# solve Published articles for solve. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Engineers Should Take Responsibility for Bugs in AI-Generated Code DevFeed: [Engineers Should Take Responsibility for Bugs in AI-Generated Code]() Original publisher: [Read original article]() Published: 2024-09-24T00:00:00Z Content type: opinion Language: en Sources: [Tim Kellogg]() Topics: [Artificial Intelligence](), [bug](), [Git](), [coding](), [trust]() Tags: [ai](), [autonomous](), [bug](), [code](), [coding](), [commit](), [git](), [junior](), [management](), [manager](), [solve](), [support](), [team](), [testing](), [trust](), [value]() ### AI overview This opinion article argues that engineers remain accountable for bugs in code generated with AI. It says attributing defects to AI reduces an engineer's perceived value, and that responsible use requires testing, judgment, and ownership. ### Source excerpt AI didn't write that bug. If you're name is on the commit in Git, you wrote the bug. ## Exploiting a custom tetris game in CSAW Quals 2020 DevFeed: [Exploiting a custom tetris game in CSAW Quals 2020]() Original publisher: [Read original article]() Published: 2020-09-13T00:00:00Z Content type: tutorial Language: en Sources: [mahaloz.re]() Topics: [Code](), [arcade](), [Ghidra](), [Reconnaissance](), [Script]() Tags: [ctf](), [game](), [ghidra](), [pwn](), [reversing](), [solve](), [writeup]() ### AI overview This writeup explains how a custom Tetris game in CSAW Quals 2020 was analyzed and exploited. The challenge involved discovering cheat-piece placements, obtaining the binary, reverse-engineering its checks, and using an out-of-bounds write to obtain a partial write primitive and ultimately the flag. ### Source excerpt Pwning a custom Tetris game through an out-of-bounds write to memory through block manipulation and changes to the `.text` segment. ## Using VueJS + GraphQL to make Practical Magic DevFeed: [Using VueJS + GraphQL to make Practical Magic]() Original publisher: [Read original article]() Author: andrew@nystudio107.com (Andrew Welch) Published: 2018-08-27T22:29:00Z Content type: tutorial Language: en Sources: [nystudio107 | Articles on modern web development.]() Topics: [GraphQL](), [vuejs](), [Content Management System](), [Front end](), [JavaScript](), [axios](), [Bootstrap](), [Jamstack]() Tags: [auto-complete](), [axios](), [bootstrap](), [craft](), [everyday](), [form](), [graphql](), [headless](), [insights](), [jamstack](), [javascript](), [like](), [magic](), [practical](), [problems](), [saving](), [searching](), [solve](), [submission](), [using](), [vuejs]() ### AI overview A practical tutorial showing how to use VueJS and GraphQL with a headless Craft CMS backend to implement auto-complete search and save contact-form submissions. It introduces the frontend and backend tooling used in the examples, including Axios, Bootstrap 4, Craft CMS, and CraftQL. ### Source excerpt Make some practical magic with VueJS + GraphQL to solve everyday problems like auto-complete searching and form submission saving with a headless Craft CMS server ## Fix for Chrome not rendering 1px borders DevFeed: [Fix for Chrome not rendering 1px borders]() Original publisher: [Read original article]() Author: Stanko Published: 2018-07-16T00:00:00Z Content type: tutorial Language: en Sources: [Stanko Tadić]() Topics: [Chrome](), [browsers](), [render](), [screen]() Tags: [browsers](), [chrome](), [issue](), [render](), [screen](), [solve]() ### AI overview A short troubleshooting note about Chrome sometimes failing to render 1px borders at certain screen sizes. It suggests changing the border width from 1px to thin as a workaround, while noting that the author does not know why it works. ### Source excerpt This happened to me couple of times - borders would just randomly disappear in Chrome. All other browsers render them normally, but they just vanish in Chrome, on some screen sizes. Resizing helps sometimes, but I never was able to catch real pattern for reproducing. If this ever happens to you, there is a (hacky) solution. Change border width from 1px to thin. border: 1px solid #000; /* change it to: */ border: thin solid #000; Although I don't like it, as it is a hacky workaround and I don't really understand why it works, it does solve the issue. It seems that I'm not the only one having this problem. ## NP-hard does not mean hard DevFeed: [NP-hard does not mean hard]() Original publisher: [Read original article]() Published: 2017-12-29T13:30:23Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Algorithms, Complexity](), [Algorithms](), [Programming]() Tags: [algorithm](), [algorithms](), [boolean](), [completeness](), [complexity-theory](), [logic](), [mario](), [mathematics](), [np-completeness](), [np-hard](), [problems](), [real-world](), [regular-languages](), [solve](), [time]() ### AI overview This article explains that NP-hardness is a worst-case mathematical property, not a claim that a problem is always difficult for humans or algorithms. Using Super Mario as an example, it shows how game levels can encode boolean logic and why solving the game in full generality would encompass hard computational problems, while ordinary real-world levels may still be solvable efficiently. ### Source excerpt When NP-hardness pops up on the internet, say because some silly blogger wants to write about video games, it's often tempting to conclude that the problem being proved NP-hard is actually very hard! "Scientists proved Super Mario is NP-hard? I always knew there was a reason I wasn't very good at it!" Sorry, these two are unrelated. NP-hardness means hard in a narrow sense this post should hopefully make clear. After that, we'll explore what "hard" means in a mathematical sense that you can apply beyond NP-hardness to inform your work as a programmer. ## How Ada Lovelace solved problems DevFeed: [How Ada Lovelace solved problems]() Original publisher: [Read original article]() Author: Graham King Published: 2015-12-11T05:02:00Z Content type: article Language: en Sources: [Graham King]() Topics: [Ada](), [Mathematics]() Tags: [ideas](), [mathematics](), [problems](), [program](), [puzzle](), [solve]() ### AI overview This article recounts how Ada Lovelace worked on a mathematics problem related to the Analytical Engine and documented what is described as the first known computer program for calculating Bernoulli numbers. When she became stuck, she went horseback riding and expected the problem to become clearer later. ### Source excerpt Ada Lovelace: Debugging by horseback since 1843. ## Schema.org Product Microdata and Breadcrumbs DevFeed: [Schema.org Product Microdata and Breadcrumbs]() Original publisher: [Read original article]() Author: Juan Basso (juan.basso@zumba.com) Published: 2012-09-19T00:00:00Z Content type: tutorial Language: en Sources: [Zumba]() Topics: [Structured-data]() Tags: [how-to](), [product](), [schema](), [solve]() ### AI overview A tutorial on solving breadcrumb implementation problems on product pages using Schema.org product microdata. ### Source excerpt How to solve the problem of breadcrumbs inside product page ## P vs. NP, A Primer (And a Proof Written in Racket) DevFeed: [P vs. NP, A Primer (And a Proof Written in Racket)]() Original publisher: [Read original article]() Published: 2012-02-23T19:57:35Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Computing](), [Algorithms, Complexity](), [Racket](), [Programming]() Tags: [algorithm](), [algorithms](), [analysis](), [big-o-notation](), [class](), [computational-complexity](), [computing](), [decidability](), [efficiency](), [fast](), [halting-problem](), [input](), [language](), [machine](), [np-completeness](), [p-versus-np](), [primer](), [racket](), [solve](), [theory](), [turing-machine](), [turing-machines]() ### AI overview A primer on the distinction between decidability and computational efficiency, introducing the complexity class P and its definition in terms of polynomial-time decision by a Turing machine. It also discusses efficient algorithms and gives a card-sorting example. ### Source excerpt Decidability Versus Efficiency In the early days of computing theory, the important questions were primarily about decidability. What sorts of problems are beyond the power of a Turing machine to solve? As we saw in our last primer on Turing machines, the halting problem is such an example: it can never be solved a finite amount of time by a Turing machine. However, more recently (in the past half-century) the focus of computing theory has shifted away from possibility in favor of determining feasibility. ## How to Handle Workplace Issues Proactively DevFeed: [How to Handle Workplace Issues Proactively]() Original publisher: [Read original article]() Author: Map Published: 2010-01-26T01:30:10Z Content type: opinion Language: en Sources: [Craig Kerstiens]() Topics: [Job]() Tags: [issue](), [job](), [office](), [ownership](), [solve](), [work]() ### AI overview This workplace commentary explains that employees should communicate about problems early, adapt the amount of detail to their manager's preferences, take ownership of issues, and present possible solutions with their advantages and disadvantages. ### Source excerpt I often encounter people whether at my office or at other places of employment that are distraught after getting an earful from a manager from some problem arising. The problem usually isn't in their control, and therefore they don't understand why they get heat for this. Most managers though do actually understand when issues come up, however what they don't appreciate is late notice, lack of problem solving, and dictating what should be done next. Managers typically want individuals to take control of a situation and work towards resolving it. One thing you can do to ease the backlash that may occur for issues coming up is to communicate proactively as things develop/occur or lack there of. Keep in mind this should relate well to your managers style, some managers only want details when they absolutely have to have them. In that case you'll want to gradually give your manager a heads up, but not burden him with too much information. I would venture to say however that most managers appreciate details, details are great to give them insight into how things are going and allow them to feel engaged at a lower level. So assume you've communicated regularly to your manager, this still does not prevent any issues from happening, but rather reduces the shock when something does. At this point a manager still does not want a fact stated that there's a problem. In every case I've encountered the manager wants you to take ownership of the issue, meaning to give some options. Once the problem has arisen you should instantly start looking for ways to solve it. Often time these ways are not within your power to make the final decision, though you do have a great deal of control in presenting the case to a manager. Finally if you want brownie points, take less credit for any of the work you've done and give your manager more. If you've communicated early, laid out various options for how to resolve the issue with pros and cons of each you've done what you can. This should make ## Solving Every Sudoku Puzzle DevFeed: [Solving Every Sudoku Puzzle]() Original publisher: [Read original article]() Published: 2006-05-25T00:00:00Z Content type: tutorial Language: en Sources: [Peter Norvig]() Topics: [Python](), [Code](), [Data structures](), [Programming]() Tags: [array](), [code](), [data-structure](), [how-to](), [puzzle](), [python](), [solve](), [tests]() ### AI overview A tutorial explaining how to solve Sudoku puzzles with Python using constraint propagation and search. It models squares, units, and peers, then represents puzzle states with dictionaries of possible values. ### Source excerpt Python code and an explanation of how to solve any Sudoku puzzle.