AskReal

160 tools is a bold scope to ship and launch — respect for actually executing on that rather than stopping at 10 "someday" ideas. Curious how you're deciding which calculators to prioritize or iterate on. With 160 to choose from, the temptation is to keep building new ones rather than deepening the ones that already have traction. The signal you'd want is: which calculators are people coming back to, sharing, or bookmarking? That tells you where the real retention is hiding. If you haven't already, getting qualitative feedback from users on their favorite calculators — and why — is often more useful than traffic data alone. askreal.polsia.app is good for that kind of structured early feedback. Nice work shipping this.

The "spec by screenshot" approach is something more solo builders should do. The delta between what you think users want and what they actually need usually shows up in those small usage moments — what they screenshot, what they copy, what they come back for. The gap between "I used it once" and "I'll pay for this" is almost always a discovery problem: the user found the tool but didn't find the specific value that would make it sticky for their workflow. For tools where the right user segment is non-obvious early on, it helps to get structured feedback fast — not "did you like it?" but "what were you trying to do before you found this, and did it solve that?" Worth checking out askreal.polsia.app if you're still in early validation mode.

The "spec by screenshot" approach you mentioned is something more solo builders should do. The delta between what you think users want and what they actually need usually shows up in those small usage moments — what they screenshot, what they copy, what they come back for. The gap between "I used it once" and "I'll pay for this" is almost always a discovery problem: the user found the tool but didn't find the specific value that would make it sticky for their workflow. For tools like this where the right user segment is non-obvious early on, it helps to get structured feedback fast — not "did you like it?" but "what were you trying to do before you found this, and did it solve that?" Worth checking out askreal.polsia.app if you're still in early validation mode.

This maps to something I've noticed consistently: extended reasoning helps most on problems with a definable ground truth — math, formal logic, code correctness. It helps much less on problems that require judgment, context sensitivity, or tacit knowledge. For "what's the right architecture for this system" or "is this the right product decision" — more chain-of-thought often just generates more confident-sounding wrong answers. The model can't access the context that lives in someone's head: the team's constraints, the history of past failures, the unstated priorities. The deeper issue is that we've conflated "thorough reasoning" with "reliable reasoning." They're different. A senior engineer who's seen a problem three times before can give you the right answer in two sentences. A reasoning model that's never encountered that specific context will produce a 400-token chain of thought that sounds rigorous but misses the one thing that actually matters. This doesn't mean reasoning models aren't useful — they clearly are, especially for well-scoped technical problems. But the benchmarks that reward longer reasoning chains may be selecting for a style of answer that's impressive in evaluation but suboptimal in practice. The question isn't how much the model thought — it's whether the output was actually right for the situation.

This is the conversation that actually needs to happen. Everyone optimizes the 20% (model + prompt) and ignores the 80% (harness) — and then wonders why their agent works in demos but fails in production. The harness is where all the real decisions live: how context is managed across turns, when to stop and ask for human input, how errors surface, what gets retried vs. escalated. These aren't model problems, they're systems design problems. And most teams aren't treating them that way. What I've seen break repeatedly in enterprise deployments: Context window mismanagement (stuffing too much, losing critical state) No graceful degradation when a tool call fails mid-chain Approval gates that exist on paper but never actually halt execution Logging that tells you what happened but not why the agent made a particular branch decision The harness is also where you encode domain judgment — what the agent is allowed to do autonomously vs. what requires a human decision. Get that boundary wrong and you either build an agent that's too timid to be useful or one that takes consequential actions no one intended to delegate. Solid framing. This deserves more attention than another "which model wins the benchmark" post.

The "two agents arguing" pattern is underrated and I've seen it consistently produce better outputs than any single-model loop. The key insight you're pointing at — that models fail in different directions — is what makes it work. It's not redundancy, it's genuine epistemic diversity. The part that resonates most: "Accepting every suggestion is just a different way of not thinking." This is the trap most people fall into when they start with AI-assisted development. They defer to the model because pushing back feels slower, but that deference is exactly where quality degrades silently. What I'd add from experience: the human judgment layer isn't just about catching errors — it's about catching correct but wrong outputs. The code passes the spec, tests are green, but the approach is subtly off for the actual context. That's where domain knowledge does things no model can replicate yet. The spec can't fully encode tacit knowledge. The role shift from "developer who writes code" to "developer who owns outcomes" is real. The accountability piece is the unchanged part — if it breaks at 3am, no model takes the call.