AI Tools Tech People Should Actually Try

Most AI tool lists are not useful.

They are either too broad, too hype-driven, or too disconnected from the work technical people actually do. A developer, product manager, data person, designer, founder, or operator does not need 50 AI tools. They need a small set of tools that reliably improves the work they already have to do.

The better question is not:

"What is the best AI tool?"

The better question is:

"What could I actually ship, understand, automate, or decide better this week if I used AI in the right place?"

This article is a practical starting point. It is not a complete market map. It is a shortlist of tools and categories worth testing if you are building products, writing software, researching markets, documenting systems, automating operations, or working with technical teams from HCMC, Vietnam, or anywhere else.

The rule is simple: do not adopt an AI tool because it is impressive. Try it because it removes a real bottleneck.

If a tool cannot help you finish a real task, it does not belong in your stack yet.

1. ChatGPT for turning messy thinking into usable work

ChatGPT is still one of the most useful general-purpose AI tools for technical people because it sits across several workflows: writing, research, planning, coding support, data analysis, and structured thinking.

The important part is not using it as a chatbot. The value comes from using it as a working environment.

Use it for:

• turning messy notes into clear plans- drafting technical documentation
• preparing meeting summaries and follow-ups
• reviewing tradeoffs before a decision
• generating first-pass checklists
• doing structured research with source constraints
• explaining unfamiliar code or architecture
• turning a rough idea into an article, spec, or product brief
  

OpenAI's current ChatGPT feature set includes capabilities such as deep research, canvas, projects, file work, and tasks, depending on plan and availability. For technical people, the most useful pattern is to keep work organized by project rather than restarting from a blank chat every time.

A concrete use case: the "Monday morning technical plan"

Imagine a small team in HCMC has an idea after a Saturday meetup: a lightweight directory of local AI builders. By Monday morning, they need to know whether it is a weekend toy, a community asset, or a real product experiment.

This is where ChatGPT is useful. Do not ask: "Is this a good idea?"

Ask it to help you structure the decision:

I want to test a small directory for AI builders in Vietnam.
Create a one-week validation plan with:- the riskiest assumptions- 10 interview questions- a simple landing page outline- what data we should collect- what would make us stop- what would make us keep building
Assume we have 2 technical people and no paid ads.

The output will not be the strategy. It will be the first draft of your thinking. The value is that you can start the week with assumptions, questions, and a test plan instead of a vague feeling that "we should build something."

Another use case: make your work legible

One underrated AI workflow is turning technical progress into language other people can understand.

A developer can paste a rough commit summary, issue notes, and test output, then ask ChatGPT to produce:

• a pull request description- a product-facing changelog
• a client update
• a list of risks still open
• a short LinkedIn post about the technical lesson

That is not glamorous, but it is valuable. Many strong technical people lose opportunities because their work is invisible to everyone outside the repo.
When not to use it:

• as the final authority on facts
• as a replacement for domain review
• to generate public technical claims without verification
• to make decisions where the cost of being wrong is high

The practical habit: use ChatGPT to accelerate thinking, then verify the output before it becomes a decision, article, commit, or client deliverable.

2. Codex, Cursor, Claude Code, or GitHub Copilot for serious coding work

AI coding tools are no longer only autocomplete. The category has shifted toward agents that can inspect a codebase, propose changes, run commands, write tests, and help move work from issue to pull request.

That does not mean every developer should use every coding agent.
You should test one serious coding workflow and understand where it fits.
Good candidates:

• Codex for agentic software work, code review, refactoring, and multi-agent development workflows.
• Cursor for developers who want an AI-native editor with codebase context and agent workflows.
• Claude Code for terminal-based agentic coding and repo exploration.
• GitHub Copilot for teams already working deeply inside GitHub, issues, and pull requests.

Use coding agents for:

• understanding unfamiliar repositories
• generating tests
• refactoring repetitive patterns
• writing migration scaffolds
• reviewing pull requests
• explaining failures
• prototyping small features
• improving documentation around code

A concrete use case: the weekend web app sprint

Here is the kind of workflow worth testing.

• Friday night:
  • Write a one-page product brief.
  • Define the smallest possible feature set.
  • Create 5 issues: setup, database, core flow, analytics, deploy.
• Saturday:
  • Ask a coding agent to inspect the repo and propose an implementation plan.
  • Give it one issue at a time.
  • Ask it to add tests or at least create a manual QA checklist.
  • Review every change before moving to the next task.
• Sunday:
  • Ask it to write a launch checklist.
  • Ask it to inspect the app for missing empty states, validation, and broken flows.
  • Ask it to draft the README, environment setup, and first user feedback form.

The "wow" is not that the AI writes code. The "wow" is that the boring glue around shipping becomes lighter: tests, docs, setup notes, small refactors, PR descriptions, and QA checklists.

A concrete prompt that actually works

You are helping me ship a small weekend web app.
First inspect the repo and summarize:
1. framework and package manager
2. app structure
3. how to run tests or checks
4. highest-risk files
5. the smallest implementation plan for this issue
Do not edit files until the plan is clear.

That prompt is much better than: "Build my app."

Coding agents are strongest when the task is narrow, the context is available, and the human stays in the review loop.

Do not use them blindly for:

• security-sensitive code without review
• complex architecture decisions
• production changes you cannot test
• large refactors without version control
• tasks where you cannot evaluate the result

The practical habit:

Treat AI coding tools like fast technical collaborators, not magic.

They are useful when the task is clear, the repo is under version control, and you can review the output.

3. NotebookLM for turning sources into memory

If you work with documents, research papers, strategy notes, transcripts, specs, or long reports, NotebookLM is worth testing.

Its advantage is that it works from sources you provide: PDFs, websites, YouTube videos, audio files, Google Docs, Google Slides, and more. For technical people, that makes it useful for understanding a body of material without losing the source context.

Use it for:

• digesting long technical documents
• preparing for meetings from source material
• summarizing research papers
• comparing product specs
• turning event recordings into notes
• creating study or planning aids
• building context before writing an article or strategy doc

A concrete use case: turn an event into community intelligence

Most meetups disappear after the last photo is posted.
That is a waste.
Imagine you have:

• speaker slides
• a recording or transcript
• notes from three conversations
• links shared in the group chat
• a few post-event comments

Put those sources into NotebookLM and ask:

Using only these sources, summarize:
- the 5 strongest technical themes
- tools people mentioned
- unresolved questions from the audience
- practical lessons for builders in HCMC
- possible follow-up article ideas
- people or projects we should ask permission to mention
Separate direct source evidence from your interpretation.

Now a one-night event becomes reusable community memory. It can feed a recap, a follow-up workshop, a builder interview, a tool review, or a better question for the next meetup.

For The Sandbox, this is especially useful. The blog should not only publish opinions. It should collect signal from the local technical scene and turn it into something builders can use later.

Another use case: stop losing research inside chat apps

If a team is exploring payments, AI coding tools, hiring, or remote-work infrastructure, the research often gets scattered across Telegram, Slack, Google Docs, and browser tabs.

NotebookLM is useful when you already have the material and want to ask questions across it:

• "What do these sources agree on?"
• "Where do they contradict each other?"
• "What should we verify before publishing?"
• "What are the open risks?
• "What is the shortest explanation for a technical reader?"

When not to use it:

• when you need broad web discovery from scratch
• when your source material is weak or incomplete
• when you need a final opinion rather than a source-grounded synthesis

The practical habit: use NotebookLM when you already have the material and need to understand it faster.

4. Gemini Deep Research or ChatGPT deep research for decisions that need evidence

Search is still useful, but deep research tools are better when the task requires synthesis across many sources.
Gemini Deep Research and ChatGPT deep research are worth testing for questions that need more than a quick answer:

• market landscape research
• competitor scans
• technical trend reports
• event/community research
• policy or regulation summaries
• tool comparisons
• preparation for a strategy decision

The key is to give constraints. Ask for trusted sources. Ask it to separate facts from interpretation. Ask it to show uncertainty. Ask it to make the final answer useful for a specific decision.

A concrete use case: should we build, partner, or write?

Suppose you keep hearing the same question from technical people in Vietnam:
"Which AI tools are worth paying for?"
That could become:

• a blog article
• a workshop
• a community survey
• a paid consulting offer
• a lightweight tool directory
  

Before building anything, use deep research to map the landscape.
Bad prompt:

Research AI tools

Better prompt:

Research AI tools for small technical teams in Vietnam that need coding support, research synthesis, workflow automation, product design, and content production.
Prioritize official sources, pricing pages, product docs, and recent credible reviews.
For each category, summarize:
- what the tool is actually good for
- what kind of team should test it
- what claims need verification
- what privacy or security questions to ask
- whether this is article-worthy, workshop-worthy, or not worth covering yet
Separate facts from interpretation.

The output gives you more than a list. It gives you a decision map.

Another use case: competitor research before a small launch

If you are launching a micro-SaaS, a local directory, a dev tool, or an automation service, do not ask AI to validate your ego.

Ask it to find reasons your idea may fail.

I am considering a small product that helps remote developers in Vietnam track client work, invoices, payment status, and weekly updates.
Research existing alternatives and summarize:
- direct competitors
- indirect substitutes
- what users complain about
- pricing patterns
- features I should not build first
- a narrow wedge that might still be worth testing

That is a much stronger use of AI than asking it to write a motivational business plan.

When not to use it:

• for quick lookup tasks
• for private data you should not expose
• for facts that need direct confirmation from a primary source
• when you do not have time to read the sources it returns

The practical habit: use deep research for decisions, not trivia.

5. n8n, Make, or Zapier for workflows that should not live in someone's head

AI becomes much more useful when it connects to real workflows.

That is where automation tools matter.

The main options to test:

• n8n if you want more control, self-hosting options, and technical workflow building.
• Make AI Agents if you want visual automation with AI agents and app integrations.
• Zapier if your team already uses many SaaS tools and wants faster business automation with governance controls.

Use them for:

• routing form submissions
• summarizing inbound messages
• creating CRM updates
• generating weekly reports
• sending event follow-ups
• turning community submissions into a content backlog
• connecting AI outputs to existing tools

A concrete use case: the builder-submission engine

The Sandbox should ask technical people one recurring question:

"What are you building right now?"

The manual version is simple:

• someone fills out a form
• someone reads the response
• someone tags the topic
• someone follows up
• someone adds it to a content backlog

That is manageable for five submissions. It breaks at fifty.

An automation tool can turn it into a lightweight system:

1. A form collects the builder's name, project, stack, technical surprise, and what kind of help they want.
2. The automation stores the response in a spreadsheet, Airtable, Notion, or CRM.
3. An AI step summarizes the project in one paragraph.
4. It tags the submission by topic: AI, dev tools, data, product, design, remote work, community.
5. It drafts a polite follow-up email asking for permission before anything is published.
6. It adds strong submissions to an article backlog.
7. It creates a weekly internal digest: "5 builders to follow up with."

That is a real workflow. It saves time, protects permission, and turns community energy into editorial signal.

Another use case: client or community follow-ups

After an event, workshop, or co-working day, the most valuable work often happens after everyone leaves.

An automation can:

• collect attendee feedback
• summarize repeated questions
• identify people who asked for follow-up
• draft a recap email
• create a list of article ideas
• remind the team to send personal introductions

This is where AI quietly becomes useful. It does not replace the relationship. It makes sure the relationship does not get lost because everyone was busy the next morning.

When not to use automation:

• when the workflow is not yet understood manually
• when mistakes could create legal, financial, or customer harm
• when nobody owns maintenance
• when an AI step is added only because it sounds modern

The practical habit: document the workflow first, automate second, add AI third.

6. Figma AI for turning product ideas into something people can critique

For designers, product managers, and frontend developers, Figma AI is worth testing because it sits close to actual product design work.

It can help with early drafts, prototyping, visual exploration, and organizing design work. That does not replace design judgment. It reduces the friction of getting from a blank canvas to something a team can critique.

Use it for:

• early UI exploration
• quick prototypes
• naming and organizing layers
• generating first design directions
• testing layout ideas
• communicating product concepts before code

A concrete use case: make the product argument visual

A lot of early product conversations stay too abstract.

Someone says:

"We should build a dashboard for remote developers."

That could mean anything.

Use Figma AI to create a first-pass interface for a very specific scenario:

A dashboard for a freelance developer in Vietnam managing three international clients.
The screen should show:
- this week's deliverables
- unpaid invoices
- timezone-safe meeting windows
- blocked tasks
- a weekly client update draft
- risk alerts

The first version will not be perfect. That is fine.

The point is to make the idea visible enough for critique:

• Is this actually the right workflow?
• What is missing?
• What is too much?
• Would a developer use this every week?
• Which part should be built first?

Good design work often starts when the team can finally point at something and disagree clearly.

Another use case: bridge product and engineering

Before asking a developer to build a feature, use Figma to clarify:

• main user flow
• empty states
• error states
• mobile constraints
• what data appears on each screen
• what can be faked in a prototype
• what needs real backend logic

This saves engineering time because the conversation moves from "make it nice" to "this is the state we need to support."

When not to use it:

• as a replacement for user research
• as a final brand or product design system
• to generate UI that nobody evaluates
• when accessibility, responsiveness, and implementation constraints matter

The practical habit: use Figma AI to make the first version faster, then use human judgment to make it correct.

7. Canva for making useful communication assets fast

Not every technical person needs to become a designer. But most technical people need to communicate.

Canva is useful for fast visual communication: event posts, simple explainers, article graphics, slides, thumbnails, and social assets. Its AI features are less interesting as "magic" and more interesting as a way to reduce production time for small communication tasks.

Use it for:

• LinkedIn graphics
• event announcement visuals
• simple carousels
• article thumbnails
• internal explainers
• lightweight brand-consistent templates

A concrete use case: turn one article into five distribution assets

A blog article should not die after one link post.

Take an article like "What HCMC Developers Are Actually Building Right Now" and turn it into:

• a LinkedIn carousel with 5 patterns from the article
• a simple event slide for discussion night
• a one-page PDF handout
• an Instagram story asking "What are you building?"
• a cover image for the blog
• a speaker intro slide for a meetup recap

Canva is useful here because the job is not high-end design. The job is consistent communication.

The idea still matters more than the asset. A plain but clear visual with a specific question will usually beat a beautiful generic graphic.

Another use case: make technical ideas easier to share

If you are explaining an AI workflow, a launch checklist, or a remote-work setup, turn it into a simple diagram:

• input- tool- human review
• output- risk
• next action

That visual can become a LinkedIn image, a workshop slide, or an internal SOP.

Technical people often underestimate how much clarity is created by packaging an idea well.

When not to use it:

• for complex product design
• for final technical diagrams that need precision
• when the output looks generic or overproduced
• when a plain text post would be clearer

The practical habit: use Canva to communicate faster, not to compensate for weak ideas.

The small AI stack I would actually test

If you are a developer, product person, operator, or technical founder, do not start by trying everything.

Start with a small stack:

• One general assistant: ChatGPT or Gemini
• one coding workflow: Codex, Cursor, Claude Code, or GitHub Copilot
• one source-grounded research tool: NotebookLM
• one deep research tool: ChatGPT deep research or Gemini Deep Research
• one automation tool: n8n, Make, or Zapier- one design/communication tool: Figma AI or Canva

Then run a two-week test.

The two-week AI workflow test

Pick one real project. Not a toy prompt. Not a fake productivity experiment. Something you already need to do.

For example:

• publish a technical article
• launch a small web app
• prepare a workshop
• research a new market
• organize community submissions
• improve onboarding docs
• create a repeatable client update workflow
  

For two weeks, track six questions:

• Did it save time?
• Did it improve quality?
• Did it help you make a better decision?
• Did it reduce repetitive work?
• Did it create new review risks?
• Did it make your work more legible to other people?

If the answer is not clear after two weeks, the tool probably does not belong in your workflow yet.

What to avoid

There are three traps worth avoiding.

First, do not confuse AI output with progress. A long document, a generated app, or a polished design is not automatically useful.

Second, do not let tools replace taste. Technical people still need to know what good looks like: good code, good writing, good design, good research, good product decisions.

Third, do not build private workflows on tools you do not understand. If an AI tool touches client data, source code, customer records, or financial information, review its privacy, permissions, and failure modes first.

The best AI users are not the people with the longest tool list.

They are the people who know exactly where AI helps, where it fails, and where human judgment still has to lead.

So the next time someone asks, "Which AI tool should I use?", do not start with the tool.

Start with the stuck point.

What are you trying to ship? What are you trying to understand? What keeps slipping through the cracks? What decision needs better evidence? What work is invisible because nobody has time to explain it?

That is where the right AI tool becomes obvious.