Dev builds undo feature for puzzle game, forced to refactor entire state system

·9 views

A developer working on a grid-based puzzle game called Traceroute hit a significant architectural problem mid-week while implementing an undo feature. Because all previous game rules edited the path state directly in memory, there was no way to retrieve an earlier version of the path — a requirement undo depends on. Fixing the issue meant rewriting five already-completed rules to produce new copies of game state rather than mutating existing ones. The refactor also exposed a latent bug involving stray empty data entries left behind after certain clicks. Beyond the undo work, the developer fixed an animation glitch on rejected moves and ended the week beginning work on level-completion logic.

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)

Developer Checklist: How to Safely Evaluate an AI Agent Before Granting File Access

Developers are urged to rigorously test AI agents before connecting them to real files, team tools, or production data. The recommended approach involves using synthetic data, defining a bounded task with a clear acceptance checklist, and granting only the minimum permissions the test requires. Controlled failure scenarios — such as removing required columns or making output folders read-only — help reveal whether an agent asks for guidance, halts, or silently invents answers. Evaluators should inspect the actual output artifact for errors, unexpected changes, and reproducibility rather than relying on convincing chat responses alone. Workflow automation should only follow after manual testing is consistently predictable, with clear controls over who can trigger actions and where logs are stored.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Over 40% of Agentic AI Projects May Fail by 2027 Due to Weak Risk Controls

Gartner projects that more than 40% of agentic AI projects will be cancelled by 2027, with inadequate risk controls cited as the primary reason rather than cost or unclear ROI. The problem is especially acute for fintech and healthtech companies, where production failures can trigger compliance reviews and regulatory scrutiny. A key underreported issue is 'evidence override' — a generation-side failure where the AI model ignores correctly retrieved information and produces a confident-sounding but incorrect response. Unlike standard API failures that produce visible errors, agentic loops fail silently by continuing to retrieve and hallucinate, compounding errors across multi-step reasoning chains. Experts note that most teams misdiagnose the problem as faulty retrieval, when the real fix requires output validation to check how the model used the evidence it was given.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why 'Do-Everything' AI Agents Fail in Production and How to Fix Them

Monolithic AI agents that handle all tasks through a single system prompt may appear efficient in demos, but they introduce serious reliability problems at scale. As system prompts grow to cover multiple functions, new instructions can silently disrupt existing behaviors, making regressions difficult to trace due to a lack of isolation between concerns. Running every request — even simple ones like order status checks — through a large, complex prompt also wastes compute resources on unnecessary reasoning. Because routing, execution, and formatting are collapsed into one inference call, behavioral drift in any area has no containment boundary and can reach users undetected. Experts recommend separating concerns: using a lightweight intent classifier, dedicated single-purpose functions, and deterministic code for routing and state management, making each component independently testable.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

When to use a headless browser for scraping — and when a simple fetch will do

A developer explains the practical decision-making process behind choosing between a headless browser and a simple HTTP fetch when scraping web data. The key test involves comparing the raw HTML response from a server against the live DOM rendered in a browser — if data appears only after JavaScript runs, a browser becomes necessary. While scraping a UK book gift-card site called National Book Tokens, the developer found the shop location data was absent from the raw HTML but present in a hidden DOM element populated by client-side JavaScript. Rather than launching a full Chromium instance inside a Firebase Function, the developer connected to a remote headless browser via WebSocket using puppeteer-core, keeping the browser's role minimal. The broader lesson is that headless browsers should handle only JavaScript execution and DOM settlement, with all subsequent data extraction done through faster, more reliable string operations.

0 comments Read more at DEV Community