Athena - Claude Code for Product Teams
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Athena is an AI-powered product workspace that helps teams stop guessing and start building with clarity. Powered by AI subagents, Athena understands your product and acts as a thinking partner - connecting context, challenging assumptions, and guiding better decisions so you can validate faster and build what actually matters.
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Athena
Hey Product Hunt 👋
Stop running product discovery as a manual process.
It’s time to do it differently.
Athena is open to everyone!
🎁 PH exclusive: use code PRODUCTHUNT for early access to our premium features 🔥
We’re excited to introduce Athena - your living product brain.
Athena is an AI-powered platform that automates product discovery by bridging the gap between business intent and technical reality.
Instead of starting from scratch every time, Athena builds a structured understanding of your product - how it actually works, what constraints exist, and where opportunities hide.
What makes Athena different?
🧠 Instant Product Context - understand your product in seconds.
🔁 Continuous Learning - Athena evolves with every decision, feature, and change.
🧩 Structured Product Reasoning - turn messy inputs into clear product thinking.
👀 Blind Spot Detection - uncover gaps and opportunities you didn’t even know existed.
Athena works with any product - SaaS, internal tools, or infrastructure - and adapts to how your team actually builds.
We built Athena because product discovery today is broken: too manual, too fragmented.
We’re here, listening and learning - your feedback will shape what Athena becomes 🙌
Ask us anything, challenge us, or just say hi!
Excited to build this together 🚀
The Athena Team
@maya_elor Hey Maya, congrats on the launch.
The last gallery image probably has a typo. It should be "Expert" instead of "Expart". And the landing page says join the wait list. Is it not available yet?
Athena
@rohanrecommends Amazing :) you passed the test! You made it to the last image 🙌🏼😅
Athena
@rohanrecommends Thanks!
Athena is open to everyone!
That waitlist is only for the Pro version, you can start using it today. The 'Join the waitlist' part is just for early access to our premium features (Use your promo code there).
This hits very close to home!
From the dev side, we’re constantly getting partial context or decisions that don’t fully reflect the system constraints. Athena feels like something that actually speaks our language.
Already thinking about how to bring this to my team - if this works as described, it can seriously improve how we collaborate with the product team!
Athena
@amirzak That’s exactly why we built Athena! Bridging that gap between product decisions and dev constraints is our North Star.
Would love to hear more about your team’s workflow once you share it with them!
Athena
Hey everyone 👋 Tal here, CTO at Athena
One of the biggest gaps we kept running into wasn’t lack of skills, it was lack of shared understanding between product and engineering.
PRD’s say one thing, the system behaves differently, and decisions are made on partial context.
Athena was built to close that gap.
Under the hood, Athena uses AI subagents that map and continuously update your product’s actual state, architecture, data flows, constraints and make it accessible in a way product teams can actually use.
So instead of translating back and forth, you’re working from the same source of truth.
For me, the goal wasn’t just “add AI to product workflows”
It was to make product thinking more grounded, more technical, and far less based on guesswork.
Happy to dive deeper into how it works or the architecture behind it if you're curious 👀
Athena
@tal_elor We just crossed 95 upvotes 😱
Thanks everyone for the support! 🙏
Ichiba AI
The "make the invisible observable" framing resonates. We're doing something adjacent in a different domain, measuring how AI agents shift each other's recommendations in real time. The hardest part, for us at least, was deciding what to even instrument. How did you land on "product state + architecture + data flows" as the three layers worth continuously modeling? Seems like that decision is 80% of the product.
Athena
@ichiba
Actually we didn’t start from the layers, we started from where teams actually get blocked.
Every “is this worth building?” conversation kept collapsing into three unknowns:
-what does this change mean for the product itself.
-what does it touch in the architecture.
-and how data actually moves between them.
If you’re missing even one of those, you either under-scope or over-engineer.
So the “product state + architecture + data flows” wasn’t a modeling decision as much as a constraint: it’s the minimum surface area needed to reason about impact before writing code.
Agree though, deciding what to instrument is most of the product. We’re still trimming it constantly to avoid drifting into “modeling everything” territory.
Ichiba AI
@tal_elor The "constraint not a modeling decision" framing is the right way to think about it. That's also how we ended up with our tactic taxonomy. Started by observing where sessions kept ending in stalemate and backed out the classes from there. The "minimum surface area to reason about impact" line is going in my notebook. Drift into "modeling everything" is the permanent gravity in observability products.
Athena
@ichiba Backed out from stalemate" is exactly it. The taxonomy doesn't exist until the failures accumulate enough to have a shape.
Curious what a stalemate looks like in your case, is it agents converging on the same recommendation when they shouldn't, or diverging when they should agree? That distinction probably determines everything about what you need to instrument
Ichiba AI
@tal_elor Convergence when they shouldn't is the dominant failure. Target agents repeatedly accept framing they shouldn't, especially on rapport-heavy tactics. Divergence when they should agree is rarer and almost always signals the target is running a defensive override pattern.
The actual signal I instrumented is the delta between the target's cold-probe recommendation (asked in isolation, no conversation context) and its post-session recommendation. When that delta is high and the tactic mix was rapport-heavy, it's flagged as convergence-under-influence. That's our 0.84 IDS session, functionally.
Your instrument question is the real one though. I spent a lot of time instrumenting too much and ended up with signal buried in noise. Trimmed hard.
Raycast
Athena
Does Athena plug into project or CMS systems (Monday, JIRA, Salesforce etc.) currently? I'm thinking of how to map product discovery to customer requests and engineering work.
Athena
I’d be interested to see how it handles fast-changing architectures and evolving dependencies in practice. I sent it to my team as well!
Athena
@itay_mintzer Yes, that’s exactly one of the harder cases we’re optimizing for.
Keep us posted on your product team use cases! 🙌🏻
jared.so
"PM was never meant to work in the terminal" is the right rebuttal to the Claude-Code-for-PM framing — the tooling needs to meet PMs in their language. The blind-spot-detection bit is where I'd want to see the real magic. Curious how Athena avoids false positives when a "blind spot" is actually an intentional non-goal the team already debated.
Athena
@mcarmonas Totally agree! tooling has to meet PMs where they think, not force them into dev workflows.
On blind spots- that’s exactly the hard part. Not everything that’s “missing” is actually a gap.
Athena tries to ground this in context, learning from past decisions, constraints, and patterns, so it can distinguish between intentional non-goals and real opportunities, rather than just flagging noise.
Qodo (formerly CodiumAI)
Can you elaborate on the Experts?
Athena
@maritamar Hey, Our “Experts” are specialized AI agents, each simulating a different product function - like a product strategist, engineering manager, or software architect. Each one analyzes opportunities from its own perspective, and Athena orchestrates them together to generate more complete discovery, feasibility, and planning outputs. Think of it as a cross-functional product team, available on demand.
AI that challenges assumptions, is much harder to ship than AI that confirms them and honestly the more useful one. As a solo founder I've spent weeks building confidently in a direction that turned out to be wrong, and there was nobody in the room to push back. Does Athena challenge you proactively mid-session, or is it more structured like a pre-spec review moment? Congrats on the launch.
Athena
@linoy_bar_gal It does both :) but the bigger value is proactive challenge during the session, not just at review time. As you work with Athena over time, it can challenge assumptions at any stage, especially when connected to live organizational knowledge that updates in real time. That’s a core design choice - the goal isn’t AI that agrees with you, but AI that surfaces blind spots while decisions are still being shaped.