... 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 ... ... anyone actually felt a meaningful change in the products themselves? It doesn't really seem that way. Comment from Tal Elor(@tal_elor): @andrew_king10  I agree. I would add that it is not only real-time vs

... 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 Maya Elor(@maya_elor): @amard_sonal  Exactly ... ... data is usually there, it s just scattered across different tools or trapped in people's heads. Relying on intuition when we can actually centralize and simulate those scenarios is a huge missed opportunity. That s exactly why we built 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 Tal Elor(@tal_elor): @olive_mwangi  Honestly, I really loved this point "especially that if people can t understand it instantly, they won t stick around ... ... 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

... 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 Maya Elor(@maya_elor): @emre_yilmaz_easyparser  Thanks, glad ... ... liked the topic! On your question - 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

... 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