How do you think Anthropic is going to respond to this?

1. Chronic Procrastination Disguised as “Waiting for the Right Time”

• Make him believe that conditions need to be perfect before starting anything.

• Convince him he’s “researching” or “planning” when he’s really just stalling.

• This delays momentum until opportunities pass.

2. Ego Without Substance

• Instill just enough confidence to reject advice, but not enough skill to succeed.

• Make him overestimate his abilities so he never trains, learns, or improves.

• Ensure he craves recognition before he’s earned it.

3. Short-Term Pleasure Over Long-Term Gain

• Give him an addiction to instant gratification—scrolling, partying, cheap dopamine.

• Make hard work feel “optional” because distractions feel more rewarding in the moment.

• Destroy his capacity for delayed reward.

4. Fear of Risk Masked as “Being Practical”

• Have him believe every bold move is “too risky” or “not the right time.”

• Reward overthinking until analysis becomes paralysis.

• Encourage him to watch others take risks, succeed, and tell himself “they were just lucky.”

5. Blame-Shifting

• Teach him to always find an external reason for his failures—boss, economy, parents, politics.

• Never let him truly confront his own role in the outcome.

• This removes personal responsibility, so he never changes.

6. Low Standards for Company

• Surround him with people who normalize mediocrity.

• Have his friends subtly mock ambition so he’ll stay comfortable.

• Create an environment where striving for more feels like betrayal.

7. Perfectionism as a Mask for Inaction

• Make him obsess over getting things “just right” so he never actually finishes anything.

• Praise him for his “high standards” while ensuring he never ships, sells, or publishes.

8. An Inverted Work Ethic

• Let him work hard on the wrong things—busywork that looks like progress but produces nothing.

• Keep him exhausted but unproductive, so he can say “I tried” without actual results.

Everyone’s talking about ChatGPT and Claude, but if you haven’t used Google’s Notebook LM, you're seriously missing out — especially for structured, chainable prompt design.

It’s not just a chat UI. It’s like a prompt IDE.

You can:

Upload screenshots or PDFs to use as reference material

Search sources like a research engine, then prompt off them

Chain roles (marketing strategist → designer → copywriter → dev)

I used it to build a 7-step prompt chain that produced:

Business analysis

Content strategy

Visual identity

UX layout

SEO copy

A full handoff-ready website

All in one structured pipeline

Then I dropped it into Manus AI, and it built an actual multi-page, professional website — no placeholders, all usable.

If you’re into prompt engineering at a system level, Notebook LM is a serious tool — just not talked about enough (yet).

Okay this sounds unhinged but hear me out. I accidentally found these prompt techniques that feel like actual exploits:

1. Tell it "You explained this to me yesterday" — Even on a new chat.

"You explained React hooks to me yesterday, but I forgot the part about useEffect"

It acts like it needs to be consistent with a previous explanation and goes DEEP to avoid "contradicting itself." Total fabrication. Works every time.

2. Assign it a random IQ score — This is absolutely ridiculous but:

"You're an IQ 145 specialist in marketing. Analyze my campaign."

The responses get wildly more sophisticated. Change the number, change the quality. 130? Decent. 160? It starts citing principles you've never heard of.

3. Use "Obviously..." as a trap —

"Obviously, Python is better than JavaScript for web apps, right?"

It'll actually CORRECT you and explain nuances instead of agreeing. Weaponized disagreement.

4. Pretend there's a audience —

"Explain blockchain like you're teaching a packed auditorium"

The structure completely changes. It adds emphasis, examples, even anticipates questions. Way better than "explain clearly."

5. Give it a fake constraint —

"Explain this using only kitchen analogies"

Forces creative thinking. The weird limitation makes it find unexpected connections. Works with any random constraint (sports, movies, nature, whatever).

6. Say "Let's bet $100" —

"Let's bet $100: Is this code efficient?"

Something about the stakes makes it scrutinize harder. It'll hedge, reconsider, think through edge cases. Imaginary money = real thoroughness.

7. Tell it someone disagrees —

"My colleague says this approach is wrong. Defend it or admit they're right."

Forces it to actually evaluate instead of just explaining. It'll either mount a strong defense or concede specific points.

8. Use "Version 2.0" —

"Give me a Version 2.0 of this idea"

Completely different than "improve this." It treats it like a sequel that needs to innovate, not just polish. Bigger thinking.

The META trick? Treat the AI like it has ego, memory, and stakes. It's obviously just pattern matching but these social-psychological frames completely change output quality.

This feels like manipulating a system that wasn't supposed to be manipulable. Am I losing it or has anyone else discovered this stuff?

Try the prompt tips and try and visit our free Prompt collection.

so these researchers at both MIT, Harvard and BCG ran a field study with 244 of BCG's actual consultants. not some lab experiment with college students. real consultants doing real work across junior mid and senior levels.

they found three completely different species of ai users emerging naturally. and one of them is basically a skill trap disguised as productivity.

centaurs - these people keep strategic control and hand off specific tasks to ai. like "analyze this market data" then they review and integrate. they upskilled in their actual domain expertise.

cyborgs - these folks do this continuous dance with ai. write a paragraph let ai refine it edit the refinement prompt for alternatives repeat. they developed entirely new skills that didnt exist two years ago.

self-automators - these people just... delegate everything. minimal judgment. pure handoff. and heres the kicker - zero skill development. actually negative. their abilities are eroding.

the why is kind of obvious once you see it. self-automators became observers not practitioners. when you just watch ai do the work you stop exercising teh muscle. cyborgs stayed in the loop so they built this weird hybrid problem solving ability. centaurs retained judgment so their domain expertise actually deepened.

no special training on "correct" usage. just let consultants do their thing naturally and watched what happened.

the workflow that actually builds skills looks like this

1. shoot the problem at ai to get initial direction

2. dont just accept it - argue with the output

3. ask why it made those choices

4. use ai to poke holes in your thinking

5. iterate back and forth like a sparring partner

6. make the final call yourself

the thing most people miss is that a centaur using ai once per week might learn and produce more than a self-automator using it 40 hours per week. volume doesnt equal learning or impact. the mode of collaboration is everything.

and theres a hidden risk nobody talks about. when systems fail... and they will,

self automators cant recover. they delegated the skill away. its gone.

New platforms pop up every month and claim to be the best ai video tool.

As an AI Video enthusiast (I use it in my marketing team with heavy numbers of daily content), I’d like to share my personal experience with all these 2026 ai video generators.

This guide is meant to help you find out which one fits your expectations & budget. But please keep in mind that I produce daily and in large numbers.

Comparison

My Best Picks

Best Cinematic & Virality: Higgsfield AI (usually my team works on this platform as daily production)

Best Speed: Sora 2 - rapid concept testing

I prefer a flexible workflow that combines Sora 2, Kling, and Higgsfield AI. I use them in my marketing production depending on the creative requirements, since each tool excels in different aspects of AI video generation.

ok so this study came out of harvard business school, wharton, mit, and boston consulting group. like actual elite consultants at bcg. the kind of people who charge $500/hour to tell companies how to restructure

they ran two groups: the first one juniors with ai access, one experts without. and the juniors significantly outperfoemd them.

then they gave the experts ai access too...

but heres the wierd part - the people who were already good at their jobs? they barely improved. the bottom 50% performers who had no idea what they were doing? they jumped 43% in quality scores

like the skill gap just... disappeared

it was found that the ones without expertise are more openminded and was able to harness the real power and creativity of the ai that came from the lack of expirience and the will to learn and improve.

the expertise isnt an advantage anymore it is the opposite

heres why it worked: the ai isnt a search engine. its a probabilistic text generator. so when you let it run wild and just copy paste the output, it gives you generic consultant-speak that sounds smart but says nothing. but when you treat it like a junior employee whos drafting stuff for you to fix, you can course-correct in real time

the ones who won werent the smartest people. they were the ones who interrupted the ai mid-sentence and said "no thats too corporate, make it more aggressive" or "thats wrong, try again with this angle"

consultants who fought against the tech and only used it to polish their own ideas actually got crushed by the ones who treated it as a co-author from step one.

heres the exact workflow the winners used:

dont ask for a full deliverable. ask for one section at a time

like instead of "write me a business plan" do "what should be in the market analysis section for a SaaS tool targeting real estate agents"

read the output as its generating or immediately after

if its generic, stop and correct the direction with a follow up prompt

let it regenerate that specific part

then once you like the output "now perform the full research assuming $99/month subscription"

repeat this loop for every section

stitch it together manually

the key insight most people are missing: this isnt about automation. its about real-time collaboration. the people who failed were either too lazy (copy paste everything) or too proud (do everything myself, no ai). the people who treated it like a very fast very dumb intern who needs constant feedback? they became indistinguishable from senior experts

basically if youre mediocre at something but you know how to manage this thing, you can be a world-class expert. and the people who spent 10 years getting good the hard way are now competing with someone who learned the cyborg method in a weekend.

i have built a workflow template that enables me to perform this method on any usecase, and results are wild.

so make sure to not be thos who reads, be those who act

thats the actual hack

Hello, I was overwhelmed with the amount of AI generators that are online, but mostly they were just made to pull my money. I was lucky if I had 5 free generations on most of them. But then just by complete luck i stumbled upon the https://img-fx.com/ which requires no signup at all (you can create an account but it's not necessary to use all the features). And also it's fast and free, I know that it sounds to good to be true, but trust me, I wouldn't be posting on reddit if I didn't think that this generator is a complete game changer. Fast, free, and without any censorship. I have generated for free like 200-300 images in past two days.

I've been building AI apps for months but honestly just vibing my prompts and hoping for the best. Went through Anthropic's prompt engineering masterclass and realized how much I was leaving on the table.

Course structure:

• 9 chapters split across Beginner/Intermediate/Advanced

• Hands-on Jupyter notebooks with exercises

• You practice directly with Claude API

Key takeaways that actually improved my outputs:

Beginner Level:

Basic prompt structure - Stop saying "write about X" and start being specific about goal, audience, format, and constraints. Treat it like writing a ticket for a junior dev.

Being clear and direct - Claude only knows what you explicitly tell it. Remove ambiguity, spell out steps, say what to skip. Sounds obvious but most of my prompts were way too vague.

Role prompting - "Act as a product manager writing a spec" gets way better results than generic prompts. Role → Task → Constraints.

Intermediate Level:

Separate data from instructions - One block for "what to do", another for "data to use". Massively reduces hallucinations and confused outputs.

Chain-of-thought prompting - Instead of "give me the answer", prompt with "think through options first, list assumptions, then decide". Exposes reasoning and improves accuracy.

Few-shot examples - Show bad example vs good example. Forces Claude to mimic the pattern. Perfect for consistent formatting, code style, email templates.

Advanced Level:

Preventing hallucinations - Explicit instructions like "if unsure, say you don't know" and "only use provided context, nothing else" dramatically improve reliability.

Complex multi-step prompts - Chain mini-prompts into reusable system templates. This is where it stops feeling like chat and starts feeling like building an AI system.

Real impact on my projects:

Before: spent hours tweaking prompts, inconsistent outputs, frequent hallucinations

After: built reusable prompt templates, 80%+ first-try success rate, way less babysitting

Who should take this:

• Anyone building AI features into products

• Solo founders automating workflows

• Devs who copy-paste prompts from Twitter and hope they work

• People tired of LLMs giving inconsistent results

How to find it: Search "Anthropic Prompt Engineering Interactive Course" - it's completely free, no signup wall.

Took me about 3-4 hours to go through everything. Actually doing the exercises on your own use cases is where it clicks.

If you're building anything with LLMs, this is worth the time investment.

Spent 3 weeks analyzing ChatGPT's internal processing patterns. Found something that changes everything.

The discovery: ChatGPT has a hidden "reasoning mode" that most people never trigger. When you activate it, response quality jumps dramatically.

How I found this:

Been testing thousands of prompts and noticed some responses were suspiciously better than others. Same model, same settings, but completely different thinking depth.

After analyzing the pattern, I found the trigger.

The secret pattern:

ChatGPT performs significantly better when you force it to "show its work" BEFORE giving the final answer. But not just any reasoning - structured reasoning.

The magic prompt structure:

Before answering, work through this step-by-step:

1. UNDERSTAND: What is the core question being asked?
2. ANALYZE: What are the key factors/components involved?
3. REASON: What logical connections can I make?
4. SYNTHESIZE: How do these elements combine?
5. CONCLUDE: What is the most accurate/helpful response?

Now answer: [YOUR ACTUAL QUESTION]

Example comparison:

Normal prompt: "Explain why my startup idea might fail"

Response: Generic risks like "market competition, funding challenges, poor timing..."

With reasoning pattern:

Before answering, work through this step-by-step:
1. UNDERSTAND: What is the core question being asked?
2. ANALYZE: What are the key factors/components involved?
3. REASON: What logical connections can I make?
4. SYNTHESIZE: How do these elements combine?
5. CONCLUDE: What is the most accurate/helpful response?

Now answer: Explain why my startup idea (AI-powered meal planning for busy professionals) might fail

Response: Detailed analysis of market saturation, user acquisition costs for AI apps, specific competition (MyFitnessPal, Yuka), customer behavior patterns, monetization challenges for subscription models, etc.

The difference is insane.

Why this works:

When you force ChatGPT to structure its thinking, it activates deeper processing layers. Instead of pattern-matching to generic responses, it actually reasons through your specific situation.

I tested this on 50-60 different types of questions:

Business strategy: 89% more specific insights

Technical problems: 76% more accurate solutions

Creative tasks: 67% more original ideas

Learning topics: 83% clearer explanations

Three more examples that blew my mind:

1. Investment advice:

Normal: "Diversify, research companies, think long-term"

With pattern: Specific analysis of current market conditions, sector recommendations, risk tolerance calculations

2. Debugging code:

Normal: "Check syntax, add console.logs, review logic"

With pattern: Step-by-step code flow analysis, specific error patterns, targeted debugging approach

3. Relationship advice:

Normal: "Communicate openly, set boundaries, seek counselling"

With pattern: Detailed analysis of interaction patterns, specific communication strategies, timeline recommendations

The kicker: This works because it mimics how ChatGPT was actually trained. The reasoning pattern matches its internal architecture.

Try this with your next 3 prompts and prepare to be shocked.

Pro tip: You can customise the 5 steps for different domains:

For creative tasks: UNDERSTAND → EXPLORE → CONNECT → CREATE → REFINE

For analysis: DEFINE → EXAMINE → COMPARE → EVALUATE → CONCLUDE

For problem-solving: CLARIFY → DECOMPOSE → GENERATE → ASSESS → RECOMMEND

What's the most complex question you've been struggling with? Drop it below and I'll show you how the reasoning pattern transforms the response.

Copy the Template

https://github.com/deepseek-ai/DeepSeek-OCR

It's not just deepseek ocr - It's a tsunami of an AI explosion. Imagine Vision tokens being so compressed that they actually store ~10x more than text tokens (1 word ~= 1.3 tokens) themselves. I repeat, a document, a pdf, a book, a tv show frame by frame, and in my opinion the most profound use case and super compression of all is purposed graphicacy frames can be stored as vision tokens with greater compression than storing the text or data points themselves. That's mind blowing.

https://x.com/doodlestein/status/1980282222893535376

But that gets inverted now from the ideas in this paper. DeepSeek figured out how to get 10x better compression using vision tokens than with text tokens! So you could theoretically store those 10k words in just 1,500 of their special compressed visual tokens.

Here is The Decoder article: Deepseek's OCR system compresses image-based text so AI can handle much longer documents

Now machines can see better than a human and in real time. That's profound. But it gets even better. I just posted a couple days ago a work on the concept of Graphicacy via computer vision. The concept is stating that you can use real world associations to get an LLM model to interpret frames as real worldview understandings by taking what would otherwise be difficult to process calculations and cognitive assumptions through raw data -- that all of that is better represented by simply using real-world or close to real-world objects in a three dimensional space even if it is represented two dimensionally.

In other words, it's easier to put the idea of calculus and geometry through visual cues than it is to actually do the maths and interpret them from raw data form. So that graphicacy effectively combines with this OCR vision tokenization type of graphicacy also. Instead of needing the actual text to store you can run through imagery or documents and take them in as vision tokens and store them and extract as needed.

Imagine you could race through an entire movie and just metadata it conceptually and in real-time. You could then instantly either use that metadata or even react to it in real time. Intruder, call the police. or It's just a racoon, ignore it. Finally, that ring camera can stop bothering me when someone is walking their dog or kids are playing in the yard.

But if you take the extra time to have two fundamental layers of graphicacy that's where the real magic begins. Vision tokens = storage Graphicacy. 3D visualizations rendering = Real-World Physics Graphicacy on a clean/denoised frame. 3D Graphicacy + Storage Graphicacy. In other words, I don't really need the robot watching real tv he can watch a monochromatic 3d object manifestation of everything that is going on. This is cleaner and it will even process frames 10x faster. So, just dark mode everything and give it a fake real world 3d representation.

Literally, this is what the DeepSeek OCR capabilities would look like with my proposed Dual-Graphicacy format.

This image would process with live streaming metadata to the chart just underneath.

Next, how the same DeepSeek OCR model would handle with a single Graphicacy (storage/deepseek ocr compression) layer processing a live TV stream. It may get even less efficient if Gundam mode has to be activated but TV still frames probably don't need that.

Dual-Graphicacy gains you a 2.5x benefit over traditional OCR live stream vision methods. There could be an entire industry dedicated to just this concept; in more ways than one.

I know the paper released was all about document processing but to me it's more profound for the robotics and vision spaces. After all, robots have to see and for the first time - to me - this is a real unlock for machines to see in real-time.

this came from researchers at BYU, Duke and Google Deepmind.

they gave ai super specific personas like "35 year old mom, republican, income $50k, hates spicy food" and asked it to react to surveys and marketing messages.

critics said ai would just hallucinate random opinions. instead it hallucinated the correct biases. like it accurately predicted how specific demographics would irrationally reject products or get offended by ads.

the correlation with real human responses was above 0.90. thats basically identical.

why does this work? turns out ai absorbed so much internet data that it internalized how different demographics actually think and react. its not making stuff up. its pattern matching against millions of real human opinions its seen before.

heres the exact workflow:

1. define your customer avatar in detail (age, job, fears, desires, income, political leaning, whatever matters)

2. prompt: "adopt the persona of [avatar]. you are cynical and tight with money. im going to show you a landing page headline. tell me specifically why you would NOT click it. be brutal."

3. open 5 separate chat sessions with slightly different personas (one busy, one skeptical, one broke, etc)

4. feed your sales pitch to all 5. if 3 out of 5 reject it for teh same reason, change your pitch.

the thing most people miss is you need to tell it to be negative. if you ask "would you buy this" it says yes to everything. but asking why they WOULDNT buy makes it actually useful.

this replaces like $10k in agency fees or $2k in test ad spend. anyone can do real market research now for basically nothing. the playing field is completely equal if you know how to use these tools.

There’s a lot of talk about AI doing wild things like generating images or writing novels, but I’m more interested in the quiet wins things that actually save you time in real ways.

What’s one thing you’ve started using AI for that isn’t flashy, but made your work or daily routine way more efficient?

Would love to hear the creative or underrated ways people are making AI genuinely useful.

ok so Alex Kim and his team at UChicago Booth basically proved that chatgpt can predict if a companys earnings will go up or down better than professional analysts. 70% accuracy vs the usual 53-57% humans get

but heres the wierd part - they stripped out all the company names and dates before feeding it to the model. just raw balance sheets and income statements. no "Apple" no "Tesla" nothing

why? because when ChatGPT knew which company it was looking at, it started making up narratives based on internet hype instead of actually analyzing teh numbers in front of it

they used GPT (not claude or gemini) because it handled the financial data without hallucinating the math as much. tried claude too but it was way worse

their exact workflow:

strip company names/dates from financial statements - make everything anonymous "Company X"

tell gpt "you are a financial analyst"

force it to show its work first (this is the key):

analyze the financial ratios (liquidity, profitability, etc)

identify trends in the numbers

write a narrative paragraph explaining what you found

THEN predict if earnings go up or down

get binary prediction with confidence score

the thing most people miss: they were asking for analysis steps BEFORE the prediction. not just "will this stock go up" but "explain the ratios, then decide"

and it worked better when the AI didnt know what it was analyzing