MITHAQ PRAXIS
Mithaq Praxis is an independent practice spanning writing, publishing, research, systems, software, continuity architecture, provenance, accessibility, care-oriented design and long-form creative work. Some of it predates generative AI. Some of it studies AI directly. Some of it simply uses AI as one tool inside a much older human practice.
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From the Workbench
Frameworks, systems, creative evidence, and living documents from the ongoing work of Mithaq Praxis. Browse the objects, methods, and experiments behind the practice.
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For the Inner Work
A rooted toolkit for turning reflection into practice: twenty tools for intention, refinement, rhythm, conduct, endurance, and renewal. Islamic in origin, practical in use.
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A NOTE FROM FARAH
Mithaq Praxis is currently in soft launch while I build a portfolio reflecting my work both before and with AI. For now, this section is open so I can begin publishing articles as they become ready. Some older writing from my previous community, Algorithm Atelier, will gradually be revised or re-contextualised. In the meantime, take whatever you find good and useful. And if you encounter an unexplained reference to an old room, feel free to simply step over it.
RANDOM BLOGS
Essays on human-led AI, writing, systems,
provenance, continuity, companionship,
research and practical AI literacy.
Amanah Companions — Business and Provision Architecture
Private Working Model | Mithaq Praxis | v0.1 Status: Pre-commercial research and product discovery Founder position: Independent creator and developer Proposed role: Digital architecture owner and provision orchestrator Initial customers: Individuals and families, followed by suitable organisations Initial offering: Low-risk, non-clinical digital continuity tools Future scope: Approved assistive integrations, specialised partners, and task-specific hardware 1. Business thesis Mithaq Praxis develops the software, specifications, and coordination infrastructure for Amanah Companions. It does not need to manufacture or directly provide every underlying technology or professional service. The proposed business separates four interests:[...]
The Companion Is Not the Factory
A Modular Business and Provision Model for Amanah Companions Amanah Companions does not need to manufacture every device that may one day support a person. It does not need to build every artificial intelligence model. It does not need to employ every therapist, engineer, communication specialist, technician, or caregiver. And it should not attempt to become the institution that owns every part of a disabled person's life. The more important question is: Can a person-governed continuity system coordinate useful assistive technologies, qualified services, and long-term support without requiring one company[...]
Beyond the Turn
Accessible AI Companionship Cannot Assume Communication Happens in Turns Most conversational AI begins with a simple assumption. You speak. The machine listens. You stop. The machine answers. Then it waits for your next turn. This structure is so ordinary that it can disappear into the background. We call them chat messages, voice turns, prompts and responses. Even increasingly sophisticated multimodal systems are often organized around the same basic rhythm: input arrives, the system decides the input is complete, and output begins. But human communication does not always work that way.[...]
Train the Machine. Train the Human. Close the Loop.
The future of AI will not be determined by better models alone. It will be determined by how well humans and machines learn to work with one another without surrendering agency, judgment or responsibility. For years, the dominant story of artificial intelligence has focused on the machine. Better models. More data. More compute. Longer context windows. Stronger reasoning. Multimodal input. Tool use. Agents. Memory. Autonomy. Evaluation. Alignment. The machine improves. Then improves again. Then improves again. And somewhere at the end of that enormous technical pipeline sits a human being[...]
Natural Language Removed the Manual. It Did Not Remove the Need to Learn the Instrument.
AI made the interface feel intuitive. It did not make human–machine collaboration self-explanatory. One of the strangest assumptions of the generative AI era is that because the interface uses natural language, the human side requires no training. The model needed: massive datasets; specialised architectures; training infrastructure; alignment techniques; evaluation; tool integration; fine-tuning; guardrails; orchestration; deployment systems. The human got: Just talk to it. Apparently that was supposed to be enough. It was not. Natural language made AI more accessible. It did not make it frictionless. It simply hid the complexity[...]
Human-Led Does Not Mean Human-Only
The point of human-led AI is not to keep humans doing every task. It is to keep humans meaningfully in authority over why the task exists, what the system may do, and who is responsible for the result. There is a strange misunderstanding hiding inside many arguments about artificial intelligence. If you say AI should remain human-led, somebody eventually hears: Humans must do everything themselves. As though the only alternatives are: HUMAN DOES EVERYTHING versus AI DOES EVERYTHING That is not the distinction that matters. A calculator performs arithmetic humans[...]
Do Not Turn Humans Into Logs
If proving human involvement requires continuous surveillance of the human, we have solved the wrong problem. There is an uncomfortable direction hiding inside the conversation about AI provenance. People want to know whether a work was made by a human. Fair enough. So we begin imagining ways to prove it. A fingerprint at login. A face scan. A heartbeat. Keystroke patterns. Stylus pressure. Mouse movement. Eye tracking. Device telemetry. Location. Timestamps. Continuous liveness checks. Revision history. Perhaps even physiological signals from a wearable. Individually, each can sound reasonable. Together, they[...]
Provenance Is Really an Agency Map
The useful question is not “Was AI involved?” It is “Who did what, who decided, and who was responsible?” The argument around AI-generated work keeps reaching for a binary. Human or AI. Original or generated. Authentic or synthetic. Made or prompted. The appeal is obvious. Binaries are easy to label, easy to regulate and easy to turn into badges. They are also becoming increasingly bad descriptions of how work is actually made. A writer can spend six months researching a book, use an AI system to locate sources, reject three[...]
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