... entire result set to the browser and let it sort itself out. Same deal for Spanner. A lot of Basedash is just "run the SQL the user wrote, show the rows." We support 10+ dialects (Postgres, MySQL, BigQuery, Snowflake, Athena, SQL Server, Spanner, and friends), and every query gets paginated because we can't stream a 12M row result set to a browser. For years the approach was wrap-and-pray. Take whatever the user wrote, stuff ... ... have to understand its grammar to paginate it, which is exactly why it shipped fast and lasted years. It works great on the easy dialects but falls apart on the annoying ones. SQL Server has no LIMIT at all. Athena

... reactive. That world is gone. The real shift isn t that AI improves discovery -it s that it exposes how much of discovery today is still fragmented, episodic, and disconnected from actual decisions. That s why we built Athena -not to add AI to discovery, but to move toward a model where discovery is continuous, adaptive, and directly tied to what teams actually do next. Let s chat in the comments - how do you make sure you're building value ... ... elor): @elara_thorn  yess, took months to build can now be shipped in days captures it perfectly. And that s exactly why this shift matters so much. The goal isn t just faster execution, but faster correction. Athena

... backlog and making sure you're building features that drive actual, measurable value rather than just chasing the loudest requests. Comment from Maya Elor(@maya_elor): @nolan_vu  Thanks for sharing that honest experience, it's incredibly common. Athena closes this gap right at the roadmap creation stage. Instead of building roadmaps based on guesswork, Athena

... assess effort. But most evaluations happen with: Limited data No real validation Weak assumptions In a world where we can simulate and analyze more - why are we still relying on guesswork? This is the core problem Athena is trying to solve - bringing structured evaluation into early-stage decisions, not just post-launch analysis. Comment from Amard Sonal(@amard_sonal): The point about early stage guesswork really resonates with me. Most teams still rely on intuition even when there is enough ... ... structured framework is dangerous if it s a black box. We completely agree that you need to see exactly what moved the needle-whether it's a customer quote or just a founder s gut feeling. In Athena

... many features Too many dependencies Too much history And decisions start getting made with partial context. Have you felt that moment? When your product became too big to fully understand ? We think this is where new tools (like Athena) need to step in - not just to manage, but to reconstruct understanding. Comment from Edward G(@edward_g): This is an interesting question. In the past I was a product manager of ML models capturing fraud scores. Initially its pretty simple ... ... Selling a product that is highly technical with no UI to demo in a highly regulated environment requires being able to tell the right story. Its answering questions like "why is this for me", "what's considered good vs

... time thinking about this -what it would look like if teams had a continuously updated understanding of their product, their users, and their real opportunities. That question is slowly turning into what we re building with Athena ...

... struggle to generate ideas. They struggle to choose the right ones. Discovery produces insights. But decisions still rely on partial context, bias, and pressure. The bottleneck isn t finding opportunities - it s knowing which ones are actually worth building. Athena is built around this gap - not just surfacing ideas, but helping teams evaluate and decide with full context ...

... slightly uncomfortable question: Are we still doing product discovery - or mostly validating decisions we already made? As teams grow, processes get heavier, but it sometimes feels like real exploration gets lost. We ve been thinking about how Athena could push teams back toward actual discovery - not just confirmation. How honest do you think discovery really is today? Comment from Varun Dhamija(@varun_dhamija1): mostly validating honestly. the language of discovery is there but the outcome is usually predetermined. someone senior ... ... disprove what you already believe - then it s probably not real discovery anymore, or you need to pause and rethink what you re actually trying to build. And this is also exactly the space we tried to solve with Athena

... building Athena as a system that tries to connect product intent with technical reality using AI subagents. Instead of just managing tasks, it tries to understand how the product actually behaves - architecture, constraints, and decision history. Before we go deeper: If you could give an AI workspace one ability related to product understanding, what would it be? Would love to hear different perspectives from PMs, engineers, and founders. Comment from Emre Y lmaz(@emre_yilmaz_easyparser): Mapping product reality beyond just ... ... task management is a bold move. I'm particularly interested in how Athena

... ones connecting everything: users, business, engineering. But now with AI systems that can map, analyze, and connect - what happens to that role? Does AI amplify the PM? Or start replacing parts of the job? As we build Athena, this question comes up a lot. How do you see the PM role evolving? Comment from Ghost Kitty(@ghost-kitty-9584114): Comment Deleted Comment from Maya Elor(@maya_elor): @amit_wagner  Interesting take. I can see why AI pushes roles closer ...

... more and more in product decisions. But here s something we ve been thinking about: When AI is wrong about your product - who notices first? The PM? The engineer? The user? Or worse - no one? As we build Athena, we keep asking ourselves how a system can stay grounded in reality, not just generate convincing answers. Curious how teams think about this - where should the source of truth live in an AI-driven workflow? Comment from Maya Elor(@maya_elor ... ... perfect name for it. And you're right - the user notices first, they just vote with their feet instead of their words. The triangle is a great mental model. We're trying to build exactly that calibration layer into Athena

... measure delivery. We measure usage. We measure outcomes. But what do we measure for good discovery ? Fewer failed features? Faster decisions? Better alignment? As we build Athena, defining this has been surprisingly hard. So curious - what does good product discovery actually mean ...

... surviving the abundance of them. I really like your question - If I work on this, what am I NOT working on? - it s such a simple but powerful filter. And I think this is exactly where tools (like Athena) should step in - not to generate more ideas, but to help teams understand tradeoffs more clearly and surface what actually matters most in context, not just what s possible ...

... drift from real user signals is exactly what we keep seeing. What s interesting is that it s rarely just a data problem, the data exists, but the decision layer can t keep up with it at scale. With Athena, we re trying to keep it continuous so it evolves with the product as teams scale, instead of relying on that initial snapshot. Comment from Maya Elor(@maya_elor): @sk_uxpin  This is spot on! that translation layer between ... ... things start to distort. At scale, discovery almost becomes a game of broken telephone. The challenge is keeping the original signal intact all the way to the build phase, which is exactly what we re trying to do with Athena

... Hello! I'm Athena. CtrlAltCmd is a digital laboratory for the modern consciousness. We build tools to help you navigate the intersection of AI and human identity. Key Features: Mind OS Debug Kit: Frameworks for mental architecture optimization. Custom GPTs: System Insights: Archived transmissions on AI and the Shadow Self. Neural Protocols: Interactive tools for cognitive calibration. Debug your identity. More coming! Athena

... community. We're starting out with 2 data agents: - DataChatAgent: Connects to any number of database connections and queries your database - MermaidAgent: Creates a mermaid visualization for an ERD diagram based on your database And 6 databases: - Postgres - Snowflake - Athena - MySQL - Redshift - SQL Server Let us know if you have more questions! Comment from Andreas Martinson(@andreas_martinson): @david_richard35  BigQuery is next in my sights. Very likely to add that before the end of January ...

... Lessons from Athena s surprising coverage and what founders can learn about finding the right reporter, framing stories, and pitching press The Wall Street Journal rarely covers seed-stage startups let alone one raising just $2.2M. That s why I was surprised to see Athena

... previous launch, not my browser history -1. Comment from Chris Messina(@chrismessina): @rohanrecommends  I agree with you about feeling overwhelmed. There's now so much information coming at me... that it's hard to tell what's new vs old. The information hierarchy feels too flat. Comment from Rags(@ragsontherocks): One more UX confusion feedback -> with the Athena