Launching today
Revolte
AI for Software Engineering
156 followers
AI for Software Engineering
156 followers
Revolte is for engineering teams to turn intent into production-ready software faster, safer, and with more control. Its agents plan changes, generate code, run quality and security checks, create PRs, support deployment, monitor runtime behavior, and surface risks early. Engineers approve the important decisions. Revolte handles the delivery heavy lifting. Built for higher delivery throughput across SDLC, stronger governance, and more value shipped per engineer.
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Launch Team / Built With
Hey Product Hunt π
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Raj here, founder & CEO of Revolte.
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For years, Iβve built and worked with engineering teams where the same pattern kept showing up:
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Writing code was rarely the only bottleneck.
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The real drag was everything around the code: setting up environments, running tests, managing deployments, fixing broken builds, triaging incidents, checking quality, and keeping delivery moving across disconnected tools.
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Coding assistants have made developers faster inside the IDE.
But software delivery is much bigger than the IDE.
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Thatβs why we built Revolte.
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Revolte is AI for Software Engineering, an agentic platform that helps engineering teams move from intent to production with humans in control.
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Give Revolte a ticket or requirement, and its agents can help plan the implementation, work against your actual codebase, generate code, run checks, create the PR, support deployment, monitor runtime behavior, and surface what needs attention.
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But the important part is this:
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Revolte does not remove engineering judgment.
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Every meaningful change goes through human review. Engineers see the diff, the reasoning, the checks, and the rollback path before anything moves forward.
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We built it this way because production software cannot run on blind automation. It needs context, governance, and control. Our belief is simple:
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AI should not just help engineers type faster.
AI should help engineering teams ship better software faster.
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Revolte is built for teams that want more delivery throughput without adding more delivery chaos.
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Weβd love for you to try it, break it, test it on something real, and tell us where it falls short.Β
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https://revolte.ai/
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And if youβre an engineering leader thinking about how agents can safely enter your SDLC, Iβd be happy to talk through the governance side with you.
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Thanks for checking us out,
Raj.
@rajagopalanarΒ congrats on the launch Raj. How do you avoid a pile up at the judge gate (agent coded and now there's a stack of reviews)?
@zolani_matebeseΒ honestly, the pile-up at the judge gate is the thing we lose sleep over, so great that you went there straight away.
A few things we've built around exactly this:
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First, the workflows are configurable. You can gate how many run in parallel, so you're not suddenly drowning in 20 PRs at once. Teams pace the output to match their review capacity.
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Second, Revolte's output isn't "here's some code, good luck." Each workflow comes with full context: what was done, why, what the tests cover, what the security scan flagged. Review goes a lot faster when you're not reverse-engineering what the agent was thinking.
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Third, trust builds over time. Teams start spotting which workflow types consistently produce clean output and streamline approval for those, keeping the deeper review for the higher-risk stuff.
And honestly, the pile-up problem is often a sign that the team's review culture needs a rethink alongside the tooling, which is a conversation we actively have with teams onboarding to Revolte.
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Really appreciate you pushing on this one.Β
The approval gating for critical decisions is the right design. Most SDLC agents fail because they either go fully autonomous (risky) or require constant hand-holding. We've felt that tension building agents that touch production. Having it handle quality checks, PRs, and deployment monitoring while preserving human review for high-stakes calls is solid. How does it decide what triggers an approval gate? Is that configurable per repo or risk-scored?
@retain_devΒ Really appreciate the comment β youβve clearly felt this pain firsthand and that framing is spot on.
Hereβs a simple development workflow in Revolte to answer your question.
A feature ticket comes in β the planning agent breaks it down β coding agent implements it β tests are auto-generated β SAST/DAST runs β PR is raised. The dev reviews and approves at the end. No hand-holding in between.
Now the gate question: thatβs configurable. Teams define approval rules at the workflow level β a routine feature build on a low-risk service can flow straight through, anywhere a particular artifact to be finally used in the product as code or test script or design, the it will go through approval gate. No output will be used or merged to exisiting repository finally without human approval.
The risk-scoring side is something weβre making smarter over time β more context-aware, less purely rule-based.
But the principle stays the same: full autonomy where itβs safe, human judgement where it matters.
let me know if this answers the question
AI that works inside the engineering workflow is a different bet than AI that sits alongside it. The context problem in code is real. Getting it to reason about system trade-offs isn't just a file-level concern. We've been building in the customer success for developer tool companies space, and Revolte touches on something we think about a lot. What's your approach to handling context across large multi-repo codebases?
@shivam_jaiswal21Β Very aligned with that view. We donβt think the context problem gets solved by pushing more files into a bigger prompt window.
Our approach is to treat context as a system graph, not a repo dump. For larger multi-repo environments, Revolte maps the service boundaries, dependencies, APIs, ownership, deployment paths, PR history, tickets/specs, and runtime signals around the change being made. The agent then pulls the relevant slice of context for that workflow instead of trying to reason over the entire estate at once.
The important part is also separating βcode contextβ from βdelivery contextβ. A change is rarely just about the files touched. It has impact across tests, environments, release rules, observability, and sometimes adjacent services.
We are still early in how far this can go, but that is the bet: context has to be structured around how engineering teams actually ship, not just how code is stored.
Would love to compare notes given your lens in the dev tool space.
@shivam_jaiswal21Β
Really glad this resonates β and youβve put your finger on something we spent a lot of time on.
Hereβs how we approach it: for every codebase, Revolte maintains a separate entity called an App/Service. Each one builds its own context β the structure, dependencies, patterns, and history specific to that service. That context isnβt shared or muddled across repos; itβs scoped and owned.
When a workflow runs, the agents work within that context. When a dev picks up Revolte Code (our CLI) to review and approve the output, theyβre working from the same context β so thereβs no gap between what the agent understood and what the human sees.
But the part weβre equally serious about is context poisoning β because bloated, unstructured context is almost as bad as no context. Agents hallucinating based on irrelevant noise is a real failure mode. Our approach is a clear structural flow built into Revolte that governs how context is built, what gets included, and what doesnβt. Meaningless context without reference doesnβt make it in.
Itβs an area weβre continuously tightening β because getting the reasoning right at a system level, not just file level, is what actually makes autonomous workflows trustworthy at scale.
mailX by mailwarm
How do you decide what counts as an important decision that requires engineer approval in comparison to something the agent can auto apply?
@othman_katimΒ
Great question β and honestly one we debated a lot internally.
The agent never auto-applies anything. Every autonomous output β whether it's a code change, a test suite, a design decision, or a deployment config β is raised as a pull request to the relevant human stakeholder. Developer, tester, architect, whoever owns that stage of the workflow.
The PR isn't only a quality check. It's an ownership handoff. It makes the human the deliberate decision-maker. The workflow is designed so that people are defining intent upfront and reviewing outputs at the back β and the AI is doing the actual work in between.
We believe that's the right inversion. Not "AI assists the human" in the traditional copilot sense, but "human sets direction, AI executes, human signs off." The accountability stays with people while work is done by AI.
It also means the system is naturally auditable β every decision has a human who reviewed and approved it, with full context of what the AI produced and why.
We're still evolving how this works across different team roles, so genuinely curious whether that model resonates with how your team thinks about it. π
This feels like Cursor meets Jarvis π How accurate is the task execution in real world coding workflows?
@nithin_raju1Β Good question and an important distinction β task execution accuracy is really asking whether the agent actually completes the job end to end, or gets lost halfway through.
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The honest answer is that the pipeline structure is what makes execution reliable in practice. There's a planner step that runs before any code gets written β it maps out what the task actually involves and what done looks like. That upfront planning step is what prevents the agent from going off in the wrong direction and only discovering it three commits later.
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From there each step in the pipeline β codegen, testgen, regression β has a defined scope and exit condition. It's not one agent trying to freestyle the whole thing. And if something fails at any stage, it surfaces there rather than quietly producing something broken that reaches a PR.
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Where execution gets harder is with tasks that span multiple services or involve calls that need real domain judgment β those are the ones where we've deliberately kept humans in the loop rather than pushing for full automation. The goal isn't maximum autonomy, it's reliable execution on the work that can run cleanly, and a clear handoff when it can't.
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What we're seeing with customers is that the volume of tasks completing end to end without constant intervention has gone up significantly. That's the real signal on execution for us.
Congrats on the launch team! Quick question though: how is this actually different from Cursor or Claude Code? Trying to figure out where Revolte fits in my stack.
@keerthana07Β totally fair question, and honestly the one we get most! πͺ
Cursor and Claude Code are brilliant tools, but they're essentially a smarter pair of hands for the developer. You're still driving, still prompting, still context-switching between coding and everything else.
Revolte works at a different level. It's not just helping you code, it's handling the whole delivery cycle autonomously: planning, coding, test generation, security scanning, PR creation, all running as one connected workflow with no one babysitting each step.
The dev or tester comes in at the end, opens Revolte Code, reviews what the agents produced, makes any edits, and approves. That's the moment of human judgement, not the hours of groundwork before it.
So in your stack: Cursor and Claude Code assist the developer writing code. Revolte assists the entire delivery process, from ticket to merged PR, with specialised agent personas for development, automation testing, non-functional testing, bug fixing and more.
If Cursor is a smart co-pilot, Revolte is the autonomous crew. β€οΈβπ₯
"AI for software engineering" could be five different products, so I honestly can't tell what this is yet. A code editor? An agent that opens PRs? A Copilot-style layer with more context? What does one normal task look like start to finish, and what happens when the repo has no tests and no spec to work from? That's the case that breaks most of these for me.
But I'm also glad you exist guys, I'm just here to challenge you haha
Congrats for the launch
@fberrez1Β this is exactly the comment I was hoping someone would write, so thank you for grilling it. πͺ
Fair point that "AI for software engineering" could mean five things, so to be specific: Revolte is an autonomous agentic platform for the whole SDLC. You define a workflow β planning, coding, test generation, SAST, DAST, bug fixing, PR creation β and the agents run it end to end, no human babysitting every step.
One normal task: a feature gets requested β planning agent breaks it down β coding agent implements β tests generate automatically β security scans run β a PR is raised. You open Revolte, review, tweak, approve. Done.
And the no-tests, no-spec case is exactly where Revolte earns its keep, because that's what breaks most tools. No spec? The planning agent builds one. No tests? The test agent writes them in the same flow. You come in at the end to approve, not to hand-hold.
So please keep challenging us, this is the good stuff. And congrats right back for asking the real question. β€οΈβπ₯