How to Get the Best Results When Working With a Software Agency

·1 views

A software agency's output depends heavily on how the client manages the relationship, not just the agency's technical ability. Clients should provide a clear problem statement, defined user context, and desired outcomes rather than a rigid feature list, allowing the agency to apply its expertise fully. Appointing a single decision-maker on the client side helps avoid slow feedback loops caused by conflicting input from multiple stakeholders. Teams should establish a regular cadence — such as weekly check-ins with working software — and maintain a shared backlog to catch issues early. Red flags in an agency include giving estimates without ranges, avoiding questions about users or business goals, and going quiet for extended periods.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

React useLocalStorage Hook Fixes SSR, Tab Sync, and State Persistence Bugs

A common React pattern of pairing useState with localStorage contains at least four bugs: it crashes under server-side rendering, throws on corrupted data, ignores updates across browser tabs, and lets multiple components using the same key fall out of sync. The useLocalStorage hook from @reactuses/core addresses all these issues while maintaining an API identical to useState. It automatically selects the appropriate serializer based on the default value's type, meaning numbers and objects are restored correctly without manual parsing. The hook also supports functional updates and safely returns null when a persisted key has been removed. Designed to be TypeScript-first, it aims to replace hand-rolled localStorage wiring that developers typically write and debug repeatedly across projects.

0 comments Read more at DEV Community

ProgrammingHacker News ·

Developer Reflects on Growing Fatigue With AI Language Model Hype

A developer named Alec Scollon has published a personal blog post describing what he calls 'LLM burnout,' a sense of exhaustion surrounding large language model technology. The piece, shared on Hacker News, reflects a growing sentiment among some in the tech community who feel overwhelmed by the constant stream of AI-related content and tools. Scollon suggests that the relentless pace of LLM development and promotion has made it difficult to maintain genuine enthusiasm for the technology. The post has sparked early attention online, pointing to a broader conversation about AI fatigue in developer circles.

0 comments Read more at Hacker News

ProgrammingDEV Community ·

Mitii AI Coding Agent Hits 78% Success Rate on 500+ Tasks Using Local Qwen3-Coder 30B

Mitii, an open-source AI coding assistant built for fully local execution, was manually benchmarked across 515 tasks using the Qwen3-Coder 30B model running via Ollama, with no data sent to external APIs. The agent passed 400 of 515 tasks, achieving an overall 78% success rate across three operational modes: Agent, Plan, and Ask. Ask Mode performed strongest on hard tasks at 87%, largely due to its built-in impact analysis that requires user confirmation before modifying code. Security-related tasks saw an 87% pass rate, while the weakest area was medium-difficulty semantic retrieval, which scored 63%. The benchmark highlights that locally run large language models can be viable for production-grade coding agents, though improvements in context routing remain a priority.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Three cache bugs broke a dashboard's badges — and how each was fixed

A development team building a cross-site updates dashboard implemented a cache-first design, but quickly encountered three separate bugs that all produced the same user-facing symptom: badges not persisting as expected. The first issue was that cache restoration only triggered when the dashboard was opened, meaning a site-list badge rendered at boot saw an empty cache and hid all badges; the fix was moving restoration to DOMContentLoaded so it runs once at app startup. The second problem was a 7-day cache TTL based on an assumed usage pattern that did not match reality, where users checked the dashboard infrequently and relied on badges between visits; the team extended the TTL to 30 days and added a tooltip showing how old the cached data was. A third bug caused maintenance on a single site to invalidate the entire cache, wiping badges for all other sites. Each fix highlighted a broader principle: cache restoration logic should be tied to app boot rather than the first consumer that happens to use it, and longer TTLs should be paired with visible data-age indicators rather than shorter expiry windows.

0 comments Read more at DEV Community