From Chaos to Code: 5 Lessons from the APAC Disaster Management AI Skills Jam

During the critical first 24 hours of a disaster, responders often face a paralyzing surge of fragmented information. To mitigate this "data paralysis," the APAC Disaster Management AI Skills Jam was convened in Bangkok. Orchestrated by the OpenAI Academy and DataKind, the initiative combined a strategic workshop on March 30, 2026, with an intensive, hands-on technical sprint on June 11–12, 2026.

Utilizing a "Build. Test. Learn." framework, 60 officials and developers from 13 Asian nations engineered field-ready crisis management solutions. Tobias Meixner, founder of Hubql, served as a technical mentor, providing expertise in OpenAI Codex and co-developing AI-driven workflows for the Indonesian Ministry of Environment.

The following five lessons highlight how data-driven initiatives help humanitarian professionals leverage AI during crises.

1. Transitioning from Individual Prompts to Reusable Workflows

Optimizing AI utility in disaster response requires a transition from unplanned and situational interaction toward 

. In the "ChatGPT 201" track, participants leveraged "Deep Research" and "Projects" to synthesize structured briefs that clearly separate verified facts from uncertainties.

This methodology prioritizes high-fidelity, structured data over speculative output. For instance, by employing AI to synthesize cross-regional adaptations to urban flooding, responders extracted verifiable, localized insights rather than generic guidance, ensuring technical demonstrations translated directly into deployable field tools.

2. The "Build. Test. Learn." Model for High-Pressure Environments

To address the acute information overload of early-stage crisis response, the workshop prioritized rapid prototyping over theoretical discourse. This approach accelerated the transition from abstract concepts to functional assets in environments where time is the most critical resource.

By integrating senior government officials with technical architects, the workshop ensured that every prototype was anchored in operational reality.

Tobias Meixner collaborated with Indonesian officials to engineer sophisticated agentic capabilities. These solutions automated the extraction of data from legacy documentation, transformed manual templates into standardized digital reports, and generated intuitive visualizations to facilitate public education on climate adaptation.

3. Prioritizing "Calm" Over "Correctness" in Responsible AI

In crisis management, the psychological impact of a digital tool is a 

. The workshop's Image Challenge underscored that the efficacy of AI-generated visuals—such as heat-safety advisories or evacuation signage—depends entirely on their clarity, cultural resonance, and ability to project authority.

Operationalizing responsible AI within public safety demands a specialized UI/UX philosophy: digital assets must be engineered to prevent panic and preempt misinformation. Ensuring public order through AI-assisted communication is a foundational requirement, necessitating rigorous human-in-the-loop oversight and validation.

Ultimately, an image that is technically accurate but induces public distress is a failure; a truly "responsible" solution is one that actively reinforces population stability and trust.

4. The Humanitarian Industry is "Document-Driven"

A prevailing misconception suggests that humanitarian AI is focused primarily on robotics. In practice, the sector is fundamentally document-centric and data-intensive, relying on the efficient processing of massive information flows.

The "Data Analysis + Excel" track demonstrated that the majority of manual labor involves parsing legacy records and maintaining complex spreadsheet trackers. The strategic value of AI lies in its ability to eliminate this administrative friction. During the workshop, technical builders empowered officials to create agents capable of:

Automating these labor-intensive processes is a high-impact innovation. When administrative overhead is reduced, decision-makers reclaim critical time for high-stakes, life-saving leadership.

 as a pivotal "AI teammate." Codex acts as a bridge, enabling non-technical domain experts—the officials who best understand local terrain and policy—to collaborate directly with engineers. This shift democratizes the development lifecycle.

Codex enables disaster management professionals to lead the architecture of functional prototypes and dashboards without writing underlying code. This ensures that digital products are precisely calibrated to field requirements. When the domain expert becomes the lead architect, the gap between technical potential and on-the-ground utility is closed.

Conclusion: The Future of Digital Response

 proved that the use of sophisticated AI tools like Codex is creating a new global standard for emergency readiness.

By prioritizing reusable workflows and institutionalizing "calmness" as a product requirement, government officials can effectively navigate the volatility of the first 24 hours of any crisis.

At Hubql, we recognize that the principles of structured data, rapid prototyping, and outcome-driven design are universal—whether managing a flood in Bangkok or a product launch in San Francisco. If AI can stabilize a disaster response, it can certainly help your organization navigate its most complex operational challenges.

 and let's discuss how we can help you deal with your real business challenges.

APAC officials and developers built AI workflows for disaster response in Bangkok, with Tobias Meixner serving as a technical mentor.

APAC Disaster Management AI Skills Jam

For decades, the "Technical Co-Founder" was the startup ecosystem's gatekeeper. If you couldn't write code or afford a dev shop, your business idea was dead on arrival. We called it the "syntax tax"—a high-entry barrier that prioritized coding literacy over domain expertise.

On May 29, 2026, at Dee Dar Bar in Bangkok, we witnessed the collapse of that barrier. The 

 proved the era where code was the primary innovation bottleneck is over.

The revolution is being fueled by a concept called "Vibe Coding." Rather than manual syntax entry, Vibe Coding allows founders to program via natural language conversation.

, an AI app builder that has moved the industry beyond simple low-code/no-code drag-and-drop tools into a realm of natural language programming.

In Bangkok, even seasoned software engineers abandoned traditional IDEs. Developers who once spent weeks on architecture switched to Lovable to ship complex products in a fraction of the time.

The technical founder is evolving into a high-level architect, focusing on 

Industry Experts are the New Software Architects

As the technical barrier falls, we see the "democratization of the stack." Domain expertise is becoming more valuable than computer science degrees.

In the age of Lovable, the person who understands the problem is the person who builds the solution. For example, a founder at the event launched a community-centric sports platform with zero prior coding experience. They translated their understanding of athlete networking into a functional digital ecosystem.

When the "how" becomes an abstraction layer, the "why" becomes the only competitive advantage. Software creation and community building have merged.

Solving "Hyper-Local" Problems with Production-Ready Logic

The most impressive takeaway from the Bangkok showcase was the shift from "toy" prototypes to production-ready software. These weren't just front-end mockups, but sophisticated tools designed to handle real business challenges.

Some of the standout projects built during the event include:

These projects demonstrate that "Vibe Coding" embeds complex business logic into internal tools that directly impact a company's bottom line.

The most dramatic element of this revolution is the radical compression of time. The hackathon ran for just four hours.

Within that session, founders with no tech background solved business problems that would have previously required a six-month roadmap and a significant budget. This is a total collapse of the traditional development cycle.

It is easier than ever for founders to turn ideas into reality. This isn't just a lower barrier; it is a total collapse of the traditional development cycle.

 marks the moment where the distinction between "technical" and "non-technical" founders became irrelevant.

We are entering an era where every SME owner—from the agricultural exporter to the neighborhood cafe manager—has the power of a software engineering team at their fingertips. The focus has moved from "thinking about building" to "shipping."

Since the technical "how" is no longer an obstacle, the only question remaining is: What problem in your business would you choose to solve today?

 and let's discuss how we can help you solve a business problem or bring a new idea to life in weeks instead of months.

Bangkok meetup and four-hour hackathon where founders and developers used Lovable to turn local business problems into production-ready tools.

Lovable Meetup & Hackathon Thailand 2026

Last Monday we had the opportunity to attend the inaugural OpenClaw Builders Bangkok meetup at Cleverse. It was a great evening with AI builders, developers, and researchers coming together to explore agentic AI systems using OpenClaw.

OpenClaw Builders Bangkok is a monthly community meetup for AI builders, developers, marketers, and researchers working hands-on with agentic AI systems. The inaugural session featured a beginner-friendly workshop where attendees set up their own OpenClaw instance on cloud infrastructure.

The event was organized by SCB 10X and Cleverse with speakers including Dr. Tanwa from SCB10X, Mr. Dejchard Techakasem from AWS, Sandra (KAITO advisor), and Gareth and Rio from the Claw Collective.

What made this meetup stand out was the format. The split between beginner and advanced sessions allowed both newcomers and experienced builders to get value from the evening. Less slides, more hands-on demos, open discussions and Q&A worked really well.

The agenda covered a solid range of topics from introduction to the OpenClaw ecosystem, guided VPS setup walkthroughs, AI model selection strategies, to cost optimization and security configurations.

A few observations from the discussions that stood out:

Cost and token consumption remains the number one concern among builders working with OpenClaw. While costs are steadily decreasing, it is still the primary factor teams evaluate when considering adoption.

There is a noticeable gap in security and guardrails implementation for teams. Many builders are still figuring out best practices for running agentic AI systems safely in production environments.

A recurring question was when dedicated agent frameworks outperform direct Claude usage. The answer depends on the complexity of the orchestration needed, but for multi-step workflows with tool use, frameworks like OpenClaw provide clear advantages.

Perhaps the most surprising observation was how few teams have systematically implemented AI for eliminating repetitive tasks. There is a significant opportunity for teams to automate more of their day-to-day workflows.

At Hubql, we use a combination of Obsidian, Markdown, Claude, and OpenClaw in our workflows. This stack allows us to maintain a structured knowledge base while leveraging AI for development and automation tasks.

OpenClaw Builders Bangkok is a monthly meetup, and we look forward to attending future sessions. If you are building with AI agents in Bangkok, this is a great community to be part of.

The inaugural OpenClaw Builders Bangkok meetup at Cleverse with hands-on agentic AI workshops, cost optimization, and security discussions.

OpenClaw Builders Bangkok March 2026

Another founder reached the same point we keep seeing with AI-built products.

The app worked.
The product idea was validated.
Users could sign up and the core workflow was already there.

But the setup behind it was still prototype infrastructure.

In this case, the founder had built the product in Lovable using the default Lovable Cloud setup. That was the right decision at the start. It got them to a working product fast. But once they needed proper outbound email, safer database workflows, cleaner deployment, and a real development environment, the default setup stopped being enough.

This is what we changed to move the app from Lovable Cloud to Supabase in production.

This is what makes these situations interesting. Founders usually do not get stuck because the idea is weak or because Lovable failed to generate something useful. They get stuck because there is a difference between:

That gap usually shows up in the same places:

Once real users are involved, those details stop being optional.

We added a dedicated email subdomain in Resend for product emails instead of relying on the root domain setup. This matters because auth flows, invites, password resets, and notifications are part of the actual product experience. If email delivery is fragile, the product feels fragile too.

We connected Supabase with GitHub so database changes can be applied through deployment rather than being handled ad hoc.

That turns schema changes into something versioned, reviewable, and repeatable. For early-stage teams, this is one of the biggest upgrades in operational maturity because it removes a lot of hidden risk from shipping.

We linked Lovable to a dedicated development environment instead of treating production as the default place where everything happens.

That gave the founder a safer place to test changes, validate flows, and separate experimentation from live user impact.

We connected Vercel with GitHub for automatic deployments and connected the production domain to the deployed app.

This is simple on paper, but it matters a lot in practice. A clean deployment path reduces manual mistakes, shortens release cycles, and makes it easier to keep moving without accumulating operational chaos.

Finally, we migrated the application data to Supabase, including users and files.

This is the part founders often underestimate. A migration is never just about tables. The real work is in preserving:

That is where prototype systems become real production systems.

There are a few practical lessons here for founders building with Lovable, Bolt, Cursor, or similar tools.

Separate dev and production earlier than you think

The sooner you create separate environments, the easier everything gets later.

Different domains, different data, and different deployment flows save an enormous amount of pain once the product starts changing quickly.

That means versioned, repeatable, and tied to deployment rather than handled manually in a rush. It also means being careful not to treat migrations as a dumping ground for data manipulation that belongs elsewhere.

Many founders discover email infrastructure only when they are about to launch or when a user reports a broken auth flow.

That is too late. Email is part of onboarding, trust, and product reliability.

If users, files, and permissions are involved, migration is not only infrastructure work. It is product work. The experience has to survive the transition, not just the database rows.

Use Lovable as an accelerator, not as the final architecture

But speed at the start does not remove the need for engineering judgment later. It simply changes when that judgment becomes necessary.

The pattern is that AI tools are very good at helping founders get to a validated prototype quickly, and that same speed creates a second phase of work once the product is clearly worth taking seriously.

In other words, the problem is not that the founder moved too fast.

The problem is that the app has now earned a better foundation.

If your team has a Lovable app that works but does not yet feel production-ready, Hubql can help harden the setup without throwing away what already works.

Case study: moving a Lovable app to a production-ready Supabase setup with real environments, email, deployments, and data migration.

From Lovable Cloud to Supabase in Production

A founder builds something with Lovable, Bolt, or Cursor. It works. Users sign up. Revenue trickles in. The idea is clearly validated.

And then, usually somewhere between 200 and 500 users, things start to break in ways that are hard to explain and even harder to fix.

We've been auditing vibe-coded applications for the past year. Not to judge the founders who built them — the decision to use AI tools to move fast is a smart one. But the results of those audits have been consistent enough that they're worth sharing publicly, because most founders walking into this situation don't know what they don't know.

The most urgent issue, in nearly every audit we conduct, is security. And this isn't a minor inconvenience — it's the kind of vulnerability that can kill a company overnight.

According to Veracode's 2025 GenAI Code Security Report, nearly 

45% of AI-generated code contains security flaws

, and newer or larger models showed no meaningful improvement in this regard 

Unit 42, the research division of Palo Alto Networks, has documented that when AI agents generate authentication functions, they routinely neglect critical controls like rate limiting and input validation 

The Tea Dating app breach in 2025 became a widely studied case: a misconfigured storage bucket exposed 72,000 user images because the AI-generated code chose a simpler implementation over a secure one 

Another high-profile case involved a vibe-coded social platform called Moltbook, where a misconfigured Supabase database exposed 

1.5 million API keys and 35,000 user email addresses

 to the public internet. The root cause, security firm Wiz found, was not a sophisticated hack but the shortcuts inherent in vibe-coded development 

Even when the security issues are manageable, the underlying architecture of vibe-coded apps almost always needs work before serious growth is possible.

AI agents optimize for "working code" — code that runs without immediate errors. They don't naturally optimize for code that performs well under load, that can be maintained by a team, or that can be extended without extensive refactoring.

: a codebase that works today but becomes progressively harder and more expensive to build on.

A 2025 analysis found that AI-assisted coding created technical debt at a dramatically accelerated rate. What previously took a developer six months of messy development, a vibe-coded app can create in a weekend 

Apiiro's research on Fortune 50 enterprises documented a 

10x increase in security vulnerabilities per month

 between December 2024 and June 2025 as AI code generation became widespread — growing from roughly 1,000 to over 10,000 monthly findings 

. No error logging. No uptime monitoring. No alerts when something breaks.

This isn't laziness. It's simply that AI tools prioritize getting the feature working over instrumenting it.

The result is that founders often find out something is broken when a user complains — not when the system reports it.

None of this is a reason to stop building with AI tools. The speed advantage is real, and for early validation, it's exactly the right approach.

The issue is what happens when validation is complete and growth begins.

"We don't let interns push code to production without proper reviews, and we should do exactly that with agents."
— Andrej Karpathy, who coined the term "vibe coding" 

The apps that successfully make the leap from prototype to product aren't the ones that were built differently. They're the ones whose founders recognized that 

the vibe-coding phase was a launchpad, not a final destination

 — and acted on it before the cracks became crises.

JumpTarget

 IT Pro, "Vibe coding security risks and how to mitigate them," October 2025.

 Unit 42 / Palo Alto Networks, "Securing Vibe Coding Tools," January 2026.

 Medium / Adnan Masood PhD, "Beyond the Vibe: A Deep Dive into the Dangers of Vibe Coding," July 2025.

 Towards Data Science, "The Reality of Vibe Coding: AI Agents and the Security Debt Crisis," February 2026.

 Outsourcify, "Vibe Coding Rescue: Scaling Your AI MVP to Production," February 2026.

 Pixelmojo, "The AI Coding Technical Debt Crisis: What 2026–2027 Holds," January 2026.

Ready to harden your Lovable app for production?

 that delivers a prioritized findings report in 5 business days — written for founders, not engineers. If you need a broader review of your AI-assisted workflow and recurring bug sources, start with the 

After auditing 10 vibe-coded apps, the same problems appear almost every time. Here is what breaks, when it breaks, and why it is completely predictable.

10 Vibe-Coded App Audits: What We Found

We Audited 10 Vibe-Coded Apps. Here's What I Found.

You built something real without writing a line of code. That's remarkable, and it's no longer unusual.

According to industry data, 63% of vibe coding platform users in 2025 were non-developers [1].

The tools have democratized software creation in a way that seemed impossible just three years ago. But there's a question that comes up in nearly every conversation we have with founders who've built with AI tools: "At what point do I need to bring in someone technical?" The honest answer isn't "immediately." And it isn't "never." It's a set of specific signals that tell you when the nature of your risk has changed.

Signal 1: You've stopped being able to explain what the app does under the hood.

 In the early days of vibe coding, even non-technical founders have a decent mental model of how their app works. They know what happens when a user signs up, where data is stored, how payments are processed. But as the codebase grows through hundreds of AI-generated iterations, that mental model starts to drift from reality. If you've reached a point where you genuinely don't know what your app is doing with user data, you have a problem that's about more than code.

Signal 2: You're handling sensitive data from real users.

 The moment you're storing names, emails, payment information, health data, or anything users consider private, you've entered a different risk category. A 2025 report from Unit 42 (Palo Alto Networks) found that most organizations using vibe coding tools had performed no formal risk assessment on those tools, and very few were monitoring the security outcomes of AI-generated code [2]. Sensitive data without proper security architecture isn't just a technical problem; it's a legal and reputational one.

Signal 3: An investor or enterprise customer has asked to review your infrastructure.

 This is the moment many founders first encounter the gap between "it works" and "it's production-ready." Investors doing technical due diligence ask questions about architecture, security practices, test coverage, and deployment pipelines. Enterprise customers ask about compliance certifications, data residency, and incident response procedures. These aren't unreasonable questions. They're the questions that reveal whether you've built a prototype or a business.

Signal 4: You've had your first unexplained outage or data incident.

 If something has broken in production and you couldn't diagnose it within a few hours, that's a signal. If user data was ever exposed, even briefly, that's a more urgent one. These events are almost always symptoms of the structural issues that accumulate in vibe-coded apps: no error logging, no monitoring, no alerting, fragile integrations with third-party services.

Signal 5: You're trying to hire your first engineer and they've said the codebase is "a mess."

 This one stings, but it's valuable information. A developer who reviews a vibe-coded codebase and expresses serious concern isn't being elitist. They're telling you that the maintenance burden of that codebase is going to slow down everything they try to do. According to a CodingIT analysis, teams relying heavily on AI-generated code often find they're "constantly revisiting past work and fixing AI-generated messes" rather than building forward [3].

2. What You Actually Need (It's Not a Full Rewrite)

Many non-technical founders hear these signals and assume they need to scrap everything and start over. That's almost never true.

A 2025 survey by Seedium, which has worked with numerous vibe-coded startups, found that most AI-generated apps are 70–80% structurally sound [4].

The 20–30% that needs work tends to cluster around the same areas: authentication and authorization, database security, API design, and deployment infrastructure.

What you need isn't a rewrite. It's a systematic audit that identifies exactly which parts of your app carry risk, followed by targeted engineering to address those risks in priority order. The distinction matters enormously for both timeline and cost. A full rewrite of a Lovable or Bolt MVP typically takes three to four months and effectively discards all the user-informed iteration that shaped your current product. A targeted audit and remediation of the critical issues typically takes four to six weeks and preserves everything your users have shaped.

3. How to Talk to a Developer Without Getting Lost

One of the most common frustrations non-technical founders express is feeling talked past in technical conversations. Here are three questions that cut through the jargon and get to what matters.

"What would break first under ten times our current traffic?"

 This question forces a concrete, practical answer about the most fragile parts of the system.

"If a sophisticated attacker looked at this codebase, what would they target?"

 This question reveals whether your developer has actually thought about the adversarial case, or just the happy path.

"What would it cost to bring this to a standard that a mid-size enterprise would accept?"

 This question anchors the conversation in real-world expectations rather than engineering idealism.

The goal isn't to become technical. It's to become an informed buyer of technical services — one who knows enough to ask the right questions and evaluate the answers.

 Second Talent, "Top Vibe Coding Statistics & Trends [2026]," January 2026.

 Zencoder, "5 Vibe Coding Risks and Ways to Avoid Them in 2025," April 2025.

 Seedium, "How to Make Your Vibe-Coded App Secure and Scalable," October 2025.

63% of vibe coding users in 2025 were non-developers. Knowing when to bring in real engineering expertise is one of the most critical decisions you'll make.

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