DEV Community: BrainGrid

The AI Coding Workflow That Ships One Feature: Idea to Merged PR

Nico Acosta — Mon, 27 Jul 2026 11:13:59 +0000

Last week someone on r/codex asked a question that has no business being asked anymore: "How do you use Codex like a professional AI builder?" The tools are a year past magic. The models are good enough that "build me a login page" produces a login page. And yet the most upvoted answer was not a clever prompt or a secret setting. It was a workflow, hand-assembled by someone who had clearly been burned: write a spec first, break it into a phased plan, give each phase a definition of done, plan before you code, then review the diffs like a lead engineer.

Read that again. The top answer to "how do the pros use this AI tool" was not about the AI tool at all. It was a process for working around it. The fastest path to shipping something real with an agent turns out to be the part of software development that has nothing to do with the agent. That is the contradiction this whole piece rests on, and it is the most useful thing you can learn this year: the AI coding workflow that actually ships is mostly the workflow you build around the AI.

The workflow everyone keeps reinventing

Spend an hour reading how working builders describe their setup and you notice they have all converged on the same shape without coordinating. Addy Osmani's LLM coding workflow going into 2026 describes a virtuous cycle where the AI writes code, automated tools catch issues, and the AI fixes them. The most-shared System Design newsletter version lists five steps and the second one is literally "Plan Before You Code." The dev.to post that keeps circulating is titled, with no irony, "Separate Planning From Execution." Different people, different tools, same skeleton.

Here is that skeleton, the one a thousand builders are rebuilding by hand in a thousand slightly different ways:

flowchart LR
  A[Idea] --> B[Spec: what done means]
  B --> C[Plan: slice into phases]
  C --> D[Build: agent writes the code]
  D --> E[Verify: check against the spec]
  E -->|passes| F[Merged PR]
  E -->|fails| D

The shape is not the interesting part. The interesting part is which step does the work. Most people assume the agent, the "Build" box, is where the magic lives, because that is the box that got a thousand times better. They are wrong, and you can prove it to yourself. Take a strong agent and give it a vague idea. You get a confident, plausible, wrong result. Now take a mediocre agent and give it a precise spec with a clear definition of done. You get something you can actually ship. The power is not in the model. It is in the box labeled "what done means," and that box is the one no tool writes for you.

One feature, start to finish

Abstractions are easy to nod along to and useless when you sit down to build. So walk one real feature through the loop: a password reset flow, the kind of thing every app needs and nobody wants to write.

The amateur version is one sentence. "Add password reset." You paste it into your agent, watch it generate four hundred lines across six files, see a form appear in the preview, and merge it because it looked done. Three days later a user emails that the reset link works twice, or never expires, or silently fails for anyone with a plus sign in their email. The demo worked. The feature did not. You shipped the gap.

Now the version the r/codex commenter was describing. Before any code, you write down what done actually means, in conditions a person can check:

The user requests a reset by email. A one-time link is sent that expires in 30 minutes and works exactly once. An invalid or expired link shows a clear error, not a crash. After a successful reset, all existing sessions are logged out. An unknown email address returns the same neutral confirmation as a known one, so the form cannot be used to discover who has an account.

That is five testable conditions, and notice what just happened. You did not write code. You did not need to. But you now have something the one-sentence version never had: a definition of done that exists outside your head, that the agent can build against, and that you can check the result against later even though you cannot read a line of the code that produced it. The spec is the deliverable. The code is downstream of it.

From there the rest of the loop has somewhere to stand. You slice the work into phases, send the agent off to build, and when the pull request comes back you do not stare at the diff feeling unqualified. You walk your five conditions. Request a reset, click the link twice, watch the second click fail. Wait thirty-one minutes, confirm the link is dead. Enter an email that does not exist, confirm the message gives nothing away. You are reviewing behavior against a checklist you wrote, which is exactly the kind of review a non-engineer is qualified to do. We went deep on that skill in reviewing a pull request when you cannot read code; the point here is that the checklist is what makes the review possible at all.

Why the spec is the workflow

There is a backlash worth taking seriously. A sharp comment on a recent Hacker News thread called the whole agentic-workflow paradigm "a narrative trend pushed by AI companies to get people to 10x their token consumption." That critic is half right, and the half they are right about is the half that matters. A loop with no definition of done is exactly what they describe: an expensive way to keep rolling the dice. You prompt, the agent generates, you squint, you do not like it, you prompt again, and every turn burns credits while you converge on nothing, because there is nothing for the loop to converge toward.

The thing that turns that slot machine into a workflow is the one ingredient the skeptics never mention. Not a better model. Not more autonomy. A spec the output can be measured against. With it, every loop iteration has a target and a stopping condition: done is when the evidence matches the spec. Without it, "done" is whenever you get tired of pulling the lever. The definition of done is not a nice-to-have step in the workflow. It is the workflow. Everything else is plumbing.

This is also why the workflow does not get easier as the models get better, which is the part that surprises people. A more capable agent makes more decisions per turn, and every decision you did not write down is one it makes for you, silently, in a direction you will discover later. The more the agent can do on its own, the more the written spec is the only thing standing between its speed and your weekend. The hard part moves toward planning, not away from it.

Where this stops being a hand-rolled process

You can run this entire loop yourself with sticky notes and discipline, and many good builders do. But look at what you are actually maintaining by hand: a definition of done for every feature, a way to slice it into buildable phases, a record of which conditions were checked and which were not, and a memory of what this product is supposed to do that survives from one session to the next. That is real work, and it is the same work whether you are one person or a team of fifty. The reason it keeps getting reinvented in Reddit comments is that nobody ships it as a product.

That is the gap BrainGrid sits in. You describe the feature in plain language, and the Planning Agent turns it into a requirement with explicit acceptance criteria, asking the clarifying questions you did not think to answer, the kind that catch the expired-link and plus-sign-email cases before any code exists. The Builder Agent then builds against that spec, either in a managed cloud sandbox with a live preview or in your own GitHub repo with Claude Code, Cursor, or Codex. When it is done, verification checks the result against every acceptance criterion, so the pull request arrives with evidence attached instead of a diff you have to decode. That is the full loop, Plan, Build, Verify, Repeat, and the part that does the work, the captured definition of done, is the part the product remembers so you do not have to rebuild it every project. We cover the planning half in depth in building the BrainGrid way, and the spec craft itself in how to write acceptance criteria an AI agent can verify.

The honest limit: a spec does not write itself, and a bad one ships a bad feature with full confidence. If you write down the wrong conditions, the agent will satisfy them perfectly and you will have built the wrong thing precisely. The workflow does not remove your judgment about what good looks like. It just makes sure that judgment gets recorded once, up front, where it can actually steer the build, instead of living as a fuzzy feeling you compare the result against at the merge button.

So the next time you catch yourself asking how the pros use the latest agent, remember what the pros actually answered. They did not share a prompt. They shared a workflow, and the center of it was the least glamorous step there is. Write down what done means before you build. The agent got fast. Deciding what it is allowed to call finished is still your job, and it is the only job that ships.

FAQ

What is an AI coding workflow?

An AI coding workflow is the repeatable process you wrap around an AI agent to turn an idea into shipped software, rather than just prompting an agent and hoping. In practice it has converged on a consistent shape: write a spec that defines what "done" means, slice the work into phases, let the agent build against the spec, then verify the result against your original conditions before merging. The agent handles the code generation; the workflow handles everything that makes the code generation aim at the right target. The most common mistake is treating the prompt as the workflow when the spec is the part that actually does the work.

How do I set up an AI coding workflow?

Start with the step everyone skips: before any code, write down what done looks like as a short list of conditions a person can check, not as a one-line prompt. Then break the feature into phases small enough to review one at a time, send each to your agent, and when it returns, walk your conditions against the running result instead of reading the diff. Keep a record of what this product is supposed to do so each new session does not start from zero. You can do this manually with notes and discipline, or use a tool that captures the spec, runs the build against it, and verifies the result for you.

Is planning really necessary if the AI model is good enough?

Yes, and counterintuitively it matters more as models improve, not less. A more capable agent makes more decisions per turn, and every decision you did not specify is one it makes on its own, in a direction you discover after the fact. A strong model with a vague request produces a confident, plausible, wrong result; a modest model with a precise definition of done produces something you can ship. The work that decides the outcome is in the spec, because that is the only thing the agent's speed has to aim at. Skipping planning is how a loop becomes an expensive way to keep rolling the dice.

How do I review an AI pull request if I can't read code?

Review the behavior, not the syntax. Read the summary and confirm the scope matches what you asked for, then open the preview and walk through your acceptance criteria the way a real user would, including the failure cases like expired links, wrong inputs, and empty forms. Notice how many files changed and be more cautious of a sprawling change than a small one. Approve only when the running feature satisfies every condition you wrote up front. This works precisely because you defined done before the build, so the review is checking a list instead of guessing against a fuzzy memory.

What's the difference between vibe coding and an AI coding workflow?

Vibe coding is prompt, generate, eyeball, repeat, with no written definition of done and nothing to verify against, which is great for prototypes and a fast way to ship hidden gaps when the app matters. An AI coding workflow adds the missing piece: a spec the output is measured against, so each iteration has a target and a clear stopping condition. The difference is not how good the model is or how fast you go. It is whether there is anything in the loop that can say "not yet," which is what turns generation into shipping.

BrainGrid turns your idea into acceptance criteria, builds against them with your choice of agent, and verifies every change with evidence, so the workflow you keep rebuilding by hand is just the product. Try it at braingrid.ai.

Originally published on the BrainGrid blog.

Agentic Coding: How to Start When You Don't Have a CS Degree

Nico Acosta — Fri, 24 Jul 2026 11:15:15 +0000

The most useful skill in agentic coding is one you already have if you have ever managed a person. Not a framework, not a language, not a CS degree. The ability to say what you want clearly enough that someone else can build it, and to tell whether what came back is actually right. MIT's own course on this, part of its Missing Semester series, lands on the same mental model: you are not the coder anymore, you are the manager of an intern who does the nitty-gritty work but needs direction and occasionally does the wrong thing.

That reframe is good news and a warning at the same time. Good news, because it means the barrier to entry is judgment, not syntax, and judgment is something a domain expert often has in surplus. The warning is the part nobody puts in the tutorial: agentic coding will get you eighty percent of the way to a working product faster than you can believe, and then quietly strand you in the last twenty. Knowing where that cliff is, before you walk off it, is the difference between shipping and starting over.

What agentic coding actually is

Start with a clean definition, because the term gets blurred with everything adjacent. Google Cloud puts it plainly: agentic coding is a software development approach where autonomous AI agents plan, write, test, and modify code with minimal human intervention. The key word is autonomous. A traditional AI assistant waits for you to type and suggests the next line. A coding agent takes a high-level instruction, breaks it into steps, reads and writes files across your project, runs commands, checks its own work, and keeps going until the task is done.

That is the difference between AI coding and agentic coding, which is the question most beginners actually have. Autocomplete finishes your sentence. An agent runs the errand. MIT's course defines the agent as a conversational AI model with access to tools: reading and writing files, searching the web, invoking shell commands. The model is the brain; the tools are the hands. Give it a goal and it operates your codebase the way a contractor operates on a job site, not the way a dictionary suggests a word.

The tools you would actually start with are mature now. Claude Code and Codex run in your terminal and drive multi-step work from the command line. Cursor and Windsurf put the same agent inside a code editor. You do not need to know which is best on day one. You need to know that all of them are doing the same thing: taking your intent and turning it into actions, then showing you the result.

How to start without a CS degree

The first move is the smallest possible real task, not a whole app. MIT's example is a good shape: take a script you already have and tell the agent, turn this into a proper command-line program with argument parsing. One change, one file, something you can read the result of. You are not testing whether the agent is smart. You are learning the loop: describe, watch, correct, repeat.

The single skill that compounds from there is writing a good specification, and it is more art than science. The MIT course warns about both failure directions in one breath:

You want the input to the agent to be descriptive enough so that the agent does what you want it to do, but not overly descriptive to the point where you're doing too much work yourself.

MIT, "Agentic Coding," The Missing Semester of Your CS Education

Too vague and the agent guesses, usually wrong. Too detailed and you are writing the code in English, which defeats the point. The sweet spot is stating the what and the how-you-will-know-it-worked, and leaving the how to the agent. Here is the difference in practice.

Vague: "Build me a login page."

Specific: "Build an email-and-password login page. Show an inline error if the email format is invalid. Lock the account for five minutes after five failed attempts. On success, redirect to /dashboard. Unauthenticated users who reach /dashboard redirect back to /login."

The second one is not code. It is a clear description of done, written in plain language, and it is exactly the skill a non-engineer can own. The agent handles the implementation. You hold the standard.

The last starting habit is keeping a rules file. Every agent looks for a project-level file, usually AGENTS.md, where you write down your conventions and, more usefully, every mistake the agent has made before so it stops repeating them. Treat it as the onboarding doc for your intern. It is the lowest-effort reliability you will ever buy.

And yes, you can start for free. Most agents have a free tier or a low-cost entry plan, and open-source options run on your own machine. Cost is not the barrier. The barrier is knowing what to ask for and recognizing when the answer is wrong.

The trap: the 80 percent problem

Here is the cliff. Agents are stunningly good at the part of software that looks like a demo, and stubbornly weak at the part that makes it a product. The pattern is common enough that it has a name, the 80 percent problem:

The agent wrote code that works. The agent did not write code that survives.

Augment Code, "The 80% Problem"

The first eighty percent is the happy path: the feature does the thing when you use it the expected way. The missing twenty is everything that makes it real. Error handling for the inputs you did not anticipate. Security applied across the whole system, not one form. Logging so you can tell why it broke. The edge cases. And that twenty percent does not just sit there waiting politely. Left unaddressed, it compounds into technical debt that is more expensive to retrofit than it would have been to build correctly the first time, because by then the duplication and inconsistency are spread across files you never read.

This is the moment most beginners hit and misread. The demo worked, so the project feels ninety percent done, and then every new change breaks two old things and nobody can say why. We told one version of that story in I built a vibe-coding mess, and dug into why that final stretch is so stubborn in The Second 80%. It is not that you did agentic coding wrong. It is that generating code and producing a trustworthy product are different jobs, and the agent only volunteered for the first one. The honest trade-off of this entire approach: speed at the start, a debt at the finish, and the debt is invisible until you are standing in it.

The skill that closes the gap

So if the bottleneck is not generating code, what is it? Andrej Karpathy, who coined the term vibe coding and then this year declared it obsolete for serious work, drew the line that matters at Sequoia's AI Ascent:

Vibe coding is about raising the floor for everyone in terms of what they can do in software. Agentic engineering is about preserving the quality bar of professional software.

Andrej Karpathy, Sequoia AI Ascent 2026

Raising the floor is what gets you the eighty percent. Preserving the quality bar is what gets you the twenty, and Karpathy is explicit about how: you work with your agent to design a spec that is very detailed, basically the docs, and then get the agents to write against it. The skill is not coding. It is specifying precisely and verifying honestly. That is the thing the agent cannot do for you, because it is the definition of what right means, and the agent is the one being checked.

This is the gap BrainGrid was built to close, and it is built for exactly the person without a CS degree. BrainGrid is the system that takes an idea to a live product you can trust. You describe the feature in plain language, the way you described that login page above, and the Planning Agent asks the clarifying questions a senior engineer would, surfaces the edge cases you did not think of, and turns your intent into a requirement with explicit acceptance criteria. That requirement is the spec your agent works against and the standard your result is checked by.

From there you choose how to build. Hand the scoped tasks to Claude Code, Cursor, or Codex in your own setup, or let the Builder Agent build it in a managed sandbox with a live preview and open a pull request. Either way, verification closes the loop: every acceptance criterion is checked against the result, and the feature is not done until the evidence says it does what you meant. The twenty percent stops being the part you discover in production and becomes the part you specified up front. That is the whole loop, Plan, Build, Verify, Repeat, and it is the same discipline we cover in agentic engineering and in spec-driven development.

Strip away the tool names and the trending terms and the argument holds on its own: agentic coding lowered the cost of producing software to almost nothing, which means the scarce thing is no longer typing. It is knowing what you want and proving you got it. If you are a domain expert who has been told you cannot build because you cannot code, that equation just moved in your favor. The work that is left is the work you were always good at.

FAQ

What is agentic coding?

Agentic coding is a software development approach where autonomous AI agents plan, write, test, and modify code with minimal human intervention. Instead of suggesting the next line like autocomplete, a coding agent takes a high-level instruction, breaks it into steps, reads and writes files across your project, runs commands, checks its own work, and iterates until the task is complete. You direct it and review the result; it does the implementation.

What is the difference between AI coding and agentic coding?

Traditional AI coding assistants are reactive: they wait for you to type and offer suggestions or answer questions. Agentic coding tools are autonomous: they take a goal and execute it end to end, navigating files, running terminal commands, and self-correcting when a test fails. The simplest way to put it is that an assistant finishes your sentence, while an agent runs the errand.

What is the 80 percent problem in agentic coding?

The 80 percent problem is the gap between the working code an agent reliably produces, roughly eighty percent of a solution, and the production-grade remaining twenty percent: error handling, security, observability, edge cases, and compliance. Agents are excellent at the happy-path demo and weak at the parts that make software survive real use. Left unaddressed, that twenty percent compounds into technical debt that costs more to retrofit than it would have to build correctly from the start. The fix is defining the full standard up front so the agent builds to it, not just to the demo.

Can you do agentic coding for free?

Yes. Most coding agents offer a free tier or a low-cost entry plan, and there are open-source agents you can run on your own machine at no cost. Cost is rarely the real barrier to starting. The harder part is learning to write a clear specification and to recognize when the agent's output is wrong, both of which are free to practice on small tasks.

Do you need to know how to code to do agentic coding?

You do not need a CS degree, but you do need judgment. The skill that matters most is writing a clear specification, stating what you want and how you will know it worked, and verifying the result honestly. MIT's own framing is that working with a coding agent is like managing an intern: you provide direction and catch mistakes, the agent does the detailed work. Domain experts who know exactly what good looks like in their field often have the most important skill already. Tools like BrainGrid's Planning Agent turn a plain-language idea into a structured requirement with acceptance criteria, so the specifying and checking happen without writing code yourself.

BrainGrid is the AI Product Planner that turns your plain-language idea into a spec your coding agent can build against and be checked against, so the last twenty percent stops being a surprise. Try it at braingrid.ai.

Originally published on the BrainGrid blog.

Harness Engineering, Explained: What Separates Top Agentic Engineers

Nico Acosta — Thu, 23 Jul 2026 11:11:33 +0000

OpenAI spent five months building an internal product where humans wrote zero lines of code, and the engineers on that team were busier than ever. Roughly a million lines shipped across more than 1,500 pull requests, every one of them authored, reviewed, and merged by agents. The work did not disappear. It moved into something the team calls the harness, and in the four months since, the idea has completed its arc from insider habit to formal discipline. There is now an open-source framework, AutoHarness, whose entire premise is that an agent is just a model plus a harness, and the harness is the part you engineer.

Here is the claim worth testing: what separates the builders getting production-grade output from agents is not the model they picked or the prompts they write. It is whether they built the environment around the agent. And inside that claim hides a second one that matters even more, because the harness, for all its power, cannot generate its own most important input.

What harness engineering actually is

The term has a precise origin story. Mitchell Hashimoto, the creator of Terraform and Vagrant, described the practice in his AI adoption journey in early February 2026, as the step where his agent use stopped being frustrating and started compounding:

Anytime you find an agent makes a mistake, you take the time to engineer a solution such that the agent never makes that mistake again.

Mitchell Hashimoto, "My AI Adoption Journey"

Six days later, OpenAI gave the discipline its formal name. Ryan Lopopolo's post described how their agent-first team operates: engineers do not write the code, they build the structure, documentation, and automated checks that keep agents on track. The post's core principle fits in four words: humans steer, agents execute. The metaphor is deliberate. A harness, in the original sense, is the equipment that channels a powerful animal's strength in a useful direction. Nothing about the harness makes the horse stronger. It makes the strength go somewhere.

So a working definition: harness engineering is the practice of designing the environment around an AI coding agent, the context it reads, the tools it can call, and the checks that catch its output, so the agent produces the right result instead of a plausible one. Your agent's raw capability is rented from a model provider, and it is the same capability everyone else rents. The harness is the part you own.

What a harness is made of

Birgitta Böckeler, writing the most rigorous treatment of the subject on martinfowler.com, defines the harness as everything in an agent except the model itself, and sorts its parts into two families. Guides act before the agent does: the AGENTS.md file that carries your conventions, the architecture notes, the bootstrap scripts that set up a working environment. Sensors act after: linters, type checkers, test suites, and review agents that inspect what came out and feed problems back in.

The whole practice reduces to one move, applied relentlessly. When the agent does something wrong, you do not correct it in chat. You encode the correction where every future run will hit it.

The difference is easy to see side by side. Chat correction: "No, use our date helper, not raw Date()," and the agent complies, and that knowledge evaporates the moment the session ends. Harness correction: a lint rule that flags raw Date() plus one line in AGENTS.md explaining the helper, and no agent in any future session ships that mistake again. The first is a conversation. The second is an asset. Top agentic engineers are the ones with eight months of accumulated corrections sitting in their repo, which is why their agents seem eerily reliable and yours seems forgetful. Same model. Different harness.

flowchart LR
    S[Spec with acceptance criteria] --> A[Agent run]
    G[Guides: AGENTS.md, conventions, scripts] --> A
    A --> C{Sensors: tests, linters, review}
    C -->|fail| F[Feedback to agent]
    F --> A
    C -->|pass| P[PR you can trust]

The harness governs how. It cannot decide what.

Now for the part the harness explainers skip, and the reason the diagram above starts where it starts. Every component of a harness shares one property: it evaluates the agent's output against a standard somebody already wrote down. The linter knows your formatting rules. The type checker knows your interfaces. The test suite knows the behavior somebody specified. Feed the harness a vague goal and it will happily help the agent build the wrong thing correctly, with clean types, passing tests it wrote for itself, and conventions intact.

Böckeler lands on exactly this limit, and her conclusion is the reframe that should change how you spend your effort:

A good harness should not necessarily aim to fully eliminate human input, but to direct it to where our input is most important.

Birgitta Böckeler, "Harness engineering for coding agent users"

Where is your input most important? Not in the lint rules. The agent can draft those. Not in the test scaffolding. The agent writes that too, and the harness checks it. The one input the harness cannot generate, verify, or even detect the absence of is the definition of what you are building and how you will know it is right. A harness with no spec is scaffolding around an empty lot. This is also what distinguishes the harness from its sibling buzzword: the loop schedules and re-runs your agent, the harness shapes and checks each run, and the spec tells both of them what done means. We covered the loop layer in What Is Loop Engineering?; the punchline there was that the loop's verifier needs a standard. The harness is that verifier, and it needs the standard just as badly.

This is the gap BrainGrid fills. BrainGrid is the system that takes an idea to a live product you can trust, and it sits exactly at the harness's missing input. You describe the feature in plain language, and the Planning Agent interrogates it the way a senior engineer would, surfacing edge cases and turning intent into a requirement with explicit acceptance criteria. That requirement becomes the harness's reference point. Run the build either way: hand the scoped tasks to Claude Code, Cursor, or Codex over MCP inside your own harness, or let the Builder Agent build in a managed sandbox with a live preview and open the PR. Then verification closes the loop the way a linter never can, checking the result against each acceptance criterion and holding the feature short of done until the evidence says it does what you intended. Code review tells you the code is well-written. Verification tells you it does what you meant.

The honest costs

Two trade-offs, because the discipline has them. First, a harness is a second codebase. Hashimoto's rule sounds light, but engineering a permanent fix for every mistake is real, ongoing work, and on a legacy codebase with years of unwritten conventions, the backfill is steep. Budget for it like infrastructure, because that is what it is.

Second, the harness has a ceiling, and you should know where it is. Böckeler's warning is that no current check, mechanical or model-based, reliably catches the agent that misunderstood the assignment. The failure mode that actually burns builders is not malformed code. It is well-formed code that solves the wrong problem. That failure is invisible to every sensor in the harness except one: a written statement of what the right problem was. The better your agents get, the more this holds, because more capable agents produce more output per unit of your attention, and every unit of output that nobody specified is a decision nobody made.

If you are building with Claude Code or Cursor today, here is what this means concretely: your edge is no longer in the prompt box. An hour spent writing one more clever prompt is worth less than an hour spent adding the lint rule, the AGENTS.md entry, and the acceptance criteria that make the next hundred prompts land. The builders pulling ahead right now are not prompting better. They are accumulating a harness, and they are feeding it specs.

Start your harness this week:

Create an AGENTS.md file and add one entry for every mistake your agent has made twice. (Claude Code skills are the structured version of the same move.)
Wire your existing checks into the agent's path: tests, linter, type checker, so failures feed back automatically.
Before the next feature, write the acceptance criteria first, in BrainGrid or anywhere, so the harness has something real to check against.

Strip away the OpenAI post, the GitHub stars, and the new vocabulary, and the argument stands on its own: an agent's output is only as trustworthy as the environment that checks it, and the environment is only as good as the intent you wrote down. The harness is how the agent stops repeating mistakes. The spec is how it stops making the expensive one.

FAQ

What is harness engineering?

Harness engineering is the practice of designing the environment around an AI coding agent so it produces reliable results: the context files it reads (like AGENTS.md), the tools and scripts it can call, and the automated checks (tests, linters, review agents) that catch its output and feed problems back. The core move is encoding every correction permanently in the environment instead of repeating it in chat.

Who coined the term harness engineering?

Mitchell Hashimoto, creator of Terraform, described "engineering the harness" in his "My AI Adoption Journey" post on February 5, 2026. OpenAI formalized the term six days later in Ryan Lopopolo's February 11 post about building an internal product with zero human-written code. The underlying word is older: a harness has long meant the scaffolding around a model, and the equestrian metaphor, equipment that directs strength, is intentional.

What is the difference between harness engineering and spec-driven development?

They cover opposite sides of the same problem. Spec-driven development defines what to build before the agent starts: requirements, behavior, acceptance criteria. Harness engineering shapes how the agent works and checks what it produced: conventions, tools, tests, and feedback. A harness verifies output against a standard, and the spec is that standard, so each practice is incomplete without the other.

What is the difference between a harness and a loop?

The harness is the environment around a single agent run: what the agent knows, what it can touch, and what checks its work. A loop is the orchestration above it: the scheduler that finds work, runs the agent again and again, and decides when to stop. The loop re-runs the agent; the harness makes each run trustworthy. Both depend on a spec to define what done means.

Do you need to be an engineer to benefit from harness engineering?

The principle transfers even if you never write a lint rule. Any builder can keep a running file of corrections their agent must follow, and any builder can define acceptance criteria before the agent starts. Tools handle the rest: BrainGrid's Planning Agent turns a plain-language idea into a requirement with testable criteria, and its verification checks the build against each one, which is the harness's feedback layer without the infrastructure work.

BrainGrid is the AI Product Planner that gives your harness the one input it cannot generate: a spec with acceptance criteria to check every build against. Try it at braingrid.ai.

Originally published on the BrainGrid blog.

What Is Loop Engineering? Stop Prompting, Start Designing Loops

Nico Acosta — Wed, 22 Jul 2026 11:13:41 +0000

Every few months the AI building world agrees on a new thing you are supposed to be doing, and this month it is loop engineering. The pitch is clean: you do not prompt your coding agent one step at a time anymore. You design the loop that prompts it for you, point it at a goal, and walk away.

It is a real shift, and it is worth understanding. But the part the loop tweets leave out is the interesting part. The loop takes prompting off your plate and hands you back a harder job, one you cannot automate your way out of. Knowing what that job is tells you exactly where to put your effort.

What loop engineering actually means

Loop engineering is the practice of designing a system that prompts and orchestrates an AI coding agent on a schedule, instead of you typing each prompt by hand. Addy Osmani, whose explainer became the reference for the term, defines it plainly:

Loop engineering is replacing yourself as the person who prompts the agent. You design the system that does it instead.

Addy Osmani, "Loop Engineering"

A loop is roughly five pieces plus one. Automations that fire on a schedule and find work. Worktrees so two agents do not collide. Skills that write down project knowledge the agent would otherwise guess. Connectors that plug the agent into your real tools. Sub-agents, so the one that writes the code is not the one that grades it. And the sixth piece, the one that makes the whole thing hold together: a memory that lives outside any single run and remembers what is done and what is next.

The wave started with two sentences from people who would know. Peter Steinberger, who built the open-source agent gateway OpenClaw and now works at OpenAI, put it as a standing reminder:

Here's your monthly reminder that you shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents.

Peter Steinberger on X

Days earlier, Boris Cherny, who leads Claude Code at Anthropic, described on stage where his own job had gone:

I don't prompt Claude anymore. I have loops running that prompt Claude and figuring out what to do. My job is to write loops.

Boris Cherny, head of Claude Code at Anthropic

Steinberger's post hit 6.5 million views in a week. It landed because the pieces ship inside the products now: /goal in Claude Code defines a testable end state and keeps working until it is true, with a separate model grading whether it is done. The leverage point moved, and everyone felt it at once.

The job the loop hands back

Here is the contradiction at the center of the hype. A loop running unattended is also a loop failing unattended. Take prompting off your plate and you have not removed the work, you have moved it somewhere worse: into runs you were never watching, with far too much output to read by hand. Akshay Pachaar said it sharply this week:

Loop engineering takes you off prompting. It takes you off curating context. It takes you off babysitting a single run. It does not take you off debugging. It just moves the debugging somewhere worse, into runs you were never watching.

Akshay Pachaar on X

Stack the layers up, prompt to context to harness to loop, and one job survives all of them. Closing the loop on failure. You can only walk away from a loop if you trust the thing checking it, and a checker you do not trust drops you right back into reading every output by hand, which is the exact work the loop was supposed to take off you.

So the real question loop engineering raises is not "how do I build the loop." Both Claude Code and Codex ship the loop. The real question is "what does my checker check against." A loop with no definition of done is just a faster way to generate work you still have to inspect line by line.

Why this moves the work upstream, not away

Watch what happens to the human as each layer of automation arrives. Prompt engineering asked you to write a good message. Context engineering asked you to curate what the model sees. Loop engineering asks you to design the system that does both on a schedule. At every step the manual labor shrinks and the judgment concentrates. The thing you cannot hand off is deciding what you actually want and stating it precisely enough that a machine can check the result.

That is a spec. Not a vague ticket, not a paragraph in Slack, but a requirement with testable acceptance criteria: the agent built this, here is how we know it is right. The sub-agent that "verifies against the spec," in Osmani's words, needs a spec to verify against. The loop's memory, the file that survives between runs, has to hold something worth remembering. Both of those are the same artifact, and writing it is the work that does not disappear.

Consider the before and after. Before: you babysit a single run, catch the drift in real time, correct it prompt by prompt. After: the loop runs ten times overnight and you wake up to ten pull requests, half of them subtly wrong in ways no human would have written, and no clear standard to grade them against. The loop made you faster at producing work and slower at trusting it. The fix is not a better loop. It is a definition of done the checker can actually use.

Where BrainGrid fits in a loop

This is the gap BrainGrid was built for. BrainGrid is a product factory that runs the loop Plan, Build, Verify, Repeat, and it owns the two pieces a loop cannot generate for itself: the plan and the standard.

You describe a feature in plain language. The Planning Agent asks the clarifying questions a senior engineer would, surfaces the edge cases, and writes an engineering-grade requirement with acceptance criteria. That requirement is the spec your loop's verifier checks against. It is also the memory: it persists outside any single run, so when tomorrow's automation fires, the context is already there. The agent forgets between sessions. BrainGrid does not.

From there the loop runs either way. Load the scoped tasks into Claude Code, Cursor, or Codex over MCP and let your own harness drive them, or let BrainGrid's Builder Agent build the requirement in a managed sandbox and open a pull request, with AI code review checking the result against the acceptance criteria item by item. Verification stops being a thing you hope the loop got right and becomes a thing the loop can prove. That is what makes walking away safe. This is the same discipline we cover in agentic engineering, and the full five-stage version lives in The BrainGrid Loop.

So is it real, or just another buzzword

Both, the way most of these are. The mechanics are real and genuinely useful: scheduled, self-checking agent runs are a meaningful jump in capability, and if you have not tried /goal in Claude Code you should. The buzzword part is the implication that designing the loop is the whole job. It is not. It is the part that got automated. The part that did not, defining what done means and giving the checker something real to check, is where the work went. Put your effort there, and the loop pays off. Skip it, and the loop is just a faster way to dig the hole we wrote about in I built a vibe-coding mess. The loop-as-a-running-joke version is our take on the Ralph Wiggum plugin.

FAQ

What is loop engineering?

Loop engineering is the practice of designing a system that prompts and orchestrates an AI coding agent automatically, on a schedule, instead of typing each prompt yourself. A loop typically combines scheduled automations, isolated worktrees, project skills, tool connectors, and sub-agents that check each other's work, plus a memory that persists between runs. The goal is to move from holding the agent's hand through every step to designing the system that runs it for you.

Is loop engineering real or just another AI buzzword?

It is both. The underlying mechanics are real and shipping today in Claude Code and Codex, and scheduled self-checking agent runs are a genuine step up. The buzzword part is the suggestion that building the loop is the whole job. The loop automates the prompting, but it hands back the harder work: defining what "done" means so the loop's checker has a standard to verify against. Designing the loop is the easy half. Closing the loop on failure is the half that survives.

What is the difference between loop engineering and prompt engineering?

Prompt engineering optimizes a single message you send by hand. Loop engineering designs the system that sends those messages for you, on a cadence, and checks the results. Prompt engineering is one turn; loop engineering is the machine that runs many turns unattended. The skill moves from wording a request to specifying a goal precisely enough that an automated checker can confirm it was met.

Does Claude Code support loop engineering?

Yes. Claude Code ships the core pieces: /loop re-runs a prompt on a cadence, /goal keeps working until a condition you defined is true with a separate model grading whether it is done, and hooks plus scheduled tasks let you run autonomous work on an interval. Codex offers the same primitives through its Automations tab. The pieces are in the products now, which is why the term took off.

What does loop engineering not solve?

Verification and your own understanding. A loop running unattended is also a loop making mistakes unattended, and the more code it ships that you did not write, the larger the gap between what exists and what you actually understand. The loop can take you off prompting, context curation, and babysitting a single run. It cannot take you off deciding what you want and confirming the result matches. That is why a clear spec with acceptance criteria, which is what BrainGrid produces, is the piece that makes a loop trustworthy enough to walk away from.

BrainGrid is the AI Product Planner that gives your agent loops the one thing they cannot generate for themselves: a spec with acceptance criteria the verifier can check against. Try it at braingrid.ai.

Originally published on the BrainGrid blog.

Building the BrainGrid Way: From Idea to Shipped Software

Nico Acosta — Tue, 21 Jul 2026 11:13:53 +0000

A clear mental model for turning ideas into working software. Four stages, no guesswork: Idea, Epic, Requirements, Build.

A founder messaged us last month. She'd spent six weeks of evenings and weekends building a booking app for independent dog walkers. Screens designed. Rough prototype working. Real conversations with walkers in her neighborhood who wanted it.

Then she hit a wall.

"Every time I add something new, something old breaks. I asked the AI to add payments and it rewrote my booking flow. I have three different versions of user profiles and I don't know which one is right anymore. I've started over twice."

Two restarts. The idea was solid. The tools were capable. She'd skipped the thinking and jumped straight to the building.

BrainGrid exists to close that gap.

Idea. Epic. Requirements. Build.

Idea — messy thought becomes clear direction. Opens in your browser. Nothing to install.
Epic (a chapter of your product) — what to build and in what order
Requirements (a single feature, fully described) — what "done" looks like
Build — ship it with Claude Code, one command at a time

Here's what this looked like for her.

Stage 1: Idea

She typed her rough idea into BrainGrid: "Booking platform for independent dog walkers. Local, not corporate. Walkers set their own rates. Owners book based on neighborhood and reviews."

The planning agent didn't start building. It pushed back:

"Who's your primary user — the dog owner or the walker? Which one do you need to attract first to make the marketplace work?"

"How do owners verify a walker is trustworthy? Background checks? Reviews? A rating threshold before they can accept bookings?"

"Rover, Wag, and Care.com all serve this space. What specifically makes a local-first model better — and for which neighborhoods?"

Not template questions. The agent had already pulled Rover's 35% service fee, Wag's walker churn rates, and three local alternatives in her city. It used that research to press her the way a sharp co-founder would over coffee.

Twenty minutes later, she had something six weeks of coding never gave her: a clear picture of who she was building for (dog owners in walkable neighborhoods where Rover felt impersonal), her real differentiator (hyper-local, walker-first), and what she was deliberately not building (GPS tracking, enterprise features, anything that competes on scale).

Stage 2: Epic

Her product broke into four epics — four chapters:

Walker profiles and verification — the foundation everything else depends on
Search and booking — the core experience
Payments — how money moves
Reviews and trust — what keeps people coming back

The agent mapped the dependencies: you can't search for walkers who don't have profiles. You can't process payments for bookings that don't exist. You can't review a walk that hasn't happened. Each epic builds on the one before it.

This is where her first two attempts broke. She'd jumped straight to the booking flow — the exciting part — without nailing down how walker profiles worked. When she added payments, the AI had no context for what a "booking" contained, so it invented its own version. Two data models. Conflicting assumptions. Restart.

Epics prevent that. Each chapter declares what it owns. Epic 2 knows walker verification lives in epic 1. It references those decisions instead of reinventing them.

Stage 3: Requirements

Each epic breaks into requirements — the individual features. If an epic is a chapter, a requirement is a scene. One clear thing that needs to exist.

For "search and booking," BrainGrid generated:

REQ-1: Search for walkers by neighborhood and availability
REQ-2: View walker profile with rates, bio, and verification status
REQ-3: Book a walk for a specific date, time, and dog
REQ-4: Send booking confirmation to both owner and walker

These aren't sentences. They're full specifications. REQ-3 included details she hadn't considered: what happens when two owners book the same slot? What if a walker hasn't set availability? What error appears if payment isn't connected?

She didn't write those edge cases. The agent did — because it had read every sibling requirement, checked what REQ-1 and REQ-2 already decided, and filled the gaps. Nothing contradicted. Nothing re-decided.

Every requirement gets a readiness score from 1 to 5. REQ-3 came back as a 3. The agent flagged a missing cancellation policy. She typed one sentence: "Owners can cancel free up to 2 hours before the walk. After that, the walker keeps 50%." Score flipped to 5. Ready to build.

One sentence. The difference between code that works and code you argue with later.

Stage 4: Build

The next day, she opened Claude Code and typed:

/build REQ-3

BrainGrid fetched the full requirement, created a branch on GitHub (feature/REQ-3-book-a-walk), and Claude Code broke the work into four tasks — each one sequenced with dependencies. Then Claude started building. No confirmation prompts. No waiting. Task by task:

TASK 1: feat: Create bookings table with time-slot constraints ✓
TASK 2: feat: Build booking API with conflict detection ✓
TASK 3: feat: Add booking form with date/time picker ✓
TASK 4: feat: Send booking confirmation to owner and walker ✓

Database first. API second. Frontend last. Each task was implemented, validated, committed, and updated in BrainGrid before the next one started — the same ordering she'd failed to maintain manually across two restarts, now handled automatically because the requirement specified the dependencies upfront.

Before it finished, the agent verified every acceptance criterion she'd written. The time-slot conflict check returned her exact error message: "This walker is already booked at that time. Here are their next available slots." The cancellation policy she'd added to reach readiness 5 was implemented word for word. Only after every criterion checked out did the requirement move to review.

She stopped debugging. She started reviewing. Not as a programmer hunting bugs, but as a product owner checking work against her own words. You don't write the code. You answer the questions. BrainGrid ships the feature.

The loop

She shipped REQ-3 and moved to REQ-4. Then the next epic. Each cycle ran faster — the system already knew her data model, her naming conventions, every decision from the previous requirements.

By week three, she had a working MVP in front of real dog walkers in Park Slope. Not a prototype. Not a demo. And when those walkers started requesting features — schedule changes, recurring bookings — she didn't panic. She typed each one into BrainGrid and watched it move through the same four stages, faster each time, because the system already knew everything she'd built.

Most builders think their bottleneck is code. It isn't. The bottleneck is knowing what to build, in what order, with enough precision that the result actually works. BrainGrid front-loads that thinking. By the time you type /build, the hard decisions are already made.

Type your idea in like she did. Watch your first epic land in three minutes. No installs. No code.

Originally published on the BrainGrid blog.

How I Vibe Coded a SaaS MVP in a Weekend (Step by Step)

Nico Acosta — Mon, 20 Jul 2026 11:14:04 +0000

I spent a week hunting for a SaaS idea I could build in a weekend. Following the tips in our post, How to Get SaaS Product Ideas, I lurked on IdeaBrowser -- a site that posts a scored startup idea every day.

None of the ideas resonated with me. But I dutifully read every line of the proposals. One of the tips that other founders recommend is solving a problem that you have faced. "Benefits finder for seniors" -- I don't know anything about senior benefits. But the pattern grabbed me: helping people navigate unfamiliar systems. That's when I remembered my own move to the UK.

When I landed in the UK in 2020, it was the height of the pandemic. I had my visa -- but I needed NHS numbers, driver's licenses, utilities, banking. Nobody tells you how any of it works. What if there was an app for that?

Let's build an immigration concierge application that helps people moving to a new country navigate the processes and systems.

The idea is clear. But how do you go from concept to working app without engineering experience?

How will we get started?

Vibe coding tools now let non-coding founders build SaaS MVPs. You describe what you want in plain English. The tool builds it. But AI tools sometimes give bad advice or incorrect information. The best way to get good results is through clear, precise prompting.

The same holds for writing software. Just type what you want and the bot builds it. But AI agents can't read your mind. Leave out one crucial detail and the entire feature gets built wrong. When I build without planning, I focus on getting something I can see -- rather than building a foundation for future growth. Sometimes this works, but other times everything feels patched together and the code looks rickety.

To build my SaaS, I want to start with a solid foundation -- even if it means I won't see a working UI right away. Rather than jumping in and building a tool that works for the UK, we need a foundation: the ability to add many countries. Billing is easier to wire in early. Should there be an admin panel to change and add features?

These are the essential "behind the scenes" pieces that let you scale. Build them now, and adding your second country takes hours instead of weeks. But am I missing anything else? I could really use a product manager to help me flesh out any other pieces I'm missing.

Product Management

In larger organizations, a product manager takes each idea, turns it into a story, and builds requirements for the development team. This process takes hours of meetings -- refining ideas, debating edge cases, clarifying scope -- but it works. The better everything is spelled out in the requirements, the better the first version of the software will be.

But it's just me, my laptop, and my idea. How do I get that same rigor without the hours of meetings? I'll use BrainGrid to turn my prompts into requirements and tasks. BrainGrid's AI Product Planner takes my prompts and adds details I wouldn't have thought of -- fewer meetings, better specs, less rebuilding. That's spec-driven development.

What Does the MVP Actually Look Like?

My initial prompt to BrainGrid describes the task I want to undertake:

Help me create a product plan for: internationalConcierge

International concierge. When I moved to the UK - I got my visa, and was allowed to enter the country. I rented a place to live. But there was no manual on what to do next. How do I get my NHS number (did you know there are 2 NHS numbers you need?). How do I get private insurance? A drivers license? Banking? What else to do arrivals to a new country need to become assimilated into the system.

Im imagining an app that would compile checklists for new arrivals to a country, available as a subscription (for individuals) or sold to companies - who move many employees to a new country. Let's spec out a web application that helps newcomers to a country learn how to navigate the systems that are new to them.

If I had jumped straight into Claude Code, it would have built a UI just for the UK. That looks great... until you want to add the US, Canada, or Australia. Then you realize you've created a vibe coded mess -- no database structure for multiple countries, no admin panel. You'd be forced to rebuild everything.

BrainGrid took my rough idea and created an Epic user story (EPIC-1) -- a high-level feature that groups related requirements. The full epic lives in the GitHub repository. Here's the one-line synopsis: "Help people relocating internationally get set up in their new country by providing clear, country-specific checklists for healthcare, banking, licensing, and insurance." The Epic described exactly what I'm hoping to build.

With the Epic created, BrainGrid is ready to create requirements documentation to make the epic come to life.

With just one click, BrainGrid begins creating the requirements for the epic user story. After a few minutes, it has come up with 5 requirements:

We can see from the requirement names that BrainGrid is thinking about storage, multiple countries, checklists, payments, and enterprise account management. The glue that holds it all together -- but not the stuff you think about off the bat.

This foundation will make expanding the application an organic process rather than a lot of band-aid fixes for stuff I would have initially missed.

Now comes the fun part: turning these requirements into working code.

Vibe Code the MVP

Once we have the requirements, you can read each requirement and make any changes you need (the REFINE step). With the first few requirements in EPIC-1, there are no changes needed. Once you have refined (or just approved the requirement), we can Start Building.

In this step, BrainGrid takes your detailed requirement and breaks it down into tasks -- the steps required to build the requirement into my application. This is similar to a sprint planning meeting (where dev teams break features into technical tasks).

BrainGrid follows these steps:

Phase 1: Gather Context
Phase 2: Plan Tasks
Phase 3: Create Tasks

Instead of an exhausting multi-hour meeting with the whole team, BrainGrid builds out the tasks required to complete the requirement in just a few minutes.

I will build my application using Claude Code. I have connected BrainGrid to Claude, so Claude can just read the tasks that have been created.

Each of the requirements for EPIC-1 generate around 10 tasks to be completed. BrainGrid creates a summary describing the 10 tasks, why they are important, what they do, and why the order of the tasks matters:

BrainGrid also documents "highlights" in the tasks:

This early on, I would not be thinking about error messages or responsive design. But with BrainGrid, it's all in there. There is also an implementation guide to make sure the steps are followed properly and everything is completed as expected:

At the end of the implementation guide, BrainGrid gives you a command to audit the acceptance criteria:

acceptance review https://github.com/[YOUR-REPO]/pull/[PR-NUMBER] against REQ-3

BrainGrid and Claude check the requirements and test the code to ensure it meets the acceptance criteria. Claude generates a report showing the criteria were met. This closes the loop: spec, code, validation, ship.

Building progress

I created EPIC-1 on a Friday afternoon, and BrainGrid/Claude created the requirements and tasks. Friday evening, while streaming a movie, all of the tasks for requirements 1&2 were completed.

Progress is being made -- it's nothing beautiful yet, but with minimal effort on my part, feeding the BrainGrid tasks into Claude Code has created the outlines of a great looking product.

I continued this process through the 5 requirements for EPIC-1. Upon completion, I have a fully working MVP prototype -- ready to show potential customers and collect early feedback. (To get the application to work, I did run database migrations at Supabase (Claude Code provided the SQL for these), but I did not complete the Stripe integration.)

I upgraded my email to become an admin, adding an admin button to the dashboard. Admins can add new countries (note that the UI automatically added the flag next to the name of the country):

Admins can also add and edit items in the checklist:

If you have a company, you can add seats and invite employees who are moving to a new country to join the site:

What I Learned from building a SaaS with BrainGrid

I have built many small applications with vibe coding tools. Scripts to pull API data, Discord bots. Very simple applications that can be described in a couple of sentences. But building a full SaaS application is a bigger deal.

One of the suggestions given by successful SaaS builders is to "look at the unsexy." In many ways, BrainGrid began the development of my application by looking at the not-sexy stuff: databases, libraries, integrations. It isn't fun to build. It isn't glamorous. But without it, the application would not have functioned the way I wanted it to.

I certainly would not have thought about an admin panel for the developers to add new countries or tasks. And the enterprise login, with different invites and seats, would have taken me days to flesh out before even building. It's probably not exactly what I want -- but I can now work with BrainGrid and Claude Code to optimize from a completed MVP.

What Surprised Me

I started this project on a whim. The fact that in just a few hours I could reach this level of complexity astounded me. Clearly the checklist data is simplistic and would require research to get the correct details -- but the app itself is built! Editing from "Lorem Ipsum" data points means I'd be working in the part of the application where I have expertise -- not joining database tables or figuring out why Stripe isn't working properly.

If you are a SaaS builder, and want to build your app with Vibe coding tools, you can! But adding BrainGrid into the mix is an added superpower: AI product planning on top of the Vibe coding tools means better code - faster. Try BrainGrid when you begin building your SaaS MVP.

Frequently Asked Questions

What is an immigration concierge service?

An immigration concierge service helps people navigate the practical systems of a new country -- healthcare registration, driver's licenses, banking, utilities, and other processes that locals take for granted. Unlike visa and legal immigration services, a concierge focuses on post-arrival setup, guiding newcomers through unfamiliar bureaucracies step by step.

How do I find startup ideas using AI tools?

AI idea generators like IdeaBrowser deliver a scored startup idea daily, evaluated on Opportunity, Problem, Feasibility, and timing. Use these ideas as springboards, not blueprints. Strip the idea to its core pattern, then map that pattern onto a domain you personally understand. Your experience is your competitive advantage.

Can I vibe code a SaaS product without engineering experience?

Yes. Vibe coding tools like Cursor, Claude Code, and Lovable let non-engineers build working products by describing what they want in plain English. The key is writing clear specs before you start. BrainGrid's product planner AI pushes the vibe coding tools to create real specifications and detailed tasks.

What are the best AI startup idea generators?

IdeaBrowser delivers daily scored ideas with market analysis. ValidatorAI scores and validates existing ideas. IdeaProof provides AI-powered market research. For broader research, Y Combinator's Request for Startups lists investor-validated problem categories.

How much does it cost to vibe code an MVP?

With AI coding tools, the primary costs are tool subscriptions ($20-100/month for Cursor, Claude Code, etc.) and your time. A focused MVP like an immigration checklist can be built in a weekend with $50-100 in tool costs. The bigger investment is content research and validation -- budget more time for research than for coding.

Originally published on the BrainGrid blog.

Builder Story: Saymon and Core Care

Nico Acosta — Sun, 19 Jul 2026 11:11:48 +0000

"The feeling I have when using BrainGrid is that I have a PM with me."

Saymon

▶ YouTube video player

Background: The Senior Engineer Gone Solo

Who is the builder?

Saymon is a software engineer from Brazil with eight years of professional experience. He has led teams of eight at top agencies, shipped features for apps with more than 9 million daily active users, and taken products from concept to seed funding. For the past two years, he has worked as an independent consultant, helping companies launch MVPs, secure funding, improve code quality, and develop growth strategies.

Saymon is not a vibe coder learning to build. He is a senior engineer who already knows how to build, and who recognized that the bottleneck was never the code itself.

The Shift to Agentic Development

About a year and a half ago, Saymon made the transition to an agentic workflow. He describes it like the jump from JavaScript to TypeScript — a J-curve where the upfront investment pays off dramatically.

His conviction was simple: writing code is not really what a software engineer should be doing. Solving problems through software is. When tools like Cursor and Claude Code matured enough to handle the writing, Saymon shifted his energy to where it mattered most: research, architecture, and decision-making.

His Development Setup

Saymon's current stack is built for speed and cost-efficiency:

IDE: Cursor
Primary coding agent: Claude Code (Opus 4.5 for most tasks, Sonnet 4.5 for large-context work requiring the million-token window)
Research: Gemini Deep Research as his go-to, supplemented by Claude Code research and GPT Deep Research
Planning and specs: BrainGrid

He still reads and reviews every line of code. But he no longer writes most of it.

Vision: Taking Care of the People Who Take Care of Others

What is he building?

Saymon is building Core Care, an all-in-one application for therapists in Brazil. The platform covers patient management, financials and payment handling, telemedicine, and one-on-one video sessions. As Saymon describes it, it is essentially seven startups consolidated into a single product.

Who is it for?

Core Care targets therapists in the Brazilian market first, with a long-term vision to go global. Saymon describes the mission clearly: "Take care of the people who take care of others."

What does it solve?

Therapists currently juggle fragmented tools for scheduling, billing, patient records, and telehealth. Core Care brings everything into one platform so they can focus on their patients instead of their software stack.

Long-term vision

From day one, Core Care has been architected with internationalization and globalization built in. Saymon is not building a local tool. He is building a platform designed to scale globally from its foundation.

The Turning Point: Choosing BrainGrid

The pain point: Product thinking without a product team

As a solo consultant working across multiple client projects, Saymon faced a recurring problem. He had access to great product management tools, but they had no awareness of the codebase. He had powerful coding agents, but they did not understand product context. He was the bridge between the two, and that bridge was expensive.

When working across different projects, he would have to context-switch constantly. If he needed to understand how something worked in a project that was not on his machine, he had to ask another developer to research it and report back. The gap between planning and engineering was real, and it cost hours every week.

Saymon tried building his own system. He tried multiple existing solutions. None of them bridged both sides — product management context and codebase awareness — in a single tool.

Then he found BrainGrid, joined the waitlist, and started using it.

What made BrainGrid different

BrainGrid gave Saymon something no other tool could: the ability to do requirements planning and task breakdown with knowledge of the codebase. It combined the product management layer with engineering awareness so he no longer had to be the translator between the two worlds.

"It is the same as having all the tools that I need for product management and also all the tools that I need for understanding at least the code base."

How BrainGrid Changed His Workflow

From organizing to decision-making

The biggest shift was not speed. It was where Saymon spent his time. Before BrainGrid, a large portion of his hours went to organizing: writing tasks, defining acceptance criteria, structuring plans. Necessary work, but as Saymon admits, "sometimes boring."

BrainGrid removed the boring part. What remained was pure decision-making. Saymon now spends his time evaluating options, refining architecture, and making calls. Not formatting Jira tickets.

"It removes the boring part. It's just decision-making. Okay, yeah, let's do things this way."

His BrainGrid workflow in practice

Saymon's process follows a clear pattern:

Understand the problem. Whether it is a client project or Core Care, he starts by deeply understanding the need and the problem being solved.
Research. He studies how other companies and apps solve similar problems, cross-referencing with his own experience.
Architecture. He sketches the high-level approach, including what to build and in what order.
Spec with BrainGrid. He brings his architecture into BrainGrid and asks it to help create specifications. BrainGrid solves the blank-page problem and produces a first draft that Saymon then reviews and refines.
Task breakdown. BrainGrid generates granular tasks with acceptance criteria. Saymon uses BrainGrid's MCP and CLI within Claude Code to pull tasks directly into his agent.
Pre-task enrichment. Before executing, Saymon layers additional context onto each task. That means Supabase checks for database tasks, design system consistency prompts for UI tasks, and API validation for backend routes.
Test flow generation. Based on the tasks, Saymon uses BrainGrid to generate the complete set of test flows he will need for QA. One recent feature produced 25 tasks and 13 distinct testing flows.
Implementation. He runs each task through Claude Code, reviews the output, and moves to the next.
QA and review. AI-assisted code review catches issues and bugs, followed by manual QA against the flows generated earlier, then automated tests.

Outcomes: Faster, More Organized, Higher Quality

A client feature that generated $1 million in revenue

One client came to Saymon with a hard deadline for a feature that would enable a major event. The challenge: eight interconnected apps that needed to work together, with architecture decisions that would determine long-term maintainability.

Without BrainGrid, the research and planning alone would have taken two full days of six to seven hours each. With BrainGrid, Saymon had a working plan in 30 minutes. He spent the rest of the day refining it.

The result: a feature that would have taken three to four weeks was delivered in just over two weeks. The client used it for an event that generated $1 million in revenue.

A side-by-side quality comparison

Saymon ran his own experiment. He built the same SaaS MVP twice:

Without BrainGrid: Two days to a working MVP using Claude Code with direct prompting. The code was messy, lacked structure, and would require significant refactoring to maintain.
With BrainGrid: Four extra hours of upfront planning, then a similar two days of coding. But the result had more features, a cleaner architecture, and code that was far easier to maintain and extend.

The difference was not just speed. It was confidence.

"I move with confidence on my own code."

The feeling of having a PM

For Saymon, the most meaningful change is not a metric. It is a feeling. As an individual contributor, he finally has the experience of working with a product manager — someone (or something) that handles the tedious but critical work of organizing requirements, writing acceptance criteria, and maintaining structure.

For his consulting work, the impact extends to teams. BrainGrid closes the gap between product managers and engineers, making both more productive. It is not replacing either role. It is making the handoff seamless.

"What BrainGrid is helping with is closing the gap between the engineers and the product managers and making both of them more productive."

What He Wants Next from BrainGrid

Saymon's feature request is focused on adoption and accessibility:

IDE integration without requiring GitHub access. When working as a consultant across multiple client projects, getting GitHub access for each organization adds friction. Having BrainGrid integrated directly into the IDE — similar to how Cursor or Slack adopted bottom-up strategies — would make onboarding new engineers and new projects dramatically easier.

It is the kind of feedback that comes from someone managing multiple teams across multiple codebases, where every step of setup friction multiplies across every project.

Final Thoughts

Saymon's story is not about learning to code with AI. It is about a senior engineer who already knew how to build and found the missing layer that let him operate like a full product team.

With BrainGrid, he is not just building faster. He is building more organized, more maintainable software — and shipping features that drive real business outcomes.

Core Care is just getting started, but the foundation is built for global scale. And Saymon has the workflow to match.

Check out Core Care, coming soon to therapists in Brazil and beyond.

Originally published on the BrainGrid blog.

Building BrainGrid with BrainGrid: Spec-Driven Development with Claude Code

Nico Acosta — Fri, 17 Jul 2026 11:12:19 +0000

How we ship features in under an hour — from half-baked idea to tested, deployed code.

48 Minutes

Here's what a typical feature build looks like for us:

10:02  /specify "Add time range filter to credits health dashboard"
10:03  → REQ-287 created with 8 acceptance criteria

10:03  /build REQ-287
10:03  → Branch: feature/REQ-287-time-range-filter, 5 tasks linked
10:04  → Task 1: Add date range picker component (in progress)
10:11  → Task 1 complete, validation passed
10:11  → Task 2: Update API endpoint with date params (in progress)
10:18  → Task 2 complete, validation passed
       ...
10:42  → All 5 tasks complete, yarn validate:fix passes

10:42  → PR created: "feat: add time range filter to credits health dashboard (#1287)"
10:43  → braingrid requirement review
10:44  → 8/8 acceptance criteria validated against PR diff
10:45  → Code review approved, merged to dev

10:46  → Agent writes test spec: time-range-filter.md
10:47  → agent-browser opens dev deployment
10:48  → Database seeded with test data for known date ranges
10:48  → agent-browser navigates to dashboard, selects "Last 7 days"
10:49  → agent-browser snapshots table, verifies filtered results
10:50  → Database query confirms filter matches actual data
10:50  → Test spec updated: status: PASSED

48 minutes. Idea to tested, merged feature. The spec took 60 seconds. The human reviewed diffs, approved the PR, and made one adjustment to a component's padding. The test ran against the live dev deployment — not a mock, not a stub — with real data verified at the database level.

Every feature in BrainGrid is built this way. Not as a marketing exercise — because it's the fastest workflow we've found. This post explains how it works.

The Problem

Vibe-coding works until you merge it, deploy it, and realize you forgot the error state. Or the loading state. Or what happens when the user has zero credits and clicks the button anyway.

You didn't think about those cases because you're moving fast — and that's fine for prototypes. But the AI didn't think about them either, because you never told it to. It built exactly what you described: the happy path. Everything else is missing.

The fix isn't to slow down or stop using AI. It's to give the AI a requirement that already thought through the edge cases, error handling, and loading states — so you don't have to. The AI that writes your spec thinks like a product engineer. It asks the questions you'd skip. The AI that implements the spec executes like a production software engineer, because that's what a professional requirement demands.

That's the entire philosophy: start with requirements, not code.

The Workflow: Four Commands

1. `/specify` — Turn an idea into a requirement

/specify "Trial users should see upgrade prompt instead of buy credits when out of credits"

AI refines your one-liner into a structured requirement. Here's what REQ-375 looked like after /specify:

REQ-375: Trial users should see upgrade prompt instead of buy credits

Problem:
  Trial organizations see the same credit exhaustion messaging as paid orgs,
  offering "Top-up credits" — but trial users can't purchase credit packs.

Solution:
  Detect trial status, display trial-appropriate messaging with only the
  upgrade CTA. Paid orgs continue seeing both options.

Components to modify:
  - out-of-credits-banner-wrapper.tsx (fetch subscription status)
  - out-of-credits-banner.tsx (conditional render by trial status)
  - low-credits-banner.tsx (agent overlay variant)

Acceptance Criteria:
  ✓ Trial org + 0 credits → top banner shows "Your trial has run out
    of credits" with only "Upgrade plan" button
  ✓ Trial org + 0 credits → agent overlay shows "Upgrade now" only
  ✓ Paid org + 0 credits → shows both "Top-up credits" and "Upgrade plan"
  ✓ Trial org + 1-50 credits → standard low credits message (not trial-specific)
  ✓ Loading state → show paid behavior until trial status confirmed
  ✓ Error state → fall back to paid behavior, log error
  ✓ Status changes reflect consistently across both banners

That's condensed. The full requirement also included a data fetching strategy (React Query with 5-minute stale time), props interfaces, error/loading state specifications, and a message variation table mapping every condition to its banner variant, message copy, and CTA buttons.

A well-structured requirement has these components:

Problem statement — what's broken or missing, in user-facing terms
Solution summary — the approach, not the implementation
Scope — which files/components are affected (so the AI doesn't wander)
Acceptance criteria — testable given/when/then conditions that define "done"
Edge cases and error handling — loading states, failures, boundary conditions
Out of scope — what this requirement deliberately doesn't cover

The AI generates all of this from a single sentence. You type one line, the AI writes the full spec — problem statement, acceptance criteria, edge cases, scope — and you review it. Most of the time it's 80% right. You fix the 20%, move on. We catch bad specs about 20% of the time — the AI assumed a modal instead of inline editing, or missed an auth check, or scoped too broadly. Editing a spec takes seconds. Debugging a wrong implementation takes an hour.

2. `/breakdown` — Turn the spec into tasks

/breakdown REQ-375

This is more than "split the work into chunks." The AI assembles context from three sources: the full requirement (acceptance criteria, edge cases, technical decisions), your codebase structure (repository analysis, file tree, existing patterns), and related documentation. It then generates atomic implementation tasks — each scoped to a single concern — with explicit dependencies between them. The AI knows which files exist, which hooks and components are already in your codebase, and how they're structured.

Here are the actual tasks generated for REQ-375:

TASK-1: Create useSubscriptionStatus hook
  → New hook: src/hooks/use-subscription-status.ts
  → Fetch from /api/organizations/[orgId]/subscription
  → React Query: cache key ['subscription-status', orgId], staleTime 5min
  → Return { isTrialSubscription, isLoading, error }
  → On error: default isTrialSubscription to false (safe fallback)

TASK-2: Update out-of-credits-banner with trial support
  → File: src/components/out-of-credits-banner/out-of-credits-banner.tsx
  → Add isTrialSubscription: boolean prop
  → Trial + 0 credits: render "Your trial has run out of credits" + "Upgrade plan" only
  → Paid + 0 credits: keep both "Top-up credits" and "Upgrade plan" (existing behavior)

TASK-3: Update out-of-credits-banner-wrapper
  → File: src/components/out-of-credits-banner/out-of-credits-banner-wrapper.tsx
  → Call useSubscriptionStatus hook
  → Pass isTrialSubscription to banner component
  → While loading: default to paid behavior (no flicker)

TASK-4: Update low-credits-banner with trial support
  → File: src/components/agent/agent-pane/low-credits-banner.tsx
  → Trial + 0 credits: show "Upgrade now" only
  → Trial + 1-50 credits: standard "You have only X credits left" (not trial-specific)

TASK-5: Run validation
  → yarn validate:fix (type-check + lint + format + test)

These aren't vague tickets. They're prompts — each one tells the agent exactly which file to modify, which pattern to follow, which prop to add, and what the expected behavior should be. The spec already made the design decisions (cache key, stale time, fallback behavior), so the tasks are pure execution.

3. `/build` — Start implementing

/build REQ-375

This runs a four-step flow:

Fetches the build plan from BrainGrid with requirement details and the full task array
Creates a feature branch — feature/REQ-375-trial-upgrade-prompt — and associates it in BrainGrid so everything is linked
Creates and links tasks in Claude Code, connecting each to BrainGrid so status syncs automatically
Starts implementing the first task immediately — no "shall I proceed?" prompts

The agent picks up tasks sequentially — implements the code, runs yarn validate:fix, and if validation passes, marks the task complete and moves to the next. If validation fails, it reads the error, fixes the issue, and re-runs before moving on. You watch in real time and course-correct when needed.

You can steer focus by appending instructions: /build REQ-375 start with the data fetching hook — Claude adjusts task priority accordingly.

4. `braingrid requirement review` — Validate acceptance criteria against the PR

braingrid requirement review

This is where the spec earns its keep. The command auto-detects the requirement from the branch name and the PR number from git, then uses AI to perform the review. It fetches the PR diff from GitHub and the full requirement with acceptance criteria from BrainGrid, then the AI reasons about whether each criterion is satisfied by the actual code changes — tracing criteria to specific lines in the diff:

Reviewing PR #1288 against REQ-375...

Acceptance Criteria:
  ✅ Trial org + 0 credits → banner shows "Upgrade plan" only
     → out-of-credits-banner.tsx:42 — conditional render on isTrialSubscription
  ✅ Paid org + 0 credits → shows both buttons
     → out-of-credits-banner.tsx:38 — default branch renders both CTAs
  ✅ Loading state → paid behavior until status confirmed
     → out-of-credits-banner-wrapper.tsx:18 — isTrialSubscription defaults to false
  ✅ Error state → fallback to paid behavior
     → out-of-credits-banner-wrapper.tsx:22 — catch block sets isTrialSubscription = false
  ...

7/7 acceptance criteria validated.

Did the implementation miss an edge case? Is there a criterion with no corresponding code change? You find out before the PR merges, not after users report a bug.

Here's what it looks like when a criterion fails — say the agent implemented the top banner but forgot to update the agent overlay:

Reviewing PR #1288 against REQ-375...

Acceptance Criteria:
  ✅ Trial org + 0 credits → banner shows "Upgrade plan" only
     → out-of-credits-banner.tsx:42 — conditional render on isTrialSubscription
  ❌ Trial org + 0 credits → agent overlay shows "Upgrade now" only
     → low-credits-banner.tsx — no trial-specific conditional found,
       still renders standard "Top-up credits" CTA for all org types
  ✅ Paid org + 0 credits → shows both buttons
     → out-of-credits-banner.tsx:38 — default branch renders both CTAs
  ...

6/7 acceptance criteria validated. 1 failed.

The AI traces each criterion to specific lines in the diff. It's not grepping for keywords — it's reasoning about whether the code changes actually satisfy the criterion. It catches semantic gaps that compile and lint clean: a component that handles the trial state but renders the wrong CTA text, or an error fallback that works correctly but doesn't match the criterion's specified behavior. Where it can't catch you is runtime logic errors where the code reads correctly but behaves wrong — "the conditional exists but the boolean is inverted." That's exactly what the next layer is for.

AI-Driven Testing

After the PR merges, testing follows the same pattern: AI writes the test, AI runs the test, humans review. This is the layer that catches what code review can't — behavioral bugs in the running application.

The agent writes a markdown test spec, then executes it by driving a real browser against the deployed app while verifying data at the database level:

Test: Credits Top-Up with Stripe
──────────────────────────────────────────────────
Setup:  Query initial balance → 1,998 credits
Step 1: agent-browser → navigate to /settings/billing
Step 2: agent-browser → click "Top-up credits", select $10 / 1,000 credits
Step 3: agent-browser → fill Stripe test card, click Pay
Step 4: agent-browser → wait for redirect, verify success message
Verify: Query final balance → 2,998 credits ✅
──────────────────────────────────────────────────
Result: PASSED
  - Credits added to ORGANIZATION account (not USER)
  - Transaction type: OVERAGE_PURCHASE
  - Credits expire after 1 year
  - Stripe event payload verified in events table

agent-browser drives the actual user flow — clicking buttons, filling forms, navigating pages. MCP servers give the agent direct database access for setup and verification. The agent reads the spec, executes each step against the running app, and appends actual results including event payloads and database state changes it observed.

The defense is layered: spec review catches missing implementations, requirement review catches criterion-to-code gaps, and browser tests catch behavioral bugs in the live app. The real gap — a bad spec and a bad test — is the same gap human engineering has. The difference is that every layer runs automatically.

When It Breaks

The happy path is nice. Here's what happens when things go wrong.

The spec itself is wrong. This is the one failure mode no amount of automation catches — because every downstream layer executes the spec faithfully. If the spec says "show a modal" and you meant inline editing, the implementation will be correct according to the wrong spec. That's why /specify walks you through clarifying questions before generating the requirement, and why you review the spec before /breakdown. The human is the checkpoint. If you rubber-stamp a bad spec, everything downstream is on you.

The AI misunderstands the spec. Different from above — this is when /specify generates acceptance criteria that don't match your intent and you catch it. We catch bad specs about 20% of the time — the AI assumed a modal when we wanted inline editing, missed an auth requirement, or scoped too broadly. Editing a spec takes seconds. Debugging a wrong implementation takes an hour.

A task fails validation. The agent runs yarn validate:fix after every task. If types break or tests fail, it reads the error, fixes the code, and re-validates before marking the task complete. You see this happening in real time. If it gets stuck in a loop, you intervene — but that's rare because the task description already specified which patterns to follow.

requirement review flags a gap. A criterion shows no corresponding code change. The agent either missed it or decided it was out of scope. You see exactly which criterion failed and can either implement it or mark it as intentionally deferred.

The test fails. agent-browser snapshots the DOM and the agent reads the actual state. "Expected 'Upgrade plan' button, found 'Top-up credits' button." The error is usually obvious from the snapshot. The agent can fix the code and re-run, or you can investigate manually. Element refs (@e1, @e2) change after every page interaction, so the agent re-snapshots after each step — stale refs are the most common failure mode and the tooling handles it.

Patterns Worth Stealing

These apply to any AI-assisted development workflow, with or without BrainGrid:

Specify before building. A few minutes of upfront clarity saves an hour of rework. This is the single highest-leverage thing you can do.
Tasks are prompts. Write task descriptions as if you're prompting an AI — because you are. Include file paths, patterns, and APIs.
Automate the things humans forget. Task status sync, validation, branch naming conventions. If the developer has to remember to do it, they won't.
Validate against the spec, not just the code. Code review catches bugs. Spec review catches missed requirements. Browser tests catch behavioral regressions. You need all three.
The agent should just start. After an explicit build command, don't ask "shall I proceed?" The user already expressed intent.
Test against real infrastructure. Database queries, browser interactions, deployed endpoints. Mocks hide bugs.
Memory compounds. Store learnings persistently. Your future self (and your agents) will thank you.
Handle errors reactively. Don't pre-check if every tool is installed. Run it and handle failure if it occurs.

Try It in 5 Minutes

You don't need the full setup to start. The core loop — specify, break down, build — works with just three things:

## 1. Install the CLI
npm install -g @braingrid/cli

## 2. Create an account and authenticate
##    → Sign up at app.braingrid.ai, then:
braingrid login

## 3. Initialize in your project
braingrid init

## 4. Open Claude Code, then type:
/specify "your feature idea here"

## 5. Break it into tasks
/breakdown REQ-XXX

## 6. Start building
/build REQ-XXX

That's it. No MCP servers, no hooks, no browser automation. Add those later when you want database verification, automated task sync, or AI-driven testing. Start with the spec.

Under the Hood

The workflow runs on Claude Code with a few extensions. Everything above works out of the box. Everything below powers the full experience but is optional.

Skills are markdown files that teach Claude domain knowledge. They load automatically when relevant context is detected — you don't invoke them manually. We use braingrid-cli (the spec-driven workflow), agent-browser (E2E testing), frontend-design (UI that doesn't look AI-generated), and memory (persistent learnings via mem0).

Hooks run shell scripts in response to tool calls. Our most important hook: when Claude marks a task complete, a PostToolUse hook syncs the status to BrainGrid automatically. You never manually update a task tracker.

MCP servers give Claude authenticated access to external services. Supabase for database queries — you control the scope (read-write on dev, read-only on production, or production off entirely — your call). Axiom for production logs (read-only). Playwright for browser automation. mem0 for persistent memory. When Claude needs to verify a migration, it queries the dev database directly. When it needs to test a flow, it drives a browser against the staging deployment. No context switching.

Persistent memory via mem0 stores learnings across sessions: "AuthKit login: wait 1500ms after email submit for password field." "Credit expiration cron uses billing_anniversary_day, not created_at." Before starting work, the agent searches memory. After discovering something reusable, it stores it. This compounds — month-old debugging insights surface exactly when needed.

Links & Resources

BrainGrid

Web App: app.braingrid.ai
CLI (@braingrid/cli): npmjs.com/package/@braingrid/cli
GitHub: github.com/BrainGridAI/braingrid
Documentation: braingrid.ai

Claude Code

Claude Code: docs.anthropic.com/en/docs/claude-code
Skills: docs.anthropic.com/en/docs/claude-code/skills
MCP (Model Context Protocol): modelcontextprotocol.io

Tools

Vercel agent-browser: github.com/vercel-labs/agent-browser
mem0 (Persistent Memory): github.com/mem0ai/mem0
Supabase: supabase.com
Axiom: axiom.co
Playwright: playwright.dev

Originally published on the BrainGrid blog.

How to Get SaaS Product Ideas: 7 Proven Methods for Solo Founders

Nico Acosta — Thu, 16 Jul 2026 11:14:04 +0000

You know your industry inside and out. You've seen the gaps -- the broken workflows, the tools that don't exist, the problems everyone complains about. With AI coding tools, you don't need to be an engineer to build the solution. The hard part isn't building. It's knowing what to build.

There's a lot of advice out there. In this post, we'll focus on the processes successful SaaS founders have used to come up with ideas and build them into profitable products.

By the end of this guide, you'll have 7 proven methods to find validated SaaS ideas that match your expertise -- ready to prototype with vibe coding tools.

What Makes a Good SaaS Idea?

The best answer to this comes from Paul Graham's classic essay: How to Get Startup Ideas. From this article, a great SaaS idea is:

Something you (and others) want: Think of a problem that constantly slowed you down at work. If you ever thought "I wish there was a tool for this," others likely felt the same.
Something you can build: AI coding tools make this less of a hurdle than ever before.
Others haven't recognized the opportunity: Maybe folks are frustrated by a problem but haven't turned the solution into a product yet.

It's more nuanced than three bullet points. You might want to build the next Airbnb. But the founders just wanted to rent out their couch. They started small. You should too.

Start small. Rather than aiming to be the next biggest thing, aim to be the best at one small thing.

What should you build? Paul Graham says to ignore two mental filters that kill good ideas:

The "sexy" filter: Forget flashy consumer apps. The real money is in solving boring problems. Think: expense tracking for small law firms, inventory management for bakeries, appointment scheduling for HVAC companies. These aren't exciting -- but customers will pay $50-200/month to avoid the headache.
The "schlep" filter: A tedious, awful task everyone avoids. If everyone hates doing a certain process, that's your opportunity. Stripe is the classic example: setting up billing on ecommerce sites was a tedious, painful job. Stripe took that schlep and made it easy. Now millions of companies use it.

You have the criteria. Now let's find that idea.

The Paul Graham Recipe for Solo Founders

Paul Graham (PG) writes that "the way to get startup ideas is not to try to think of startup ideas." Find a problem no one else has solved. If you've personally experienced it, even better.

Just Start Building

"The best way to discover startup ideas is to become the sort of person who has them and then build whatever interests you." Vibe coding tools have dramatically lowered the barrier to build an MVP. 95% of startups fail. You'll have failures too. The key is failing fast -- in days, not months.

Action: Pick a problem that bugs you. Build an ugly MVP this week. Launch it publicly. If no one wants it, move on fast.

Stick to Your Domain Strengths

Your personal experience is your superpower. Building a SaaS on what you know beats jumping into an unknown field. What do people in your industry need?

What's Missing in Your World?

Have you ever thought "I'd pay someone to fix this"? Maybe that someone is you. Reach out to others in your field and find out if it's a market gap you could fill.

Is There a Niche the Big Players Are Missing?

Is there an unmet need that large companies overlook? Maybe they think it isn't "big enough" to pursue. But perhaps it's just big enough for a solo founder to get a foothold.

Live in the Future, Then Build What's Missing

Live in the future: Join Discord servers, follow builders on X, test new AI tools daily. Stay on the cutting edge of your field so you spot problems before they're obvious.

Build what's missing: When you see a gap forming, build for it now. The future of your field will create needs for tooling that doesn't exist yet. Start now and you'll be there when demand becomes urgent.

You have the framework for evaluating ideas. Now you need to find them. Here's where successful founders go hunting.

Mine Reddit for Pain Points

Reddit is your focus group of thousands. Founders complain openly about real problems. Each complaint is a potential SaaS idea.

The Iceberg Effect: One Complaint = 26 Silent Customers

For every founder who complains on Reddit, 26 others have the same problem. That one Reddit post complaining about a broken workflow? It's proven demand hiding in plain sight.

Direct Feedback Channels

Already have an MVP? Reddit communities full of founders are an excellent place to get early feedback and suggestions.

You can find ideas on many subreddits. If you're in a specific domain, there's probably a subreddit for it. Here are some go-to channels:

`r/SaaS`

With 203k users and 15,000 posts a week, r/SaaS is a font of knowledge and conversation. A top post analyzed 9,300+ "I wish there was an app for this" posts and found most frustration posts happen Mondays and Tuesdays.

Use critical thinking though. Many of the complaints in the post ("recipe posts are too long") aren't SaaS-worthy problems. Pull the nuggets of insight and ignore the rest.

`r/Entrepreneur`

PG warns against entrepreneurship classes -- you learn by doing, not studying terms. But reading the trials of other entrepreneurs can sharpen your approach. With ~500k members, you'll find ideas and hard-won experience from people at every stage.

`r/Startups`, `r/Marketing`, `r/SaaSMarketing`

Don't wait until launch to think about payments or marketing. Reading these subs as you build keeps you thinking about better ways to sell your product. Posts here can kickstart slogans or product names while you're still building.

Filter Reddit advice through common sense. Many posts are AI-generated noise.

Action: Join r/SaaS and r/Entrepreneur today. Spend 30 minutes reading top posts from this week. Bookmark 3 problems worth solving.

YC's Request for Startups

Y Combinator is one of the most well-known startup incubators. They publish a Request for Startups page listing problems they want solved. Think of this as a pre-vetted list of SaaS ideas -- if YC wants to fund it, there's market demand. The Spring 2026 list weighs heavily on AI products.

The Spring 2026 list highlights AI-native product development tools -- the exact category powering the vibe coding movement. This investor interest confirms growing demand for tools that help non-engineers build software. The screenshot above actually describes the tools being built here at BrainGrid.

Browse previous RFS entries too. They reveal where smart money sees market gaps. If YC wants to fund startups in these fields, customers are ready to pay for working solutions.

Action: Read YC's RFS page. Pick one problem that matches your domain expertise. That's your starting point.

Learn from Serial Indie Hackers

Indie hackers build fast, launch ugly, and iterate based on revenue. They ship 10 products to find 1 winner. Learn their patterns to avoid wasting months on bad ideas.

Levelsio

Pieter Levels @levelsio has created multiple startups with combined monthly revenues of ~$250k. His most famous products are RemoteOK and NomadList.

In a 2023 interview, he says "indie hacking" no longer exists, but has evolved into entrepreneurship. What used to be hacking is now just the way companies are formed.

He talks openly about his failures. He built a fancy YouTube analytics platform that no one wanted. His takeaway: "quick and dirty" MVPs work best. Super-polished means you've spent too much time on features people might not want. Get feedback on the MVP first, then build based on what you've launched.

John Rush

John Rush is another prolific SaaS founder who builds in public on Twitter and Substack.

His 24 SaaS projects generate $2M ARR combined. His 3-month playbook to $10k MRR:

Find a popular SaaS, and identify one sector of their audience.
Learn about their pain points in a popular feature in that SaaS.
Begin conversations -- build a following -- while learning about the pain point.
Launch a waitlist for the fix.
Build the MVP.

Key insight: Rush validates demand before building. He creates 5 waitlists, then builds only the one with the most signups.

You don't have 114k followers. So adjust: build 3 waitlists featuring your ideas. DM 50 people individually. Manual hustle beats viral tweets when you're starting out.

Tools for Systematic Idea Discovery

Beyond manual research, AI-powered tools can accelerate the process. IdeaBrowser delivers a fresh SaaS idea every day, scoring each on Opportunity, Problem, Feasibility, and "Why Now" timing. It also integrates directly with vibe coding tools to help you start building right away.

Warning: You won't be the only reader. If you build an IdeaBrowser suggestion, narrow it down. Don't build "AI resume builder" -- build "AI resume builder for nurses re-entering the workforce after career breaks." The more specific your audience, the less competition you'll face. Kaleen Canevari did exactly this -- a Pilates instructor who built a multi-tenant studio management platform for her exact domain.

Validate Before You Build

This is the step most founders skip -- and it's why most fail. SaaS products rarely fail because they're poorly built. They fail because no one wants them. Validation prevents this.

John Rush's waitlist approach works well here. Build landing pages for a few different ideas. Each page explains the problem and promises a solution. Add a waitlist signup form and promote in relevant communities (Reddit, LinkedIn groups, Discord, and forums). The idea with the most signups wins -- build that one.

But don't fixate on raw signup numbers. Most solo founders don't have 100k followers to drive traffic. Focus on the quality of responses instead. Are people DMing you asking when it launches? Are they sharing the page with colleagues? Are they describing exactly how they'd use it? A handful of passionate, specific replies is a stronger signal than a hundred casual signups.

By communicating on social media, you get a feel for what features potential customers are missing. Listening to their needs helps you prioritize "what's next" on the feature list -- and tells you whether you're solving a real problem or an imagined one.

Your SaaS Ideation System: Next Steps

You now have 7 proven methods for finding SaaS ideas:

Personal experience -- Problems you've faced in your domain
Paul Graham's framework -- Unsexy problems, schlep opportunities, living in the future
Reddit mining -- Real pain points from r/SaaS, r/Entrepreneur, and niche subreddits
YC's Request for Startups -- Investor-validated categories
Indie hacker playbooks -- Levelsio and John Rush's processes
AI discovery tools -- IdeaBrowser and daily idea scoring
Validation tactics -- Waitlists, pre-selling, and conversations

What to do this week:

Pick 3 pain points from your domain experience.
Search Reddit for similar complaints using the subs listed above.
Create landing pages for your top 2 ideas.
Share them in relevant communities and track the engagement.

Don't wait for the perfect idea. 95% of startups fail, so better to figure that out when you've invested days, not months. Launch early, iterate fast, and pivot when the data tells you to.

Once you've validated an idea and gathered feedback, turn those insights into a structured product plan. That's where tools like BrainGrid help -- transforming feature requests and customer feedback into detailed requirements and build-ready tasks for your AI coding tools.

In a coming post, we'll walk through taking a validated idea from waitlist to shipped MVP using vibe coding tools and BrainGrid.

Frequently Asked Questions

How do I find a good SaaS idea?

Start with problems you personally experience in your domain of expertise. The best ideas come from noticing gaps rather than brainstorming solutions. Monitor communities like Reddit where people share frustrations, and validate demand before building anything.

What are some micro SaaS ideas?

Popular micro-SaaS categories include AI-powered tools. Instead of general resume builders or content generators, build for a specific category of user. Instead of general automation tooling, automation for specific industries (real estate, restaurants). Create features unbundled from larger platforms. Focus on serving one sub-audience exceptionally well rather than building for everyone.

How do I validate a SaaS idea without coding?

Create a landing page with a waitlist signup before writing any code. Share your problem hypothesis in relevant communities and gauge responses. Consider pre-selling access or running "smoke tests" with "coming soon" buttons to measure interest. Build a raw MVP tool, and gauge the response.

Can you build a SaaS by yourself?

Yes—the micro-SaaS model is designed for solo founders. Successful products often generate $5,000-$30,000 monthly recurring revenue with 70-80% profit margins. AI coding tools like Claude Code, Cursor, BrainGrid, and Lovable make it faster than ever for non-engineers to ship working products.

What does Y Combinator look for in startups?

YC's current Request for Startups prioritizes AI-native product development, financial services, agency businesses using AI, government technology, and manufacturing. These areas signal where experienced investors see market timing and opportunity, though you should narrow down to specific problems.

How do successful indie hackers find product ideas?

Prolific indie hackers like Pieter Levels and John Rush build many small products, share progress publicly, and iterate quickly. They accept high failure rates as part of the process. Rush's playbook: find a popular SaaS, identify a sub-audience, and build one highly-used feature for that audience.

Originally published on the BrainGrid blog.

Introducing AI Product Planner: Structure your ideas for AI

Nico Acosta — Wed, 15 Jul 2026 11:18:33 +0000

You have ideas. Probably too many of them.

They live across docs, notes, Slack threads, and half-finished prompts. Turning those ideas into something your AI coding tool can build beyond a fragile prototype is where most builders get stuck.

Code is not the bottleneck anymore. Planning is.

Today, we are introducing BrainGrid AI Product Planner, a new way to bring structure to product thinking before a single line of code is written.

What's New

AI Product Planner gives you a clear plan for what you are building and what is actually ready to build.

Everything lives in one place. No scattered notes. No guessing what comes next.

Four capabilities, one goal: help you ship with clarity.

Unified Plan view

Epics and requirements organized in a single structured hierarchy.
AI-guided specification

Planning agents ask smart clarifying questions to turn vague ideas into buildable specs.
Readiness scoring

A clear signal for what is ready to build and what still needs work.
Dependency management

Visualize blockers before they slow you down.

Why We Built It

Builders kept telling us the same thing.

“I know what I want to build, but I don't know how to get it out of my head in a way AI can reliably execute.”

AI coding tools are excellent at implementation. They are not designed to help you think through product strategy, scope, sequencing, or the bigger picture.

That missing step is where most AI-built apps start to break.

We built AI Product Planner to fill that gap. It meets you where you are, whether that is a half-formed idea or a detailed concept, and helps you turn it into a plan that can actually be built.

How It Works

1. Create your plan

Start with a project. This could be a small feature, a single change, or an entire product.

BrainGrid organizes your thinking into a clear product plan. Epics give you the big picture. Requirements keep things concrete and actionable.

2. Specify with AI guidance and clarifying questions

Select a requirement to specify. The planning agent asks the kinds of questions an experienced product manager and tech lead would ask.

What is the user flow?

What are the edge cases?

What constraints matter?

You work one requirement at a time, without losing sight of the overall plan. Large initiatives become manageable.

The result is a fully specified requirement with the context AI coding tools need to build it correctly.

3. Check readiness scores

Each requirement receives an AI-evaluated readiness score from 1 to 5.

Score	Meaning
1–2	Needs more definition
3	Good for discovery, not ready to build yet
4–5	Ready to build

Low scores tell you where to refine your thinking. High scores mean you can confidently hand work off to your coding agent.

4. Manage dependencies

Most features depend on other work being completed first. AI Product Planner makes those relationships explicit.

You can see what is blocked and what is blocking it before development starts.

5. Build with your favorite coding agent

Send structured tasks to Claude Code, Cursor, Codex, Replit, or Lovable via MCP, CLI, or simple copy-paste. Each task includes all the context your AI coding agent needs to build it correctly.

BrainGrid stays focused on planning. Your coding agent stays focused on execution.

From idea to execution

A typical workflow looks like this:

Bring your ideas into a plan
Organize them into epics
Specify the most important requirements
Check readiness scores
Set dependencies
Ship by handing high-readiness requirements to your coding agent via CLI, MCP, or copy-paste

Your Plan view becomes the source of truth. You always know what you are building, why it matters, and what comes next.

Get Started

AI Product Planner is available now for all BrainGrid builders.

Open your dashboard →

Create a project, add your first epic, and let the planning agent help you turn your next idea into something buildable.

Originally published on the BrainGrid blog.

7 Best Vibe Coding Tools in 2026 (Tested & Ranked)

Nico Acosta — Tue, 14 Jul 2026 11:14:19 +0000

Ship Your SaaS in Weeks, Not Months—No Developer Required

Non-technical founders are launching paid products in 2-4 weeks using AI coding tools. This guide helps you pick the right one and avoid costly rebuilds.

What is vibe coding? You describe what you want in plain English, and AI agents write all the code. You iterate together until it works—often in hours, not months.

Is it really possible to build a working SaaS this way? Yes. Today's tools make it achievable, and they're improving rapidly. But it isn't always easy—AI agents make mistakes, features can break, and some tools fit your workflow better than others.

By the end of this guide, you'll know:

Which tool matches your technical comfort level and budget
How each tool handles the same simple app (so you can compare directly)
Why detailed specs prevent expensive rebuilds later

So, what tool should you pick to hit the ground running?

How to Choose Your Vibe Coding Tool

Tool choice paralysis is real—and overwhelming. Each vibe coding tool works differently. Understanding your needs and technical abilities narrows your choice to one or two options instead of a dozen. Then try them out and see which fits your way of thinking.

Here are a few more questions that may help you narrow your selection:

1. How Comfortable Are You with Git and Version Control?

GitHub is more than just the website that stores your code and helps developers collaborate. Git gives you version control—you can move back to older states of the codebase if mistakes happen. With Git branches, you keep your working code in the main branch and work on features in other branches. When the new features are ready, you merge them into main.

If you're not familiar with Git—that's fine. Browser-based tools like Bolt and Lovable handle version control automatically.

If you're learning Git or already comfortable with it, IDE-based tools let you use that skill for more control.

Why this matters for vibe coding: AI agents make mistakes. Without version control, one bad prompt can break your working app with no way to recover. Git lets you experiment fearlessly—if the AI breaks something, roll back to the last working version in seconds. (New to Git? Read our Git version control guide for AI builders.)

2. Control vs Speed: What Matters More Right Now?

Speed: Auto-accept all AI changes without reviewing them. You'll churn out features quickly, but the codebase becomes messy. If your SaaS grows and you hire developers later, they'll spend time refactoring before adding new features.

Control: Review each change, understand what the AI created, and iterate until the code does exactly what you need. This approach is slower, but produces higher-quality code that you (or a future dev team) can follow and maintain.

3. What's Your Monthly Budget and Usage Pattern?

How much vibe coding will you do every day? If it is just an hour or two a day, you can probably get away with the base plans that are offered. Most vibe coding tools are very affordable to start - and you may find yourself using more than one.

As your usage increases, you will exceed the base usage plans, and can expect to pay overage amounts. If those amounts continue to grow, you can upgrade to higher usage plans.

4. Are You Handling Customer Data Yet?

You've probably heard warnings about sending private data to an LLM. The same applies to your customer data. When your code queries include personally identifiable information (PII), you leak private data—putting both your business and your customers at risk.

The best approach is to use a test environment with only test data in your database. The 2nd best approach is to edit all logging and error messages to remove PII (but this is slow, and easy to forget when crunching through a really pesky problem.)

Even if your vibe coding platform is SOC2 compliant, it is not ok to share PII from logs, etc. to the coding platform.

5. Will you Move to a Dev Team?

Is your product a simple tool you can maintain the same way you built it? Or do you envision a larger product that you'll eventually hand off to developers? If handoff is likely, lean toward controlled vibe coding—clean code is easier to maintain than the spaghetti code that arises from prioritizing speed.

Now that you know what to consider, let's see these factors in action. Below, we compare the most popular tools by building the same app with each—so you can see exactly how they differ.

The Most Popular Tools in Early 2026

We rank tools from least technical (no Git, no IDE) to more advanced (Git knowledge, coding IDE). If you're a non-technical founder, start at the simpler end and work your way up as your skills grow. If you're already a developer, jump to the more technical tools.

For each tool, we'll build the same small SaaS-like app—Founder Daily Pulse: a check-in app with 3 simple questions that gives you feedback on your day.

The prompt that I will use with each tool is:

Daily Pulse is a tiny app that helps founders check in with themselves once a day and get a clear snapshot of how they're actually doing.

Instead of tracking everything, it asks just three quick questions:

How's your energy?

How focused do you feel?

How stressed are you?

From those inputs, Daily Pulse generates a simple visual summary and a short, human-readable insight.

Lovable: Browser-Based Full-Stack Builder (Hosted MVP Platform)

Lovable is built into the browser. There is no code, there is no IDE. You just tell the Agent what to build and you start going.

Lovable Pro costs $50/month and includes 200 credits. I built the sample application on the free tier and couldn't easily determine the exact credit usage.

Giving Lovable our prompt, it created a webpage that uses a number of common development technologies:

Vite
TypeScript
React
shadcn-ui
Tailwind CSS

It created a test, a 404 (page not found) placeholder, and a nicely working app.

You can try the app yourself at Lovable.

Lovable has a built in design tab that allows you to easily adapt and change the design of your application.

Here we can adapt the images, fonts or colors:

Lovable is a great tool to begin your vibe coding journey—no coding requirements, no GitHub dependencies. We've written a tutorial on how to download a Lovable project for when you're ready to move to more advanced tools or hand off to a developer. Looking for a direct replacement? See our 10 best Lovable alternatives comparison.

While Lovable excels at simplicity, Bolt.new adds more production-ready features like built-in authentication and databases.

Bolt.new: Browser to Deployed App

Bolt.new, like Lovable, is browser-based. You can begin without even logging in. After entering the prompt, Bolt immediately started building:

A few minutes later, the Bolt version of our app was ready. Bolt built in a database for logins and to store pulse scores over time. It included login by default—making it easy to scale to many users. In Bolt's settings, you can even enable Google sign-in.

Once signed in, we have 3 sliders, again 1-10. The user submits their result:

The bars are green for good values and red for worse ones. Supabase (an open-source database tool) stores the data and displays yesterday's results alongside today's—although it appears to show the same values for both.

You can try the Bolt.new app

When publishing, Bolt discovered a security issue:

Supabase scans for insecure passwords. In this case, I chose to ignore the warning—but if I were building a real SaaS, I'd fix this.

The free Bolt.new account includes 1M tokens—I used ~232k tokens to build this app. Pro accounts cost $25/month and include 10M tokens.

v0 by Vercel: UI-Focused Component Generator

v0 by Vercel is also browser-based. Sign up free and start prompting. Vercel built our application with buttons rather than sliders and defaulted to a dark mode design:

Vercel can integrate and upload code to GitHub, and also has a built in design studio to change colors, fonts, and other design features of the application:

I built this application with V0's free tier, which allows $5 of usage per month and 7 messages per day. Pro plans start at $20/month with $20 of credits and the ability to purchase more.

Replit: Browser-Based Development Environment (Learning Platform)

Replit is another browser based tool, and based on the number of ads in front of it on Google by competitors, a pretty popular choice for developers.

The Replit app had two introductory pages and a required login before I could perform my daily check-in:

Replit chose sliders for the selection:

The Replit app was the first that didn't complete the task. The "Complete Check-in" button does nothing, and publishing claimed success but the URL doesn't work. Both issues could likely be resolved with additional prompts, but I wanted to keep prompting consistent across all tools.

Building the app used 335 of my daily free credits. Pro plans start at $20/month, include $25 of credits, and remove the "made with" badge. In my opinion, the app over-engineered the setup with 2 intro pages and required sign-in—then didn't actually score my daily pulse.

Cursor: AI-Powered Code Editor

Cursor is an IDE-based agentic AI. Installed on your computer, it replaces VS Code (it's a fork, so it looks familiar). Cursor is designed for developers—with autocomplete for coding in addition to vibe coding features.

The prompt generated 3 files: HTML, CSS, and JavaScript. Unlike browser tools, Cursor doesn't automatically publish your app to the web, but the code is on GitHub. You can open the page directly in a browser:

I built this application with Cursor's free tier. Cursor Pro starts at $20/month. (See our Cursor pricing breakdown for details.)

Claude Code: Terminal-Based AI Agent

Claude Code installs into your terminal and interacts with your code from the command line. It works best alongside a coding IDE and requires basic Git knowledge to manage branches. When creating the sample app, Claude offered several technology stack options:

I selected #2 a React application framework.

The Claude app uses sliders and works similarly to the other applications. Claude Code costs $20/month for the Pro tier. (See our Claude Code pricing breakdown for details.)

Windsurf: Agentic AI-First IDE

Windsurf, like Cursor, is a standalone IDE built for vibe coding with AIs.

Windsurf also created a Vite application. Interestingly, it built a single-page site with sliders and results on the same page. The code saves results in localStorage, indexed by day—a smart approach that makes adding a database easier as the app grows.

Windsurf starts at $20/month and scales with you and your team.

After building the same app seven times, a pattern emerged: each tool interpreted our prompt differently. This reveals a challenge in vibe coding that no single tool solves.

The Missing Layer: Why All Tools Need Specs

Building the same application across all these tools revealed fascinating differences. While most results were similar, the variance was significant: entry flows (immediate vs. multi-page signup), UI choices (sliders vs. buttons), and backend approaches (no storage vs. localStorage vs. full Supabase database).

It's clear the AI agents weren't sure what to build, so they made their own choices. Sometimes that's great—but it can also lead to expensive rework.

Vague Prompts Lead to Vague Implementations

We've all encountered LLMs giving unexpected responses—often due to vague prompts (or, in my case, lazy prompts). The better structured your prompt, the better the response. This effect is magnified in vibe coding: one poorly written prompt, and agents build features you never wanted or needed.

Specs Turn Ideas into Clear Instructions

In traditional software development, product managers turn vague ideas into clear requirements. The vibe coding equivalent is BrainGrid, an AI Product Planner. BrainGrid translates your prompts into detailed requirements that specify exactly what each feature should do.

For example, entering our daily pulse prompt into BrainGrid produced an epic user story, which was then converted into specific requirements:

These 8 requirements take the daily pulse much further than any individual AI agent did. BrainGrid's Product Planner made logical extensions: Do we need a database? If we're tracking usage over time, can we build trends and analyze historical data?

By adding these ideas to the epic, agents can build not just an MVP, but a framework for a SaaS that grows and evolves. For our MVP, we'd implement requirements 1 and 2—but they'd build on a solid, future-proof foundation.

BrainGrid creates detailed tasks with prompts that feed directly into your vibe coding tool of choice. Your simple prompts become detailed, specific instructions designed for successful implementation.

The Spec-First Workflow

By creating specifications, BrainGrid helps you understand exactly what you're building and how you plan to grow. Building an MVP in one prompt is amazing—but taking time to plan and build a foundation first makes growing your SaaS easier over time. (Learn more about this approach in our guide to spec-driven development.)

Works with Any Tool

The requirements and tasks BrainGrid creates work with all the tools above. BrainGrid's MCP installs into your vibe coding tool of choice, letting agents read requirements and tasks directly.

Why This Matters for Non-Technical Founders

You want to move fast—but also safely and productively. Using BrainGrid to create your tasks requires more upfront thinking, but the resulting prompts are stronger. You're more likely to get what you want on the first try, avoiding costly rework later.

With or without BrainGrid, you still need to pick your primary vibe coding tool. Here's how to decide.

Which Tool Should You Choose?

All the tools we tested did a solid job building an MVP. The best tool is the one that matches (or slightly stretches) your technical skills and preferred workflow.

If you have less coding experience: Browser-based tools are excellent. You get working code without worrying about Git. Most include design tools for tweaking colors, fonts, and UI elements right in the browser.

If you're comfortable with Git and want to see the code: IDE-based tools (and Claude Code as a terminal agent) are a great choice. They let you work in the code like a developer while still leveraging AI agents to build new features.

No matter which tool you choose: BrainGrid helps create better prompts for your agents. Quality prompts lead to quality code—and fewer expensive rebuilds.

Best of luck on your vibe coding journey!

Frequently Asked Questions

What are the best vibe coding tools for beginners?

Bolt.new and Lovable are best for complete beginners because they handle Git and deployment automatically in the browser. You describe your app, they build and deploy it without requiring terminal commands or version control knowledge. For beginners willing to learn basic Git, Cursor offers a good middle ground with visual code editing and AI autocomplete.

Which vibe coding tool is best for non-technical founders?

Claude Code is best for non-technical founders who think in product requirements rather than code syntax. You describe features in detail and Claude executes autonomously across multiple files. Alternatively, Bolt.new works well if you prioritize speed and don't need to understand the underlying code—great for validating ideas quickly before committing to deeper learning.

How do I choose between Cursor and Claude Code?

Choose Cursor if you want to see and understand code as it's written—you'll review changes line-by-line and learn programming patterns along the way. Choose Claude Code if you prefer describing outcomes and letting AI handle implementation details autonomously. Both cost $20/month, so the decision comes down to control preference: hands-on (Cursor) vs autonomous execution (Claude Code). For a deeper comparison, see our Claude Code vs Cursor guide.

What's the difference between browser-based and IDE-based AI coding tools?

Browser-based tools (Bolt, Lovable, Replit, V0) run entirely in your web browser with zero setup—no installing software or configuring environments. IDE-based tools (Cursor, Claude Code, Windsurf) require local installation and basic Git knowledge but give you full control over your codebase. Browser tools are faster to start but have limitations for complex projects; IDE tools have a learning curve but work better for professional-grade applications.

Do I need to know Git to use vibe coding tools?

Browser-based tools require no Git knowledge—they handle version control automatically. IDE-based tools benefit from basic Git understanding (commit, branch, revert) which takes 1-2 days to learn. You can start without Git, but you'll hit limitations within a week when you need to undo mistakes or manage different feature versions safely.

How much do vibe coding tools cost per month?

Base plans cost $15-20/month for most tools, but realistic budgets range from $60-100/month for part-time building to $180-300/month for full-time development. The difference comes from usage overages (exceeding monthly AI request limits), hosting costs, database services, and supporting tools. Set spending caps in tool settings to avoid surprise bills—start with $50-100 cap and adjust based on your building pace.

When should I graduate from Lovable to professional tools?

Graduate when you hit customization limits (usually after 2-3 months of active building) or when code export becomes necessary. Signs you've outgrown Lovable: spending more time working around platform limitations than building features, needing custom backend logic the platform doesn't support, or requiring integrations Lovable doesn't offer. Migrate to Cursor or Claude Code when you need full control over your codebase and professional deployment workflows.

Can I use multiple vibe coding tools together?

Yes, most successful builders use multiple tools strategically: Bolt for quick prototypes, Cursor or Claude Code for production features, v0 for polished UI components, and BrainGrid for spec creation. Start with one primary tool for 2-4 weeks until comfortable, then add specialized tools for specific tasks. Using 2-3 tools in combination is common—the key is having a clear primary tool and adding others only when you hit limitations.

What Are Common Pitfalls in Vibe Coding?

Pitfall 1: Blindly Accepting AI Code

AI agents will give you code and explain how it works—but they can be wrong. Review the code and verify it does what you expect. Blindly accepting changes can move things forward, but also break things in ways the AI can't fix—leading to frustrating rabbit holes and lost time.

Pitfall 2: Skipping Version Control (Git)

Made a mistake? (Maybe you ignored Pitfall 1.) Version control lets you "go back" to the working version and try your prompts again. Without it, one bad change can cost hours of rework.

Pitfall 3: Building Without Clear Requirements

In the daily-pulse example, the requirements seemed clear—but each agent interpreted them differently. The more detailed your prompt, the less likely agents will get "creative" with unwanted features.

Pitfall 4: Not Setting Spending Caps

All vibe tools have monthly limits. You can extend spending, but set a monthly cap—otherwise, you might be surprised by your bill.

Pitfall 5: Ignoring Security Until Launch

Vibe coding focuses on product, but you must also ensure code security. Work with your agents to review security before launch. You don't want to ship a SaaS with a database wide open to the internet.

Originally published on the BrainGrid blog.

7 Best Replit Alternatives in 2026 (Free & Paid Options)

Nico Acosta — Mon, 13 Jul 2026 11:15:55 +0000

Looking for Replit alternatives? You're not alone. Replit is great for getting started—no setup needed, code right in your browser. But as your projects grow, you might hit some limits.

Maybe your database keeps crashing. Or the AI suggestions don't match your code style. Perhaps you need better team collaboration, or you're looking for a free Replit alternative that's more powerful.

We've tested the platforms developers actually switch to when they outgrow Replit. Here are the best options, whether you're coding in Python, JavaScript, or building web apps.

Quick Comparison: Best Replit Alternatives 2026

Here's a quick look at the top Replit alternatives we'll cover:

Tool	Best For	Key Feature
BrainGrid AI	Planning before coding	Helps clarify what to build
GitHub Codespaces	Teams and companies	Works with GitHub, consistent setups
Cursor	AI coding help	Understands your whole project
Bolt.new	Quick prototypes	Builds apps from text prompts
v0 by Vercel	Professional UI components	Generates beautiful React components
Emergent.sh	Full-stack AI apps	Multi-agent system builds complete apps
Lovable	AI app builder	Builds full apps from descriptions

Looking specifically at Lovable? Check out our Lovable alternatives guide for more options in that space.

Now let's dive into each one to help you find the best Replit alternative for your needs.

1. BrainGrid AI: Plan Before You Code

What it is: BrainGrid helps you figure out what to build before you start coding. It's not a code editor—think of it as a planning tool that works with any coding platform.

The problem it solves: Ever ask an AI to "add user login" and get something that's missing password reset, or doesn't work with your existing code? That's because the requirements weren't clear from the start.

How it works:

You describe what you want to build
BrainGrid asks smart questions: What happens if something goes wrong? How does this connect to your existing features?
You get a clear plan with specific tasks
Then you code in Replit, Cursor, or any tool you prefer

Key features:

Asks questions you might not think of
Breaks big features into smaller, clear tasks
Works with any coding tool (Replit, Cursor, etc.)
Helps avoid "that's not what I meant" moments

Good for:

When requirements keep changing
Complex projects where details matter
Teams that need to plan before coding
Anyone tired of rebuilding features

Bottom line: If you keep rebuilding features because the requirements weren't clear, BrainGrid helps you get it right the first time.

2. GitHub Codespaces: Best for Teams

What it is: GitHub Codespaces lets you code in the cloud using VS Code. It connects directly to your GitHub projects, so your whole team works in the same environment.

The problem it solves: Ever heard "it works on my computer"? When everyone has different setups, things break. Codespaces fixes this by giving everyone the exact same environment.

How it works:

You create a config file that describes your setup (Node version, Python packages, etc.)
Save it to your GitHub project
Anyone who opens the project gets the same environment automatically
No more "install this, install that" meetings

Key features:

Everyone gets the same setup (no more "works on my machine")
Works in your browser or on your computer
Built-in AI coding help (GitHub Copilot)
Secure and trusted by big companies
Works great with GitHub projects

Good for:

Teams that need everyone on the same page
Companies that need security and compliance
Projects already on GitHub
When onboarding new team members takes too long

How to switch from Replit: Export your project to GitHub, add a simple config file, and you're done. Takes about an hour.

Bottom line: If your team struggles with different setups or you need enterprise security, Codespaces is worth considering.

3. Cursor: Best AI Coding Assistant

What it is: Cursor is like VS Code but with super-smart AI that understands your whole project, not just the file you're working on.

The problem it solves: Replit's AI (Ghostwriter) only sees the file you're editing. It doesn't know about your other files, your code style, or how your project is organized. Cursor sees everything, so its suggestions actually fit your code.

How it works:

Install Cursor on your computer (it's not browser-based like Replit)
Open your project
The AI learns your code style and project structure
When you ask for changes, it suggests code that matches your existing patterns

Key features:

AI understands your whole project, not just one file
Select code, press Cmd+K, describe what you want—it edits it
Works with multiple AI models (Claude, GPT-4)
Works offline once installed
Built-in Git support

Good for:

When AI suggestions keep missing your code style
Developers who want smarter AI help
Projects where code quality matters
Anyone comfortable coding on their computer

How to switch from Replit: Download your project from Replit, open it in Cursor. The AI learns your code in minutes.

Bottom line: If you want AI that actually understands your project and suggests code that fits, Cursor is the best option.

4. Bolt.new: Build Apps Instantly from Text

What it is: Bolt.new turns your text descriptions into working apps instantly. Type what you want, get a live app with a shareable link—no setup, no deployment steps.

The problem it solves: Need a working demo by end of day? Replit still requires setup and configuration. Bolt.new skips all that and just builds your app.

How it works:

Type what you want: "Build a task manager with a calendar view"
Bolt.new creates the full app (frontend, backend, database)
Deploys it automatically
Gives you a link to share

It handles React, Node.js, databases, and even mobile apps (React Native) automatically.

Key features:

Builds full apps from text descriptions
Deploys instantly with shareable links
Works for web and mobile apps
No configuration needed
Great for showing ideas to clients

Good for:

Quick prototypes and demos
Non-technical founders testing ideas
Agencies showing concepts to clients
When speed matters more than perfect code

Bottom line: If you need a working app fast to show an idea, Bolt.new is the fastest option. Perfect for prototypes, not for production apps.

5. v0 by Vercel: Professional UI Components

What it is: v0 by Vercel generates beautiful React components that look professionally designed. It's not a full coding environment—it's a tool for building UI components quickly.

The problem it solves: Replit's AI can generate code, but the UI often looks basic or unpolished. v0 creates components with proper spacing, typography, and visual hierarchy—the kind of UI that looks hand-crafted.

How it works:

Describe what you want: "A pricing table with three tiers"
v0 generates React component code with shadcn/ui
Copy the code into your project
Customize as needed

It's built by Vercel and works perfectly with Next.js and shadcn/ui.

Key features:

Generates professional-looking components
Works with React and Next.js
Uses shadcn/ui (popular component library)
Components have proper design (spacing, typography, etc.)

Good for:

Building Next.js apps
When you care about how your app looks
Developers who know the difference between "functional" and "professionally designed"
Accelerating UI work

Bottom line: If you're building Next.js apps and want professional UI components fast, v0 is perfect. It generates code that actually looks good.

6. Emergent.sh: Full-Stack AI App Builder

What it is: Emergent.sh is a full-stack AI coding platform that builds complete applications. You describe what you want using natural language, and Emergent's multi-agent system builds it for you.

The problem it solves: Replit's AI (Ghostwriter) helps you code, but you still do most of the work. Emergent uses multiple specialized AI agents that work together to build entire apps—code, tests, deployment, and optimization.

How it works:

Describe your app idea in plain English
Emergent's agents work together:
- Builder Agent creates the code
- Quality Agent runs tests
- Deploy Agent handles deployment
- Optimizer Agent improves SEO and accessibility
You get a complete, working application

It runs in your browser on Google Cloud. You can build with React, Next.js, Expo, or Python, and you have full access to the code.

Key features:

Multi-agent system (different AI agents for different tasks)
Builds complete full-stack apps
Runs in browser (no local setup)
Works with React, Next.js, Expo, Python
Full code access and export
Often more reliable than single-agent systems

Good for:

Developers who want full-stack control with AI help
Non-technical founders building production apps
Teams that need reliable AI workflows
Complex projects that need multiple AI agents

Bottom line: If you want AI that builds complete apps (not just helps you code), Emergent's multi-agent system is designed to be more reliable than single-agent tools. Great for full-stack projects.

7. Lovable: AI App Builder from Descriptions

What it is: Lovable is an AI web app builder that creates full-stack applications from text descriptions. Describe your app idea in plain English, and Lovable builds it for you—no coding required.

The problem it solves: Replit still requires you to code, even with AI help. Lovable lets non-technical founders describe what they want and get a working app. It's popular with people who want to validate ideas fast without hiring developers.

How it works:

Describe your app idea
Lovable generates the full-stack application
You get a working app you can customize
Deploy when ready

The appeal is speed—ideas become clickable demos in minutes instead of weeks.

Key features:

Builds full-stack apps from descriptions
No coding knowledge required
Fast prototyping and validation
Great for non-technical founders
Customizable generated code

Good for:

Non-technical founders validating ideas
Quick prototypes and demos
When you need an app fast
People who want to build without coding

Bottom line: If you're not technical but want to build apps fast, Lovable turns ideas into working software quickly. Perfect for validating ideas, less ideal for complex production apps.

How to Choose the Best Replit Alternative for You

Don't pick based on features. Pick based on what's actually slowing you down.

What Problem Are You Trying to Solve?

"My database keeps crashing"

→ Try GitHub Codespaces for more reliable infrastructure

"The AI doesn't understand my code"

→ Use Cursor for smarter AI, or BrainGrid to plan better so any AI works

"Onboarding new team members takes forever"

→ GitHub Codespaces sets up environments automatically

"I need a working demo by end of day"

→ Bolt.new builds apps instantly, or use BrainGrid first if you need to clarify requirements

"Requirements keep changing while I code"

→ BrainGrid helps you plan first, then code in any tool

"I want professional UI components fast"

→ v0 by Vercel generates beautiful React components

"I need full control"

→ Cursor on your computer gives you complete control

Using Multiple Tools Together

You don't have to pick just one. Many successful teams use different tools for different jobs:

Planning: Use BrainGrid to figure out what to build

Coding: Use Cursor, Codespaces, or your preferred tool

Quick prototypes: Use Bolt.new when you need something fast

Deployment: Use whatever deployment method works for you

Example workflow:

Plan in BrainGrid → Get clear requirements and tasks
Code in Cursor → AI understands your project
Build UI with v0 → Professional components fast
Deploy normally

The best teams mix tools to solve different problems. You don't need one tool that does everything.

Quick Comparison Table

Feature	Replit	BrainGrid	Codespaces	Cursor	Bolt.new	v0	Emergent.sh	Lovable
Setup	Instant	Web-based	Seconds	Local	Instant	Web	Browser	Browser
AI Help	Basic	Planning	Copilot	Advanced	Full-stack	UI gen	Multi-agent	Full-stack
Team Features	Basic	Planning	Enterprise	Limited	Sharing	Limited	Good	Basic
Deployment	Built-in	N/A	Manual	Manual	Instant	Manual	Built-in	Built-in
Best For	Learning	Planning	Teams	Local dev	Prototypes	UI components	Full-stack AI	App builder

Frequently Asked Questions About Replit Alternatives

What are the best free Replit alternatives in 2026?

v0 by Vercel is completely free for generating UI components. GitHub Codespaces offers free hours monthly for individual developers.

BrainGrid helps you plan your projects before you code.

Emergent.sh and Lovable also offer free options to get started.

What are the best Replit alternatives for Python?

GitHub Codespaces and Cursor both work great with Python. Replit is still actually one of the best options for Python beginners, but if you need more power or team features, Codespaces is the way to go.

Are there free alternatives to Replit?

v0 by Vercel is completely free. GitHub Codespaces offers free hours monthly. Emergent.sh and Lovable both offer free options.

These free options are great alternatives to Replit's paid plans.

Can I migrate my Replit projects?

Yes! Most alternatives work with GitHub:

Export your Replit project to GitHub (Replit has a built-in export feature)
Import to your new tool:
- GitHub Codespaces: Just open the repo, add a simple config file
- Cursor: Download and open locally
- Emergent.sh/Lovable: Import from GitHub or start fresh

Most projects migrate in under an hour.

Which Replit alternative has the best AI?

Cursor's AI understands your whole project, not just one file. This makes its suggestions much more useful.

BrainGrid helps you plan better, so any AI (including Replit's) builds the right thing.

GitHub Copilot (in Codespaces) is also very good, though not quite as smart as Cursor.

Do any Replit alternatives work offline?

Cursor works completely offline once you install it (it runs on your computer).

Most cloud-based tools (Codespaces, Emergent.sh, Lovable, v0) need internet.

What's the best Replit alternative for teams?

GitHub Codespaces is best for bigger teams—it has security features, consistent environments, and works great with GitHub.

Emergent.sh is best if you need AI-powered full-stack app building with multi-agent systems.

Lovable is great for non-technical founders who want to build apps from descriptions.

Which tool is best for learning to code?

Replit is still excellent for absolute beginners—it's the easiest to get started.

Once you're past the basics:

v0 by Vercel helps you learn professional UI design
Emergent.sh teaches full-stack development with AI
GitHub Codespaces teaches professional workflows

Can I use these for production apps?

Yes, but choose carefully:

GitHub Codespaces: Great for production (used by big companies)
Cursor: Perfect for production (full control on your computer)

Better for prototypes:

Bolt.new: Fast but not built for production
Replit: Can work but has limits at scale

Planning only:

BrainGrid: Helps you plan, doesn't host apps

Final Thoughts: Finding Your Replit Alternative

Replit is great for getting started, but everyone hits limits eventually. Maybe your database keeps crashing, or the AI isn't helpful, or you need better team features.

The key: Don't switch because "Replit is bad." Switch because something specific is blocking you.

Quick decision guide:

Requirements keep changing? → BrainGrid helps you plan first
Need enterprise features? → GitHub Codespaces
Want smarter AI? → Cursor
Need instant prototypes? → Bolt.new or Lovable
Want professional UI components? → v0 by Vercel
Need full-stack AI apps? → Emergent.sh
Building apps without coding? → Lovable

The smart approach: Try one tool on one project first. See if it actually solves your problem. Then expand gradually.

Most successful teams use different tools for different jobs. BrainGrid for planning. Your preferred coding environment for building. Specialized tools when needed.

Bottom line: There are great Replit alternatives out there. The best one depends on what's actually slowing you down. Find your blocker, pick the tool that fixes it, and test it out.

The tools are ready. What problem are you trying to solve?

Originally published on the BrainGrid blog.