How I Think Individuals Should Use AI

Start With the Actual Need

Custom software has a long tail. The first version is only the beginning. Someone has to manage accounts, permissions, backups, updates, data exports, errors, security issues, hosting, and the moment a connected service changes its API. AI can lower the effort required to create code, but it does not make those operating responsibilities disappear.

There is also a judgement problem. A generated application may look complete while storing sensitive information badly, accepting invalid inputs, hiding failure, or producing calculations nobody verified. If the person commissioning it cannot recognise those problems, asking the same model that wrote the code whether the code is good does not create independent review.

Experimenting is fine. A personal script that renames copies of local files is not the same risk as an application handling customer payments or health records. My objection is to making custom code the first answer to an ordinary problem. Before building, I would search for a maintained tool that already solves most of it. Brownsmith Dynamics keeps a public coding-tools directory for exactly this kind of discovery, including open-source and self-hosted options that can often be configured around an individual's needs.

The Useful Unit Is the Workflow

A workflow is more than an automation. Automation usually describes a step that runs when something happens: copy an attachment, rename a file, send a reminder, or add a row. A workflow explains how several steps form a result. It identifies the starting information, the decisions, the sequence, the tools, the expected output, the checks, and what happens when the normal path breaks.

Imagine a weekly research brief. The task may require collecting sources, excluding repeated stories, checking dates, separating reported facts from opinion, extracting relevant numbers, drafting a summary, and asking a human to approve it before distribution. 'Research this topic every Friday' is a prompt. The workflow is the definition of what acceptable research looks like and how the result moves from collection to approval.

This planning matters because AI is better at specific tasks than at silently maintaining a long chain of command. It may perform one search well, then forget an exclusion rule three steps later. It may draft a convincing report before noticing that one source failed. It may treat an optional suggestion as permission to send the result. Breaking the work into visible stages gives the agent less room to improvise around missing state.

Do Not Force Every Task Into Chat

A general chat assistant is useful for thinking, drafting, explanation, and contained tasks. It becomes awkward when a workflow needs scheduled runs, persistent files, several tools, memory across sessions, or results delivered somewhere else. At that point the individual may need a different environment rather than a longer message pasted into the same chat window.

Hermes Agent is one example. Its documentation describes tools, reusable skills, persistent memory, and scheduled tasks that can run with selected skills and deliver results to files or messaging platforms. That shape may suit a personal research routine, monitoring job, or recurring administrative task better than starting every run from a blank conversation. It also asks the user to understand credentials, tool permissions, model choice, and where the agent runs.

The right answer may be much simpler. A spreadsheet formula, email rule, calendar template, note-taking system, browser extension, or existing automation service can remove the friction without involving an agent at all. I would rather see someone use a boring tool reliably than operate an impressive agent they cannot explain.

Move Beyond One-Off Prompts

A skill is a package of instructions and supporting material that teaches a compatible agent how to perform a particular kind of work. It might explain how to review a document, research a market, prepare a meeting brief, check a spreadsheet, or format a report. The user calls the skill when the task appears instead of rewriting the procedure from memory every time.

The open Agent Skills specification allows a skill to include a `SKILL.md` file, scripts, references, and assets. The important part for an ordinary user is not the folder structure. It is that the procedure becomes reusable and inspectable. You can improve one step, add a missed edge case, or replace a stale reference without rebuilding the whole assistant.

You do not have to create every skill yourself. Search for skills published by people or organisations you trust, read what they instruct the agent to do, inspect any bundled scripts before allowing them to run, and adapt them to your tools and risk. A useful skill can produce better output, introduce a real QA checklist, and make the agent appear smarter because it no longer has to guess the procedure.

That last sentence deserves care. The underlying model did not become more intelligent. You gave it relevant knowledge, a sequence, examples, and a standard for acceptance. Good context improves the conditions under which the model works. It does not remove the need to review the result.

More Context Has a Cost

Specific instructions often improve repeatable work because the agent does not have to rediscover preferences, steps, and checks on every run. They also consume context. If the provider charges by tokens, repeated instructions and growing transcripts can increase the cost. Even where pricing is bundled, a crowded context leaves more material for the model to weigh on every step.

For a workflow that saves hours, this can be a sensible premium. The mistake is assuming that every additional instruction improves the answer. A giant procedure can contain conflicts, stale advice, duplicated rules, and branches irrelevant to the current task. The agent may spend a long time following the process while losing sight of the outcome. Skills that load only when needed help because they keep specialised instructions out of unrelated requests.

Eventually a long prompt stops being a workflow design. If the process needs durable state, conditional branches, retries, parallel work, human checkpoints, or recovery after failure, an orchestration layer may be justified. LangGraph, from the LangChain project, models workflows as state, nodes, and edges and supports long-running execution and human review. That is development work. An individual should introduce it when the repeated value exceeds the setup and maintenance, not because the first prompt became untidy.

I use a simple rule: begin with the smallest repeatable procedure, observe where it fails, and add structure only where the failure demands it. A saved instruction may be enough. Then a skill. Then a scheduled agent such as Hermes. Then, for genuinely complex work, a designed workflow in LangGraph or another orchestration system. Complexity should be earned.