2026-02-27 15:05:32
Building AI agents in 2025 means constantly assembling a patchwork of tools. Resume parsers here, content generators there, text analysis somewhere else.
I got tired of it and built the AI Tools MCP Bundle — 8 tools in one endpoint, in MCP format so it plugs directly into Claude and Cursor without any glue code.
Here's a quick demo — resume parsing in Python:
import requests
response = requests.post(
"https://ai-tools-mcp-bundle.p.rapidapi.com/parse-resume",
json={"resume_text": "Jane Smith, Senior ML Engineer, 7 years..."},
headers={
"x-rapidapi-key": "YOUR_KEY",
"x-rapidapi-host": "ai-tools-mcp-bundle.p.rapidapi.com"
}
)
print(response.json())
# → { "name": "Jane Smith", "role": "Senior ML Engineer", "years_exp": 7, ... }
Or content generation:
response = requests.post(
"https://ai-tools-mcp-bundle.p.rapidapi.com/generate-content",
json={"type": "blog", "topic": "Why MCP is the future of AI tooling", "tone": "professional"},
headers={...}
)
Why I chose MCP format: The Model Context Protocol lets AI assistants like Claude call your tools natively in their reasoning loop. No prompt engineering to get output in the right shape — the tool schema does that for you.
All 8 tools, one subscription:
Resume parsing, job matching, content generation, sentiment analysis, keyword extraction, readability scoring, AI content detection, and prompt rephrasing.
Free tier available (50 req/mo). Paid from $14.99.
👉 https://rapidapi.com/phaniavagaddi/api/ai-tools-mcp-bundle-resume-content-and-text-analysis
What are you building with MCP? Let me know in the comments.
2026-02-27 15:04:09
In the devlog-ist/landing project, we recently tackled an interesting challenge: ensuring post publication dates were correctly localized across all portfolio themes. This involved making sure that month names and other date elements respected the application's locale, as set by the SetLocale middleware. The fix centered around using translatedFormat() instead of format() within the Go templates.
The initial implementation used Go's format() function to display post dates. While this worked fine for the default locale, it failed to adapt when users switched to different languages. For instance, month names would remain in English even when the application was set to display in French or Spanish.
translatedFormat()
The key to solving this was the translatedFormat() function. This function, unlike format(), is locale-aware. It retrieves the translated month names and other date elements from the application's localization resources, ensuring that the dates are displayed correctly in the user's chosen language.
Here's a simplified example of how the change was implemented within the Go templates:
{{/* Before */}}
<time datetime="{{ .Date.Format "2006-01-02" }}">{{ .Date.Format "January 2, 2006" }}</time>
{{/* After */}}
<time datetime="{{ .Date.Format "2006-01-02" }}">{{ .Date.translatedFormat "January 2, 2006" }}</time>
In this example, replacing .Date.Format with .Date.translatedFormat ensures that the month name ("January") is rendered according to the current locale.
This seemingly small change has a significant impact on the user experience. By correctly localizing dates, we provide a more seamless and intuitive experience for users from different regions. It also enhances the overall professionalism and polish of the devlog-ist/landing project.
When working with dates in Go templates, remember to use translatedFormat() instead of format() to ensure proper localization. This simple change can greatly improve the user experience for international audiences.
2026-02-27 15:02:32
For most of my career, I have lived in the middle tier. I built REST APIs. I designed microservices. I wired service meshes, API gateways, orchestration engines, and Sagas. I helped teams decompose monoliths and celebrated when we finally had clean domain boundaries and independently deployable services. And yet, if we are honest, we replaced one kind of rigidity with another.
We moved business logic out of monoliths but we hardcoded coordination logic into workflow engines and orchestration layers. We spread the intelligence across dozens of services, but the way those services talk to each other is still scripted, fixed, and brittle.
That is the layer now being reimagined. Not the databases. Not the systems of record. Not even the REST APIs underneath.
The layer that changes is the coordination layer the middle tier itself.
Five Core Responsibilities Nobody Talks About before proposing change, we need clarity about what exists. In most enterprises, the middle tier handles five core responsibilities.
It routes requests. An API gateway receives traffic and decides which service should handle it.
It orchestrates workflows. A customer onboarding request might require identity verification, credit checks, account creation, compliance screening, and notifications all in a specific sequence with carefully coded failure handling.
It isolates domains. The customer service knows customers. The orders service knows orders. The claims service knows claims. When workflows span domains, the middle tier stitches them together.
It translates data. One system calls it an "account." Another calls it a "profile." A third expects a completely different shape. The middle tier maps, transforms, and reconciles.
It handles exceptions. When services fail, time out, or return unexpected responses, the orchestration layer decides what to retry, what to compensate, and what error to surface.
The Problem: Every Workflow Is Pre Scripted
This is serious engineering work. It is not trivial. It is not accidental.
But it is entirely pre scripted.
Developers anticipate the workflows and encode them. The system behaves exactly as programmed no more, no less. When reality deviates from what was anticipated, we don't adapt. We patch. We extend the script. We add another conditional branch. Over time, the coordination layer becomes as rigid as the monolith it replaced.
From Scripted Workflows to Runtime Reasoning
The change underway is not incremental optimization. It is a shift in where intelligence lives.
Instead of scripting every workflow in advance, we introduce a reasoning layer that receives a goal and determines the coordination path at runtime.
Conceptually, the architecture simplifies into four layers:
The systems stay. The data stays. The REST APIs stay.
What changes is how coordination happens.
Static Orchestration vs. Runtime Composition
In the traditional model, orchestration logic is static. Someone writes:
"If fraud check passes, call claims validation. If validation passes, create payment. If any step fails, return error code."
In an agentic model, the system receives a goal:
"Approve this claim, check for fraud indicators, and notify the customer."
The coordinator does not follow a hardcoded sequence. It reasons.
It identifies that fraud analysis is required. It determines that claim validation must precede payment. It recognizes that notification depends on final status. It adapts if a service is slow. It retries intelligently. It can even escalate to a human if necessary.
The difference is not randomness. It is runtime composition.
Scripted orchestration assumes the world is predictable.
Agentic orchestration assumes the world is dynamic.
The Protocol That Makes This Feasible. This shift would not be feasible without a standardized way for agents to interact with enterprise systems. That is where Model Context Protocol (MCP) enters.
MCP was introduced by Anthropic in late 2024 as an open standard for exposing system capabilities to AI agents. By 2025, it had been adopted across major AI providers and contributed to neutral governance under the Linux Foundation.
It is important to be precise here. MCP does not replace REST.
Your REST APIs remain exactly where they are. What MCP does is standardize how those APIs are described and invoked by agents. Each system exposes its capabilities as tools clearly defined actions with structured inputs and outputs.
In traditional microservices, integration complexity grows with the number of consumer provider pairs. In an MCP world, each system is wrapped once. Every agent can use it.
Instead of writing custom integration clients for every service-to-service interaction, agents discover and invoke tools through a uniform interface. Integration complexity grows linearly, not combinatorially.
That is a structural improvement, not a cosmetic one.
A2A: When Agents Talk to Agents
Coordination Beyond Tool Calls
As enterprises introduce specialized agents fraud agents, claims agents, compliance agents coordination becomes agent-to-agent rather than purely agent-to-system. This is the purpose of Agent-to-Agent (A2A) protocol.
A2A standardizes how agents delegate tasks, negotiate responsibilities, and exchange results over HTTP-based transports. It allows independently developed agents to collaborate without tight coupling.
Adoption today is still maturing. MCP has seen broader uptake so far, largely because tool integration is the first problem enterprises solve. Multi-agent collaboration comes next.
But the pattern is clear:
MCP handles agent-to-tool communication.
A2A handles agent-to-agent coordination.
Together, they create a composable reasoning layer.
The Hidden Cost of Domain Specific UIs,The most visible impact of this architecture may not be in the middle tier at all. It may be in the client layer.
Today's enterprises have an application for every domain. HR has a portal. Finance has a dashboard. Operations has its console. Each UI encodes domain specific logic.
When coordination becomes agentic, the client no longer needs to encode domain intelligence. It only needs to capture intent.
A user can ask:
"What is our exposure on outstanding claims in the southeast region, and are any flagged for fraud review?"
In the current world, this requires navigating multiple systems and reconciling results manually. In an agentic architecture, the reasoning layer determines which tools to invoke, gathers cross-domain data, synthesizes the response, and returns a coherent answer.
The client becomes thinner. The middle tier becomes smarter.
This does not eliminate specialized UIs overnight. But over time, the proliferation of portals becomes harder to justify when a unified intent interface can coordinate across domains.
Constrained Reasoning, Not Uncontrolled Autonomy An architecture that reasons at runtime introduces new responsibilities.
Guardrails become essential. Agents must operate within defined policies. Tool access must be permission scoped. Outputs must be auditable. Decision traces must be retained.
Auditability as a Feature
Fortunately, agentic systems naturally produce logs of tool invocations, delegations, and reasoning steps. With proper observability, auditability improves rather than degrades.
This must be engineered with the same rigor applied to distributed systems over the past decade but the raw material is there.
The Transition Path
You Don't Need to Rewrite Anything
This evolution does not require a rewrite. Enterprises can move incrementally:
Wrap existing systems as MCP tools.
Introduce specialized agents in high value domains.
Add coordination between agents where cross domain workflows matter.
Gradually simplify client experiences.
Microservices remain the foundation. Domain decomposition remains valid. REST remains plumbing.
The difference is that we stop writing brittle coordination scripts and start expressing goals.
From Build Time Composition to Runtime Reasoning We spent a decade breaking monoliths apart. Then we rebuilt rigidity through orchestration layers, workflow engines, and integration code.
The next phase is not further decomposition. It is a shift in when composition happens.
Why This Is Inevitable
This is not hype. It is a logical extension of everything enterprise architecture has been evolving toward: loose coupling, standardized interfaces, independent deployability now applied to coordination intelligence itself.
The scripted middle tier solved yesterday's problem.
The agentic middle tier addresses tomorrow's complexity.
That is the shift.
Thanks
Sreeni Ramadorai
2026-02-27 15:01:17
So your resume made it past the ATS. Congrats — you're in the top 30%. But then... silence. Ghosting. The black hole.
Here's what nobody tells you: passing ATS is just the entrance exam. The real rejection happens in the next 30 seconds.
I've been building SIRA — an AI resume optimizer — for over a year now. In that process, I've had conversations with recruiters, hiring managers, and dozens of developers who've been through brutal job searches. And I kept hearing the same thing: "I don't understand why I'm getting ghosted after getting past the filters."
So I started asking recruiters directly. Here's what I found.
Everyone talks about the 6-second rule. But the actual human review for developer resumes is closer to 30 seconds — and recruiters aren't reading. They're pattern matching.
They're scanning for three signals:
That's it. In 30 seconds. If any of these three fail, you're out — even if your resume is technically perfect.
Look at most developer resumes and you'll see bullets like:
- Developed and maintained REST APIs using Node.js
- Collaborated with cross-functional teams
- Participated in code reviews
A recruiter looks at this and thinks: "So... they had a job."
Compare that to:
- Rebuilt payment API in Node.js, reducing average response time from 800ms to 120ms — directly tied to a 12% increase in checkout completion rate
Same job. Completely different signal. The second version shows impact, not activity.
Here's something I never thought about when I was first putting together my resume: the order and framing of your jobs tells a story.
If you went from Senior Engineer → Backend Developer → Junior Role, even if the junior role was a strategic pivot into a new domain, it looks like regression. Recruiters don't have time to decode your narrative — you have to make it obvious.
Add a one-liner context note when needed:
Backend Developer @ StartupXYZ (Deliberate pivot to ML infrastructure — reduced team)
One line. Saves the recruiter the confusion. Keeps you in the pile.
I see this constantly: a skills section that lists 40+ technologies in a flat comma-separated list.
Skills: Python, JavaScript, React, Vue, Angular, Node.js, Django, Flask, FastAPI, PostgreSQL, MySQL, MongoDB, Redis, Docker, Kubernetes, AWS, GCP, Azure, Terraform, Jenkins...
This doesn't impress anyone. It actually hurts you. A recruiter looking for a senior backend engineer sees this and wonders: "Do they actually know any of these, or do they just list everything they've touched?"
Better approach — group by proficiency:
Expert: Python, FastAPI, PostgreSQL, Docker
Proficient: React, Node.js, Redis, AWS (EC2, Lambda, RDS)
Familiar: Kubernetes, Terraform, GCP
Now you're communicating confidence, not just coverage.
I'll share a few real things I've heard (paraphrased, names withheld obviously):
"When I see a resume with no dates on the education section, I immediately wonder if they're hiding something. Just put the dates."
"I skip resumes that use the word 'passionate' in the summary. It's meaningless. Show me what you shipped."
"If I can't figure out your seniority level in the first 5 seconds, I'm moving on. It's not my job to figure that out."
That last one hit me hard, honestly. Because most developers write resumes for themselves — to be comprehensive, to showcase everything — instead of writing for the 30-second scan.
After building SIRA and processing thousands of resumes, we've found that the ones that convert to interviews consistently follow this structure:
For each job entry:
[Action verb] + [What you built/changed] + [Measurable outcome]
For your skills section:
For your summary:
When I was building SIRA, the initial idea was just "fix ATS keywords." But after digging deeper into where applications actually die, it became clear that ATS is table stakes. The real work is optimizing for the human scan.
So SIRA now runs two layers of analysis: one that checks keyword alignment with the job description (ATS layer), and one that evaluates impact density — how many of your bullets have a measurable outcome vs. just describing activity.
You can try it at sira.now or directly through @sira_cv_bot on Telegram. Drop in your resume and a job description — it'll tell you exactly where you're leaking opportunities.
Most of us spend hours agonizing over which projects to include, whether our summary sounds smart enough, whether we should list that 2-month contract role. And almost none of us think about how a human reads this document in 30 seconds.
That mismatch is why strong engineers with great experience get ghosted while less experienced candidates get callbacks. It's not about what you've done — it's about how quickly a stranger can extract the signal from your document.
Fix that, and the callbacks start coming.
What's the biggest resume mistake you've made — or seen others make? Drop it in the comments. I read everything.
2026-02-27 15:01:06
We Crawled 3,000,000 URLs and broke Vercel at 2:13AM.
Building an AI Visibility Engine with Google Gemini
This is a submission for the Built with Google Gemini: Writing Challenge
At 2:13 AM, Vercel failed again.
This time it couldn’t find a package that absolutely existed.
Error: Cannot find module '@repo/ui'
The package was there.
The exports were correct.
The workspace was configured.
But Vercel insisted it didn’t exist.
That was the night I stopped thinking of this as “an SEO tool.”
We were building infrastructure.
And Google Gemini was sitting in the middle of that chaos, not as magic, but as a force multiplier.
Before we wrote a single API route, I was already deep in architecture mode.
At Valnee Solutions, I led the technical planning for ReachSaga. That meant long nights mapping tentative user flows, sketching system boundaries, and pressure-testing assumptions before they became code.m
On TLDraw, I mapped:
On Linear, I broke everything into:
That upfront planning didn’t remove friction, but it prevented chaos.
And when things broke (they did), we had structure to fall back on.
This wasn’t a hackathon project.
This was an internal MVP build at Valnee Solutions for a client-facing platform.
The problem we were solving:
Brands track SEO.
They don’t track AI visibility.
They don’t know:
SEO is search-engine focused and we were building for AI platforms, classified as Answer Engine Optimization.
At a high level:
Keywords -> Topic Clusters -> Prompt Simulation -> AI Visibility Scoring -> Content Generation -> Site Optimization Scoring
Core modules:
Tech stack (condensed):
And in the middle of all this: Google Gemini.
One of the first architectural pivots Gemini helped structure was our adaptive sitemap ingestion model.
We started with this assumption:
MAX_URLS = 500
Safe. Predictable. Easy.
Then we ingested a site with 3,000,000 URLs, and another with 12.
That’s when we redesigned the logic into:
if total_urls < 1500:
crawl_all()
else:
prioritize_by(
depth,
traffic_probability,
keyword_match_score
)
enforce_absolute_cap(1500)
Hard limits aren’t scalable, adaptive scoring is.
Gemini helped decompose that reasoning clearly and quickly.
apps/
web/
workers/
packages/
ui/
db/
config/
Clean. Modular. Future-proof.
Until Vercel defaulted to Yarn v1. The project required Yarn v4 via Corepack.
What followed:
@repo/ui not foundEPERM unlink errorsrootDirectory misconfigurationIt looked like a registry issue.
It wasn’t. It was workspace resolution failure caused by version mismatch.
Another unexpected battle: mirroring a deploy branch from one repository into main of a private repo in another account.
Sounds simple.
It wasn’t.
We hit:
CI/CD isn’t a “later” feature, it’s part of the product.
Gemini helped draft and refine the GitHub Actions YAML, but we still had to understand:
AI helped structure.
We had to validate.
That pattern repeated across the entire project.
The biggest shift wasn’t technical.
It was conceptual.
We thought we were building an SEO tool.
We were building an AI visibility engine.
That meant:
When we added:
target_platform
tone_profile
structure_type
intent_class
The content engine stopped being “blog generation.”
It became multi-channel orchestration.
That was the moment the architecture clicked.
ReachSaga is live at:
The platform is deployed in production with:
500 URLs felt safe.
It wasn’t. Adaptive systems scale. Static caps break.
Gemini was strongest when:
It struggled when:
The biggest insight:
AI doesn’t reduce thinking.
It rewards better thinking.
Scoring normalization should’ve been designed earlier.
Worker scaling should’ve been planned earlier.
I underestimated deployment complexity. I won’t again.
To be perfectly candid
Gemini was incredibly strong at turning messy thoughts into structured systems.
It’s powerful. But it’s not autonomous. And that’s fine.
The human still defines reality.
The near future includes the following:
Soon, we'll get closer to the Phase 2 of our project, incorporating GEO.
SEO optimizes for search engines.
AEO finds out whether or not any given AI platform is citing us.
GEO figures out how to make the AI cite us in exactly the way we need.
2026-02-27 15:00:24
If you have been shipping with ai coding tools lately, you have probably felt the trade-off in your hands. You can describe an app, watch thousands of lines appear, and demo something real in an afternoon. But the moment that code runs on your laptop, your API keys, browser sessions, and files sit one prompt away from becoming part of the experiment.
A recent real-world incident made this painfully concrete. A security researcher demonstrated that, by modifying a single line inside a large AI-generated project, an attacker could quietly gain control of the victim’s machine. No suspicious download prompt. No “click this link” moment. Just the reality that when you cannot review what gets generated, you also cannot reliably defend it.
The core lesson is simple and uncomfortable. Vibe coding shifts risk from writing code to executing code. The danger is not that AI writes “bad code” in the abstract. The danger is that it produces a lot of code quickly, and it often runs with permissions your prototype does not deserve.
Here is the pattern we see most often with solo founders and indie hackers. The build starts as a no code app builder style flow, or a low code application platform workflow with an AI chat maker UI. Then it becomes a real product. Users sign up. Payments enter the picture. Secrets land in environment variables. That is the point where “it works” stops being the bar.
Right after you internalize that, the next step is to move the dangerous parts out of your personal machine and into a controlled environment.
A practical way to do that early is to run prototypes against a managed backend where permissions, auth, storage, and isolation are already designed in. That is exactly why we built SashiDo - Backend for Modern Builders. It lets you keep the speed of ai generate app workflows, while avoiding the habit of giving bots local access to everything.
Traditional app security failures usually need a trigger. You click a malicious attachment. You paste credentials into the wrong place. You install a compromised dependency. In the incident above, the attacker’s leverage came from something scarier. The victim did not need to do anything at all after starting the project. That is what makes “zero-click” style compromises so damaging in practice.
There are three reasons vibe-coding workflows create a new class of problems.
First, the review surface explodes. When an AI tool generates thousands of lines you did not author, it becomes normal to run code you do not understand. That makes it easy for malicious or compromised changes to hide in plain sight.
Second, the tooling often has deep local privileges by default. If your AI agent can read your filesystem to be helpful, it can also read secrets. If it can run commands to build and test, it can also execute unexpected payloads.
Third, the “project” is rarely just code. It is config files, local caches, credentials, and tokens. That is why a single line added in the wrong place can turn a harmless demo into full device access.
This is also why professor Kevin Curran’s warning lands with experienced engineers. Without discipline, documentation, and review, the output tends to fail under attack. The discipline part matters because ai coding is less forgiving when you skip basic software hygiene.
You do not need a full security program to make good decisions. You need a simple model of what can go wrong.
Start with the assets. In almost every vibe-coding project we see, the highest value items are: API keys and tokens, user data, payment and analytics dashboards, and your local machine’s browser sessions and SSH keys.
Then map the paths.
An attacker can target the AI tool itself, its plugin ecosystem, or shared project artifacts. They can also target your own workflow. For example, sharing a project link, pulling “helpful” code snippets from community chat, or granting the agent permission to access a folder full of keys.
Finally, map the outcomes. In the worst cases, a hidden change does not just break your app. It turns your environment into the attacker’s environment.
If you want a compact set of categories that maps well to these failures, the OWASP Top 10 (2021) is still the best common language. You will recognize the usual suspects, like broken access control and injection. But in vibe coding, the biggest driver is often the same. Lack of visibility.
If your goal is to keep building quickly while reducing the odds of an “ai coding hacks” moment, you are looking for guardrails more than features.
A secure setup typically has three layers.
At the device layer, isolation matters. Running agentic AI directly on your daily laptop is convenient, but it makes compromise catastrophic. Microsoft’s Windows Sandbox overview is a good example of the direction you want. A disposable environment. A fresh state each run. Clear boundaries.
At the identity layer, least privilege matters. Disposable accounts for experiments and short-lived credentials reduce blast radius. This aligns with the broader “assume breach” mindset found in the CISA Zero Trust Maturity Model.
At the software layer, supply chain visibility matters. If you cannot answer “what dependencies did the agent add” you are already behind. CISA’s guidance on SBOMs, like Shared Vision for SBOM, is worth reading because it explains why modern software is as much about components as code.
In practice, here is the checklist we see working for solo founders.
None of this removes the value of vibe coding. It just puts your workflow back inside a security boundary.
For early demos, local execution is fine. The break point usually happens when one of these becomes true.
You start storing user content, like images, audio, or documents. You introduce authentication and password reset flows. You add push notifications. You accept payments or connect to production third-party APIs. Or you hit a growth threshold where a single security mistake impacts more than a handful of beta users.
That is when local-first, agent-heavy workflows create two kinds of pain.
The first is security pain. It becomes normal for your agent to have access to the same files and sessions you use for everything else.
The second is operational pain. Even if the prototype works, you now need APIs, a database, background jobs, and a place to host and scale. If you try to bolt those on late, you often end up shipping with default settings and unreviewed permissions.
This is the moment where a managed backend is less about convenience and more about risk containment.
For commercial intent decisions, it helps to compare options by what they protect you from, not what they promise.
| Option | What It’s Great For | Where It Breaks | Best Fit |
|---|---|---|---|
| Vibe coding on your main laptop | Fastest first demo, quick iteration | Large blast radius. Hard to review. Secrets leak risk | One-off experiments with no real data |
| Vibe coding in a sandbox or dedicated machine | Safer agent execution | Still need backend, auth, storage, scaling | Early builders who want speed plus containment |
| Roll your own backend (self-host) | Maximum control | DevOps tax, patching, uptime, backups | Teams with infra experience and time |
| Managed backend (BaaS) + AI front-end | Faster path to production-grade primitives | You still own app logic and access rules | Solo founders going prototype to launch |
If you are in the last category, this is where SashiDo - Backend for Modern Builders fits naturally. We built it so you can move from “the agent generated an app” to “this is a real service” without building a DevOps stack first.
In a typical ai coding workflow, you need a database, APIs, auth, file storage, realtime updates, background jobs, serverless functions, and push notifications. In SashiDo, those are first-class features. Every app includes a MongoDB database with CRUD APIs, complete user management with social logins, object storage backed by AWS S3 with a built-in CDN, JavaScript serverless functions in Europe and North America, realtime via WebSockets, scheduled and recurring jobs, and unlimited iOS and Android push notifications.
If you want to validate this quickly, our Getting Started Guide shows how to stand up a backend and connect a client app without building your own infrastructure.
When comparing managed backends, you might also look at alternatives like Supabase, Hasura, AWS Amplify, or Vercel depending on your stack. If you do, keep the evaluation grounded in what you need for your launch. Auth model, database fit, scaling knobs, background job support, and how much operational responsibility you retain.
For reference, we maintain comparison pages that highlight the practical differences. You can start with SashiDo vs Supabase, SashiDo vs Hasura, SashiDo vs AWS Amplify, and SashiDo vs Vercel. The point is not that one is “best” in a vacuum. The point is to choose the backend that reduces your risk and workload for the kind of app your ai coding tool is producing.
People often ask for the best ai for vibe coding as if the answer is purely about code quality or speed. In practice, the deciding factor is whether the workflow gives you control over permissions and execution.
If the tool can run code, read files, and manage dependencies, then your security posture depends on what it is allowed to touch. The safer tools make boundaries obvious. They separate “generate text” from “execute actions.” They support running inside isolated environments. They make it easy to inspect diffs and changes.
The most reliable pattern is to let AI help with generation and refactoring, then run builds and deployments inside a controlled pipeline. This is also why agentic AI on personal devices keeps landing in headlines. It is powerful, but without guardrails it is also extremely insecure.
It is tempting to look for an ai coding detector or ai coding checker that can tell you whether the output is safe. These tools can help, especially when they flag obvious secrets, risky dependencies, or suspicious patterns. But they are not a replacement for isolation and access control.
A detector can tell you “this looks machine-generated” or “this string resembles a key.” It cannot reliably answer, “does this project contain a hidden execution path that only triggers under specific conditions?” That is why the first line of defense should be limiting what the project can touch.
Use checkers for what they are good at. Consistency, linting, scanning for known issues, and catching accidental leaks. Then build the real defenses around execution boundaries and least privilege.
Moving to a managed backend does not magically make your app secure. You still need to design access rules and avoid shipping admin-level APIs to clients.
What it does change is the reliability of your foundation. Your database is not a file on your laptop. Your auth system is not a half-finished prompt output. Your storage and CDN are not an ad-hoc bucket with unknown permissions. Your background jobs do not run on a machine that also holds your personal SSH keys.
At SashiDo, we see this shift most clearly when indie hackers add auth late. They often start with a “just store users in local storage” approach because the AI suggests it. Then they realize password resets, social logins, token expiry, and account takeover protection are a product in themselves.
That is why we include a complete User Management system by default, and why our documentation focuses on concrete, buildable flows rather than marketing promises.
If you are dealing with higher stakes workloads, it is also worth reviewing our security and privacy policies to understand where the platform’s responsibilities end and where yours begin.
The other anxiety we hear constantly from the vibe-coder-solo-founder-indie-hacker crowd is cost volatility. The pattern is predictable. A demo hits social media. Traffic spikes. The backend bill surprises you. Then you start turning features off.
The best defense is not a perfect forecast. It is picking an architecture that can scale in predictable steps.
In SashiDo, scaling is designed around clear knobs. You start with an app plan and scale resources as needed. If you want the current pricing and what is included, always check our live pricing page, because rates and limits can change over time. The key point for planning is that you can begin with a free trial and then scale requests, storage, and compute as real usage arrives.
When you hit compute-heavy workloads, like agent-driven processing or bursty realtime features, that is when our Engines become relevant. Our write-up on the Engines feature explains how isolation and performance scaling work, and how usage is calculated.
If you only change a few habits this week, make them these.
Do not run agentic tools with access to your home directory “because it’s easier.” Do not store production secrets in files the agent can read. Do not let an AI tool auto-install dependencies without checking what it added. Do not treat “it compiled” as a security signal. And do not assume that because the code came from a well-rated tool, the project is safe.
Instead, build a workflow where you can move fast and contain failures. Use isolation for execution. Use disposable credentials. Use automated scanning for obvious leaks. Then move the backend into a managed environment before you start collecting real users.
The big shift in ai coding is not that software became easier to write. It is that software became easier to run without understanding it. That is how you get a single hidden change turning into full device access, and how you end up with a “zero-click” style compromise in what looked like a harmless prototype.
The fix is not to abandon vibe coding. The fix is to treat AI output as untrusted until proven otherwise, and to move execution and data behind boundaries you control.
If you want to keep shipping quickly without giving bots deep local access, it helps to put your database, auth, storage, and jobs behind a managed backend. You can explore SashiDo - Backend for Modern Builders to sandbox AI agent-driven apps, add production-ready auth and APIs, and start with a 10-day free trial with no credit card required. For the most up-to-date plan details, refer to our live pricing page.
The best “coder for AI” is the workflow that lets you constrain what the model or agent can execute, not the one that generates the most code. Look for strong boundaries, reviewable diffs, and isolated execution. If the tool can run commands or access files, your ability to limit permissions matters more than raw generation quality.
The most common failures are hidden code changes, leaked secrets, and overly broad permissions. In vibe coding, attackers do not need you to understand the code. They need you to run it. That is why isolating execution and using disposable credentials reduce risk even when you cannot fully review every generated file.
Move off local-first setups once you add real auth, start storing user content, connect to paid APIs, or expect public traffic. Those are the points where compromise affects users, not just your demo. A managed backend also helps when you need background jobs, push notifications, or predictable scaling without building DevOps.
They help with specific problems like finding accidental secrets, spotting known vulnerable dependencies, and enforcing basic hygiene. They do not replace isolation or access control, because they cannot reliably prove a large project has no hidden execution paths. Use them as a safety net, not as your primary defense.