GTCHO

Hey HN,

We just launched the V1 homepage for Actory AI, where we’re building autonomous software quality assurance agents for startups and scaleups that need to ship fast without sacrificing stability.

Our platform automatically analyzes builds, flags bugs, and provides intelligent test coverage recommendations—no need to write endless test cases or rely on fragile scripts. We’re focused on making QA invisible, intelligent, and integrated into your dev flow.

Check out our new site: https://actory.ai LinkedIn: https://www.linkedin.com/company/actory-ai

We’re in the early stages and would love your feedback: • What matters most to you when it comes to automated QA? • How do you currently handle QA in fast-release cycles? • What would make this valuable enough for you to adopt?

Thanks in advance for taking a look. Excited to share this journey with you.

— Daryl, CTO @ Actory AI

#ShowHN #QA #DevTools #AutonomousAgents #Startup #AI #SoftwareTesting

100% agree with the need to elevate QA rather than replace it. With Actory AI https://actory-website.vercel.app/, we found the sweet spot was letting the agent watch for regressions and PR risk patterns, while human testers focused on creative edge cases and UX paths. QA gets more strategic, less repetitive—morale actually went up once they weren’t stuck rerunning the same flows.

We are pre launch check us out!

With Actory AI https://actory-website.vercel.app/ we found the sweet spot was letting the agent watch for regressions and PR risk patterns, while human testers focused on creative edge cases and UX paths. QA gets more strategic, less repetitive—morale actually went up once they weren’t stuck rerunning the same flows.

We are pre launch any feedback is welcome.

I am actually baking this idea directly into Actory AI https://actory-website.vercel.app/. It keeps a full historical context across tickets, commits, and diffs—so when it flags an issue, it’s not just looking at static tests, but learned behavior patterns. It’s also queryable in natural language, so PMs or engineers can ask: “When did this flow last break?” and get back full traces.

We are pre launch any feedback would be loved.

I hear you and to be clear, this isn’t about replacing talented QA teams. It’s about offloading the repetitive and pattern-based parts of QA so human testers can focus on more strategic, exploratory, and usability-driven work.

In the case I saw, the agent handled things like regression patterns, diff analysis, and known-risk detection across thousands of past issues. The QA team actually became more valuable because they weren’t stuck rerunning the same test plan for the fifth time that week. It was augmentation, not replacement.

That said, I totally agree if a team is just rubber-stamping PRs, the issue isn’t automation, it’s expectations and leadership.

Fair point. The team I know using this runs a SaaS platform with around 25 engineers. Before the agent, their QA team was stuck doing triage on weekends after bugs hit prod. Now, the agent blocks PRs that resemble patterns from past bugs—things like changes to concurrency-heavy areas that previously caused memory leaks.

It hasn’t replaced QA, but it shifted their role. Now they spend more time analyzing what the agent flags instead of rerunning test plans. It’s not perfect but it’s made a big difference in stability and team morale.

Also in the process of building: Actory AI https://actoryv3.vercel.app/

Really good points. On quality it’s not replacing human insight, but it is exceptional at pattern recognition and coverage at scale. It catches edge cases that tend to get missed and never forgets past regressions. The best results I’ve seen come from pairing the agent with human QA. The agent does ambient monitoring and flags suspicious behavior. Humans then dig deeper.

Cost-wise, it’s surprisingly reasonable. The version I saw ran in containers that spun up based on commit activity or deploy frequency. So if no one is pushing code, it's idle. But during launches or busy dev cycles, it ramps up. Much cheaper than staffing a full team to maintain 24/7 vigilance.

Completely agree. Linking incidents back to code changes is one of the most valuable things a team can do but it's rarely done well. In this case, the agent actually learns from that full timeline production incidents, support tickets, commit diffs. It surfaces patterns you’d never catch manually, like an issue that only appears under high concurrency.

Also yes on chat querying. One of the most useful parts was letting PMs ask questions like “Has this bug happened since April?” and getting a full trace across releases. The idea of automating grooming using historical story similarity is spot on too. This could easily save teams hours per sprint.

After going through YC’s Winter 2024 batch with a product we bootstrapped from zero, I’ve seen firsthand how much one bet can change everything — not just for the business, but for the founder behind it. That journey inspired the next one.

Off the momentum of that experience, I’ve launched a syndicate — Scalable Ventures — to back ambitious, technical founders solving real-world problems. The focus: startups inside the ecosystems I now know well — YC, Microsoft, Google, and adjacent spaces where builders are quietly changing how things work.

This isn't about being a gatekeeper — it’s about opening the gates wider. Operator-led capital for founders who move fast, ship smart, and serve the world with what they’re building.

More soon. In the meantime, if you're building or investing, I'd love to connect. https://scalablevc.com

This is one of the most forward-thinking posts I’ve seen on HN in a while — and it captures something I’ve been seeing firsthand.

As a YC founder now building a syndicate (Scalable Ventures), we’re intentionally building around this exact shift: high-agency, post-accelerator, solo or lean-team founders using AI to skip the old playbook. The traditional "pre-seed → seed → Series A" model doesn't apply the same way anymore — and you're absolutely right, many of these founders will choose profitability and independence over chasing unicorn status.

Our thesis is: fund the founder, not the form factor. We’re starting to explore creative models like revenue-sharing, early founder grants, and performance-based follow-ons — especially for bootstrapped, cashflow-positive companies that don’t want to raise in the traditional sense.

https://www.scalablevc.com/HackerNews

Would love to connect and explore ideas.

Thanks for the transparency — this is the kind of post more early-stage investors need to read before jumping into angel investing.

I’m a YC founder who recently launched a syndicate called Scalable Ventures, and this post hits on a key reason we’re building it: to reimagine how early-stage investing gets done, especially for operator-led investors who want real upside — not just optionality or dilution surprises years later.

Your story illustrates a growing reality: early angels often take on venture-level risk but receive sub-venture returns. We’re structuring our deals to better align incentives for angels — including deeper diligence, co-investor discipline, and shared reporting (not just “hope you hear from the CEO someday” vibes).

If you’re ever open to advising newer syndicates or just sharing more about your experience, I’d love to connect. Either way, thank you for sharing this — it’s exactly the kind of insight the ecosystem needs.

https://www.scalablevc.com/HackerNews

Ask HN: YC Founder Building a Transparent Venture Syndicate – Would Love Feedback

I'm Daryl, a YC F24 founder (Getcho) now launching Scalable Ventures – a founder-first syndicate focused on transparency, lightweight capital, and helping early-stage startups go to market before they raise.

We’re experimenting with tools to help founders pitch, get feedback, and build traction before they raise.

Curious: what would you want from a syndicate if you were raising now?

→ https://www.scalablevc.com

The pleasure is all mine and if you'd like to work together: [email protected]