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A developer has created a free, unofficial iOS app for finding Clash of Clans base layouts, not affiliated with Supercell. The app lets players filter bases by Town Hall level so that lower-level layouts are excluded for higher-level accounts. It separates layouts into modes for war, farming, and Clan War League, and allows a single tap to copy a layout directly into the game. According to the developer, usage data suggests players favor bases with fewer copies, since those are harder for opponents to prepare for.
The author argues that vague bug descriptions in Slack threads give coding agents too little to work with, leading to patches that miss the actual problem. Instead, they recommend a brief, replayable repro such as numbered steps, a failing URL, a minimal network snippet, a short recording, or the relevant console stack trace. The piece stresses keeping the effort proportionate, so the repro is cheaper than guessing. It also offers a reusable template that captures surface, environment, steps, expected and actual results, and artifacts, along with rules for keeping recordings useful. Slack is positioned for routing and triage, while the agent chat or pull request serves as the artifact layer.
Friday is a free, MIT-licensed desktop app that runs coding agents such as Claude Code, Codex and Cursor side by side on a user's computer. After each run, the coding tool reads the session locally and records lessons like decisions, pitfalls, and conventions for that repository. When a user corrects an agent, that correction is passed to every agent that later works in the same repository, regardless of tool. If a mistake recurs, Friday can run a user-approved read-only or test command after changes and returns failures to the agent, and lessons unused for 60 days are retired. All lessons are viewable and editable in Settings under Memory, stored locally per repository, and the author notes the feature is new with no data yet on its effectiveness.
A team behind Leadolovets, a service that scans open Telegram chats for buying intent, found that keyword search returned 542,795 matches but only 14,774 were real requests. Their LLM filter appeared to fail under parallel load, returning empty responses in 118 of 225 calls. The real cause was a billing check that returns an empty result instantly for accounts with expired subscriptions, so the model was never called. After fixing the test harness, the model answered all parallel and batched tests without failure. Measured on 100 requests from paying customers, the filter removed 95% of junk while losing about 18% of good requests, a figure the team later estimated closer to 5% after reviewing labeling errors.
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