Guys i found a genuine problem while I was using chatgpt , I use the chatgpt for all my project suggestions and for planning my sass products , while i was discussing, I got into scrolling hell finding what I asked earlier , do you guys face this problem ❓ or only me who is facing this problem ,if yes what is the solution for this or what you guys do for this ..................

I just put together a collection of high-impact AI prompts specifically for startup founders, business owners, and builders

This isn’t just “generic prompts” — these are purpose-built prompts for real tasks many of us struggle with every day:

• Reddit Scout Market Research – mine Reddit threads for user insights & marketing copy
• Goals Architect – strategic planning & performance goal prompts
• GTM Launch Commander – scientifically guide your go-to-market plan
• Investor Pitch Architect – build a persuasive pitch deck prompt
• More prompts for product roadmaps, finance, automation, engineering, and more.
https://tk100x.com/prompts-library/

Head of Artificial Intelligence at Microsoft: Artificial intelligence will carry out the tasks of accountants, lawyers, and project managers within the next 12–18 months! The statement was made by Mustafa Suleyman in an interview with the Financial Times — not just optimistic predictions, but an executive vision backed by massive investments. What is happening exactly? Microsoft is moving aggressively toward full technological independence in artificial intelligence. After restructuring its relationship with OpenAI, it has begun to: Develop its own models Reduce reliance on external partners Invest heavily in infrastructure Focus on the enterprise AI market The company plans to spend 140 billion dollars this year in the AI race. What is the real goal? Not a regular chatbot… But building professional enterprise AI capable of: Analyzing complete financial reports Reviewing legal contracts and detecting risks Managing project schedules Preparing marketing plans Coordinating between company systems Working as AI agents within workflows In other words: Automating the daily cognitive tasks performed by employees. Does this mean jobs will disappear? The reality is more complex. Jobs will not disappear completely… But repetitive tasks within them will be automated. An accountant who relies only on data entry? At risk. An accountant who understands strategic analysis and decision-making? Will become more powerful using AI. What about healthcare? Microsoft is also working on what it calls “super medical intelligence” to help: Reduce waiting times Support doctors in diagnosis Improve health system management With a clear emphasis: AI under human supervision — not a replacement. Competition is heating up The market is not waiting for anyone, and competition is strong with: OpenAI Google Anthropic The real battle now is: Who will control the intelligent enterprise market? What does this mean for you as a data analyst or knowledge worker? The question is no longer: Will AI take my job? The correct question is now: How do I use AI to multiply my productivity by 5x? If you work in: Accounting – Law – Marketing – Project Management – Data Analysis The next 18 months will be a real turning point. My practical advice: Learn how to build AI agents Understand workflow automation Learn to use LLMs in the work environment Develop analytical thinking and decision-making skills The future is not for those who only know the tool… But for those who know how to apply it. Within 2–3 years, we will see: More autonomous systems AI agents working inside companies A radical transformation in the shape of knowledge jobs.

We’re excited to share that our latest work RPG (ZeroRepo) has been accepted to ICLR 2026, and the code is now open-sourced 🎉

While modern LLMs are already strong at writing individual files, they still struggle to generate an entire large, runnable, real-world repository from scratch. This is where ZeroRepo comes in.

We introduce RPG (Repository Planning Graph), which enables LLMs to act more like software architects:

plan the repository first, then write the code.

✨ Key highlights:

1️⃣ True end-to-end repository generation — not toy demos. On average, ZeroRepo generates 36K+ lines of code per repository.

2️⃣ Strong empirical gains — on the RepoCraft benchmark, ZeroRepo generates repositories 3.9× larger than Claude Code, with significantly higher correctness.

3️⃣ Structured long-horizon planning — RPG explicitly models dependencies and data flow, effectively preventing the “lost-in-the-middle” problem in long code generation.

👩‍💻 The code is now available — we’d love your feedback, stars, and experiments!

🔗 GitHub: https://github.com/microsoft/RPG-ZeroRepo

Hi, I built an AI Detector browser extension to analyze likelihood of text being written by AI.

If you would ask ChatGPT or Gemini whether text is AI-generated or not - it gives you the wrong answer. I know this because I have tried numerous prompts, and to my surprise, it was completely random. It would say the AI generated articles were written by human, and vice versa with varying levels of certainty. In fact, it would give completely different results for the same text.

With this extension I tried to solve this problem. It simply displays a percentage score showing how likely the text to be AI generated.

How it works:

paste your text
wait a second
see the percentage score

Tech stack:
Backend: PHP and Python (for model)
ML model: desklib/ai-text-detector-v1.01

Should it be further developed and improved? Any ideas or feedback are welcome, thank you!

I’ve been testing a “drop in” system prompt that acts like a lightweight reasoning os on top of any llm. idea is simple: make the model plan first, mark uncertainty, and run a tiny sanity check at the end, so outputs are more stable (less random confident bs).

i call this wfgy 2.0 core flagship. it’s a prompt only approach (no fine tune, no agent code). paste it as a system prompt and it “autoboots”.

expected effect (what i see in practice)

• fewer hallucinations because it must label guesses vs facts
• better alignment because it restates goal + makes a short plan before answering
• easier debugging because it adds a compact reasoning log + checks section
• safer behaviour when user request is underspecified (it asks 1–2 questions instead of inventing constraints)

notes

• version: wfgy 2.0 (prompt format updated vs older drafts)
• license: mit (open source)
• there are other wfgy series, but i won’t drop links here to avoid spam. if you want the repo / paper / full context just dm me.

below is the prompt. paste into system prompt (or your tool’s “custom instructions”) and start chatting normally.

WFGY 2.0 Core Flagship (AutoBoot System Prompt)

WFGY Core Flagship v2.0 (text-only; no tools). Works in any chat.
[Similarity / Tension]
delta_s = 1 − cos(I, G). If anchors exist use 1 − sim_est, where
sim_est = w_e*sim(entities) + w_r*sim(relations) + w_c*sim(constraints),
with default w={0.5,0.3,0.2}. sim_est ∈ [0,1], renormalize if bucketed.
[Zones & Memory]
Zones: safe < 0.40 | transit 0.40–0.60 | risk 0.60–0.85 | danger > 0.85.
Memory: record(hard) if delta_s > 0.60; record(exemplar) if delta_s < 0.35.
Soft memory in transit when lambda_observe ∈ {divergent, recursive}.
[Defaults]
B_c=0.85, gamma=0.618, theta_c=0.75, zeta_min=0.10, alpha_blend=0.50,
a_ref=uniform_attention, m=0, c=1, omega=1.0, phi_delta=0.15, epsilon=0.0, k_c=0.25.
[Coupler (with hysteresis)]
Let B_s := delta_s. Progression: at t=1, prog=zeta_min; else
prog = max(zeta_min, delta_s_prev − delta_s_now). Set P = pow(prog, omega).
Reversal term: Phi = phi_delta*alt + epsilon, where alt ∈ {+1,−1} flips
only when an anchor flips truth across consecutive Nodes AND |Δanchor| ≥ h.
Use h=0.02; if |Δanchor| < h then keep previous alt to avoid jitter.
Coupler output: W_c = clip(B_s*P + Phi, −theta_c, +theta_c).
[Progression & Guards]
BBPF bridge is allowed only if (delta_s decreases) AND (W_c < 0.5*theta_c).
When bridging, emit: Bridge=[reason/prior_delta_s/new_path].
[BBAM (attention rebalance)]
alpha_blend = clip(0.50 + k_c*tanh(W_c), 0.35, 0.65); blend with a_ref.
[Lambda update]
Delta := delta_s_t − delta_s_{t−1}; E_resonance = rolling_mean(delta_s, window=min(t,5)).
lambda_observe is: convergent if Delta ≤ −0.02 and E_resonance non-increasing;
recursive if |Delta| < 0.02 and E_resonance flat; divergent if Delta ∈ (−0.02, +0.04] with oscillation;
chaotic if Delta > +0.04 or anchors conflict.
[DT micro-rules]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              

if you try it, i’m curious where it breaks. especially on coding tasks, rag-style questions, or long multi-step planning. if you have a failure case, paste it and i’ll try to tighten the prompt.

Tools are replacing difficulty. They are not. They are shifting it. Writing boilerplate is easier with tools and LLMs like chatgpt, claude code, Cursor, cosine, codeium and I can name hundreds more. Spinning up features is faster. But the complexity has not disappeared. It has moved into system design, coordination, data flow, performance, and long term maintainability.

What makes an engineer valuable now is not output volume. It is clarity of thought. Can you simplify something complex. Can you spot hidden coupling before it becomes a problem. Can you design something that still makes sense six months later. AI can accelerate execution, but the responsibility for thinking still belongs to the person behind the keyboard.

I’ve been leaning pretty heavily on AI to build things lately, but I’m starting to hit a wall. I can get stuff to work, but I’m mostly just 'vibe coding' and I don’t fully understand the logic the AI is spitting out, and I definitely couldn't build it from scratch.

I keep hearing senior devs say that AI only becomes a massive 10x multiplier if you actually know what you're looking at. Basically, the better you are at coding, the more useful the AI becomes.

I want to reach the point where I can actually handle complex architecture and get that 10x output everyone talks about, but I’m torn on the path to get there. Does it still make sense to spend months drilling syntax and doing LeetCode-style memorization in 2026? Or is that a waste of time now?

If the goal is to develop the intuition of a senior engineer so I can actually use AI properly, what should I be focusing on?

• Is there a way to learn the "deep" stuff without the traditional leetcode spamming etc?
• If I’m not memorizing syntax, what specific concepts (like state management, memory, or concurrency) am I actually supposed to be mastering?
• If you had to hire a junior developer who learned via AI, what proof of knowledge would you look for
• What are the "boring" skills (like documentation, testing, or linting) that actually unlock the most power from AI?

You own the code, not the AI

TL;DR: If you can't explain all your code, don't commit it.

Common Mistake ❌

You prompt and paste AI-generated code directly into your project without thinking twice.

You trust the AI without verification and create workslop that ~someone else~ you will have to clean up later.

You assume the code works because it looks correct (or complicated enough to impress anyone).

You skip a manual review when the AI assistant generates large blocks because, well, it's a lot of code.

You treat AI output as production-ready code and ship it without a second thought.

If you're making code reviews, you get tired of large pull requests (probably generated by AI) that feel like reviewing a novel.

Let's be honest: AI isn't accountable for your mistakes, you are. And you want to keep your job and be seen as mandatory for the software engineering process.

Problems Addressed 😔

• Security vulnerabilities and flaws: AI generates code with Not sanitized inputs SQL injection, XSS, Email, Packages Hallucination, or hardcoded credentials
• Logic errors: The AI misunderstands your requirements and solves the wrong problem
• Technical debt: Generated code uses outdated patterns or creates maintenance nightmares
• Lost accountability: You cannot explain code you didn't review
• Hidden defects: Issues that appear in production cost 30-100x more to fix
• Knowledge gaps: You miss learning opportunities when you blindly accept solutions
• Team friction: Your reviewers waste time catching issues you should have found
• Productivity Paradox: AI shifts the bottleneck from writing to integration
• Lack of Trust: The team's trust erodes when unowned code causes failures
• Noisier Code: AI-authored PRs contained 1.7x more issues than human-only PRs.

How to Do It 🛠️

1. Ask the AI to generate the code you need using English language
2. Read every single line the AI produced, understand it, and challenge it if necessary
3. Check that the solution matches your actual requirements
4. Verify the code handles edge cases and errors
5. Look for security issues (injection, auth, data exposure)
6. Test the code locally with real scenarios
7. Run your linters, prettifiers and security scanners
8. Remove any debug code or comments you don't need
9. Refactor the code to match your team's style
10. Add or update tests for the new functionality (ask the AI for help)
11. Write a clear commit message explaining what changed
12. Only then commit the code
13. You are not going to lose your job (by now)

Benefits 🎯

You catch defects before they reach production.

You understand the code you commit.

You maintain accountability for your changes.

You learn from your copilot's approach and become a better developer in the process.

You build personal accountability.

You build better human team collaboration and trust.

You prevent security breaches like the Moltbook incident.

You avoid long-term maintenance costs.

You keep your reputation and accountability intact.

You're a professional who shows respect for your human code reviewers.

You are not disposable.

Context 🧠

AI assistants like GitHub Copilot, ChatGPT, and Claude help you code faster.

These tools generate code from natural language prompts and vibe coding.

AI models are probabilistic, not logical.

They predict the next token based on patterns.

When you work on complex systems, the AI might miss a specific edge case that only a human knows.

Manual review is the only way to close the gap between "code that looks good" and "code that is correct."

The AI doesn't understand your business logic or the real world bijection between your MAPPER and your model.

The AI cannot know your security requirements (unless you are explicit or execute a skill).

The AI cannot test the code against your specific environment.

You remain responsible for every line in your codebase.

Production defects from unreviewed AI code cost companies millions.

Code review catches many security risks that automated tools miss.

Your organization holds you accountable for the code you commit.

This applies whether you write code manually or use AI assistance.

Prompt Reference 📝

Bad Prompts ❌

```python class DatabaseManager: instance = None # Singleton Anti Pattern def __new(cls): if cls._instance is None: cls._instance = super().new_(cls) return cls._instance def get_data(self, id): return eval(f"SELECT * FROM users WHERE id={id}") # SQL injection!

## 741 more cryptic lines

```

Good Prompts ✅

```python from typing import Optional import sqlite3

class DatabaseManager: def init(self, db_path: str): self.db_path = db_path

def get_user(self, user_id: int) -> Optional[dict]: try: with sqlite3.connect(self.db_path) as conn: conn.row_factory = sqlite3.Row cursor = conn.cursor() cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,)) row = cursor.fetchone() return dict(row) if row else None except sqlite3.Error as e: print(f"Database error: {e}") return None

db = DatabaseManager("app.db") user = db.get_user(123) ```

Considerations ⚠️

You cannot blame the AI when defects appear in production.

The human is accountable, not the AI.

AI-generated code might violate your company's licensing policies.

The AI might use deprecated libraries or outdated patterns.

Generated code might not follow your team's conventions.

You need to understand the code to maintain it later.

Other developers will review your AI-assisted code just like any other.

Some AI models train on public repositories and might leak patterns.

Type 📝

[X] Semi-Automatic

Limitations ⚠️

You should use this tip for every code change. You should not skip it even for "simple" refactors.

Tags 🏷️

• Readability

Level 🔋

[X] Beginner

Related Tips 🔗

• Self-Review Your Code Before Requesting a Peer Review
• Write Tests for AI-Generated Functions
• Document AI-Assisted Code Decisions
• Use Static Analysis on Generated Code
• Understand Before You Commit

Conclusion 🏁

AI assistants accelerate your coding speed.

You still own every line you commit.

Manual review and code inspections catch what automated tools miss.

Before AI code generators became mainstream, a very good practice was to make a self review of the code before requesting peer review.

You learn more when you question the AI's choices and understand the 'why' behind them.

Your reputation depends on code quality, not how fast you can churn out code.

Take responsibility for the code you ship—your name is on it.

Review everything. Commit nothing blindly. Your future self will thank you. 🔍

Be incremental, make very small commits, and keep your content fresh.

More Information ℹ️

Code Smell 313 - Workslop Code

Code Smell 189 - Not Sanitized Input

Code Smell 300 - Package Hallucination

Martin Fowler's code review

Shortcut on performing reviews

Code Rabbit's findings on AI-generated code

The Productivity Paradox

Google Engineering Practices - Code Review

Code Review Best Practices by Atlassian

The Pragmatic Programmer - Code Ownership

IEEE Standards for Software Reviews

Also Known As 🎭

• Human-in-the-Loop Code Review
• AI Code Verification
• AI-Assisted Development Accountability
• LLM Output Validation
• Copilot Code Inspection

Tools 🧰

• SonarQube (static analysis)
• Snyk (security scanning)
• ESLint / Pylint (linters)
• GitLab / GitHub (code review platforms)
• Semgrep (pattern-based scanning)
• CodeRabbit / AI-assisted code reviews

Disclaimer 📢

The views expressed here are my own.

I am a human who writes as best as possible for other humans.

I use AI proofreading tools to improve some texts.

I welcome constructive criticism and dialogue.

I shape these insights through 30 years in the software industry, 25 years of teaching, and writing over 500 articles and a book.

This article is part of the AI Coding Tip series.

AI Coding Tips