Developer revisits 2017 Firefall perk calculator as a record of past coding skills

·1 views

A developer has rediscovered a perk calculator web app built with a friend around 2017–2018 for the online game Firefall. The tool let players browse perks by category, assemble builds via drag-and-drop, and automatically filter options based on their chosen class. Reviewing the code now, the developer acknowledges it lacks modern organisation, clearer responsibilities, and better architecture. Despite these shortcomings, they chose to leave the project unchanged and make it publicly available on GitHub. The developer views it as a career snapshot, arguing that old projects reflect growth in problem-solving and technical decision-making, not just technology choices.

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)

Why AI agents mishandle empty tool results — and how to fix it

Developers building AI agents frequently encounter a subtle failure mode where tool calls return empty results without explaining why, leaving agents unable to respond correctly. A single empty result set can stem from at least three distinct causes — a bad query, a stale data index, or a backend timeout — each requiring a different corrective action. Engineers have proposed that tools should return structured failure states with labels, supporting evidence, and a suggested retry interval, rather than treating all empty responses as equivalent. However, defining meaningful thresholds — such as what counts as a 'stale' index — introduces subjective calibration decisions that may not suit every use case. The broader issue is framed as an authority boundary problem: the assumptions baked into a tool's failure logic are set by its author, who may not anticipate the contexts in which the tool will eventually be deployed.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Builds AI Paper Ranking Tool to Cut Weekly arXiv Review Time by 80%

A developer working on the Paper List project within the OpenNomos ecosystem built a personalized research paper ranking engine to address the inefficiency of manually browsing arXiv each week. The tool uses text embeddings and cosine similarity to score papers by relevance to a user's specific project description, rather than relying on keyword matching or citation counts. Starting as a 200-line Python script, the system learns from user feedback over time, improving its recommendations as papers are saved or dismissed. In practice, the tool reduced the developer's weekly paper review time from roughly five hours to 45 minutes, while surfacing more relevant results per session. The developer notes the improvement stems not from complex AI, but from applying a smarter filtering approach to an otherwise noisy discovery process.

0 comments Read more at DEV Community

ProgrammingHacker News ·

Count Binface

Article URL: https://countbinface.com Comments URL: https://news.ycombinator.com/item?id=48887753 Points: 42 # Comments: 10

0 comments Read more at Hacker News

ProgrammingDEV Community ·

ServeSense vs FileZilla Server: 2026 Feature and Security Comparison

A July 2026 comparison evaluates ServeSense and FileZilla Server across security, automation, and deployment criteria to help organizations select a file transfer solution. Both platforms support FTP, FTPS, and SFTP protocols, but ServeSense offers additional features including real-time monitoring dashboards, REST API, webhooks, and event triggers that FileZilla Server largely lacks. On the security front, ServeSense provides multi-level brute-force protection, DoS mitigation, and anti-timing attack controls, while FileZilla Server offers only basic protections. ServeSense is also notable for supporting non-administrator operation and a lightweight, zero-dependency installation, whereas FileZilla Server follows a more traditional setup suited to standard file transfer needs. FileZilla Server remains a widely adopted open-source option for straightforward use cases, while ServeSense targets DevOps, managed service providers, and enterprise workflows requiring greater automation and security.

0 comments Read more at DEV Community