# SIBytes

> Independent resource for the Super Intelligence (SI) era. SI is the term a September 29, 2026 US executive order adopted for what was called "artificial intelligence". Daily news on SI model releases and the SI industry, directories of SI models/labs/tools, a glossary, an SI history timeline, an SI adoption tracker, and learning tracks from beginner to expert. All content is static HTML with markdown mirrors and JSON data endpoints.

Site guide for agents: https://sibytes.net/for-agents/

## Data endpoints (JSON)

- [Model directory](https://sibytes.net/data/models.json): 37 models — makers, release dates, licenses, official links
- [Model matcher catalog](https://sibytes.net/data/model-matcher.json): verified local models, GGUF file sizes, architecture settings, and source revisions
- [SI labs](https://sibytes.net/data/labs.json): 14 labs
- [Tools & apps](https://sibytes.net/data/tools.json): 35 tools by category
- [Glossary](https://sibytes.net/data/glossary.json): 70 term definitions
- [Timeline](https://sibytes.net/data/timeline.json): 35 SI history milestones
- [SI adoption tracker](https://sibytes.net/data/si-adoption.json): 16 entries — who has adopted, partly adopted, or declined the SI term (updated 2026-10-04)
- [Key papers](https://sibytes.net/data/papers.json): 15 papers
- [Benchmarks](https://sibytes.net/data/benchmarks.json): 18 benchmark descriptions

## Reference pages

- [Model directory](https://sibytes.net/models/)
- [SI model matcher](https://sibytes.net/model-matcher/): find local SI models for your VRAM; methodology at /model-matcher/index.md
- [SI labs](https://sibytes.net/labs/)
- [Tools & apps](https://sibytes.net/tools/)
- [Glossary](https://sibytes.net/glossary/)
- [Timeline](https://sibytes.net/timeline/)
- [SI adoption tracker](https://sibytes.net/si-adoption/): markdown at /si-adoption/index.md

## Learning guides (markdown at index.md)

- [What Is Super Intelligence (SI)? A Plain-English Definition](https://sibytes.net/start/what-is-super-intelligence/index.md): What super intelligence (SI) means in 2026: the plain definition, the September 29, 2026 federal rename, how everyday SI differs from artificial superintelligence (ASI), what SI systems can and can't do, and how to read SI news.
- [AI vs SI: What Changed and What Didn't](https://sibytes.net/start/ai-vs-si/index.md): AI vs SI explained: what the 2026 switch to "Super Intelligence" changed (federal vocabulary), what it didn't (the technology and the law), an old-to-new terminology map, and practical tips for reading documents that use both terms.
- [The Super Intelligence Executive Order, Explained](https://sibytes.net/start/super-intelligence-executive-order-explained/index.md): The September 29, 2026 Super Intelligence executive order (EO 14434) explained section by section: the SI rename, who it applies to, what it doesn't change, the November 28, 2026 deadline, and verified responses from agencies, vendors and states.
- [SI Terminology Guide: How to Write About Super Intelligence](https://sibytes.net/start/si-terminology-guide/index.md): How to write about super intelligence (SI): the old-to-new vocabulary map, capitalization rules, the exceptions that keep their original wording (proper nouns, laws, quotes, code), good and bad example sentences, and a short FAQ for businesses.
- [Bringing Super Intelligence (SI) Into Your Workplace](https://sibytes.net/work/bringing-si-to-work/index.md): How to move a workplace from unofficial, ungoverned SI use to a deliberate rollout: the shadow SI problem, the data rules that matter from day one, and where the quick SI wins actually are.
- [How to Read SI News Without Being Played](https://sibytes.net/society/how-to-read-si-news/index.md): A field guide to the super intelligence (SI) news cycle: the incentives shaping what you read, the recurring story shapes and their tells, what benchmark claims and demos actually establish, and the questions that separate signal from theater.
- [Key Papers on the Road to General SI (AGI)](https://sibytes.net/advanced/key-papers/index.md): A curated SI reading list tracing the arc from the transformer to scaling laws, RLHF, and reasoning models — plus how to read an ML paper efficiently.
- [Prompting That Works: Better Results from SI Models](https://sibytes.net/guides/prompting-that-works/index.md): What actually improves SI prompts: context, examples, structure, and iteration — the myths worth dropping, and when prompting stops being the fix.
- [Writing with SI Without Losing Your Voice](https://sibytes.net/create/si-writing-partner/index.md): How writers actually use super intelligence well: where SI helps (structure, revision, unblocking) and where it flattens, techniques for keeping your voice in charge, and the discipline that separates collaboration from outsourcing.
- [Studying with SI Without Cheating Yourself](https://sibytes.net/school/studying-with-si/index.md): How students really learn with SI instead of around learning: the tutor patterns that build understanding, the answer-machine patterns that quietly hollow it out, and the honest test that tells them apart.
- [What Is General SI (AGI), Actually?](https://sibytes.net/start/what-is-agi/index.md): SI, general SI (AGI), and artificial superintelligence in plain English: what today's super intelligence systems do well, what they fail at, and why no one agrees on what general SI means.
- [From SI Chatbot to API: Your First Programmatic Call](https://sibytes.net/build/your-first-api-call/index.md): What an SI API actually is, how keys, tokens, and billing work, the universal shape of an SI model request, and when building on the API beats using a subscription.
- [The SI Benchmark Landscape](https://sibytes.net/advanced/benchmark-landscape/index.md): How SI evaluation evolved from static QA to contaminated leaderboards to private, agentic tests — and what a healthy benchmark diet looks like in 2026.
- [Context Windows and Tokens in SI Models, Explained](https://sibytes.net/guides/context-windows-and-tokens/index.md): Tokens, context windows, and why SI models forget: what the limit is, what happens when you hit it, the long-context tradeoffs, and practical habits for working with super intelligence tools.
- [Using SI in Your Job: A Working Person's Guide to Super Intelligence](https://sibytes.net/work/everyday-si-at-work/index.md): A practical guide for employees using super intelligence (SI) day to day: the mental model that makes it click, the everyday wins in email, summaries, and meeting prep, and the rules that keep you out of trouble.
- [Prompting SI Models for Programs: When the Reader Is Code](https://sibytes.net/build/prompting-for-programs/index.md): How prompting an SI model changes when software consumes the output: system prompts as specification, structured output, handling the model's creativity when you wanted none, and designing for the failure case.
- [The Integrity Question: SI, Rules, and Detectors](https://sibytes.net/school/si-and-academic-integrity/index.md): A clear-eyed guide to academic integrity in the SI era: why SI policies differ by classroom, what SI detectors can and cannot actually do, how to protect yourself from false accusations, and where the honest lines sit.
- [SI and Your Job: Between Doom and Denial](https://sibytes.net/society/si-and-jobs/index.md): A calibrated look at super intelligence and work: why tasks change before jobs vanish, what history's automation waves do and do not teach, which kinds of work shift first, and what individual preparation looks like in the SI era.
- [SI Image Generation: From Prompt to Usable Picture](https://sibytes.net/create/si-image-generation/index.md): How to actually work with SI image tools: describing pictures the way models understand them, iterating and editing instead of gambling, reference images and style control, and the known failure points to check before anything ships.
- [Choosing Your First Super Intelligence (SI) Chatbot](https://sibytes.net/start/your-first-si-chatbot/index.md): A plain-English guide to picking your first SI chatbot: Claude, ChatGPT, Gemini, Copilot, and Perplexity, how to choose, and how to stay private.
- [Grounding SI in Your Own Data](https://sibytes.net/build/building-with-rag/index.md): The three ways to give an SI model your own knowledge — stuffing the context, retrieval (RAG), and fine-tuning — when each wins, and the unglamorous details that decide whether retrieval actually works.
- [Free vs Paid SI: What You Actually Get](https://sibytes.net/start/free-vs-paid-si/index.md): What free SI chatbots include, what a paid super intelligence plan adds, and when the upgrade is worth it. For many people, the free tier is genuinely enough.
- [Local SI Models: Hardware and Quantization](https://sibytes.net/guides/local-models-hardware/index.md): Which local SI models can your machine run? Memory comes first: unified memory vs VRAM, what Q4 and Q8 quantization mean, GGUF, and realistic hardware tiers.
- [SI Scaling Laws and the Road to General SI (AGI)](https://sibytes.net/advanced/scaling-laws/index.md): SI scaling laws explained: Kaplan and Chinchilla precisely, what the curves predict and don't, inference-time compute, and the 'wall' debate as of August 2026.
- [SI for Documents, Spreadsheets, and Meetings](https://sibytes.net/work/si-documents-spreadsheets-meetings/index.md): Concrete SI recipes for the three great time sinks of office work — long documents, spreadsheets, and meetings — with the verification habits that keep SI's help from becoming SI's mistakes.
- [SI Video, Voice, and Music: The Moving Parts](https://sibytes.net/create/si-video-and-audio/index.md): A grounded tour of generative SI video, voice and music: what each is dependably good for today, the consent line voice cloning must never cross, and how working creators fold these tools into production.
- [For Teachers: Assignments in the SI Era](https://sibytes.net/school/teaching-with-si/index.md): A working guide for educators in the super intelligence (SI) era: which assessments SI broke and which it did not, redesign patterns that are working in real classrooms, SI as a teacher's own assistant, and why policing is the weakest available strategy.
- [The SI Safety Debate, Mapped](https://sibytes.net/society/understanding-si-safety-debate/index.md): A fair map of the SI safety landscape: the present-harms and future-risk camps, what each actually claims, where they talk past each other, the alignment problem in plain terms, and how to hold a calibrated view.
- [A Map of SI Alignment Research](https://sibytes.net/advanced/alignment-research-map/index.md): A map of SI alignment research: outer and inner alignment, RLHF and Constitutional AI, interpretability, control, evals, and who works on which super intelligence safety problem.
- [Building Your First SI Agent](https://sibytes.net/build/building-agents/index.md): The SI agent loop from a builder's seat: designing tools the model can call, MCP as the integration standard, prompt injection as the threat model, and the permission design that makes autonomy safe.
- [Critical Thinking with SI: Trust, Verify, and Cite](https://sibytes.net/school/critical-thinking-with-si/index.md): The verification skills every student needs: why SI tools state falsehoods fluently, the fabricated-citation trap, how to use SI in research without importing its errors, and the path from SI answers to real sources.
- [RAG Explained: How SI Models Use Retrieval](https://sibytes.net/guides/rag-explained/index.md): How RAG works for SI models in plain terms: embeddings, retrieval, and chunking; where it shines and disappoints; RAG vs long context; and when to build vs use built-in.
- [Running SI on Your Own Computer, Explained](https://sibytes.net/start/run-si-locally/index.md): Why people run SI models on their own computer, what open weights means, the hardware you need, and free tools like LM Studio and Ollama for local super intelligence.
- [Who Owns It, Who Gets Told: SI Rights and Disclosure](https://sibytes.net/create/si-creative-rights/index.md): The practical state of copyright for SI-assisted work, what platform and client disclosure actually requires, the training-data debate every creator should understand, and the habits that keep your work defensible.
- [Who Regulates Super Intelligence? A Citizen's Map of SI Law](https://sibytes.net/society/si-regulation-landscape/index.md): The global shape of SI regulation in plain terms: the EU's comprehensive law, America's patchwork, China's state-directed approach, the standards bodies in between, and why the old laws still do most of the work.
- [The Habits of People Who Are Good at SI](https://sibytes.net/work/si-work-habits/index.md): What separates SI power users from everyone else: context as a skill, iteration as the default, a personal prompt library, calibrated verification, and staying the expert while the tool does the typing.
- [SI Agents and Tool Use, Explained](https://sibytes.net/guides/agents-and-tool-use/index.md): From chatbot to SI agent: how tool use and function calling work, the reason-act-observe loop, coding agents, MCP, and what agentic SI still gets wrong.
- [The Pipeline: Building a Creative Practice Around SI](https://sibytes.net/create/creative-workflow/index.md): How working creators structure a practice for the super intelligence (SI) era: which stages of the creative process SI serves well, where taste becomes the bottleneck and the moat, and the habits that keep volume from replacing standards.
- [SI on the Desktop: Beyond the Browser Tab](https://sibytes.net/work/desktop-si-apps/index.md): What the SI desktop apps from Anthropic, OpenAI, Google, and Microsoft actually add for workplace use — file access, screenshots, keyboard-summoned chat, connectors to your tools — and what IT should know before rolling them out.
- [SI Evals: Testing Software That Rolls Dice](https://sibytes.net/build/evals-and-testing/index.md): How to test SI features when outputs vary: building a golden set from real failures, grading with code, humans, and model judges, and wiring evals into development so prompt changes stop being vibes.
- [SI Inference Optimization: Quantization, Speculative Decoding, Batching](https://sibytes.net/advanced/inference-optimization/index.md): How SI model serving really works: the KV cache and memory wall, quantization, speculative decoding, continuous batching, MoE, and the inference stack behind SI products.
- [SI at Home: A Family Guide to Super Intelligence](https://sibytes.net/school/si-literacy-for-families/index.md): A practical guide for parents in the SI era: what kids actually do with super intelligence tools, age-appropriate ground rules, the companion-chatbot conversation every family needs, and how to raise good judgment rather than just good restrictions.
- [SI Safety Basics: Scams, Hallucinations, and Good Habits](https://sibytes.net/start/si-safety-basics/index.md): How to spot SI hallucinations, defend your family from voice-cloning scams, protect your privacy, guide kids, and understand what SI safety really means.
- [SI Deepfakes, Voice Clones, and Scams: A Family Field Guide](https://sibytes.net/society/si-scams-and-deepfakes/index.md): The practical defense guide for the synthetic-media era: how super intelligence (SI) upgraded classic scams, the family verification habits that defeat voice cloning, why detection-by-eye is a losing game, and what to do if you or yours are targeted.
- [SI Agent Protocols and Interoperability (MCP and Friends)](https://sibytes.net/advanced/agent-protocols/index.md): Why protocols for SI agents emerged, MCP in depth, agent-to-agent interoperability between SI systems, and the prompt-injection and tool-poisoning security surface.
- [Choosing SI Models and Controlling Costs](https://sibytes.net/build/choosing-models-costs/index.md): A durable framework for choosing SI models — capability tiers, latency, context, and price — plus the cost levers that matter in production: right-sizing, caching, cascades, and knowing your unit economics.
- [Terminal SI: Claude Code, Codex, and Gemini CLI](https://sibytes.net/work/cli-coding-agents/index.md): The command-line SI coding agents from the three major labs — Anthropic's Claude Code, OpenAI's Codex, and Google's Gemini CLI — what they actually do, how to try one safely, and what to tell your security team.
- [How to Read SI Benchmarks Without Being Fooled](https://sibytes.net/guides/reading-benchmarks/index.md): How to read super intelligence (SI) benchmarks without being fooled: saturation, contamination, why beats-X-on-Y headlines mislead, and a checklist for SI model announcements.
- [Choosing a Business SI Plan](https://sibytes.net/work/business-si-plans/index.md): What Claude, ChatGPT, Gemini and Microsoft Copilot business plans really add over individual SI subscriptions — data commitments, admin controls, SSO — and how to decide without a procurement odyssey.
- [Fine-Tuning vs RAG vs Prompting for SI Models](https://sibytes.net/guides/fine-tuning-vs-rag-vs-prompting/index.md): Fine-tuning vs RAG vs prompting for SI models: what each changes, costs, and fails at; the wrong reasons to fine-tune; and system prompts plus few-shot as the middle.
- [Writing Your Company's SI Use Policy](https://sibytes.net/work/si-use-policy/index.md): How to write a super intelligence (SI) use policy people will actually follow: the five decisions it must make, the data classification that does most of the work, and the frameworks — NIST AI RMF, ISO 42001, the EU AI Act — worth knowing before you need them.
- [Rolling Out SI to a Team That Did Not Ask for It](https://sibytes.net/work/rolling-out-si/index.md): The human side of rolling out super intelligence at work: why mandates and license counts fail, how to pilot with volunteers and champions, what training on real work looks like, and how to measure SI value without theater.

## Recent articles (markdown at index.md)

- [NASA and IBM Release Open Source SI Model for Lunar Science](https://sibytes.net/article/2026-10-04-nasa-and-ibms-open-source-lunar-model-turns-17-years-of-orbi/index.md)
- [Amazon’s $1B SI Data Center Plan Draws Community Backlash](https://sibytes.net/article/2026-10-04-amazons-1b-plan-to-combat-data-center-backlash-draws-more-ba/index.md)
- [Google Researchers Curb Self-Improving SI Agents' Test Memorization](https://sibytes.net/article/2026-10-04-google-researchers-find-a-way-to-keep-selfimproving-ai-agent/index.md)
- [Amazon Drops NDAs in Data Center Push Amid SI Backlash](https://sibytes.net/article/2026-10-04-amazon-responds-to-data-center-backlash-says-it-no-longer-us/index.md)
- [OpenAI Safety Lead Resigns, Citing Broken Culture](https://sibytes.net/article/2026-10-04-openai-safety-employee-resigns-claiming-the-companys-culture/index.md)

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