AI tools I wish existed (sharif.io)

It's September 2025 and, riding the wave of Claude Opus 4.1, GPT-5 and “nano banana” models, a product-minded wishlist sketches dozens of single‑purpose AI apps people actually want: a nano-banana camera app that makes iPhone shots look Leica-like; vision-driven agents that add dark/light theming or decompile and debug minified code; personalized coaching that ingests fine-grained workout logs; nightly reading digests tuned to browsing time; lightweight nutrition and sleep/fitness coaches that integrate Apple Watch, Oura and other sensors; semantic search for TikTok/Reels; a paint-by-number filmmaking storyboard-to-shoot pipeline; long‑running “Deep Research” agents that spawn hundreds of subagents; and a marketplace for hyper‑specialized agents. Many entries emphasize minimalist UIs, local/offline models for privacy, and tools that augment rather than replace creators. Technically this wishlist highlights what productization of LLMs will require: robust multimodal perception (vision-to-UI, video semantic indexing), persistent memory and long‑horizon reasoning, reliable code‑then‑debug loops, data plumbing to connect sensors and services, low-latency local models, iterative human-in-the-loop feedback, and infrastructure for composing and verifying specialist agents. The ideas point to an ecosystem shift from one‑size‑fits‑all assistants toward curated, auditable micro-agents and component libraries that can be combined safely — with implications for tooling, evaluation, privacy, and marketplaces that sell trusted, niche AI behavior.