#Design system tools

[uiuxcafe]

Design system tools

Everything you need to know about design systems, and the tools to build them.

[gothteddiesdotcom]

can we start considering online job application systems as class warfare or

[reksink]

Strangely Cladded Wizards

[growthmasterylab]

discipline is a skill built in silence forged in small repeated acts your life reflects your structure your habits shape your power

[eachuisge-cc]

do you happen to have any psds saved for the cat skins? i love how high quality yours are and would like to make my own custom patterns, i understand if not tho!

I will not be releasing the psds as they are, because they're large files and they are not set up in a way that would make them easy for someone else to use. They're also 100% painted from scratch and I'll admit to feeling kind of protective over them, since releasing a template for recolors pretty much immediately relinquishes any further control over what's done with them. I could see maybe releasing a template for personal use in the future but I'd need to think about how I'd want to go about that and reorganize the layers etc into something easier to parse.

[e-bird-online]

Web Design Trends Shaping 2025

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5 Important ways to develop responsive website

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Optimize images and code for faster loading.

Use caching, a good hosting provider, and efficient coding practices.

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[bean-face]

sketches of a couple of my oc's

knight maid elf and goblin mage

@firefilledhikey figured you'd wanna see these girls

sorry the trans homunculus isn't around i don't have a drawing of that one yet

[al-n-cartoons]

I drew this one a few days ago and never posted it for reasons I forget.

Here's the current reference sheet for the Ben of A Story Told. He has about a million different pseudo names (his friends generally call xer “Benj” or “Benja” whereas xer coworkers and family call him “Ben” or “Benjamin”). His hero name is “Ten Speed”, which he came up with at the age of ten for an email address and it he never outgrew it. His car is called it, his username is it, xe has tried to ‘anonymously’ use it to fly under the radar (and failed fantastically but xe was a child so it's to be expected).

Xe is closeted in public, around family, and just generally when in xer home dimension bit is openly omnigender and poly outside of it. Xer longest standing relationship is a comet with a rock musician in Danny's hometown.

He's not too crazy about dresses (and prefers pants) but does enjoy them on occasion. His preferred style is more geared towards athletic and loose.

-

His Omnitrix scrambled out of its outer casing when he was about ten years old but it went unnoticed until he tried to take it off. Without the outer casing's hardware, xe was unable to control/select xer form nor acquire easy charge. Xe went on to wear the casing sparsely, mostly just to keep the Omnitrix from putting a drain on his body. Transforming without the outer casing is difficult to do on command and near impossible to get right.

The Ultimatrix was supposed to replace the Omnitrix and correct the old programming. The Omnitrix makes the very mature and not tantrum-like decision to cannibalize the Ultimatrix from the inside out, using its parts for replacements and rewiring the Ultimatrix to suite its needs.

The latest edition of the Omnitrix is an attempt to compromise with the original, or so Ben was told. In actuality, it was a failed attempt to deactivate the Omnitrix and replace it with something closer to the intended design. Azmuth might have outdone himself in designing the original's AI…

-

Through trial and error, Benj found a secondary source of power for the Omnitrix so as to keep it from making him sick. At the same time, he learned that he can do and tolerate certain things previously believed exclusive to energy beings. This is how xe was able to get xer ectotattoos; a kind of body art practiced by ghosts and halfas. Eyed viewers might notice that Left and Right are labeled in this image. I a, certain that the artist did not get those confused several times over and that her immense decisiveness will not cause the design and placement of the tattoos to fluctuate across art pieces.

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A decision from Rex in a desperate situation led to the Omnitrix's parameters to be irreversibly broadened. Whereas before it had cared only about protection of its data and a baseline frequency of transformations, now it monitors its host's (Benj's) vitals. A risk to Ben is a risk to the system; the Omnitrix must now generate defensive and offensive mechanisms for their shared wellbeing.

The amulet was a last ditch attempt to appease the Omnitrix after a freak accident lands Ben in an intensive care unit.

[equalseleventhirds]

contemplating the 'matrix but everyone is terrible' ttrpg i'm working on and i gotta be. so so so clear to both players and gm. that the characters are not good people who will get heroic endings.

like the thesis of the game is that these ppl discover they've been trapped in a corrupt and harmful system, and instead of trying to fight it or escape it, they believe they can hack it to give themselves benefits and play out power fantasies, and are fine using and hurting ppl to do so. the metaphor cannot be more hamfisted.

also the game mechanics are based on growing ur base of power and attacking other ppl's bases of power all while the System attacks all of u at once, and there is no win-state to the growing. grind never stops, bcos once u stop ur dead, someone else is coming to take ur power and destroy u. again the metaphor is so so so hamfisted.

[teethands]

adding pot to cenozoicraft next update

[03349656115]

appleiphone

[virtual-board-game-room]

VBR v0.8

The new VBR version v0.8 is finished and ready for download for all members.

Members of all level can download the software for free.

Version Log v0.8:

Completely new, intuitive inventory system with Drag&Drop operation

Copy function: Objects can now be copied directly in the room with shortkey "i"

Locking and phantoming an object and the status display is now integrated in the middle mouse button menu

The base ground size can now be set in the options menu

Game objects can now be randomly removed from locked bags and card decks with a single mouse click.

The high-resolution movement of objects via the number pad now also has movement via the Y axis with the "9" and "3" buttons

Fixed many major bugs and minor errors.

Virtual Board Game Room on Patreon

[newcodesociety]

A design system is a collection of reusable components and clear standards that can be assembled together to build any number of applications. Design systems aid in digital product design and development of products such as mobile applications or websites. (Source)

[aliiiiiiice]

here's something even better, the entirety of the Pathfinder 2e rules on an official website so you can play something that isn't D&D 5e

here's a free character builder too

Here is a free pdf of the players handbook

Here is a free pdf of xanathars guide to everything

Here is a free pdf to monsters manual

Here is a free pdf to tashas cauldron of everything

Here is a free pdf to dungeon master’s guide

Here is a free pdf to volo’s guide to monsters

Here is a free pdf of mordenkainen’s tomb of foes

For all your dnd purposes

[e-bird-online]

The Future of Web Design: Generative AI

The power of generative AI is transforming the landscape of web design. This video explores how AI-powered tools are automating creative processes, leading to more efficient and personalized web experiences.

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Good design is obvious. Great design is transparent.

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Visuals as a Language

Design communicates through color, typography, layout, imagery, and other visual elements. Good design translates complex thoughts into simple, understandable visuals.

[jcmarchi]

Why Large Language Models Skip Instructions and How to Address the Issue

New Post has been published on https://thedigitalinsider.com/why-large-language-models-skip-instructions-and-how-to-address-the-issue/

Why Large Language Models Skip Instructions and How to Address the Issue

Large Language Models (LLMs) have rapidly become indispensable Artificial Intelligence (AI) tools, powering applications from chatbots and content creation to coding assistance. Despite their impressive capabilities, a common challenge users face is that these models sometimes skip parts of the instructions they receive, especially when those instructions are lengthy or involve multiple steps. This skipping leads to incomplete or inaccurate outputs, which can cause confusion and erode trust in AI systems. Understanding why LLMs skip instructions and how to address this issue is essential for users who rely on these models for precise and reliable results.

Why Do LLMs Skip Instructions? 

LLMs work by reading input text as a sequence of tokens. Tokens are the small pieces into which text is divided. The model processes these tokens one after another, from start to finish. This means that instructions at the beginning of the input tend to get more attention. Later instructions may receive less focus and can be ignored.

This happens because LLMs have a limited attention capacity. Attention is the mechanism models use to decide which input parts are essential when generating responses. When the input is short, attention works well. But attention becomes less as the input gets longer or instructions become complex. This weakens focus on later parts, causing skipping.

In addition, many instructions at once increase complexity. When instructions overlap or conflict, models may become confused. They might try to answer everything but produce vague or contradictory responses. This often results in missing some instructions.

LLMs also share some human-like limits. For example, humans can lose focus when reading long or repetitive texts. Similarly, LLMs can forget later instructions as they process more tokens. This loss of focus is part of the model’s design and limits.

Another reason is how LLMs are trained. They see many examples of simple instructions but fewer complex, multi-step ones. Because of this, models tend to prefer following simpler instructions that are more common in their training data. This bias makes them skip complex instructions. Also, token limits restrict the amount of input the model can process. When inputs exceed these limits, instructions beyond the limit are ignored.

Example: Suppose you give an LLM five instructions in a single prompt. The model may focus mainly on the first two instructions and partially or fully ignore the last three. This directly affects how the model processes tokens sequentially and its attention limitations.

How Well LLMs Manage Sequential Instructions Based on SIFo 2024 Findings

Recent studies have looked carefully at how well LLMs follow several instructions given one after another. One important study is the Sequential Instructions Following (SIFo) Benchmark 2024. This benchmark tests models on tasks that need step-by-step completion of instructions such as text modification, question answering, mathematics, and security rule-following. Each instruction in the sequence depends on the correct completion of the one before it. This approach helps check if the model has followed the whole sequence properly.

The results from SIFo show that even the best LLMs, like GPT-4 and Claude-3, often find it hard to finish all instructions correctly. This is especially true when the instructions are long or complicated. The research points out three main problems that LLMs face with following instructions:

Understanding: Fully grasping what each instruction means.

Reasoning: Linking several instructions together logically to keep the response clear.

Reliable Output: Producing complete and accurate answers, covering all instructions given.

Techniques such as prompt engineering and fine-tuning help improve how well models follow instructions. However, these methods do not completely help with the problem of skipping instructions. Using Reinforcement Learning with Human Feedback (RLHF) further improves the model’s ability to respond appropriately. Still, models have difficulty when instructions require many steps or are very complex.

The study also shows that LLMs work best when instructions are simple, clearly separated, and well-organized. When tasks need long reasoning chains or many steps, model accuracy drops. These findings help suggest better ways to use LLMs well and show the need for building stronger models that can truly follow instructions one after another.

Why LLMs Skip Instructions: Technical Challenges and Practical Considerations

LLMs may skip instructions due to several technical and practical factors rooted in how they process and encode input text.

Limited Attention Span and Information Dilution

LLMs rely on attention mechanisms to assign importance to different input parts. When prompts are concise, the model’s attention is focused and effective. However, as the prompt grows longer or more repetitive, attention becomes diluted, and later tokens or instructions receive less focus, increasing the likelihood that they will be overlooked. This phenomenon, known as information dilution, is especially problematic for instructions that appear late in a prompt. Additionally, models have fixed token limits (e.g., 2048 tokens); any text beyond this threshold is truncated and ignored, causing instructions at the end to be skipped entirely.

Output Complexity and Ambiguity

LLMs can struggle with outputting clear and complete responses when faced with multiple or conflicting instructions. The model may generate partial or vague answers to avoid contradictions or confusion, effectively omitting some instructions. Ambiguity in how instructions are phrased also poses challenges: unclear or imprecise prompts make it difficult for the model to determine the intended actions, raising the risk of skipping or misinterpreting parts of the input.

Prompt Design and Formatting Sensitivity

The structure and phrasing of prompts also play a critical role in instruction-following. Research shows that even small changes in how instructions are written or formatted can significantly impact whether the model adheres to them.

Poorly structured prompts, lacking clear separation, bullet points, or numbering, make it harder for the model to distinguish between steps, increasing the chance of merging or omitting instructions. The model’s internal representation of the prompt is highly sensitive to these variations, which explains why prompt engineering (rephrasing or restructuring prompts) can substantially improve instruction adherence, even if the underlying content remains the same.

How to Fix Instruction Skipping in LLMs

Improving the ability of LLMs to follow instructions accurately is essential for producing reliable and precise results. The following best practices should be considered to minimize instruction skipping and enhance the quality of AI-generated responses:

Tasks Should Be Broken Down into Smaller Parts

Long or multi-step prompts should be divided into smaller, more focused segments. Providing one or two instructions at a time allows the model to maintain better attention and reduces the likelihood of missing any steps.

Example

Instead of combining all instructions into a single prompt, such as, “Summarize the text, list the main points, suggest improvements, and translate it to French,” each instruction should be presented separately or in smaller groups.

Instructions Should Be Formatted Using Numbered Lists or Bullet Points

Organizing instructions with explicit formatting, such as numbered lists or bullet points, helps indicate that each item is an individual task. This clarity increases the chances that the response will address all instructions.

Example

Summarize the following text.

List the main points.

Suggest improvements.

Such formatting provides visual cues that assist the model in recognizing and separating distinct tasks within a prompt.

Instructions Should Be Explicit and Unambiguous

It is essential that instructions clearly state the requirement to complete every step. Ambiguous or vague language should be avoided. The prompt should explicitly indicate that no steps may be skipped.

Example

“Please complete all three tasks below. Skipping any steps is not acceptable.”

Direct statements like this reduce confusion and encourage the model to provide complete answers.

Separate Prompts Should Be Used for High-Stakes or Critical Tasks

Each instruction should be submitted as an individual prompt for tasks where accuracy and completeness are critical. Although this approach may increase interaction time, it significantly improves the likelihood of obtaining complete and precise outputs. This method ensures the model focuses entirely on one task at a time, reducing the risk of missed instructions.

Advanced Strategies to Balance Completeness and Efficiency

Waiting for a response after every single instruction can be time-consuming for users. To improve efficiency while maintaining clarity and reducing skipped instructions, the following advanced prompting techniques may be effective:

Batch Instructions with Clear Formatting and Explicit Labels

Multiple related instructions can be combined into a single prompt, but each should be separated using numbering or headings. The prompt should also instruct the model to respond to all instructions entirely and in order.

Example Prompt

Please complete all the following tasks carefully without skipping any:

Summarize the text below.

List the main points from your summary.

Suggest improvements based on the main points.

Translate the improved text into French.

Chain-of-Thought Style Prompts

Chain-of-thought prompting guides the model to reason through each task step before providing an answer. Encouraging the model to process instructions sequentially within a single response helps ensure that no steps are overlooked, reducing the chance of skipping instructions and improving completeness.

Example Prompt

Read the text below and do the following tasks in order. Show your work clearly:

Summarize the text.

Identify the main points from your summary.

Suggest improvements to the text.

Translate the improved text into French.

Please answer all tasks fully and separately in one reply.

Add Completion Instructions and Reminders

Explicitly remind the model to:

“Answer every task completely.”

“Do not skip any instruction.”

“Separate your answers clearly.”

Such reminders help the model focus on completeness when multiple instructions are combined.

Different Models and Parameter Settings Should Be Tested

Not all LLMs perform equally in following multiple instructions. It is advisable to evaluate various models to identify those that excel in multi-step tasks. Additionally, adjusting parameters such as temperature, maximum tokens, and system prompts may further improve the focus and completeness of responses. Testing these settings helps tailor the model behavior to the specific task requirements.

Fine-Tuning Models and Utilizing External Tools Should Be Considered

Models should be fine-tuned on datasets that include multi-step or sequential instructions to improve their adherence to complex prompts. Techniques such as RLHF can further enhance instruction following.

For advanced use cases, integration of external tools such as APIs, task-specific plugins, or Retrieval Augmented Generation (RAG) systems may provide additional context and control, thereby improving the reliability and accuracy of outputs.

The Bottom Line

LLMs are powerful tools but can skip instructions when prompts are long or complex. This happens because of how they read input and focus their attention. Instructions should be clear, simple, and well-organized for better and more reliable results. Breaking tasks into smaller parts, using lists, and giving direct instructions help models follow steps fully.

Separate prompts can improve accuracy for critical tasks, though they take more time. Moreover, advanced prompt methods like chain-of-thought and clear formatting help balance speed and precision. Furthermore, testing different models and fine-tuning can also improve results. These ideas will help users get consistent, complete answers and make AI tools more useful in real work.