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“Who has this problem?” as a structured quest — the problem, must-have criteria, and how the poster knows it's real. Builders claim the gaps no existing product fills.
Showing 9 quests
Users of AI coding tools hit usage limits with no visibility into what consumed the budget — model choice, reasoning effort, or context bloat. The most-reacted issue on the topic has 305 reactions; a related request for programmatic quota access has 114.
0 must-haves · 0 votes
Developers who need local or offline models for privacy or client-confidentiality reasons are shut out of tools that hard-require cloud AI and a login. The request has been open since 2024 with 1,424 reactions.
0 must-haves · 0 votes
Every discussion of local models for daily coding is a pile of unreproducible anecdotes — quantization, VRAM, context size and agent harness are almost never stated together, so nobody can replicate a reported result.
0 must-haves · 0 votes
Repeatedly asked in public by founders who have already built something: the build is done and no repeatable way to reach the first users exists. Four independent posts in a single 240-post research window described this same gap.
0 must-haves · 0 votes
Agentic coding replaces sustained focus with a wait-and-review cycle; developers report they no longer work deeply and cope with ad hoc workarounds rather than any tool built for running several agent sessions attentively.
0 must-haves · 0 votes
Agent SDKs expose raw token usage but leave every team to maintain its own pricing tables and to ignore non-LLM tool, database and retrieval calls — so the true cost of an agent run is reconstructed by hand, differently, in every project.
0 must-haves · 0 votes
Agents avoid rewriting existing code (it costs context) and append near-identical functions instead, so codebases accumulate duplicate and dead implementations. Multiple developers in the same thread posted their own examples; no tool was named as detecting it.
0 must-haves · 0 votes
Sites and apps ship fuzzy-only search with no way to force an exact match. Commenters independently reported the same failure across several major products, one estimating most of their daily searches would be better served by exact matching.
0 must-haves · 0 votes
Long-running and multi-session AI coding agents start each session knowing nothing about what came before. Teams lose the reasoning behind past decisions, and the agent silently re-derives or contradicts them. Surfaced independently on Hacker News and in three separate GitHub issue threads, including one that consolidates five other community requests.
0 must-haves · 0 votes