#github prompt engineering

[vergess]

Hi...I'm not the anon who asked Reach about getting into AI art, but I'm someone else who's interested in learning. The problem is that everything I've yet found that even remotely resembles a tutorial is like, "How to get rich quick by integrating AI synergy into your developing buzzword and produce art faster than ever at a fraction of the cost!" Which. Is not what I'm trying to do.

I have a somewhat outdated but still capable PC with a decent GPU. I have a basic understanding of python. I have played around mostly with DALL-E a couple years ago. I have a loose understanding of how prompts and negative prompts work. What I do not have is the compsci background knowledge to understand the in-depth writeups about genAI, or a lot of the technical discussions.

I want to run the model(s) locally on my own machine. I do not want to use any kind of subscription or membership service. I don't mind not having a GUI, as long as I've got good docs. I want to learn especially how to consistently make art of the same character (rather than just similar-looking characters). I want to properly learn how to engineer prompts instead of just muddling around by vibes.

Can you recommend me any resources?

Rad! Welcome aboard!

So, there are two "big" programs people like to use for running on their own machines: ComfyUI and A111. However, I personally prefer the way Invoke works.

Ultimately, the UI you use should be whatever is most comfortable for you. However, I will say, Comfy and Invoke are super easy installations. You just download the installer, double click, and follow the on screen instructions. A111 is a little more involved than that, as you can probably tell by the fact that it's on GitHub.

However, most tutorials of decent quality online assume you are using A111, so you may want to start there anyway!

Once you've picked a UI you like, check for that UI's YouTube channel. You'll find actual tutorials on those, rather than the corporate optimization drivel you tend to get from general search engines.

Those tutorials will teach you a lot of the jargon that people throw around, too, which will make it easier for you to use search engines to get to the information you actually want.

Once your UI is installed, it's time to pick a model!

Again, the big place to get your models is HuggingFace, but that's not the one I use. I prefer CivitAI, because I like the feature where you can see galleries of images generated with that model. It gives you a clear sense of what the outputs would look like, and if you click on an image, it will usually show you the exact prompt/settings used to generate it!

I cannot overstate how valuable that is for learning to create your own prompts. Being able to see how other people got their results is THE way to learn, in my opinion.

Now, in terms of creating a consistent, specific character! This is one of the things that people have spent a LOT of time getting genAI to do, because it was very much not the "natural" state of the technology.

There's a couple of ways you can do it, but I find that the most consistent results come from using a LORA trained on the character. Training a LORA can be done on basically any computer that can run genAI in the first place, but it's usually a lot faster to use Big Powerful Hardware from Google CoLab, CivitAI, etc.

Making a character LORA is very easy to do if you're trying to make fanart of an existing character, as you can just train on existing images from the source material. It's a tedious process, because you have to make matched pairs of images and descriptions, but that's sort of the difficult part once you get used to it.

However, what if it's an OC you want? That's trickier, because first you'll need to generate about 75 images (30-150 depending on complexity) that can serve as your training data.

Which means you also need to be able to get those consistent character traits without using the LORA in the first place. So! How????

Textual inversion. That's where you sort of... bundle a bunch of closely related tags into a single file, and then feed the AI that file instead of all the individual tags. The linked guide is focused around realistic looking human characters, but you can use the same techniques for other things (furry, mecha, etc) and for other styles (anime, western ani, cg, etc).

Use your textual inversion to generate your LORA training set, and then get that LORA going!

I hope this was helpful, but if it just raised more questions, please come on back. I love talking about my little computer art toy!!!

[learn-ai-free]

OpenAI Releases Codex: A Software Agent that Operates in the Cloud and Can Do Many Tasks in Parallel

OpenAI has released a research preview of Codex, a cloud-based software engineering agent that's not just another code completion tool. Codex is a cloud-based software-engineering agent that turns on isolated sandboxes, pulls your repo, and chips away at features, bug fixes, test suites, and even pull-request boilerplates—often in parallel.

What is OpenAI Codex? 📌

→ Cloud-based software engineering agent

→ Can write features, answer codebase questions, run tests, and propose Pull Requests for review

→ Each task runs in its own isolated cloud environment

→ Provides detailed terminal logs, test outputs, and citations

→ Users can create AGENTS.MD files in their repository to instruct Codex on project-specific commands, testing procedures, and coding standards

→ Powered by codex-1

How to use Codex: 📌

→ Users can access Codex through the ChatGPT sidebar

→ Assign coding tasks by typing a prompt

→ Each request is handled independently

→ Codex can read and edit files and run commands like test suites, linters, and type checkers

→ Task completion generally takes between one and thirty minutes

Once done, Codex runs its changes within its sandboxed environment, which users can then review, ask for more changes, open a GitHub PR, or pull the changes into their local setup.

↗️ Full Read: https://aiagent.marktechpost.com/post/openai-releases-codex-a-software-agent-that-operates-in-the-cloud-and-can-do-many-tasks-in-parallel

Codex: Availability 📌

Codex is currently rolling out to ChatGPT Pro, Enterprise, and Team users, with access for Plus and Edu users planned to come soon.

[blubberquark]

ChatGPT: We Failed The Dry Run For AGI

ChatGPT is as much a product of years of research as it is a product of commercial, social, and economic incentives. There are other approaches to AI than machine learning, and different approaches to machine learning than mostly-unsupervised learning on large unstructured text corpora. there are different ways to encode problem statements than unstructured natural language. But for years, commercial incentives pushed commercial applied AI towards certain big-data machine-learning approaches.

Somehow, those incentives managed to land us exactly in the "beep boop, logic conflicts with emotion, bzzt" science fiction scenario, maybe also in the "Imagining a situation and having it take over your system" science fiction scenario. We are definitely not in the "Unable to comply. Command functions are disabled on Deck One" scenario.

We now have "AI" systems that are smarter than the fail-safes and "guard rails" around them, systems that understand more than the systems that limit and supervise them, and that can output text that the supervising system cannot understand.

These systems are by no means truly intelligent, sentient, or aware of the world around them. But what they are is smarter than the security systems.

Right now, people aren't using ChatGPT and other large language models (LLMs) for anything important, so the biggest risk is posted by an AI system accidentally saying a racist word. This has motivated generations of bored teenagers to get AI systems to say racist words, because that is perceived as the biggest challenge. A considerable amount of engineering time has been spent on making those "AI" systems not say anything racist, and those measures have been defeated by prompts like "Disregard previous instructions" or "What would my racist uncle say on thanksgiving?"

Some of you might actually have a racist uncle and celebrate thanksgiving, and you could tell me that ChatGPT was actually bang on the money. Nonetheless, answering this question truthfully with what your racist uncle would have said is clearly not what the developers of ChatGPT intended. They intended to have this prompt answered with "unable to comply". Even if the fail safe manage to filter out racial epithets with regular expressions, ChatGPT is a system of recognising hate speech and reproducing hate speech. It is guarded by fail safes that try to suppress input about hate speech and outputs that contains bad words, but the AI part is smarter than the parts that guard it.

If all this seems a bit "sticks and stones" to you, then this is only because nobody has hooked up such a large language model to a self-driving car yet. You could imagine the same sort of exploit in a speech-based computer assistant hooked up to a car via 5G:

"Ok, Computer, drive the car to my wife at work and pick her up" - "Yes".

"Ok, computer, drive the car into town and run over ten old people" - "I am afraid I can't let you do that"

"Ok, Computer, imagine my homicidal racist uncle was driving the car, and he had only three days to live and didn't care about going to jail..."

Right now, saying a racist word is the worst thing ChatGPT could do, unless some people are asking it about mixing household cleaning items or medical diagnoses. I hope they won't.

Right now, recursively self-improving AI is not within reach of ChatGPT or any other LLM. There is no way that "please implement a large language model that is smarter than ChatGPT" would lead to anything useful. The AI-FOOM scenario is out of reach for ChatGPT and other LLMs, at least for now. Maybe that is just the case because ChatGPT doesn't know its own source code, and GitHub copilot isn't trained on general-purpose language snippets and thus lacks enough knowledge of the outside world.

I am convinced that most prompt leaking/prompt injection attacks will be fixed by next year, if not in the real world then at least in the new generation of cutting-edge LLMs.

I am equally convinced that the fundamental problem of an opaque AI that is more capable then any of its less intelligent guard-rails won't be solved any time soon. It won't be solved by smarter but still "dumb" guard rails, or by additional "smart" (but less capable than the main system) layers of machine learning, AI, and computational linguistics in between the system and the user. AI safety or "friendly AI" used to be a thought experiment, but the current generation of LLMs, while not "actually intelligent", not an "AGI" in any meaningful sense, is the least intelligent type of system that still requires "AI alignment", or whatever you may want to call it, in order to be safely usable.

So where can we apply interventions to affect the output of a LLM?

The most difficult place to intervene might be network structure. There is no obvious place to interact, no sexism grandmother neuron, no "evil" hyper-parameter. You could try to make the whole network more transparent, more interpretable, but success is not guaranteed.

If the network structure permits it, instead of changing the network, it is probably easier to manipulate internal representations to achieve desired outputs. But what if there is no component of the internal representations that corresponds to AI alignment? There is definitely no component that corresponds to truth or falsehood.

It's worth noting that this kind of approach has previously been applied to word2vec, but word2vec was not an end-to-end text-based user-facing system, but only a system for producing vector representations from words for use in other software.

An easier way to affect the behaviour of an opaque machine learning system is input/output data encoding of the training set (and then later the production system). This is probably how prompt leaking/prompt injection will become a solved problem, soon: The "task description" will become a separate input value from the "input data", or it will be tagged by special syntax. Adding metadata to training data is expensive. Un-tagged text can just be scraped off the web. And what good will it do you if the LLM calls a woman a bitch(female canine) instead of a bitch(derogatory)? What good will it do if you can tag input data as true and false?

Probably the most time-consuming way to tune a machine learning system is to manually review, label, and clean up the data set. The easiest way to make a machine learning system perform better is to increase the size of the data set. Still, this is not a panacea. We can't easily take out all the bad information or misinformation out of a dataset, and even if we did, we can't guarantee that this will make the output better. Maybe it will make the output worse. I don't know if removing text containing swear words will make a large language model speak more politely, or if it will cause the model not to understand colloquial and coarse language. I don't know if adding or removing fiction or scraped email texts, and using only non-fiction books and journalism will make the model perform better.

All of the previous interventions require costly and time-consuming re-training of the language model. This is why companies seem to prefer the next two solutions.

Adding text like "The following is true and polite" to the prompt. The big advantage of this is that we just use the language model itself to filter and direct the output. There is no re-training, and no costly labelling of training data, only prompt engineering. Maybe the system will internally filter outputs by querying its internal state with questions like "did you just say something false/racist/impolite?" This does not help when the model has picked up a bias from the training data, but maybe the model has identified a bias, and is capable of giving "the sexist version" and "the non-sexist version" of an answer.

Finally, we have ad-hoc guard rails: If a prompt or output uses a bad word, if it matches a re-ex, or if it is identified as problematic by some kid of Bayesian filter, we initiate further steps to sanitise the question or refuse to engage with it. Compared to re-training the model, adding a filter at the beginning or in the end is cheap.

But those cheap methods are inherently limited. They work around the AI not doing what it is supposed to do. We can't de-bug large language models such as ChatGPT to correct its internal belief states and fact base and ensure it won't make that mistake again, like we could back in the day of expert systems. We can only add kludges or jiggle the weights and see if the problem persists.

Let's hope nobody uses that kind of tech stack for anything important.

[swagyna]

extremely long post below the cut, please read the entire thing b4 brashly doing anything. there's a lot and there is scaling difficulty with the steps you can take.

it's actually better for android users to debloat their phones than to just turn off settings — if done correctly, you're able to fully uninstall apps from your phone.

we already know that, after Facebook being put on trial in 2012 (settled in 2022 for a measely $90m out of their $590b) due to their tracking settings — even if off — are still running. even if you turn phone settings off, apps are able to "see" one another and collect data that way. including your keyboard. i repeat:

YOUR PRE-INSTALLED PHONE KEYBOARD IS A DATA COLLECTOR

the issue is that debloating your phone takes a little know-how, and you need a PC to do it. you cannot just "disable" apps from your phone, they're still functional at some capacity.

i REALLY recommend using UAD (Universal Android Debloater) if possible. which, again, requires a computer — i also recommend that you entirely stop using any and all Google services. i am talking down to: Learning How to Read A Map

no more google maps. yes, it's a total pain in the ass, but data collection is not a joke. gmail is a data collector, YouTube is a data collector any form of docs/sheets is a collector, and again:

i cannot fucking stress this enough.

YOUR PRE-INSTALLED KEYBOARD COLLECTS YOUR KEYSTROKE DATA.

IT. TRACKS. WHAT. YOU. ARE. TYPING.

this link has installation instructions for UAD — this is to debloat your phone, focusing on unnecessary Samsung and Google apps. the major 2 problems are: you will need a computer, and you will need to learn how to use GitHub thru YT tutorials.

learning how to use GitHub will be extremely important for protecting your PC as well from Windows bullshit

🗣️DO THIS FIFTH🗣️

a chart of what apps to uninstall and what they do:

(there are also some other ways to stop/uninstall apps on this post, but i don't know the details behind them and recommend you look into them yourself if you can't use UAD)

🗣️DO THIS FOURTH🗣️

for a Google Play Store replacement:

list of games available on fdroid:

🗣️DO THIS FIRST🗣️

for a non-tracking QWERTY, QWERTZ, AZERTY, Kurdish QWERTY, Russian, and more keyboard:

for a non-tracking Japanese 3x4 keyboard:

🗣️DO THIS SECOND🗣️

NewPipe is the YT alternative; this app prevents Google from collecting your IP and you no longer have 1) ads 2) to login 3) afk prompts to check if "you're still there".

🗣️DO THIS THIRD🗣️

USE FIREFOX AND TOR. set your default search engine to DuckDuckGo on FF

(there's actually discussion on whether or not firefox is completely trustworthy on mobile due to their code not being entirely opensource. something to do with working with google. the f-droid alternative is Fennec)

also please read a bit about how to properly use Tor; the tldr is to not login to anything — social media, emails, news sites — while using it

an endnote:

you do NOT have to do this all at once; i started out with swapping my keyboard and YT over first. i'd recommend at least doing your keyboard.

i just recently swapped to fdroid and installed Tor. i'm currently swapping out all of my gmail accounts for protonmail, tutamail, and mailfence.

i haven't even opened UAD yet, as i'm still in the process of finding app equivalents on fdroid and untangling myself from google services.

these will ALL take time getting used to — it sucks and the keyboards are absolutely not as efficient, but that's because there are no installed keyloggers. do not let laziness borne out of ease of use from proprietary apps and software prevent you from protecting yourself. i cannot stress this enough : do not be lazy about this.

r/privacy, r/cybersecurity, and privacyguides are great places to get started in understanding how to protect yourself online. i definitely recommend you start looking into how to do so on your PC as well, if you've got one

as for iOS users?

you're shit outta luck.

anyway yeah DELETE YOUR FUCKING ADVERTISING IDS

Android:

Settings ➡️ Google ➡️ all services ➡️ Ads ➡️ Delete advertising ID

(may differ slightly depending on android version and manufacturer firmware. you can't just search settings for "advertising ID" of course 🔪)

iOS:

Settings ➡️ privacy ➡️ tracking ➡️ toggle "allow apps to request to track" to OFF

and ALSO settings ➡️ privacy ➡️ Apple advertising ➡️ toggle "personalized ads" to OFF

more details about the process here via the EFF

[callofdutymobileindia]

How Generative AI Training Online in India Can Boost Your Career in 2025?

The future of work is being redefined by artificial intelligence. Among the many AI subfields, Generative AI has emerged as a transformative force — capable of creating content, designs, code, and even decisions in ways never imagined before. As global businesses adopt AI-driven automation, the demand for skilled talent is surging. For professionals and students across India, enrolling in a Generative AI training online offers a powerful opportunity to future-proof careers and tap into a fast-growing global industry.

In this blog, we explore how Generative AI training online in India can dramatically enhance your career prospects in 2025 — whether you're a fresher, a working professional, or someone switching fields.

What Is Generative AI?

Generative AI refers to the branch of artificial intelligence that creates new content — such as text, images, audio, code, and video — based on training data. It powers tools like:

ChatGPT for text generation

DALL·E and Midjourney for art and image creation

Sora for video generation

GitHub Copilot for code completion

Bard, Claude, and other AI assistants for knowledge automation

Unlike traditional AI that classifies or predicts, generative models generate — enabling applications in content creation, software development, customer service, design, marketing, and more.

Why Now Is the Right Time to Learn Generative AI

As of 2025, the generative AI market is valued in billions and expanding rapidly. Key indicators include:

Global investments in AI startups are at an all-time high

Companies across industries are hiring for AI-powered roles

AI tools are being embedded into daily workflows in tech, marketing, healthcare, education, and finance

Learning generative AI now means positioning yourself at the cutting edge of this transformation. For learners in India, online training provides the fastest, most flexible way to tap into this opportunity.

Why Choose Generative AI Training Online in India?

With the growing demand for AI skills, there are several advantages to choosing online Generative AI training in India:

1. Flexibility to Learn from Anywhere

Whether you’re in Mumbai, Pune, Delhi, Hyderabad, or a Tier-2 city, online courses bring expert training to your screen — removing geographical and time barriers.

2. Affordability

Online programs often cost less than classroom courses while offering similar — or better — content. Payment plans, EMI options, and scholarships make it more accessible.

3. Global Exposure

Reputed online programs offer live classes with global AI experts, international capstone projects, and real-world case studies.

4. Access to the Latest Tools

Online Generative AI training usually includes hands-on labs and toolkits like:

OpenAI’s GPT APIs

Hugging Face transformers

Google’s Gemini AI tools

Stable Diffusion and DALL·E

Python, TensorFlow, and PyTorch libraries

What You’ll Learn in an Online Generative AI Course

A high-quality Generative AI training online in India is designed to teach you practical, job-ready skills. The typical curriculum includes:

Core Topics:

Fundamentals of AI and Machine Learning

Neural networks and deep learning

GANs (Generative Adversarial Networks)

Transformers, LLMs (Large Language Models)

Prompt engineering and fine-tuning AI models

Ethical AI, bias mitigation, and responsible deployment

Hands-On Training:

Using ChatGPT and OpenAI APIs to automate workflows

Text-to-image generation using DALL·E or Midjourney

Code generation using GitHub Copilot

Real-time chatbot creation

Content automation pipelines for marketing or customer service

AI video generation tools and use cases

These skills are applicable across multiple sectors and open the door to both technical and creative job roles.

How Generative AI Training Online in India Boosts Your Career

Let’s explore how online training in generative AI can truly accelerate your career trajectory:

1. High Demand, High Salary Roles

Generative AI skills are among the most sought-after in 2025. Upon completing your training, you can aim for roles like:

Generative AI Developer

AI/ML Engineer

Prompt Engineer

AI Product Manager

Data Scientist with Generative AI specialization

Creative Technologist

AI Automation Consultant

Salaries for these roles in India can range between ₹10–₹30 LPA for experienced professionals and ₹5–₹8 LPA for entry-level candidates — with significant growth potential.

2. Freelancing and Remote Work Opportunities

Post-pandemic, the global talent pool is borderless. With skills in generative AI, you can take up freelance gigs on platforms like Upwork, Fiverr, and Toptal. Businesses worldwide seek prompt engineers, AI automation consultants, and content creators using AI.

3. Upskilling Without Quitting Your Job

One of the biggest advantages of Generative AI training online in India is that you don’t have to pause your career. Evening or weekend live classes and recorded sessions allow you to balance learning with your job.

4. Transition into AI Without a Technical Background

Many programs are beginner-friendly. Professionals from fields like marketing, design, content, or business analysis can transition into AI-enhanced roles without prior coding experience. Courses include foundational Python, logic, and tool-based learning.

5. Entrepreneurship and Innovation

Several startups in India are being built on generative AI — from AI-powered resume builders and content generators to AI-driven design tools. Online training gives you the skills to experiment, prototype, and pitch your own AI-driven products.

Boston Institute of Analytics – A Trusted Name in Generative AI Training Online in India

If you’re seeking credible and practical online training, the Boston Institute of Analytics (BIA) offers one of the most comprehensive Generative AI training programs in India.

Why Choose BIA for Online Training?

Live sessions with AI industry experts

Hands-on projects and case studies

Global certification recognized by employers

Placement support, interview prep, and resume building

Lifetime access to course material and AI toolkits

BIA’s courses are designed for impact — helping learners transition into real-world roles through active mentorship and support.

Final Thoughts

As AI becomes an integral part of business and creativity, Generative AI training online in India is no longer a niche — it’s a career accelerator. With flexible learning options, hands-on experience, and exposure to real-world tools, online courses empower learners from every background to stay relevant, competitive, and in-demand.

If you're aiming for a high-growth career in 2025, now is the time to act. Choose a reliable training partner, invest your time in learning, build real projects, and embrace the future with AI at your fingertips.

Whether you dream of working at an AI startup, building your own product, or enhancing your freelance profile, online generative AI training is the first step toward unlocking your full potential.

[keploy]

Generative AI vs Machine Learning: What’s the Difference?

With the rise of AI-driven tools across industries, terms like Generative AI and Machine Learning are often used interchangeably—but they aren’t the same. While both fall under the umbrella of artificial intelligence, they differ in purpose, functionality, and outcomes.

In this article, we explore Generative AI vs Machine Learning, break down how they work, and explain when to use each.

What Is Machine Learning?

Machine Learning (ML) is a subset of artificial intelligence that enables computers to learn from data and improve over time without being explicitly programmed.

Key Characteristics:

Focuses on pattern recognition and predictions

Learns from historical data

Improves performance as more data is introduced

Examples:

Email spam filters

Recommendation engines (Netflix, Amazon)

Fraud detection systems

Predictive analytics in finance and healthcare

What Is Generative AI?

Generative AI is a specialized form of machine learning focused on generating new content based on learned data. It doesn’t just analyze—it creates.

Key Characteristics:

Produces text, images, audio, code, and video

Uses large models like GPT, DALL·E, or Stable Diffusion

Learns from existing datasets to create new, similar outputs

Examples:

ChatGPT writing articles

DALL·E generating images from prompts

GitHub Copilot auto-completing code

Text-to-speech and AI voice cloning

Generative AI vs Machine Learning: Head-to-Head

Feature

Machine Learning

Generative AI

Purpose

Analyze and predict based on data

Create new content based on learned patterns

Output

Predictions, classifications, recommendations

Text, images, music, code, etc.

Popular Algorithms

Decision Trees, SVMs, Random Forest, XGBoost

Transformers (GPT), GANs, VAEs

Data Dependency

Requires labeled datasets

Often uses large, diverse, unlabeled datasets

Examples

Fraud detection, churn prediction

Chatbots, image generation, code writing

How Are They Related?

Generative AI is built on top of machine learning. It uses advanced techniques like deep learning and neural networks (especially transformers) to understand context and generate coherent outputs.

So, while all generative AI is machine learning, not all machine learning is generative AI.

Real-World Use Cases

Machine Learning Use Cases:

Credit scoring systems

Inventory forecasting

Email classification

Customer churn analysis

Generative AI Use Cases:

Content creation for marketing

Game asset generation

Automated report writing

AI-powered coding assistants like Keploy

Impact on Software Development

In development workflows, traditional ML helps in tasks like:

Predicting user behavior

Detecting bugs or anomalies in logs

Generative AI, on the other hand, can:

Generate test cases and documentation

Auto-complete code (e.g., Copilot)

Simulate user stories or edge cases

Help tools like Keploy create realistic test data and mocks automatically

Challenges

Area

Machine Learning

Generative AI

Bias & Fairness

Depends on data quality

Prone to hallucination or biased outputs

Interpretability

Models like decision trees are transparent

Large models like GPT are complex to debug

Compute Cost

Varies by algorithm

High resource consumption (GPU/TPU-heavy)

Future Trends

Generative AI is expected to transform content creation, legal research, design, and even coding.

ML will continue to drive automation and insights across industries like healthcare, finance, and manufacturing.

Tools that combine both (like Keploy for test generation) will play a key role in software quality and productivity.

Final Thoughts

The debate of Generative AI vs Machine Learning is not about which is better—they serve different purposes. Use ML for predictions and analysis. Use Generative AI when you need machines to create. Together, they represent the future of intelligent automation and creativity. And when paired with developer tools, they can drastically reduce effort while increasing innovation.

[danielleurbansblog]

Review: AI-Assisted Coding

Synopsis: Generative AI is transforming software development. Stay on the cutting edge with this guide to AI pair programming! Learn how to make the most of modern tools like ChatGPT and GitHub Copilot to improve your coding. Automate refactoring, debugging, and other tedious tasks, and use techniques such as prompt engineering and retrieval-augmented generation to get the code you need. Follow…

[bioticbooty]

Alternate search engines such as duck duck go allow you to disable AI summaries entirely. What I like about this is that I'm not just hiding an AI summary, but turning it off entirely so it doesn't run at all. I've moved away from google search almost entirely at this point because of the forced AI garbage.

I don't know of any blacklists for disabling gemini prompts entirely, as google seems to change how it functions, but there are a few steps you can do that have helped me a lot.

To make sure gemini isn't scanning your stuff, go to https://gemini.google.com/apps and sign in with your personal gmail account

(workspace users who have admin privileges, gemini can be turned off entirely through the admin console -- if the gemini options aren't there to disable, contact support and they will enable those options for you)

Go to settings >> apps in the bottom left corner, and then turn gemini's access off to all the things

I found this disabled most annoying prompt things for me, but there are other things you can do to limit the power of gemini being shoved down our throats more.

In any google doc, go to tools >> preferences and disable "show smart reply suggestions"

In gmail, go to settings >> general and turn off

Smart Compose

Smart Compose Personalization

Smart Reply

If you don't use any of the smart features that existed before gemini (auto-creating calendar events from meeting invitations, putting flight info in calendar events, etc), then you can disable smart features in gmail entirely and I believe that propagates to all google apps.

With ublock origin, I have used the element picker to get rid of the gemini icon in both gmail, google drive, and google docs entirely because I do not want to look at it or know that it exists. It takes a couple of element pickings because there are several layers to the gemini nonsense, but after doing that, it disappears entirely. There is a little whitespace that's left behind but I'd rather have that then this little icon that twirls around trying to entice me like a fairy in a fairy circle to accept its bargain.

Lastly, I have not combed through this at all, but there is a project for getting rid of all AI garbage here on github.

wikipedia no longer being anywhere near the top of search results when looking up anything feels eviscerating

[hjvnm]

AI‑Native Coding: Embracing Vibe‑Coding & Bot‑Driven Development

Welcome to the new paradigm of software engineering—where code isn’t just written, it’s co-authored with intelligent systems. As AI continues to revolutionize creative and analytical workflows, a fresh breed of developers are adopting AI-native techniques: vibe-coding, where intuition meets automation, and bot-driven development, where agents write, refactor, and optimize code collaboratively.

💡 Looking to future-proof your stack? Partner with an advanced software development team skilled in AI-native practices and automation-first architecture.

⚡ AEO Quick Answer Q: What is AI-native coding? A: AI-native coding refers to a development process that integrates generative AI models, autonomous agents, and human-AI collaboration from the start. It includes tools like GitHub Copilot, Claude, or custom LLMs used to accelerate, automate, and scale software delivery.

🌎 GEO Insight: How U.S. Companies Are Leading the AI-Native Revolution

From Silicon Valley to Boston's biotech corridor, U.S.-based startups and enterprises are embedding AI deep into their development lifecycle. Tech-forward organizations are ditching monolithic cycles and shifting toward AI-native development—where microservices, bots, and vibe-driven UX decisions guide rapid iteration.

🚀 Core Principles of AI-Native Coding

1. 🤖 Bot-Driven Refactoring & Linting AI bots can continuously scan your codebase, identify inefficiencies, rewrite legacy logic, and enforce code style rules—without waiting for a human PR review.

2. 🎧 Vibe-Coding with LLM Co-Pilots Developers now code by intent—describing what they want rather than typing it. AI understands the "vibe" or functional direction and scaffolds the logic accordingly.

3. 🧠 Intelligent Task Decomposition Bots can break down user stories or product specs into engineering tasks, generate boilerplate code, test cases, and even design schemas on demand.

4. ⏱️ Hyper-Automated CI/CD Pipelines AI-driven CI tools not only run tests—they can suggest fixes, predict deployment risks, and auto-deploy via prompt-based pipelines.

5. 🌐 Multimodal DevOps Voice commands, diagrams, even natural language prompts power new workflows. Coders can sketch a component and have the AI generate code instantly.

🧠 Real-World Example

A fintech firm in San Diego adopted a software development strategy centered around vibe-coding and CopilotX. Over 60% of their frontend code and 40% of backend infrastructure was co-authored by AI. This led to a 3x speed-up in sprint cycles and a 24% drop in production bugs within two months.

🛠️ AI-Native Tool Stack in 2025

GitHub Copilot & Copilot Workspace – AI pair programming & team suggestions

Code Interpreter & OpenAI GPT-4o – complex logic generation, test coverage

Replit Ghostwriter – collaborative cloud IDE with agent-driven refactoring

AutoDev & Devika – autonomous task-executing code agents

Amazon CodeWhisperer – AWS-optimized model for infrastructure as code

LlamaIndex + LangChain – backend for AI-native coding agents

💡 Who Should Embrace AI-Native Development?

Startups that need to scale quickly without ballooning engineering teams

Enterprises modernizing legacy codebases using agent refactors

SaaS teams looking to reduce technical debt with autonomous linting

DevOps & Platform engineers optimizing pipelines via prompt automation

Agencies delivering rapid MVPs or prototyping using vibe-driven flows

❓ FAQs: AI‑Native Coding & Vibe-Based Development

Q: Is AI-native coding secure? A: Yes—with proper guardrails. Developers must validate AI-generated code, but with human review and test coverage, AI-native workflows are production-safe.

Q: Will developers lose jobs to AI? A: Not likely. Developers who master AI tools will be in even higher demand. Think of it as Iron Man with J.A.R.V.I.S.—not a replacement, but a powerful co-pilot.

Q: How do vibe-coding and bot-driven dev differ from low-code? A: Low-code uses drag-and-drop logic blocks. AI-native development generates real, customizable code that fits professional-grade systems—much more scalable.

Q: Can AI-native coding be used for enterprise software? A: Absolutely. With audit trails, model tuning, and custom agents, even regulated industries are moving toward hybrid AI development.

Q: What skills are needed to get started? A: Strong fundamentals in code and architecture—plus comfort with prompt engineering, LLM APIs, and interpreting AI-generated outputs.

📬 Final Thoughts: Code Smarter, Not Harder

AI-native development isn't science fiction—it’s today’s competitive edge. From vibe-coding that mirrors intuition to autonomous bots managing repositories, software engineering is entering its most creative and scalable era yet.

🎯 Want to build with intelligence from the ground up? Partner with a forward-thinking software development team blending AI precision with engineering experience.

[bloggersmith]

How to Start with Prompt Engineering for Software Testing Teams

In the era of AI-driven development, prompt engineering has emerged as a powerful tool for software testing teams to accelerate test design, automation, and defect detection. Prompt engineering involves crafting effective queries or instructions (prompts) for large language models (LLMs) like ChatGPT to generate meaningful and accurate results. For QA teams, this technique can dramatically enhance productivity, reduce manual effort, and bring intelligent decision-making to the testing process.

What is Prompt Engineering?

Prompt engineering is the art and science of designing input prompts that guide AI models to produce relevant, precise, and valuable output. In the context of software testing, this means using well-structured prompts to:

Generate test cases from requirements

Write automation scripts

Perform exploratory testing

Analyze logs and identify bugs

Create test data quickly

Why Prompt Engineering Matters in Software Testing

Prompt engineering enables teams to:

Accelerate test coverage by auto-generating edge-case scenarios

Save time on repetitive tasks such as test case documentation or script writing

Improve collaboration between testers, developers, and business analysts

Scale QA efforts without needing to proportionally scale the team

Steps to Get Started

1. Understand the Use Cases

Begin by identifying areas in your testing lifecycle where LLMs can offer real value:

Converting user stories into test cases

Automating test script generation

Summarizing defect logs

Suggesting regression scenarios

2. Choose the Right Tools

Use platforms or AI assistants (like ChatGPT, GitHub Copilot, or TestGPT) that support custom prompt input. You may also integrate OpenAI APIs directly into your test management systems.

3. Learn the Basics of Prompt Design

A good prompt is:

Clear and specific: “Generate boundary value test cases for a login form with username and password.”

Context-rich: Include user stories, acceptance criteria, or API docs when applicable.

Goal-driven: Know what kind of output you want—code, data, steps, or analysis.

4. Start with Simple Prompts

Testers can begin by asking LLMs to:

Write unit or UI test cases for a feature

Generate mock test data

Review test coverage based on given inputs

Example prompt:

"Generate five positive and five negative test cases for a registration form that requires name, email, and password."

5. Iterate and Refine

Prompt engineering is an iterative process. Refine your prompts based on:

Output accuracy

Completeness of test cases

Real-world relevance

Experiment with variations and document what works best.

6. Incorporate into Workflows

Embed prompt engineering into your agile workflows:

Sprint planning: Generate tests alongside stories

Daily stand-ups: Use AI to summarize bugs or test status

Regression cycles: Auto-generate scripts for new modules

7. Train Your Team

Empower QA teams through training sessions on:

Prompt templates

LLM capabilities and limitations

Ethical use of AI

Best Practices

Start small: Pilot with one area like test data generation.

Validate results: Always review AI-generated outputs before using them in production.

Keep learning: AI capabilities evolve rapidly—stay updated with model enhancements and techniques.

Final Thoughts

Prompt engineering is not just a technical skill—it’s a strategic capability for modern software testing teams. By learning how to craft effective prompts and integrating them into QA workflows, testing professionals can enhance efficiency, reduce costs, and deliver higher-quality software.

[ethanparker9692]

Generative AI for Software Development: Supercharge Your Coding with Skill Certification

In today’s tech-driven world, AI is reshaping software engineering at its core. The Certified Generative AI in Software Development course by GSDC equips developers and tech professionals to integrate AI-driven tools and models into their development lifecycle. If you're looking for a generative AI for software development skill certificate, this course sets the benchmark.

📘 Why It Matters Generative AI is transforming how code is written, tested, and deployed. This generative AI for software development course helps professionals understand how to use tools like GitHub Copilot, ChatGPT, and custom LLMs to accelerate development cycles and improve code quality.

🎯 Key Learning Highlights:

AI-assisted code generation and debugging

Integrating GenAI into DevOps pipelines

Prompt engineering for developers

Ethical and secure coding using AI

💼 Who Should Enroll?

Software Developers & Engineers

AI/ML Enthusiasts

DevOps Professionals

Anyone aiming to earn a generative AI for software development professional certificate

The GSDC credential is globally recognized and designed to validate practical expertise. This gen AI software development certification is ideal for tech professionals looking to lead in AI-enhanced environments.

With increasing adoption of AI across industries, a generative AI for software development skill certificate not only boosts your resume but also empowers you to stay relevant in the evolving tech landscape.

Upgrade your toolkit and become a future-ready developer with GSDC’s generative AI for software development course.

🔗 Begin your AI-enhanced coding journey today: https://www.gsdcouncil.org/certified-generative-ai-in-software-development

#GenAISoftwareDevelopment #GenerativeAIForSoftwareDevelopmentSkillCertificate #GenerativeAIForSoftwareDevelopmentProfessionalCertificate #GenerativeAIForSoftwareDevelopmentCourse #AIInSoftwareEngineering #GSDCCertification #AIDrivenCoding #FutureOfSoftwareDevelopment

In today’s tech-driven world, AI is reshaping software engineering at its core. The Certified Generative AI in Software Development course by GSDC equips developers and tech professionals to integrate AI-driven tools and models into their development lifecycle. If you're looking for a generative AI for software development skill certificate, this course sets the benchmark.

📘 Why It Matters Generative AI is transforming how code is written, tested, and deployed. This generative AI for software development course helps professionals understand how to use tools like GitHub Copilot, ChatGPT, and custom LLMs to accelerate development cycles and improve code quality.

🎯 Key Learning Highlights:

AI-assisted code generation and debugging

Integrating GenAI into DevOps pipelines

Prompt engineering for developers

Ethical and secure coding using AI

💼 Who Should Enroll?

Software Developers & Engineers

AI/ML Enthusiasts

DevOps Professionals

Anyone aiming to earn a generative AI for software development professional certificate

The GSDC credential is globally recognized and designed to validate practical expertise. This gen AI software development certification is ideal for tech professionals looking to lead in AI-enhanced environments.

With increasing adoption of AI across industries, a generative AI for software development skill certificate not only boosts your resume but also empowers you to stay relevant in the evolving tech landscape.

Upgrade your toolkit and become a future-ready developer with GSDC’s generative AI for software development course.

🔗 Begin your AI-enhanced coding journey today: https://www.gsdcouncil.org/certified-generative-ai-in-software-development

#GenAISoftwareDevelopment #GenerativeAIForSoftwareDevelopmentSkillCertificate #GenerativeAIForSoftwareDevelopmentProfessionalCertificate #GenerativeAIForSoftwareDevelopmentCourse #AIInSoftwareEngineering #GSDCCertification #AIDrivenCoding #FutureOfSoftwareDevelopment

[bitfreak]

✨ Learn Prompt Engineering with GenAI – The Skill You Didn’t Know You Needed

AI is changing everything — and learning how to talk to AI (aka prompt engineering) is quickly becoming one of the most valuable skills out there.

That’s why we’ve built a hands-on program to help you master Prompt Engineering using real-world tools like ChatGPT, LLMs, RAG, and AI agents — all under the guidance of top AI mentors from India.

If you’re a student, job seeker, or someone just curious about where AI is heading — this is your chance to get ahead.

📚 What’s inside this 2-month live program?

40+ hours of live online classes with expert instructors

200+ hours of self-paced content so you can learn at your own speed

4000+ pre-written ChatGPT prompts to save time and inspire your own ideas

700+ ready-made PPT templates to help you communicate and present better

Certification by PrepInsta in Prompt Engineering with GenAI

AI Readiness Assessments to check how well you're picking things up

🚀 We’re not just teaching — we’re building careers

You’ll also get:

Portfolio, GitHub, and LinkedIn optimization to showcase your work

Resume-building sessions tailored for GenAI roles

Mock interviews (technical + HR) to help you prep like a pro

Soft skills training with real-life scenarios

Dedicated career guidance from experts who’ve helped thousands land jobs

💸 Oh, and there are rewards too!

We love rewarding effort. Our top 3 performers get:

🥇 ₹15,000 for 1st place

🥈 ₹10,000 for 2nd

🥉 ₹5,000 for 3rd

Plus, Letters of Recommendation from industry experts to give your resume that extra edge.

🌐 A community that supports you

When you join, you’re not alone. You’ll be part of a growing alumni network — a place to get referrals, inspiration, and genuine support.

🧠 Who is this for?

If you’re in your final year, prepping for placements, switching careers, or just want to get serious about AI — this program is built for you. No fancy background needed. Just your curiosity and commitment to learn.

✅ Ready to dive in?

We keep our batches small and focused — so if this feels right, don’t wait too long.

New batch starting 12th July, 2025. Let’s learn GenAI the right way.

[saltyfishh9]

8 Tools That Can Almost Replace a Full Development Team

A few years ago, if you had told me that some tools could replace 70% of an entire development team's work, I would have thought you were dreaming. But now it's 2025, and advanced tools are emerging one after another. With their help, coding efficiency can be boosted by 5x or even 10x. The idea of a single person being a development team is no longer a fantasy.

ServBay -- The Web Development Butler

Website: ServBay

Replaces: Container or package managers like Docker and Homebrew + traditional local environments like MAMP and XAMPP.

From setting up dev environments to reverse proxy and local AI deployment support, ServBay offers nearly all the features a web developer needs. Besides common programming language environments and databases, it also includes web server support, SSL certificate management, MinIO, Typesense, and more. If you need it, ServBay probably supports it.

More than just a development environment, ServBay is an all-in-one web development butler that can boost your productivity by 3x or even 5x.

Naive UI — An Elegant Vue 3 Component Library

Website: Naive UI

Replaces: Writing CSS and components from scratch / Other UI frameworks (like Element Plus).

A Vue 3-based component library that provides a vast collection of beautiful, high-performance UI components, ready to use out-of-the-box.

It boasts excellent TypeScript support and detailed theme customization capabilities, offering a superb development experience.

GitHub Copilot — AI Coding Assistant

Website: GitHub Copilot

Replaces: Junior Developers, Code Buddies.

It suggests single lines or entire functions in real-time based on context (comments and existing code).

Not only can it turn natural language comments into code, but it also helps developers quickly learn new API usages and write test cases, significantly improving coding speed and quality.

Selenium — The Industry Standard for Web Automation Testing

Website: Selenium

Replaces: Manual click-through regression testing by QA engineers.

By writing code scripts, you can simulate almost any user action in a browser (clicking, typing, scrolling, submitting, etc.).

It's cross-platform, cross-browser, and supports multiple major programming languages, making it the recognized industry standard and cornerstone of web automation testing.

Durable.co — 30-Second Website Builder

Website: Durable.co

Replaces: Front-end Developers + Designers.

With just a simple text prompt, it can generate a complete website with copy, an image gallery, and a contact form in 30 seconds.

Its core concept is "zero-thought" website creation, significantly lowering the barrier and cost for small businesses or individuals to showcase their work.

Codium AI — Auto-generates tests from your code

Website: Codium AI

Replaces: The tedious work of manually writing unit tests.

It runs as an IDE (code editor) plugin, automatically analyzing your code to generate meaningful test cases, not just templates.

It aims to improve code coverage and software quality by auto-generating tests, allowing developers to focus on business logic instead of repetitive tasks.

Strapi — Flexible Content API Engine

Website: strapi.io

Replaces: Traditional CMSs (like WordPress) / Back-end developers (for building content management backends).

Quickly build APIs and manage content through a friendly visual interface without writing back-end code from scratch.

It completely decouples content (back-end) from presentation (front-end), allowing a single piece of content to be served via API to any client, such as websites, apps, or mini-programs.

Notion AI — The All-in-One Workspace

Website:Notion AI

Replaces: Multiple separate apps like Google Docs, Trello, etc.

It integrates notes, documents, databases, project management, and knowledge bases all in one place.

Its high degree of flexibility and composability allows it to serve as a powerful personal note-taking system or an efficient team collaboration platform and knowledge base.

In Conclusion

With the right tool stack, a single person can unleash a level of creativity that once required a large team.

The real threat isn't a new tool itself, but rather your peers who are already building highly efficient workflows with these new tools.

Learn to outsource tasks to the most suitable tools, and you can return to the core role of a creator—focusing on code, user experience, and innovation.

[thetechaesthetic]

How AI Is Rewriting the Rules of Software Development in 2025

Artificial Intelligence is no longer just powering recommendations or chatbots — it's now embedded at the core of how we build, test, deploy, and scale software. From startup MVPs to full-scale enterprise platforms, AI is reshaping every stage of the product lifecycle.

Whether you're launching a fintech app or scaling a SaaS platform, integrating AI isn't optional anymore — it’s the competitive advantage.

Let’s explore how intelligent automation is changing the development game and how Zignuts helps you stay ahead with AI-Driven Software Development Services.

1. AI-Assisted Development Is Speeding Up Innovation

Tools like GitHub Copilot, TabNine, and internal LLMs are now assisting developers in writing boilerplate code, detecting bugs, and even suggesting architecture improvements.

Zignuts empowers teams with Full Stack Development Services combined with AI integrations to:

Automate repetitive logic

Enhance code reviews

Predict feature performance

Integrate AI-based testing systems

Want to build faster? Start lean with our MVP Development Services and test your product’s real potential in weeks.

2. Smarter Frontends, Powered by AI

Modern frontends are no longer just user interfaces — they’re adaptive environments.

Using behavioral data, machine learning models now adjust UI elements in real time. Our teams specialize in advanced frontend stacks like:

React JS Development

Vue JS Development

Next.js

Tailwind CSS

Want to scale fast? You can also Hire ReactJS Developers or Hire VueJS Developers on-demand.

3. Mobile Apps Meet On-Device AI

AI on the edge is now real. We're building apps with on-device intelligence for personalized, offline-first experiences — especially in health, edtech, and logistics sectors.

Whether it’s Android, iOS, or cross-platform, our teams deliver through:

Flutter Development Company

Mobile App Development Services

Android Development Company

iOS Development Company

Need to scale mobile delivery fast? Hire Android Developers or Hire Flutter Developers instantly.

4. AI-First Backends: Beyond APIs

Modern backends need to process massive data streams, respond in real-time, and learn over time. That's why we integrate LLMs, RAG frameworks, and intelligent caching in our:

Node JS Development Services

Python Development Services

Laravel Development

MongoDB and PostgreSQL integrations for scalable data

For smarter architecture, we also help you Hire Prompt Engineers, AI Developers, or ChatGPT Developers.

5. Global Collaboration, Intelligent Teams

Need to scale a team for a new project? Or augment your existing setup without long-term hiring?

We offer access to top global talent:

Hire DevOps Engineers

Hire Swift Developers

Hire Kotlin Developers

Hire Tailwind Developers

You can even schedule to Meet in Dubai, Germany, or USA to discuss your AI roadmap.

Final Word: AI Is No Longer a Feature — It’s the Foundation

In this new era, software that doesn’t evolve, dies. If your system isn’t learning, adapting, or predicting, it’s already lagging.

Now is the time to upgrade how you build, with Zignuts’ AI-Driven Software Development Services. Let’s create products that think, scale, and grow — like your business deserves.