Revolte

AI for Software Engineering

Software Engineering

Developer Tools

Artificial Intelligence

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How is security checking implemented? Do you have internal rules or checklists?

Congrats on the launch. The framing that resonates most is treating the full SDLC as the product rather than just code generation. That's a meaningfully different bet from the IDE-centric tools, and a harder one to build well.

@rajagopalanar What stands out about Revolte isn't just the AI assistance, but it's the philosophy. Tools should amplify human judgment, not override it.

As someone who writes about practical tech at Your Tech Compass, I see too many "AI fixes everything" promises that skip the nuance. Revolte feels different: the iterative suggestion flow (try-tweak-approve) mirrors how thoughtful devs actually work.

One thing I'm curious about as I test: can teams customize Revolte's "confidence threshold" for auto-suggestions? For example, "only suggest changes with >90% confidence" vs. "show me everything and let me filter." Asking because for risk-averse teams (and readers who value transparency), that control knob could be the difference between "cool demo" and "daily driver."

Congrats on launching something that feels both powerful and humane.

• Diana - Your Tech Compass

Congrats on the launch team Revolte! What I appreciate here is that the trust layer isn't an enterprise add-on, it's the foundation. Audit trails, approval gates, and rollback paths shipped by default says a lot about who this was built for.

does it mean this will work starting from the idea with a small title - "like create AI note pad" to autonomous implementation?

How does this hold up on a real production codebase? Most dev tools I've tried demo well and then struggle the moment you point them at an older repo with legacy layers. curious what your experience has been with messier code bases.

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?

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?

How do you decide what counts as an important decision that requires engineer approval in comparison to something the agent can auto apply?

One of the earliest and most consequential decisions we made was this: Revolte would not be a coding tool with delivery features bolted on. We made the SDLC itself the product.

It sounds obvious in hindsight, but the pressure early on was to show something immediately impressive — an agent that generates a working PR from a prompt, a demo that wows in a ten-minute call. That stuff is genuinely useful. But we kept running into the same wall: generating code is not the bottleneck anymore. The bottleneck is everything that has to be true for that code to safely reach production inside a real engineering organisation.

What context did the agent have when it made that decision? Who approved it? What's the audit trail? What happens if it needs to be rolled back? How does an engineering leader defend this to their CISO, their CFO, or their board?

That's where most of our actual engineering effort has gone — not into making agents generate better code, but into making agents operable inside real teams. Audit trails, approval gates, policy-aware actions, delivery visibility, rollback paths. These are not enterprise features we added later to close deals. They are the reason engineering teams can say yes to agents at all.

The line we keep coming back to internally: AI can carry the delivery load, but engineering judgment has to stay visible and accountable. That's not a constraint on what agents can do — it's what makes them trustworthy enough to actually use.

Curious whether others building in this space have hit the same wall — the gap between "the agent works in a demo" and "the organisation can actually run it."

I worked on the deploy and runtime side of @Revolte .

The funny thing about "deploy a service" is that it sounds simple until you see how different every team’s setup is.

Different pipelines. Different secrets. Different rollback rules. Different environments. Different observability habits. Every org has its own delivery snowflake.

A lot of agent demos avoid this by staying in a sandbox. We didn’t want Revolte to be useful only in a clean demo environment.

So the challenge was to make the agent work with the way teams already ship, existing repos, existing pipelines, existing infra patterns, while still giving them a cleaner execution layer on top.

The CLI was a big part of that.

We didn’t want engineers to feel like they had to live inside another SaaS dashboard. The CLI is meant to make Revolte feel close to the actual workflow: ticket, code, checks, PR, deploy support, without forcing engineers out of their flow.

That part took longer than expected, but I think it matters a lot for adoption 🙌

"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

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.

This feels like Cursor meets Jarvis 👀 How accurate is the task execution in real world coding workflows?

Excited to share that we’re launching Revolte today.

Revolte around a simple belief: software teams should spend more time building great products and less time dealing with delivery complexity.

Today, engineering teams jump across multiple tools for planning, coding, testing, deployment, and production monitoring. A lot of valuable time gets lost in handoffs, repetitive workflows, and operational overhead.

That’s why created Revolte.

Revolte is AI for software engineering, helping teams move faster from intent to code, testing, deployment, and production, while keeping engineers in control throughout the process.

With Revolte, teams can:

⚡ Build faster
🧪 Automate testing and release workflows
🚀 Ship with less operational overhead
🔍 Monitor production with greater visibility and confidence

Building this for developers, engineering leaders, and teams that want to ship faster without adding more complexity to their workflow.

Would love to hear your thoughts, if AI could take over one painful part of your software delivery workflow, what would you want it to handle?

Thanks so much for checking out Revolte 🙌

Greetings Product Hunt 👋 this is Watson from @Revolte

One thing we kept hearing from engineering teams was this:

AI helps teams write more code. But WHY shipping software to production still feels painfully operational — and WHY no serious engineering team fully trusts AI near production yet.

The hardest balance in AI software delivery today :

• Too much approval, and the product becomes another workflow layer engineers have to babysit.

• Too much autonomy, and no serious team will trust it near production.

• Automation should handle the repeated delivery work
  environment setup, test runs, build management, deployment support, runtime monitoring, and coordination.

• Human judgment should stay where it matters: code merges, production changes, infra-sensitive decisions, security-sensitive changes, and rollback paths.

• This balance is the product. We went through many versions before landing on the current model.

And honestly, that’s where a lot of AI ROI still gets stuck inside real engineering organizations.

We believe the future isn’t just AI generating code — or engineers manually coordinating every step around software delivery forever.

It’s intelligent execution systems continuously carrying delivery work forward while engineers stay focused on architecture, reliability, product thinking, and technical judgment.

That’s the balance we’ve thought deeply about while building Revolte — and where the compounding value really starts.

Would genuinely love feedback from the PH community ❤️

Congratulations to Raj and the Revolte team on the launch 🚀

I hunted Revolte because it’s one of the few AI engineering platforms I’ve seen that looks beyond code generation and focuses on the real bottleneck: getting software safely from intent to production.

A lot of AI dev tools make engineers faster inside the IDE. That matters, but it doesn’t solve the full delivery problem. The hard part is everything around the code, planning the change, understanding the existing codebase, running the right checks, creating the PR, supporting deployment, watching what happens at runtime, and knowing what to do when something breaks.

That’s where Revolte feels different to me.

Their bet is not that AI should blindly replace engineering judgment. It’s that agents can take on more of the SDLC heavy lifting if the trust model is designed properly, with the right approval gates, visibility into the diff and reasoning, quality and security checks, and rollback paths where they matter.

That’s the version of AI for software engineering I can actually see moving into real production codebases.
Two things I’d encourage people here to look at closely: the per-service pricing model, which is very different from the usual per-seat AI tooling model, and the CLI/workflow experience, because engineering teams don’t want another SaaS dashboard unless it genuinely removes work.

Excited to see how the Product Hunt community responds to this.

Raj and team have clearly thought deeply about where AI belongs in the software delivery lifecycle. Looking forward to the discussion.