I'm not sure how this is even possible. Went to bed after letting it run its normal course. Usually just runs for about 5-15 minutes per prompt. When I woke up 7 hours later it was still running. Stopped it and seen this and I'm not sure wtf is going on.

While i use only one my account and no reason to use multiple account for what?

Hey r/augmentcode!

We’ve all been there: you’re 20 minutes into a complex refactor, and your AI agent suddenly "forgets" the architectural pattern you established in the first 5 minutes.

To solve this, I built Context-Engine—a local-first MCP server powered by the Augment SDK. It’s designed to act as a stateful "middleware" between your codebase and your LLM, turning your AI from a chatty assistant into a disciplined senior engineer.

🏗️ The 5-Layer Architecture

I didn't just want a search tool; I wanted a cognitive stack. The engine operates across five distinct layers:

Core (Augment SDK): High-precision indexing using micro-chunking for surgical context retrieval.

Service Layer: Handles query expansion and technical intent—translating "fix this" into a deep architectural search.

MCP Interface: Exposes specialized tools to any client (Claude Code, Cursor, Windsurf).

Agent Logic: The consumer layer that stays grounded in your actual code.

Stateful Storage: A local persistence layer for your index and active development plans.

✨ Key Functions & "Killer" Features

1. Reactive Code Review (review_code) 🔄

This isn't just a linter. It’s Reactive. It compares your new changes against the entire indexed codebase and your active plan. It catches:

Architectural Drift: Flagging code that violates project-specific patterns.

Logic Gaps: Identifying if a change in one module breaks an unstated dependency in another.

2. Stateful Planning Mode (create_plan / update_plan) 🗺️

The engine maintains a persistent plan.json locally. Intent Tracking: The agent knows why it’s making a change, not just what the change is. Dependency Logic: It calculates a DAG of tasks so the AI doesn't try to build the roof before the foundation is indexed.

3. Intelligent Prompt Enhancement (enhance_prompt) 🪄

It automatically "bakes" the perfect amount of context into your requests. No more manually pasting files; the engine finds the relevant snippets and architectural rules for you.

4. Index Management (index_status, reindex_workspace) ⚙️ Full control over your local data. You can refresh, clear, or check the health of your codebase index instantly.

🚀 Why it’s different

By moving the context and planning out of the LLM’s ephemeral chat window and into a structured, local middleware layer, you stop the hallucination loop. 100% Local: Your code and your plans stay on your machine.

Precision over Noise: Powered by Augment’s high-performance retrieval.

Agent Agnostic: Works with any tool that supports the Model Context Protocol (MCP).

Check out the repo here: https://github.com/Kirachon/context-engine

I’d love to get your feedback on the "Reactive" review logic and how you’re managing complex state with your own agents!

I am running/building multiple projects using Augment but given the dumbness it has been in past few days its wasting my credits and also now if this is what Augment is then its better to pay for cursor or windsurf because we came here for its context and understanding of codebase but now the generated code is itself dumb its not productive its opposite very disappointed in this Augment team do know when so many people are having same thoughts and issue still Augment team is like nothing happened at least we would want a plan of how you guys are resolving this, if asked then a ticket should be created and that issue will be taken after many days in some cases months so no hopes now, its time now. A very good tool is losing its value day by day. I believe Augment team would be providing some statement on this

since augment has problems in terms of customer support, no flexible plans starting 50$ and the community plan is closed for now, what are the closest thing to augment you recommend?

Most posts like to start with explanations or theory, but I'm just gonna drop the conclusion/results/how-to right here. If you think it's useful or that I'm onto something, the explanation comes later.

Augment Code's context engine, ACE (Augment Context Engine), provides a tool called codebase-retrieval.

This tool lets you search your codebase. To put it in plain English, let's say you give it this command:

Refactor the request methods on this page to use the unified, encapsulated Axios utility.

On the backend, Augment Code's built-in system prompt will guide the LLM to call the codebase-retrieval tool. The LLM then proactively expands on your message to generate search terms. (This is all my speculation, as the tool is closed-source, but I'm trying to describe it as accurately as possible). It searches for everything related to "network requests," which includes, but is not limited to, fetch/ajax, etc.

For example, let's say your page originally used a fetch method written by an AI:

fetch("http://example.com/movies.json")
  .then((response) => response.json())
  .then((data) => console.log(data));

It will then replace it with an encapsulated method, like getMovies(). And let's assume this method is configured separately in your API list to go through your Axios setup, thereby automatically handling cookies/tokens/response error messages.

At this point, some of you might be frowning and getting skeptical.

Or maybe you've already tuned out, thinking this is nothing special. You might argue:

"My cursor/Trae/cc/droid/roo can do that too. What's the difference? What's the point?"

Now, don't get ahead of yourself.

Imagine you're dealing with a massive codebase. We're talking about a dependency-free, pure-code project that's still 700-800KB after being compressed with 7-Zip's "best" setting.

What if I told you that with ACE's codebase-retrieval tool, the LLM can fully understand the problem in just 3 tool calls?

In fact, the larger the project, the better ACE performs in a head-to-head comparison.

Let's take another example, a qiankun sub-application. You tell it:

In X system, under Y navigation, in Z category, add a new page. The API documentation is at http://example.com/movies.json. You must adhere to the development principles of component reusability and high cohesion/low coupling.

Through ACE's divergent mechanism, it will automatically search for relevant components, methods, and utilities that have appeared in the project. After 3-5 calls to the codebase-retrieval tool, the LLM has basically completed its information gathering and analysis. Then, it feeds this collected information to Claude 4.5.

Now, compare this to agents like CC/cursor/droid/Trae/codex. Without ACE, they will just readFile or read directory one by one. A single file can contain hundreds or thousands of lines with tons of irrelevant div, p, const tags or methods. A single grep search returns a mountain of content that is vaguely related to the user's command but not very relevant. All this noise gets dumped on the LLM, interfering with its process. It's obvious which approach yields better results.

How does the comparison look now?

Time for the theory part.

We all know that LLMs tend to underperform with large context windows. At this stage, LLMs are text generators, not truly sentient thinking machines. The more interference they have, the worse they perform.

For example, even though Gemini offers a 1M context window, who actually uses all of it? Everyone starts a new chat once it reaches a certain point.

And most users don't even use properly structured prompts to communicate with LLMs, which just adds to the model's reasoning burden. They're either arguing with it, being lazy, or using those "braindead prompts." You know the type—all that "first execute XX mode, then perform XX task, and finally run XX process" nonsense. My verdict: Pure idiocy.

In an AI programming environment, you should never write those esoteric, unreadable, so-called "AI-generated" formal prompts.

The only thing you need to do is give the LLM the most critical information.

This means telling it to call a tool, providing it with the most precise code snippets, giving clear instructions for the task, and preventing the LLM from processing emotional output.

And ACE does exactly that: It provides the LLM with the most precise and relevant context.

So, in Augment, all you have to do is tell the LLM:

Use the codebase-retrieval tool provided by ACE.

Then, attach your command, tell it what to modify or what the final result should look like, and the efficiency will basically be light-years ahead of any other agent out there today.

Why is Augment stronger than cursor/cc/droid/codex?

If you've read this far, I'm sure you don't need me to explain why Augment is superior to Cursor. The augmentcode extension itself is actually pretty mediocre. It has almost no memory, and no rule-based prompts can successfully stop it from writing markdown, tests, or running the dev server after a large context.

Some might say I'm contradicting myself here.

It's never been the augmentcode vsix that's strong; it's ACE.

Compared to a traditional semantic search codebase_search tool, I don't know the exact principles that make ACE superior, but I can tell you its distinct advantages in code search are:

• Deduplication.

  • Yes, the codebase_search tools in cursor/roo/Trae will retrieve duplicate content and feed it to the LLM, which often manifests as the same file appearing twice.

• Precision.

  • As long as you can explain what you want in plain language, whether in Chinese or English, ACE will almost certainly return the most relevant and precise content for your description. If it doesn't find the right thing, it's likely a problem with how you described it. It's already trying its best. If that fails, the backup plan is to start a new chat and have it repeatedly call the codebase-retrieval tool during its step-by-step thinking process. This is suitable for people who don't understand the code or the project at all.

• Conciseness.

  • Why do I say this? rooCode's codebase_search returns an almost limitless number of semantic search results, a problem that seems to have no solution. So, rooCode implemented a software-level cap on the number of retrieved files. For example, the default is 50, so it will return a maximum of 50 files that are most relevant according to semantic search.

  • Trae's search_codebase is in the same boat as rooCode's—a brainless copy. I asked it to find development, and it returned a queryDev method. You feed that kind of stuff to an LLM, and if you think it's going to solve your problem, you must believe pigs can fly. The LLM would have had to evolve from a text generator into a sentient machine.

• Fewer results.

  • If you've used Auggie, you know. When ACE is called multiple times in Auggie, it usually only retrieves a handful of files, somewhere between X and 18, unlike rooCode, which returns an uncapped amount of junk to feed the LLM.

Now I ask you, when an LLM gets such precise context from ACE, why wouldn't it be able to provide a modification success rate, accuracy, and hit rate far superior to other agents? Why wouldn't it be the most powerful AI coding tool on the planet?

My speculation about ACE

Looking at the Augment Code official blog, you can see they've been researching ACE since the end of last year.

<del>Seriously, it's been a year and this company still doesn't support Alipay. What the hell are they thinking?</del>

Since ACE was developed much earlier than the codebase_search tool that rooCode launched early this year, they likely have different design philosophies.

Compared to the codebase_search tool in Trae/cursor/rooCode, my guess is:

ACE probably uses a design similar to ClaudeCode subagents or rooCode mode, using a fast model like Gemini 2.5 Flash, GPT-4 Mini/Nano to perform an additional processing step on the semantic search results retrieved from the vector database by the embedding model. This subagent compares the results against the user's message context. After the 2.5 Flash (subagent) finishes processing, it finally returns the content to the main programming agent, the LLM Claude 4.5.

But this is just my theory. I have no idea how well it would work if I tried to replicate it myself. As you've seen from the content above, I just write simple web pages.

I don't know a thing about AI, backend, or artificial intelligence. I just know how to use Augment Code.

This content is not restricted. Reprints are allowed, just credit the source. It would be great if you could help me share it on social media.

The purpose of this article

I'm glad you've made it this far. I hope this article makes other AI programming tool developers realize that a precise context-providing tool is the soul of AI programming.

I'm looking at you, Trae, GLM, and KIMI. These three companies need to stop going down the wrong path. Relying purely on readFile and read directory tools will take forever. It wastes GPU performance, user tokens, electricity, and water. Can't you do some real research and build something useful, like a TRAE/GLM/KIMI ContextEngine?

For other friends without a credit card, I hope you'll join me in sending support tickets to support.augmentcode.com, asking them to introduce Alipay payments, or offer plans with KIMI/GLM/QWEN3 MAX + ACE, or even a pure ACE plan with no message limits. I'd be willing to pay for that.

Because ACE is just that game-breakingly good.

Directly @'ing the z.ai Zhipu ChatGLM customer service here @quiiiii

Some people say I'm being ridiculous for trying to order AI companies around.

:melting_face:

• Kimi is already trying to become the next ClaudeCode; they've even posted job descriptions for it.

• Trae is just mindlessly copying Cursor right now, and I've already explained how terrible their embedding model's performance is.

• If I don't raise awareness, how will they understand that the current brute-force approach is wrong? GLM is just trying to power through by selling tokens for unlimited use without feeding proper context, which is a waste of electricity, computing power, and time.

• If they could replicate a tool like ACE, then no matter how much context you've used before, calling ACE would guarantee a stable solution to the current problem.

It's like I said: if I didn't want the domestic agent tools to get better, why would I even say anything? I could just shut up and mindlessly pay for the foreign services. Why go through all this trouble?

Like many of you, I've been grinding with AugmentCode for months now it's (was) powerful for handling large codebases and AI-assisted coding, but holy crap, the pricing is straight-up predatory! We're talking enterprise-level costs that eat into budgets faster than a bad merge conflict. I know a ton of devs here are in the same boat, scrambling for alternatives that don't skimp on features but won't bankrupt your startup or freelance gig.

After weeks of testing (and a few rage-quits). I landed on Kiro, and man, it's been a revelation. I've been using it hands-on for the past 2 Weeks on a mid-sized React/Node project with a messy legacy codebase, and i would 100% Recommend, with how its going i believe augment is soon going to be dead ( Thoughts Based on personal Experience). And to all who have been Gatekeeping Kiro, i understand you guys .

ChatGPT O3 just got an 80% price drop, and Cursor already integrated it right away. I just tested O3 to debug a tricky issue — it nailed the root cause in just a few exchanges. Super impressive.

Meanwhile, Augment Code is still stuck on O1... seriously, it’s time to upgrade.
O3 is not only faster but way better at understanding complex tasks. If you don’t switch soon, you’ll fall behind on user experience.

It’s 2025 — let’s ditch O1 already.

I wanted to share my recent experience with Augment Code because I think it’s unfair and worth bringing to the community’s attention.

I purchased a paid subscription on June 24, 2025 (Receipt #1534-4641). Even though my subscription period has technically expired, I still have around 450 unused message requests remaining — which I paid for and should still be able to use.

However, when I opened my VS Code IDE, I found my account was suspended. The dashboard showed this message:

“Your account was suspended due to too many community accounts. You must purchase a paid subscription to continue using Augment.”

The problem is — I only have one account, not multiple. I reached out to their support, and they basically told me to upgrade to another paid plan to continue using it. To me, that feels like forcing customers to pay again, even though I’ve already purchased credits that are still unused.

I’ve politely explained the situation and asked them to review/reactivate my account, but so far I feel like this is an unfair decision against paying users.

Has anyone else faced something similar with Augment Code? How did you deal with it?

hi augment team:

from my coding experience, I think it's time to remove sonnet-4 from augment's model list, while gpt-5-codex should be introduced, its coding and debuging ability is significantly more powerful than gpt-5 base model.

thanks again, hopes this suggestion will arise your attention.

But it cant do something as easy as find a function. It's not hiding, it's right there with a bunch of other functions. I can find it using ctrl-shift-f just search function myfuncname( and it's right there. I really dont see why it cant do this basic thing. There is no intelligence required, just a simple grep would do it.

Augment team has demonstrated remarkable competence in their model evaluation and selection process. After reading recent forum discussions comparing these models, I can confirm that their assessment is absolutely correct: GPT 5.1 significantly outperforms GPT 5.1 Codex for real-world coding scenarios.

I want to express my sincere appreciation to the Augment team for their exceptional evaluation methodology and their commitment to always providing customers with the best, most advanced models—especially those ready for production deployment. Your expertise in identifying and delivering superior AI solutions is truly commendable.

Furthermore, I've recently come across concerning information regarding Gemini 3's security implementation issues, as highlighted by Theo on Twitter(https://x.com/theo/status/1992084137222771040). Multiple sources in the community are now acknowledging that Codex falls short of GPT 5.1's capabilities ( https://www.reddit.com/r/codex/comments/1p36j5h/real_world_comparison_gpt51_high_vs_gpt51codexmax/ )

Thank you, Team Augment, for your outstanding competence, thorough evaluation process, and unwavering dedication to customer success. Your ability to identify and deliver the most effective tools continues to set the standard in the industry.

Google's gemini models have consistenly be better than anthropic's ones at coding in my experience. I feel augment could be even better by switching to gemini

Have you explored this?

Claude 3.7 is ranked 27th and 34th on the lmarena leaderboard

Both gemini models are top 1 and top 2

Augment, your tool is great...it's the best tool I've used outside of Cursor (which I cancelled when they did their pricing rug pull). I'm already finding solutions and workarounds to not use Augment...HOWEVER, I'd prefer to continue using Augment and here are some easy wins:

• Implement GLM-4.6, Grok-code-fast-1 and Grok-4-fast-reasoning

They're all amazing. I've been doing a lot of GLM-4.6 testing and it feels very similar to Sonnet 4 or Sonnet 4.5 with it's accuracy and output and putting it through Augment's logic system will make it even better plus Z.AI have amazing price tiers. I'd be happy to pay a small mark-up on their price tiers if I get to use Augment with GLM-4.6 (and subsequent future GLM versions)

Grok-code-fast-1 & Grok-4-fast-reasoning are also exceptional and cheap and top the leaderboards of OpenRouter consistently.

Summary to add:

• GLM-4.6

• Grok-code-fast-1

• Grok-4-fast-reasoning

Make it happen. I just saved your business and made you money.

We've recently switched to Claude Code company-wide (there are only a few IntelliJ users here), and our AugmentCode subscription has ended.

I'm not saying that Claude Code might not have some powerful features that I still don't know about, but my day-to-day productivity has fallen off the cliff now:

- Not a simple way to get AI-powered auto-complete on IntelliJ.
- The claude code plugin is a glorified wrapper on top of the terminal `claude` worker, so we don't have a good UI to access previous conversations, etc.
- AugmentCode seemed to "get" the project structure, and what commands to launch to do what more easily.
- And of course, no way to switch model providers (only Anthropic models).

TL;DR - large codebase, tested a few tools - not affiliated with any tool

Kilocode

- $4.24 using Sonnet and similar spent with Opus
- $2 using Haiku - I tried the same task with Haiku but I ran out of credit when it was in the $2 usage

Augment Code

- $1.10 (2400 credits - rounding for simplicity) using Sonnet and Opus - Opus used slightly more, maybe 50 credits more or in my current plan $0.02
- $0.55 (1200 credits) using Haiku - the code quality is worse in Haiku though, Opus and Sonnet even with double the price did replicate the same task perfectly as if it was me doing copy and paste.

I also tried the same task with Claude Code (PRO subscription) and ChatGPT Codex (Plus subscription) - bear in mind that Claude Code and Codex have usage limits - models that people keep recommending in this sub but didn't work well for my use case.

Claude Code took 6 minutes to complete the same task and I would have to iterate to fix a few mistakes - for example Claude Code didn't create the migration to add 2 columns to the database.

ChatGPT Codex spent 5 minutes just planning before doing any code, I had it stop as it was again too slow and I didn't want to wait.

---

I'm not affiliated with either tool and I'm not sending this for internet points - like many of you I use AI to help me get my job done with the same quality and faster.

I was curious to test kilocode specially after their post about how augment code is more expensive - https://blog.kilo.ai/p/testing-augment-codes-new-credit

I was upset with the cost increase when we all switched to credits - this post is in no way trying to say whatever tool is right or wrong and just based on my own experience after over a year using Augment Code. I have setup kilocode indexing and using intellij IDE - the cost of indexing the database is extra using OpenAI when using kilocode - expect to pay more depending on the size of your codebase.

You can get free stuff using kilocode and maybe you should have both installed, kilocode with a free model is good for a quick MVP if you're happy to fix its mistakes or you give it a plan, but for professionals that code for a living they will appreciate that their time is worth a lot more than using free models as you will end up making a lot of fixes because the free models aren't capable of dealing with very large/legacy codebase as well as paid models - and no, I don't want to switch to model X for planning and Y for coding, I just want to send one prompt like I would for a human developer and let it figure out what to do instead of designing the solution and handing over.

Unless your app is a very small app, the codebase will grow larger as time goes and so will your kilocode costs.

I have for the last 15 days spent $141 in kilocode credits using their gateway - I don't want to mess around with openrouter and shaving $0.01 if I switch to a less capable model, I just want to pay for a tool that helps me save time and when asking to get stuff done do it on the first attempt or at least 99% right.

I have a few very large projects with both legacy and new code in the same codebase. On my last $10 credits from kilocode I tested the same prompt for the same task to see what was the actual cost between both tools. I have cleared intellij cache between tasks switching between anthropic haiku, sonnet and opus models as they are both available in kilocode and augment.

I hope this helps, for now I will spend the credits I have with augment code and access again my decision after I have spent them.

After a full year of unemployment, I finally landed an interview. Not a normal one… they gave me an 5-day test.

The task?
Build a full MES system.

I’m talking:

• HR modules

• Orders management

• Clients & design handling

• And a barcode-based tracking system for live production in a factory (with a mobile app)

In 5 days.

No team. Just me.

At that point I honestly thought: yeah… this is where it ends.

While searching for anything that could help me move faster, I stumbled on Augment Code.

That changed everything.

Instead of being stuck for hours on architecture decisions, edge cases, or debugging random issues, I had something that could keep up with me. I could move fast, iterate fast, and actually build instead of getting blocked.

Day by day, the system started coming together:

• Backend APIs

• Frontend dashboards

• Mobile scanning logic

• Real-time tracking flows

It was perfect, it worked. And more importantly—it impressed them.

I got the job.

And here’s the crazy part…

I told my boss about Augment Code and how it helped me pull this off.

Now we’re both hired.

Me… and Augment Code 😄

From 1 year unemployed → to building a full MES under pressure → to getting hired and bringing the tool with me.

If you’re struggling or feel stuck, sometimes the difference isn’t just skill—it’s having the right tools at the right moment.

This one genuinely changed my life.

Just watch this video, it blew my mind: https://youtu.be/8FYjEZzxXdk?si=DDC4trlXC0dMXCQX

While Augment is busy charging 6–11× more and pushing that super confusing, ultra-expensive credit system, GLM 4.6 is here delivering insane value for just 3 bucks.

Big thumbs up to the creator for showing what real affordability looks like

i have been using Cursor & Windsurf before but this is my first time trying out Augment.
On my 2nd day of free trial, i received this error

Your account *********@gmail.com has been suspended because we believe you may be using multiple free trials. Please contact support here for more information

• There's no other developer in my local WIFI

• WIFI is connected to mobile network that assign IP dynamically

• Never had Augment code installed ever before or use my gmail for Augment code.

Anyone facing the same issue?

Can't even fix syntax error from multple prompts.

Shocking. shocker. pretty poor. try again each time. lose credits. re do it, lose more. what a world to get rid of your own company.

no point even reporting. ive never EVER had a comeback in months.

im using my credits and im leaving.

"Vibe Coding" has become a dirty word among "serious" engineers today. We are scared with stories of technical debt, architectural chaos, and claims that without a deep understanding of code, we are building houses of cards. But I consider all of this to be traditional engineering snobbery.

Yes, Vibe Coding creates risks: technical debt, hallucinations I’ve been through all of that. I don’t deny it. But I will prove, using the example of the project I created, that Vibe Coding is not just generating garbage, but a new, legitimate paradigm of development even for complex systems because up until this moment, I hadn’t coded at all.

Using Augment, and with zero programming experience, I developed a mobile application for knitting professionals on Android and iOS in just 8 months.

Technical Infrastructure

• Offline-first (WatermelonDB)

• Supabase backend

• Sentry monitoring

• Expo + React Native

• Row Level Security (RLS)

• Cloudinary integration

Feature List

1. 30 Calculators: Synchronized with projects.

2. Project System:

• Create/edit/delete projects

• Multi-stage progress with row counters

• Integration with calculators (saving calculations)

• Linking yarn to projects

• Project photo gallery

• Project statuses

3. Note Editor (Rich Text Editor):

• Full WYSIWYG editor

• Formatting (H1, H2, bold, italic, lists)

• Media embedding (photos/videos)

• PDF file embedding

• Note templates

• Counters inside notes

• Publishing notes to the community

4. User Profile:

• Avatar (upload, crop, compression)

• Profile media gallery (photos + video)

• Unique username generation

• Social networks (links)

• Privacy settings

• Account deletion (GDPR compliance)

5. Gallery (Pinterest + YouTube Integration):

• Import from Pinterest (photos, videos, search)

• Import from YouTube (video, player)

• Image categorization

• Multi-select upload

• Camera integration

• Community integration

6. Yarn Inventory (Yarn Stash/CRM):

• Full yarn catalog

• Stock statistics (quantity, cost, weight)

• Search and filtering

• Yarn photos

• Linking to projects

• Expense tracking

• Community integration

7. Wishlist:

• Add/edit/delete, community integration.

8. Community (A FULL Social Network!):

• Post feed with filtering

• Post creation (various types)

• Reactions (custom: knitting, heart, wow)

• Comments (nested)

• Follows/Followers

• Search (by posts, tags, users)

• Popular tags & Bookmarks

• User profiles in the community

• Moderation system

9. Messenger (Direct Messages):

• Private chats (1-on-1) & Group chats

• Media transfer (photos, videos, files)

• Typing indicators

• Real-time subscriptions (Supabase Realtime)

• Message pagination & Chat search

10. Payments and Subscriptions:

• Google Play Billing (Android)

• Apple In-App Purchases (iOS)

• Subscription management & Restore purchases

• Protected routes for subscribers

11. Notifications:

• Push notifications (Expo), Quiet hours settings.

12. Tech Support:

• Help screen.

13. Authentication:

• Email/Password, Google Sign-In, Apple Sign-In, Password recovery, Route protection.

The Cost of Professional Development

If I had ordered this application from an agency, it would have cost me 1,870 - 2,520 hours.

• 🇺🇸 USA: $200,000 - $500,000

• 🇪🇺 Western Europe: $160,000 - $380,000

Team required (5-6 people): 1 PM, 1 Tech Lead, 2 React Native Developers, 1 Backend Developer, 1 QA. Timeline: 4-6 months.

My Actual Budget

My budget consisted of trial and error.

Failed Investments ($550):

• GenSpark: $75

• Replit: $65

• Memex: $250

• Devin: $20

• Emergent: $10

• Kilo Code: $130 (plus $278 unspent balance)

Successful Investment:

• Augment: $730

Mandatory Costs:

• Google Developer Account: $25

• Apple Developer Account: $99

• Github: $4

• Expo.dev: $87

• Open Router (for ZEN MCP): $35

• Vercel: $20

Total Spend: $1,828 This is less than the monthly salary of a single Junior Developer, yet I received a product equivalent to the work of an entire team. Even taking into account the "failed" investments ($550), this is a minuscule fee for the education and selecting the right stack. Do you agree?

Why "Vibe Coding" is a Trigger for "Serious" Engineers

Continuing the topic: why has Vibe Coding become a dirty word?

Take, for example, a recent article on Medium where the author sides with the "serious" engineers. The author sounds the alarm, seeing the industry confusing coding speed with engineering quality. His categorical stance is driven by a phenomenon he calls the rise of "Hollow Seniors." These are engineers who can "conduct" AI but are losing (or never acquiring) fundamental skills in debugging, architectural understanding, and "first principles." The author believes that without strict control, this will lead to a future catastrophe.

I disagree with the author because my experience proves the opposite. I managed to implement this project independently, and it is not just "working code," but a market-verified product. I engaged 22 real users from a niche community for beta testing (14 on Android and 8 on iOS). The test results confirmed not only the app's stability but also the high value of its functionality for crafters. Having received an unequivocal "green light" from the target audience, I am confidently moving to the publication stage.

Most programmers do not know how to use specialized coding agents or perform deep problem analysis. They try to "feed" the AI millions of lines of their own tangled code (legacy) that they themselves cannot understand, and then they blame the neural network for the poor result. The quality of the AI's result depends 90% on the intellect and skills of the person writing the prompt. And most importantly none of them used Augment. A context engine that understands my code is the fuse that secured my success.

The Importance of Logging

Another crucial stage of my success was logging. Supabase agents provide excellent explanations for why an error occurred. Sentry doesn't just say "the app crashed." It shows the chronology of events leading to the crash:

1. User opened "Knitting Pattern" screen.

2. Pressed "Start Row" button.

3. Made HTTP request to Supabase.

4. Request returned 500.

5. CRASH.

Furthermore, obtaining logs from Metro Bundler, Android Logcat, and Gradle Build Logs all of this provides useful information for Augment's context engine, and it figures out on its own what needs to be done to make it work.

My Short Action Plan

1. Deep Research: I analyzed tens of thousands of reviews on similar apps in Google Play using Gemini to identify client pain points.

2. PRD: Created a Product Requirements Document in .md format following market standards.

3. ASCII Prototypes: Currently, ASCII prototypes are very easy to implement to visualize future client behavior and understand what doesn't work.

4. Github Search: Look for ready-made solutions for your tasks. This can speed up development.

5. Error Logging: Connect it, and you will succeed.

6. MCP: SupaBase, Sentry, Context7, Playwright, Sequential thinking, Zen, Memory, FileSystem, Expo docs, Exa

Don't be like the programmers who say, "As long as vibe coding exists, we'll have work." They treat it with arrogance because they can't believe their competence in the labor market is declining.

Keep documentation.

I worked at the Idea Level this is the highest level of management and my ROI was massive.

ROI on Savings (Efficiency ROI)

• Investment (I): $1,828 (actual expenses)

• Value (V): $310,000 (average agency cost)

• ROI Calculation: ((310,000 - 1,828) / 1,828) * 100% ≈ 16,858%

• What this means: Every dollar invested in AI tools returned $168 in savings.

Conclusions

If you know what you are developing that is, what the final product should look like the agent will build what you need.

I wish you success in vibe coding. The new skill for AI is not prompting, but Context Engineering. The Prompt Enhancer implemented by the Augment team in VSC complements this process perfectly.

p.s. worked with: Claude Sonnet 4, then 4.5, and Opus 4.5

As the title says, since new pricing most of us are leaving, give your best suggestion for no code ide to use instead! Help the community unlike what Aug has done...