance, claim-level evidence, conflicts, freshness, and saying “source needed” out loud.
AI Engineering
Building Long-Running Agents With Permissions, Progress and Human Control
Long-running agents need more than a bigger context window: scoped permissions, visible progress, spend caps, confirmations, and a kill switch humans can trust.
AI for Creators
How Creators Can Turn Their Expertise Into an AI Product
A field playbook for creators: mine repeated questions, package source material, set hard boundaries, publish explainers, then ship one bounded workflow — not a personality bot.
AI for Families
Designing Voice-First AI for Parents and Older Adults
A human-centered design guide for family voice AI: slow clarity, confirm-before-act, privacy defaults, accessible audio UX, and never pretending to be a doctor or banker.
Founder Research
Harness Engineering: Why the Loop Beats the Model
A careful synthesis of OpenAI’s harness-engineering case study: the model is not the product — the environment, feedback loops, and verification system are.
Agent Playbooks
Managed Memory for Long-Running Agents
Separate chat history, compaction, and durable memory. Build on Anthropic’s client-side memory tool and OpenAI’s memory/compaction patterns without treating memories as ground truth.
Agent Playbooks
SKILL.md and the Rise of Production Agent Skills
Agent Skills package procedural expertise as folders — not one-off prompts. Learn progressive disclosure, SKILL.md metadata, eval-first authoring, and skills security from Anthropic’s engineering primary source.
Agent Playbooks
What Builder.io's Quality Review Agent Teaches Us About Verifiers
Generator agents write. Verifier agents check. A careful read of Builder.io’s April 2026 Quality Review Agent and a Satlyx checklist for dual-path verification without cargo-culting their product.
Industry AI
How Banks Can Deploy AI Agents in 30 Days
Ship a narrow, read-heavy banking agent in weeks — with inventories, approved knowledge, human review, and escalation — not fabricated “trillion-dollar” ROI claims.
Industry AI
93% of Insurers Don't Use AI — Here's Their Playbook
Keep the five-agent ladder; delete the unverifiable “93% don’t use AI” headline claim. Build insurance agents from explainers up — never autonomous claim denial.
Agent Playbooks
Building Your First Agent: A Non-Technical Guide
If you can write a clear job description, you can specify an agent. This field guide covers role, rules, knowledge, tests, and when not to automate.
Prompt Library
Prompt Chaining: Advanced Techniques
When one prompt collapses under complexity, chain specialized steps with explicit contracts between stages — and verify before you trust the final synthesis.
Industry AI
AI Agents for Legal: The 79% Opportunity
Keep legal judgment with lawyers. Use AI for process assist under ABA Formal Opinion 512 duties — and stop repeating unverified adoption percentages.
Industry AI
Healthcare AI: Closing the Rural Gap
Design low-bandwidth assistive health agents that expand access without claiming diagnosis — and without inventing rural “44% gap” statistics.
Industry AI
Trade Workers + AI = 192% ROI
Use AI to reduce admin load for trades — quotes, reminders, message drafts — and measure ROI with your own hours and margins. Do not treat “192%” as a proven Satlyx result.
Model Watch
Free AI Models Compared: Llama vs Mistral vs Gemma
Open models are useful when you score them against your constraints — license, hardware, latency, eval set — not against a forever leaderboard.
Case Studies
How a Small Shop Uses 5 AI Agents Daily
Five repeatable AI workflows for a small retail day: posts, replies, stock notes, expenses, feedback — measured in hours saved, not invented growth percentages.
Founder Research
10 AI Startup Ideas Nobody Is Building Yet
Ten underserved workflow wedges for AI founders — framed as research prompts, not “zero competition” or invented TAM slides.
Case Studies
The Psychology of AI Chat: Why Users Come Back
Retention is product craft: specific starts, strong first answers, scannable structure, optional memory — not invented addiction percentages.
Model Watch
Free AI Models in 2026: Groq, MiniMax M2.7, and What to Use Where
Map free-tier demos and open-weight long-context work separately — and re-verify pricing, rate limits, and model IDs before production.
Prompting
Efficient Prompting: Better AI Results With Fewer Tokens
Learn efficient prompting with weak-vs-improved examples, token counts, structured outputs, delimiters, context reduction, injection awareness, and a practical prompt analyzer.
Token Economics
Token Cost Optimisation: Reduce LLM Cost Without Breaking Quality
Calculate and reduce LLM token cost with input/output formulas, prompt caching, retries, failures, success rate, routing, output caps, and cost per successful task.
AI Engineering
How to Test AI Agents Before Users Depend on Them
Test AI agents with unit tests, mocked providers, API integration tests, streaming failures, tool timeouts, prompt injection checks, eval datasets, CI, and release gates.
AI Engineering
Model Comparison and Routing: Choose the Right AI Model Per Task
Compare and route AI models with reproducible methodology: same tasks, same prompts, same rubric, repeated runs, latency, cost per successful task, tool reliability, and fallback.
AI for Finance
AI for Finance: Monthly Close Workflow With Controls
Use AI in monthly close without autonomous posting: ERP export, validation, reconciliation, variance detection, materiality, approval trail, and management reporting.
AI for Finance
AI for Financial Research: Source Map to Memo Workflow
Build financial research memos with annual reports, filings, earnings calls, source extraction, ratio calculation, competitor comparison, citations and auditability.
AI for Legal
AI for Legal Ops: Contract Comparison Workflow
Compare contract versions with AI using redlines, clause extraction, citations, redaction, jurisdiction caveats, hallucination checks and human legal review.
AI for HR
AI for HR: Source-Grounded Policy Assistant Workflow
Design an HR policy assistant with source-grounded answers, access control, employee-data boundaries, protected-attribute risks, retention, notice and human review.