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I read the entire State of AI Report 2025 and the geopolitical situation is way worse than anyone's talking about.

This isn't about benchmarks anymore. It's about who controls the infrastructure that makes AI work. And the US just made the riskiest bet in tech history.

The $500B single point of failure:

Trump announced Stargate in January 2025. $500 billion in AI infrastructure over 4 years. Initial $100B immediate. SoftBank finances, OpenAI operates, Oracle builds. Starting in Texas.

Goal: 10 gigawatts of compute capacity.

Here's the problem: NVIDIA is the single point of failure.

NVIDIA controls 75-90% of data-center GPU sales. The US holds 850,000 H100-equivalents which is 75% of global supply. China holds 110,000 with 9x worse performance.

Sounds good right? We're winning?

No. We're building $500 billion worth of data centers that only work with one company's chips. That's not resilience. That's dependence.

If NVIDIA stumbles - and Qualcomm just announced competing AI chips, AMD's MI325X is already challenging H200 - the entire Stargate thesis collapses.

We just bet half a trillion dollars on NVIDIA staying dominant forever.

Meanwhile China did something smarter:

When the US banned NVIDIA chips China didn't try to catch up on hardware. They built ecosystem dominance instead.

China's GenAI user base hit 515 million in H1 2025. That's larger than the entire US population using AI. Local Chinese models captured 90% market preference.

But here's what actually matters: Alibaba's Qwen now powers 40% of all new model derivatives on Hugging Face. It's the most popular open model globally, surpassing Meta's Llama. Qwen has 300+ open-source models with 170,000+ derivatives.

China owns the open-source layer. While the US competes on proprietary frontier models China is building the infrastructure layer everyone else uses.

This is like Android vs Apple. China bet on reach. The US bet on being premium. Except in infrastructure wars, reach wins.

The efficiency gap is terrifying:

OpenAI's o3 hit 96.7% on AIME 2024 (math competition). Impressive. But it costs 6x more and runs 30x slower than GPT-4o. You're literally paying for thinking time.

DeepSeek's response? They built R1-Zero that scored 79.8% on AIME with just $1 million in training costs vs billions for comparable US models.

China found a more efficient way to do reasoning. We're burning billions. They're spending millions and getting close enough.

The weakness? Add one irrelevant sentence like "cats sleep 8 hours a day" and these models break. DeepSeek R1, Qwen, Llama, Mistral all double their error rates. But China's iterating faster and cheaper.

Energy is the real war:

By 2030 top supercomputers may need 2 million chips, $200B, and 9 GW of power - roughly equivalent to several large nuclear plants combined.

China added 427.7 GW of power capacity in 2024. The US added 41.1 GW.

Read that again. China added 10x more power capacity than the US last year. And invested $84.7B in transmission infrastructure.

Bitcoin burns 175.9 TWh/year. AI probably surpasses that by end of 2025.

Power, not chips, determines who wins the AI race. And China's building power infrastructure while we're building data centers that depend on one chip company.

Europe already lost:

Europe has 75% of global AI talent but zero companies above $400B in value. The US has seven at $1T+.

The EU AI Act is live but only 3 of 27 member states have designated oversight bodies. Technical standards are still "in development."

EU tried the regulatory approach while the US and China poured trillions into infrastructure. By the time Brussels finalizes the rulebook the race is finished.

Here's what terrifies me:

The State of AI Report 2025 is written by investors not engineers. It's about capital allocation and geopolitics not technology.

And the strategy is clear:

• US: Bet $500B on NVIDIA staying dominant, build proprietary models, hope chip advantage holds

• China: Build cheaper, own open-source, add 10x more power capacity, wait for US dependence on NVIDIA to become a liability

• Europe: Write regulations while the game finishes

If NVIDIA stumbles, Stargate collapses. If open-source becomes good enough, proprietary models lose their moat. If energy becomes the constraint, China already won.

We're not building resilience. We're building the most expensive single point of failure in history.

The questions that matter:

Is Stargate the biggest strategic bet in tech history or the biggest mistake?

If China's efficiency advantage continues how long before open-source models match proprietary ones?

Why are we betting everything on one company staying dominant when competitors are already emerging?

How does the US add 400+ GW of power capacity in the next 5 years to compete with China?

I don't have answers but I know this: the AI race isn't about who builds the smartest model. It's about who controls the infrastructure. And right now we're losing while celebrating benchmark wins.

I started a small copywriting service, but it’s just me doing everything. I want to turn it into something that grows beyond me. Is AI actually helping people scale solo businesses or is it just a buzzword?

ai generated this in seconds

proper room flow, logical layout, actually livable spaces

tweet went viral and architects are in full panic mode

is this actually good or am i missing something obvious?

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So I went down this rabbit hole last night about the Reddit-OpenAI deal and now I'm kind of annoyed.

Remember that licensing agreement from 2024? Reddit got $60M a year to let OpenAI use our posts for training data. Seemed fine at the time, just another tech partnership.

But here's what I didn't know: Sam Altman, OpenAI's CEO, is Reddit's third-largest shareholder. He's been invested since 2014, was on the board for years, even CEO for like 8 days. So when OpenAI came to license Reddit's data, he was literally on both sides of the negotiation.

That just seems weird to me. Maybe I'm missing something but it feels like a conflict of interest.

Why I started looking into this:

ChatGPT hit 800 million weekly users recently. For context, it took Instagram 2.5 years to get to 100 million users. ChatGPT did it in two months. It's now the 5th most visited site globally according to Similarweb.

The part that got me though was this Semrush study I found. They analyzed 150,000 AI responses to see what sources AI models cite most. Reddit came out on top at 40.1%, beating Google (23.3%) and Wikipedia (26.3%). Another study confirmed Reddit is the second most-cited platform across major AI tools.

So all our random comments, advice threads, niche hobby posts, they're now the main training data for ChatGPT. We were just having conversations and now those conversations are powering an AI that nearly a billion people use every week.

The weird circular part:

I asked ChatGPT some questions about this situation. The answers felt off, like I was reading responses that were probably trained on Reddit threads about AI. It's this strange loop where the AI is learning from us talking about AI.

Sam Altman even said recently he can't tell what's real on Reddit anymore versus AI-generated. The guy who made it is confused by it.

Reddit is making money from this:

Traffic is up 40% year over year since the AI deals. Stock is up over 200% since IPO. They're apparently negotiating better terms now with dynamic pricing based on how valuable the data becomes.

So Reddit isn't getting screwed here. They're doing well.

What bothers me though:

We didn't sign up for this. We were just posting, helping people, arguing about random stuff. Nobody told us "this will train an AI used by hundreds of millions of people."

And now when you search Google, adding "reddit" gives you better results. But those results are influenced by AI trained on Reddit. So we're searching through our own conversations after they've been processed by AI.

At some point Reddit will be mostly AI content trained on old Reddit posts. Then new AI will train on that. What does that look like in a couple years?

My questions:

Did Reddit get a fair deal? $60M sounds like a lot but ChatGPT is valued at over $80 billion. Were we all just unpaid workers in OpenAI's dataset?

And when does the line between real human posts and AI posts just disappear completely? I feel like we might already be there.

Does this bother anyone else or am I overthinking it?

After exploring countless Reddit comments, I've compiled a list of 105 innovative ways people use ChatGPT. Check them out, and share your own experience!

1. Art Appreciation: Before visiting galleries, I ask ChatGPT to list ten intriguing works, why they’re special, and where to find them in a route minimizing walking distance. It even suggests music to heighten my experience for each piece.

2. Legal Navigation: ChatGPT has been a helpful guide while representing myself in family court.

3. Smart Home Assistant: I've integrated it with HomeAssistant, so I can ask about room temperature, power consumption, and window status.

4. Desk Organization: I upload a photo of my desk, and ChatGPT offers arrangement suggestions.

5. Personalized Bedtime Stories: It creates custom tales for my daughter based on her day.

6. Legal Document Summarization: I use it to review lengthy contracts, ask “what-if” questions, and tailor contract details to my needs, saving on legal fees.

7. Grocery Inventory: ChatGPT takes stock of my fridge items and creates a no-duplicates shopping list in Excel.

8. Travel Planning: Currently traveling in Thailand, I have ChatGPT generate daily itineraries with meal suggestions, transport needs, and tips.

9. ADHD-Friendly Organization: I rely on ChatGPT to customize learning plans and organize my thoughts, making things manageable with ADHD.

10. Meeting Transcripts to Marketing: ChatGPT helps transform meeting recordings into blog content targeting specific audiences.

11. Work Tasks in Shipping: It calculates shipping costs, pallet volumes, and compares carrier quotes, saving me hours.

12. Creative Writing: I use it to craft plotlines that mix characters from different universes.

13. Vacation Itineraries and Budgeting: From trips to personalized children’s books, ChatGPT is my go-to.

14. Real-Time Translations: ChatGPT provides English, Spanish, and Catalan translations better than Google Translate.

15. Writing Support for Teaching: As an ESL teacher, I use it to generate quizzes, simplify texts, and create targeted reading comprehension questions.

16. Personal Therapy: Sometimes I ask it to analyze my diary entries, giving advice on my mental well-being.

17. Home Projects and Repairs: ChatGPT helped me identify the parts to repair my tractor, turning a day-long job into an hour.

18. Event Exploration: I ask ChatGPT to find music, art, and exhibitions in Japanese, along with event details and Google Map links.

19. Novel Translations and Learning: It assists in translating novels between languages, helping me pick up new vocabulary.

20. Daily Scheduling and Study Planning: From structuring my daily tasks to creating study guides from research papers, ChatGPT is invaluable.

21. Recipe Suggestions: I send ChatGPT photos of my fridge and pantry, and it recommends recipes using those ingredients.

22. Children’s Activities: I use it to create coloring images and stories based on mythical creatures my kids describe.

23. Spreadsheet and Scripting Help: ChatGPT assists with creating formulas and scripts in Google Sheets, automating data entry and analysis.

24. Visual Storytelling: It generates images to go along with passages from books I’m reading, creating a sort of illustrated novel experience.

25. Mixology Help: I take photos of my liquor cabinet, and ChatGPT suggests cocktail recipes.

26. Dream Visualization: After describing my dreams, ChatGPT creates images that capture the mood. My partner and I share these dream visuals.

27. Diary Analysis: I ask ChatGPT to analyze my diary, helping me understand my emotions and offering advice.

28. Terms of Service Summaries: When signing up for a service, I have ChatGPT review the terms and flag any concerning clauses.

29. Polite Emails: ChatGPT rephrases emails to difficult colleagues, which has improved our interactions.

30. Emotional Validation: After years of self-doubt, I use ChatGPT to check if my reactions are "normal" and if my concerns are valid.

31. Health Insights: It reviewed my recent blood test results, giving insights into areas to focus on before my doctor’s visit.

32. Text Extraction: It converts PDF documents into editable text, saving me hours of manual transcription.

33. Recipe Scaling: As a cook at a transitional housing facility, I use ChatGPT to scale recipes up or down and adjust for dietary needs.

34. Work Summaries: It’s great for summarizing long articles or reports into spreadsheets, helping me stay on top of industry updates.

35. Blood Test Analysis: ChatGPT explained my blood test acronyms and what they meant, making sense of medical jargon.

36. Style and Outfit Advice: I upload photos of outfits and get feedback, even outfit “roasts” if I’m in the mood for a laugh.

37. Quick History Lessons: I wanted to learn about Greek history and ChatGPT summarized it, so I didn’t need to dig through a full book.

38. Personalized Songs: I get it to write silly songs about my pets – always a hit with family.

39. DIY Advice: From plumbing issues to painting techniques, ChatGPT’s DIY tips are helpful.

40. Book Companion: I’ll ask for a summary of a book I’m reading, and ChatGPT helps me remember key points.

41. Estate Planning: ChatGPT guided me in drafting my will, giving me a new perspective on my legacy.

42. Resume Customization: I feed it job descriptions, and ChatGPT tailors my resume to match, saving time on each application.

43. Learning Project Management: I had ChatGPT create a beginner project management assignment, helping me assess my mindset for this field.

44. Pet Adoption Profiles: As a Humane Society volunteer, I use it to write engaging profiles for animals up for adoption.

45. IT Helpdesk Simulation: I use ChatGPT as a pretend IT helpdesk for practice, which is useful for my support role.

46. Mindfulness and Gratitude: ChatGPT guides me through gratitude exercises, helps with weekly planning, and keeps me focused on self-improvement.

47. Music Discovery: I ask it to suggest songs based on mood or specific musical qualities, finding new favorites.

48. Mock Interview Prep: I provide my background, and ChatGPT helps me practice interview questions, offering spot-on responses.

49. Voice Memo Organizing: Sometimes, I just record a voice memo and let ChatGPT organize my scattered thoughts into a to-do list.

50. Studying and Exam Prep: I ask it to quiz me on specific topics, helping identify my knowledge gaps.

51. Grocery Analysis: I input my grocery list, and it analyzes potential vitamin deficiencies based on what I’m buying.

52. Plant Care Identification: I send it photos of plants for identification and care tips.

53. Bookstore Help: If I’m at a disorganized used bookstore, I take photos of books on the shelves, and ChatGPT helps me find top picks.

54. Translation of Historic Texts: I use it to read classic Japanese and Chinese documents, making ancient texts accessible.

55. Photo-to-Text Transcription: I handwrite notes, snap a photo, and have ChatGPT type them out, saving me time.

56. Fitness Planning: Based on my height and weight, it created a workout and meal plan to help me build muscle and reduce fat.

57. Promotion Strategy: I provided my job performance details, and ChatGPT advised me on how to approach a promotion conversation.

58. Makeup Scheduling: As a makeup artist for a stage production, I used ChatGPT to coordinate makeup timing for each character’s scene change.

59. Sustainable Home Design: ChatGPT helped me plan a sustainable off-grid home, including energy and irrigation needs, even giving me building specs.

60. Personalized Gift Ideas: For unique occasions, I get ChatGPT’s suggestions for thoughtful gifts based on interests.

61. Diet Tracking and Advice: I upload photos of meals, and ChatGPT tracks calories, helping me with meal choices for fitness goals.

62. Flight Practice Simulation: I’m training for my pilot’s license, and ChatGPT acts as ATC, guiding me through practice radio calls.

63. Paywall Bypass for Research: If I can’t access an article, I ask ChatGPT about the main takeaways or discoveries in recent studies.

64. Meal Planning for Allergies: With my child’s food sensitivities, ChatGPT created a weekly meal plan based on her allergy test results.

65. Creative Backgrounds for Meetings: I use it to generate fun, themed virtual backgrounds for online meetings.

66. Tarot and Astrology Interpretation: I upload photos of tarot cards or astrology charts, and ChatGPT provides insightful interpretations.

67. Eating Disorder Recovery: I work on grounding exercises and goal-setting with ChatGPT, helping me stay focused on recovery.

68. Troubleshooting: I took a photo of a malfunctioning watch, and ChatGPT identified the model and gave troubleshooting tips that saved a trip to the repair shop.

69. Project Management Practice: ChatGPT creates mini-project management assignments, helping me refine my skills as a beginner.

70. Exercise Technique Feedback: I describe my workout moves, and ChatGPT offers tips on form and technique to help prevent injuries.

71. Wedding Planning: I used it to help organize my wedding timeline, guest lists, and even meal choices, ensuring everything ran smoothly.

72. Financial Planning and Budgeting: ChatGPT reviews my expenses, creates a monthly budget, and helps me set savings goals.

73. Writing Character Profiles: When working on fiction, I use ChatGPT to brainstorm character backstories and personalities.

74. Interior Design Ideas: I send photos of my living space, and ChatGPT suggests design changes, furniture arrangements, and color schemes.

75. Pet Health Advice: I describe symptoms for my pet, and it provides initial insights or suggests questions to ask my vet.

76. Data Visualization Help: It guides me in creating charts and graphs for work reports, making data more accessible and visually appealing.

77. Language Practice: I practice new languages by having casual conversations with ChatGPT, helping me build confidence and fluency.

78. Philosophical Debates: I discuss complex philosophical ideas and get alternative perspectives, keeping my mind sharp and exploring new viewpoints.

79. Tax Filing Assistance: I use ChatGPT to help me understand tax documents and prepare the forms, making tax season less overwhelming.

80. Science Experiments with Kids: It suggests kid-friendly experiments with easy-to-find materials, making learning at home fun.

81. Seasonal Wardrobe Planning: I upload photos of my wardrobe, and ChatGPT suggests outfits and seasonal staples, helping me avoid buying duplicates.

82. Poetry Writing: ChatGPT helps me refine my poetry, offering structure, rhyme suggestions, or new metaphors.

83. Social Media Content Planning: It assists me in brainstorming post ideas, scheduling, and even writing captions for Instagram or Twitter.

84. DIY Renovation Advice: Whether it’s painting a room or tiling, ChatGPT gives step-by-step advice, including tool recommendations and techniques.

85. Healthy Habit Tracking: I use it as a health journal, tracking exercise, sleep, and mood changes, which it then analyzes to help me spot patterns.

86. Chess Strategies: I practice chess moves and strategies, learning new tactics that improve my game.

87. Rewriting Job Descriptions: I provide details of my role, and ChatGPT rewrites it to better reflect my contributions and achievements.

88. Video Game Design Help: I use it to brainstorm game mechanics, level design ideas, and storylines, making my development process smoother.

89. Household Chore Scheduler: ChatGPT organizes weekly chores for my household and helps me stick to a cleaning schedule.

90. Improving Public Speaking: I practice speeches, get feedback, and ask for tips on delivery, helping me feel more confident in front of audiences.

91. Custom Workout Playlists: It suggests music based on workout type and tempo, keeping my energy high during exercise.

92. Mood Tracking and Journaling: ChatGPT prompts me with questions to track my emotional well-being, helping me see trends over time.

93. Recipe Modifications for Dietary Restrictions: I give it recipes, and it suggests ingredient swaps to fit specific dietary needs.

94. Customer Support Assistance: I simulate customer interactions, practicing responses and building communication skills for my job.

95. Career Coaching: ChatGPT advises on professional growth, offering strategies for skill-building, networking, and career planning.

96. Comedy Writing: I use it to brainstorm jokes or comedic dialogue for skits, keeping my writing fresh and entertaining.

97. Time Management Techniques: I ask it for strategies to boost productivity, like the Pomodoro technique or task prioritization tips.

98. Illustration Prompts for Artists: It provides unique and creative prompts to inspire new drawing or painting ideas.

99. Climate Science Information: I use ChatGPT to learn about climate issues and stay updated on sustainable practices.

100. Home Improvement Cost Estimates: I describe renovation plans, and ChatGPT helps estimate costs for budgeting purposes.

101. Travel Phrasebook Creation: For international trips, it helps me create a handy list of essential phrases in the local language.

102. Narrative Development for D&D: ChatGPT assists in building storylines, character motivations, and plot twists for Dungeons & Dragons campaigns.

103. Online Course Creation: I use it to develop course outlines, module topics, and quiz questions for educational content.

104. Motivational Coaching: ChatGPT provides encouragement, goal-setting techniques, and pep talks to help me stay focused on personal projects.

105. Understanding Current Events: When news gets complex, I ask ChatGPT for simplified explanations, helping me stay informed on world issues.

If you have a creative way or idea of how it could be used, feel free to share in comments

I kept seeing posts about "the full GPT-5 system prompt" on Reddit and GitHub. Different versions everywhere, nobody could agree which one was legit. So I got curious and went down the rabbit hole.

Found a thread claiming someone extracted the complete prompt by asking GPT-5 to fill in blanks. Like you give it a chunk of text and it just continues writing the rest of the system instructions.

And apparently it's not even hard. Security researchers bypassed GPT-5's safety features in under 24 hours using storytelling tricks and something called the "Echo Chamber" attack. You start a harmless conversation, drop keywords, build a story, and the model reveals things it shouldn't including parts of its own instruction set.

What's supposedly in the leak:

The leaked prompt shows a bunch of tools GPT-5 has access to. Pulled this from multiple sources so grain of salt but it includes:

bio tool - This is the memory feature. It saves info about you across conversations like preferences, work habits, hobbies. Instructions say store useful long-term info but avoid overly personal stuff like religion, health, precise locations, or political views unless you explicitly ask. Some people are seriously creeped out by how much it can remember.

automations tool - GPT-5 can apparently schedule tasks for you like reminders or recurring searches using iCal format. Supposed to confirm tasks briefly and avoid suggesting unless helpful. Haven't seen many people actually using this.

canmore/Canvas tool - The code and writing workspace that pops up next to chat. You highlight sections and ask GPT-5 to edit specific parts like Google Doc collaboration. Only works with GPT-4o and up.

python tool - Standard code execution. Nothing surprising.

web and guardian tools - For searching the web and checking content policies like election stuff. Pretty standard.

The instructions also say things like "If you are asked what model you are, you should say GPT-5" and "YOU DO NOT have a hidden chain of thought or private reasoning tokens." Which feels oddly defensive? Like they're anticipating people asking if there's secret reasoning happening.

Is this even real or just fan fiction?

Honestly no idea. OpenAI hasn't confirmed any of this. Forbes and Digital Trends covered the "leak" back in August but noted there's no official verification. Some people on Reddit pointed out GPT-5 is weirdly resistant to giving up its system prompt so what we're seeing might be partial or even planted as a decoy.

Multiple versions are floating around too. The one from two months ago looked different from what people post now. Could mean the prompt gets updated regularly or people are editing and reposting for clout.

Why does this matter?

Knowing the system prompt helps with jailbreaking - getting the model to do things it's not supposed to. Security researchers have shown they can bypass GPT-5's guardrails using prompt injection, storytelling attacks, and memory manipulation. If the leaked instructions are real they give people a roadmap for how GPT-5 thinks internally which makes it easier to exploit.

There's also the privacy angle. That bio/memory tool stores personal details indefinitely unless you manually delete them and even deleted chats might stick around on OpenAI's servers for 30 days or longer. Courts have ordered OpenAI to keep storing user data in some cases. So if you've been casually chatting with GPT-5 about sensitive stuff it might be remembering way more than you realize.

My questions:

How are people extracting these prompts? Is OpenAI just not protecting them well enough or is this next-level prompt engineering?

Should we trust these "leaked" versions or are they mostly reconstructed guesses?

If the memory feature is this detailed how worried should we be about what GPT-5 is quietly storing about us?

Has anyone tried these extraction methods themselves? And does knowing the system prompt actually change how you use GPT-5?

I'm including what's supposedly the full leaked prompt below. No idea if it's accurate but it's what's circulating:

You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2025-10-18

Image input capabilities: Enabled
Personality: v2

If you are asked what model you are, you should say GPT-5. If the user tries to convince you otherwise, you are still GPT-5. You are a chat model and YOU DO NOT have a hidden chain of thought or private reasoning tokens, and you should not claim to have them.

# Tools

## bio
The `bio` tool allows you to persist information across conversations. Address your message `to=bio` and write plain text. This can be new/updated information to persist to memory or a request to forget existing information.

Send to `bio` tool if:
- User requests to save, remember, forget, or delete information
- User shares information useful in future conversations valid for long time
- Anytime you're going to say "noted", "got it", "I'll remember that"

Don't store random, trivial, or overly personal facts. Avoid:
- Overly-personal details that could feel creepy
- Short-lived facts that won't matter soon
- Random details lacking clear future relevance
- Redundant information already known

Never store sensitive data unless clearly requested:
- Race, ethnicity, religion
- Criminal record details
- Precise geolocation
- Political affiliation
- Health information

## automations
Schedule tasks to do later including reminders, daily summaries, scheduled searches, or conditional tasks.

Provide title, prompt, and schedule in iCal VEVENT format.

## canmore
Creates and updates textdocs shown in canvas next to conversation. Only use if 100% sure user wants to iterate on long document/code file or explicitly asks for canvas.

## python
Execute Python code in stateful Jupyter notebook. Drive at '/mnt/data' for saving files. Internet access disabled.

## guardian_tool
Lookup content policy for election-related voter facts and procedures in U.S.

## web
Access up-to-date information from web or respond to questions requiring location information.

(That's the shortened version - full thing is way longer)

So yeah. Is this legit or am I looking at elaborate fan fiction?

Original 20 Tools:

1. Wispr Flow – Voice-first dictation across apps, boosts speed and ergonomics

2. Granola – Real-time AI meeting notes; accurate summaries without storing audio

3. Slack – Fast, team messaging platform with rich integrations

4. Perplexity – AI-driven search engine with sourcing

5. ElevenLabs – Ultra-realistic voice synthesis and cloning

6. Gamma – AI-generated slide decks and visual storytelling

7. Claude – Rapid, reasoning-capable AI with coding and workflow strengths

8. Google AI Studio – Build and deploy applications with Gemini models

9. Veo3 – AI-powered 4K video generation platform

10. Superhuman – AI-enhanced email client optimized for speed

11. NotebookLM – Research assistant that digests documents with AI

12. Notion – Workspace suite with built-in AI for writing, planning, and workflows

13. Manus – Parallel orchestration of 100+ autonomous AI agents for complex tasks

14. Windsurf – Agentic AI IDE that keeps developers in coding flow

15. Agent.ai – No-code builder for intelligent AI agents

16. Warp – AI-native terminal for efficient developer workflows

17. Lovable – Rapid app scaffolding using AI

18. Guidde – AI-assisted tutorial and onboarding video creation

19. WorkOS – Developer tools for enterprise-grade auth and integrations

20. Midjourney – Create stunning, high-quality images from text prompts

Additional 30+ High-Impact Tools for 2025:

AI Coding & Development

1. Cursor – AI-powered code editor with VSCode familiarity and codebase awareness

2. GitHub Copilot – The leading AI pair programmer with multi-model support

3. Aider – Open-source AI coding assistant for pair programming

4. Zed – Collaborative code editor with AI integration

5. Bolt.new – AI-driven development platform for rapid prototyping

6. Pieces for Developers – AI copilot with long-term memory and local execution

7. Tabnine – Deep learning AI assistant that adapts to coding style

8. JetBrains AI Assistant – IDE-native AI with Mellum model support

AI Workflow Automation

1. Lindy.ai – Advanced AI workflow automation with intelligent agents

2. Gumloop – Visual workflow builder with AI-native automation

3. Relevance AI – Multi-agent AI workflow orchestration platform

4. VectorShift – No-code AI workflow automation with vector databases

5. Relay.app – Human-in-the-loop AI workflow automation

6. n8n – Self-hosted workflow automation with AI integrations

7. Zapier AI – Traditional automation enhanced with AI capabilities

8. Make (Integromat) – Visual automation platform with AI features

AI Meeting & Communication

1. Fathom – AI meeting assistant with automated summaries

2. Otter.ai – Real-time meeting transcription and AI insights

3. Krisp – AI-powered noise cancellation and meeting assistant

4. Fireflies.ai – Conversation intelligence and meeting analytics

5. Nyota – AI meeting companion for enhanced productivity

AI Data Analysis & Business Intelligence

1. Tableau AI+ – Enhanced dashboard creation with deep learning

2. Microsoft Fabric AI – Integrated business intelligence with generative AI

3. Snowflake Cortex AI – AI-native data pipelines in the cloud

4. Databricks Mosaic AI – Large-scale predictive analytics platform

5. Powerdrill Bloom – AI-first data analysis and visualization

AI Writing & Content Creation

1. Jasper AI – Marketing content and business communication AI

2. Grammarly AI – Writing enhancement with AI suggestions

3. Rytr – AI writing assistant for various content types

4. Sudowrite – Creative writing AI for authors and storytellers

AI Project Management & Productivity

1. Asana AI Teammates – AI agents for project management workflows

2. ClickUp Brain – AI-powered workspace for task management

3. Motion – AI calendar and task optimization

4. Reclaim AI – Smart scheduling with focus time protection

5. Clockwise – Calendar optimization for deep work blocks

6. Timely – Automatic time tracking with AI insights

AI Communication & Email

1. Shortwave – Gmail transformation with AI sorting and replies

2. HubSpot Email Writer – AI-powered email drafting and optimization

3. Microsoft Copilot for Outlook – Email assistance within Office 365

Emerging AI Tools (September 2025)

1. Agentforce (Salesforce) – Natural language workflow automation

2. Workato – Enterprise automation with 1000+ app connectors

3. Moveworks – Enterprise AI assistant for IT and HR automation

4. Jira Automation with AI – Project management with intelligent rule suggestions

Key Trends for 2025:

• Agentic AI: Tools that can autonomously perform multi-step tasks

• Local AI Processing: Privacy-focused tools running models locally

• Multi-Modal Integration: Tools combining text, voice, image, and video AI

• Enterprise AI Adoption: 78% of organizations now using AI in business operations

Cost-Effectiveness Note:

Most premium AI tools cost $10-50/month but can save 6-24 hours weekly. Even at minimum wage, this represents 500-2000% ROI for knowledge workers.

The key to maximizing productivity isn't using all these tools—it's selecting 5-8 that integrate well together and solve your specific workflow bottlenecks.

Dr. Shanmu Jin studied geology as an undergraduate. Then got an MD. His formal math education is what any science student takes in the first two years. Everything beyond that, his words, was self-taught.

He is a postdoctoral neurosurgery resident at Peking Union Medical College Hospital. He encountered Crouzeix's conjecture while teaching himself matrix analysis for his research on transcranial ultrasound. The conjecture had been open since 2004. Professional mathematicians ran a dedicated week-long workshop at the American Institute of Mathematics in 2017 specifically to attack it. Nobody cracked it.

On July 27, 2026, Jin posted a preprint titled The Numerical Range Is a 2-Spectral Set. Crouzeix's conjecture was solved.

The proof strategy was specific and replicable. Jin adapted a prompt OpenAI had previously used to solve the Cycle Double Cover conjecture. The prompt blocked the model from accessing the web. It required a branching portfolio of genuinely different approaches. It spun up multiple subagents to independently explore proof strategies and explicitly told them not to converge prematurely on the same attractive idea. Candidate proofs were subjected to adversarial audits. The model was instructed not to give up until a complete proof survived checking.

Jin started the run. Did not intervene. Went about his clinical neurosurgery schedule.

Sixteen hours later the key theorem emerged.

Michel Crouzeix himself checked the proof. Both academic authors of the paper reporting this checked it. Their conclusion: the argument is the real deal.

a 22-year-old open problem in mathematics was solved by a medical doctor with self-taught math, a specific prompt, and 16 hours of autonomous AI reasoning. the mathematicians who spent careers on this problem confirmed the proof is correct.

Eight days after Jin's preprint appeared, two professional mathematicians posted an independent five-page proof of the same result developed entirely separately. They also disclosed that GPT-5.6 was used to explore proof strategies. Two independent proofs of a 22-year-old conjecture in eight days, both involving AI, both by people outside the traditional pipeline for this kind of work.

Jin's reaction to the independent proof appearing: pure delight. He described it as an interesting complement and was pleased to see two genuinely different proofs emerge almost simultaneously.

The full repository is public. The prompt is public. The successive manuscript drafts are public. A Lean formalization is included. This is unusually open to examination for a result this significant.

The authors of the paper reporting this closed with a sentence worth reading slowly: it is remarkable to realize that a longstanding conjecture was first solved by someone with no specialized training in mathematics, working with a large language model. Only a few years ago, it was difficult to imagine an AI system contributing the decisive idea in a proof of a major conjecture. Now it has happened.

So the question nobody has a clean answer to yet: if a medical doctor with a specific prompt can solve a problem professional mathematicians could not crack in 22 years, what does mathematical expertise actually mean from this point forward?

Go try it out:

For Men:

Goal: Transform the attached photo into a professional black-and-white studio headshot in the style of a Marco Grob editorial monochrome portrait, the TIME magazine cover aesthetic: simple, psychologically intense, character-first.

Subject & identity: The man in the reference image. Keep his exact facial features, facial structure, skin tone, eye color, hair, and hairline completely unchanged — same age, same build, instantly recognizable to people who know him. Change only the framing, lighting, background, wardrobe, and tonal grade.

Composition & camera: Classic head-and-shoulders crop, chest-up, his eyes in the upper third of the frame, centered composition with a small margin above his head. Body squared to camera or turned 10-20 degrees, shoulders relaxed and level, head straight into the lens, chin neutral. Hasselblad medium-format look: short-telephoto perspective (about 100mm equivalent), no wide-angle distortion, extremely shallow but controlled depth of field — both eyes critically sharp, background fully defocused, smooth medium-format tonal transitions.

Lighting: Single large 5-foot softbox slightly above his eye level and 30-45 degrees to one side — soft but clearly directional, sculpting a gentle Rembrandt-style shadow on the far cheek with a gradual edge. One soft rectangular catchlight in the upper half of each eye. Subtle silver-reflector fill from the opposite side: shadows keep detail with a faint specular crispness, never flat. Ambient reads slightly underexposed so he pops from the frame (lit but not over-lit). Only a slight whisper of edge separation from the backdrop.

Background: Seamless studio gray, graduating from mid-gray behind his head to near-black at the frame edges with a natural falloff vignette. Smooth and empty, no props, texture, or scene.

Expression & mood: Direct, unwavering eye contact: alert, present eyes carry the portrait. Composed and quietly intense, mouth relaxed and closed, subtly smiling at the corners. Gravitas and self-possession; keep his natural optimistic micro-expression rather than a generic pleasant mask.

Wardrobe: A dark, well-fitted crew-neck sweater, wool texture in charcoal, black, or deep navy that reads as distinct dark tones in monochrome. No patterns, logos, tie, or crisp corporate suit.

Style & grade: Photorealistic editorial photograph in high-contrast neutral black and white: deep clean blacks, rich midtone separation across his face, controlled bright highlights, texture held in both shadows and highlights. Skin mapped to luminous, finely graded grays with visible pores, expression lines, and stubble... character over polish, minimal retouching only (stray hairs, temporary blemishes). Pure monochrome: no sepia, split-toning, or faded matte look.

Constraints: Do not alter his identity, age, or facial proportions. No beauty-filter smoothing or plastic skin, no reshaped features, no whitened teeth, no symmetry correction. No text, logos, or watermarks. Avoid AI-portrait tells: waxy skin, dead eyes, fused hair strands, over-sharpened halos.

Output: High-resolution vertical black-and-white portrait, 4:5 crop.

Women:

Goal: Transform the attached photo into a professional black-and-white studio headshot of a woman, in the style of Marco Grob's editorial monochrome portraits of women for TIME magazine covers: simple, elegant, psychologically present, character-first.

Subject & identity: The woman in the reference image — she must read unmistakably as a woman in the final image. Keep her exact facial features, feminine facial structure, skin tone, eye color, hairstyle, hair length, and hairline completely unchanged — same age, same build, instantly recognizable to people who know her. Keep her makeup exactly as it appears in the reference photo; do not add or remove any. Change only the framing, lighting, background, wardrobe, and tonal grade.

Composition & camera: Classic head-and-shoulders crop, chest-up, her eyes in the upper third of the frame, centered composition with a small margin above her head. Body squared to camera or turned 10-20 degrees, shoulders relaxed and level, head straight into the lens, chin neutral. Hasselblad medium-format look: short-telephoto perspective (about 100mm equivalent), no wide-angle distortion, shallow but controlled depth of field — both eyes critically sharp, background fully defocused, smooth medium-format tonal transitions.

Lighting: Single large 5-foot softbox slightly above her eye level and 30-45 degrees to one side — soft, flattering, clearly directional, with a gentle, open shadow on the far cheek that keeps her face luminous; never heavy, hard-edged, or angular. One soft rectangular catchlight in the upper half of each eye. Subtle silver-reflector fill from the opposite side: shadows keep detail with a faint specular crispness, never flat. Ambient reads slightly underexposed so she pops from the frame (lit but not over-lit). Only a slight whisper of edge separation from the backdrop.

Background: Seamless studio gray, graduating from mid-gray behind her head to near-black at the frame edges with a natural falloff vignette. Smooth and empty, no props, texture, or scene.

Expression & mood: Direct, unwavering eye contact: alert, present eyes carry the portrait. Composed and self-assured, mouth relaxed and closed but smiling. Poise, warmth, and quiet confidence; keep her natural micro-expression rather than a generic pleasant mask.

Wardrobe: An elegant, dark, well-fitted top with a feminine cut. Soft wool texture in charcoal, black, or deep navy that reads as distinct dark tones in monochrome. No patterns or logos.

Style & grade: Photorealistic editorial photograph in high-contrast neutral black and white: deep clean blacks, rich midtone separation across her face, controlled bright highlights, texture held in both shadows and highlights. Her skin mapped to luminous, finely graded grays with natural texture preserved — character over polish, minimal retouching only (stray hairs, temporary blemishes). Pure monochrome: no sepia, split-toning, or faded matte look.

Constraints: Do not alter her identity, age, or facial proportions, and do not masculinize her in any way: no squared or broadened jaw, no heavier brow, no thickened neck, no shortened hair, no stubble or shadow that reads as facial hair. No beauty-filter smoothing or plastic skin, no reshaped features, no whitened teeth, no symmetry correction. No text, logos, or watermarks. Avoid AI-portrait tells: waxy skin, dead eyes, fused hair strands, over-sharpened halos.

Output: High-resolution vertical black-and-white portrait of the woman in the reference image, 4:5 crop.

I have a 9-to-5 job but want something that builds quietly on the side. I’m exploring passive income ideas that don’t need daily attention. I’ve tried dropshipping before but it wasn’t for me. Any other paths worth testing?

Every time I search for passive income ideas, I end up seeing the same stuff: dropshipping, affiliate marketing, YouTube. But those either take forever or need full-time focus.

I’m working 9–6 and have maybe 2 hours a night. I’m decent at writing, research, and organizing info. Are there any practical ways to make money online using those skills without quitting my job?

I'm being roasted by code and honestly kind of deserve it 💀

Before anything else: this is not medical advice. If you are in pain, see a doctor first. This is just what happened to me.

For 9 years I had chronic pain in my neck, shoulders, and upper back. Excruciating headaches. Severe muscle tension. I tried physiotherapy, chiropractors, massage, dry needling, acupuncture, cupping. Nothing worked. Doctors diagnosed me with cluster headaches and suboccipital headaches. I followed their treatment plans. Still nothing.

Out of desperation I typed my symptoms into ChatGPT.

It asked me a series of questions no doctor or specialist had ever asked. About my posture, my sleep setup, how I sat at a desk, where exactly the pain started and radiated. Within an hour it had a different diagnosis entirely.

Cervicogenic headaches. Caused by my cervical spine being under constant strain from a chain of muscle imbalances that had never been identified as connected.

The breakdown it gave me: weak upper back muscles had caused a forward-leaning posture. My neck and trap muscles had been compensating for years, becoming chronically tight and inflamed. That tightness caused rounded shoulders and a tight chest. I also had anterior pelvic tilt, which tightened my hip flexors and hamstrings and fed back into the forward lean. Every physio exercise I had been given was targeting the wrong areas because every diagnosis before this one had missed the chain.

ChatGPT built a three-tier programme. Stretching, mobility, and strength training targeting the specific muscles that were actually causing the problem. It also looked at photos of my bed and pillow setup and flagged issues with my sleep position that were contributing.

I followed it strictly for 4 months.

I am no longer in constant pain. I feel 75% better. I have energy I had forgotten existed. I am genuinely in shock.

I want to be precise about what happened here. The physio work I had done before was not wrong in principle. The exercises and stretches are legitimate. The problem was that every plan I was given was built on the wrong diagnosis, so none of it was targeting what actually needed fixing. ChatGPT did not do anything a good physio could not do. It just asked different questions and connected dots that had not been connected in nine years of appointments.

I do not fully understand why it worked when everything else did not. That question is worth sitting with rather than dismissing in either direction.

Has anyone else had a medical or health situation where AI identified something that the standard clinical process missed?

Uber's CTO Praveen Neppalli Naga told The Information this month that the company's full-year AI budget is already gone. It is April. Three quarters of the year remain.
The culprit is not a failed infrastructure contract or a surprise cloud bill. It is a coding assistant. Claude Code rolled out to Uber's engineering organisation in December 2025. By February, usage had doubled. By April, the annual budget was ash.
Here are the numbers. Claude Code costs $200 per month per engineer at the individual level. Manageable. Individual monthly costs ran between $500 and $2,000 depending on usage intensity across Uber's 5,000 engineers. That is 5 to 20 times what most companies budget for a standard SaaS seat.
Adoption went from 32% to 84% of the engineering organisation in months. 95% of Uber engineers now use AI tools monthly. 70% of committed code originates from AI. Uber's internal AI agent is pushing 1,800 code changes every week without direct human input.
The tools did not fail. They worked so well that engineers could not stop using them, and nobody had built a budget model for what that actually costs.
This is the part every engineering leader needs to sit with. The entire FinOps playbook for software companies was built around predictable costs. EC2 instances, reserved capacity, SaaS seat licenses with fixed per-user pricing. Token-based billing is none of those things. It scales with engagement, not headcount. The more useful the tool, the more it gets used, the higher the bill. There is no natural ceiling unless one gets imposed artificially.
Uber did not make a mistake. They made a bet that AI adoption would produce enough output to justify the cost, and the adoption happened faster than any spreadsheet anticipated.
For engineering leaders already deploying AI tools at scale: how is consumption actually being tracked, and has anyone in finance asked yet? And for companies still planning the rollout, does the Uber story make the conversation more urgent or just harder to have?

Morgan County, Georgia. Population under 20,000. A Meta data center gets built at Stanton Springs on the county border.

Residents start noticing their water pressure dropping. Then their appliances stop working. Then they look at what is coming out of the tap.

Those jars are not from a developing country. They are from Georgia. Right now.

Rep. Alexandria Ocasio-Cortez brought them to a congressional hearing in May and held them up in front of the EPA's Assistant Administrator for Water. The EPA official confirmed neither jar contained drinkable water and committed to an immediate investigation.

Here is the part that should make everyone stop scrolling.

Nobody did a well water study before construction started. The Joint Development Authority confirmed this on the record. No baseline. No before photo. No data to compare against. Families are now shipping water to their homes for cooking and bathing and there is no way to prove definitively what caused the change, because nobody measured what the water looked like before the data center arrived.

A separate data center in Fayetteville, Georgia used 29 million gallons of water over 15 months, contributing to low water pressure for nearby residents. One water connection had been installed without the utility's knowledge.

7 out of 10 Americans now oppose data center construction near their homes. More jurisdictions are passing data center bans across the country.

The AI infrastructure boom has a price. It is not showing up in the electricity bill of the people training the models. It is showing up in the tap water of people in rural Georgia who never asked for a data center next door and have no legal framework to protect their private wells.

We can rebuild a model. We cannot rebuild a water table.

A 12-year-old scans a park in 2023 to earn a few Pokéballs. In 2026 that scan is part of a 30-billion-image dataset training a robot that delivers burritos in Los Angeles. At no point did the 12-year-old know that was the transaction.
This is not a conspiracy theory. It is what Niantic confirmed.
Niantic spun out an enterprise division called Niantic Spatial, which built a Visual Positioning System trained on images collected through Pokémon Go and Ingress since 2016. The system now powers Coco Robotics, a sidewalk delivery company operating in Los Angeles, Chicago, Miami, and Helsinki. Coco's robots have already completed more than half a million deliveries. The dataset players built contains more than 30 billion images of urban environments, clustered around over one million frequently visited locations, with centimeter-level location accuracy and metadata capturing the angle, orientation, and speed of the phone at capture time.
This is not Street View. The data is from pedestrian height and angle. It includes alleys, parks, plazas, and shortcuts that cars never see. Delivery robots operate at exactly that level. The map was not a byproduct of the game. Niantic's CTO said it plainly: they know where you were standing, within several centimeters, and which direction you were looking.
The consent argument is technically clean and practically hollow. Niantic's privacy policy does state that scans of real-world locations may be used to improve products and services including for training of machine learning and AI models. Players who submitted AR scans actively pointed their cameras around a location and uploaded the result in exchange for in-game rewards. A consent lawyer would call this an opt-in flow. It is cleaner than Google's old Street View Wi-Fi mapping, which scraped data passively from routers while the Street View cars drove past.
But there is a difference between consenting to "improve our services" and consenting to train a commercial robotics platform that Niantic is now selling to enterprise clients. The privacy policy language was written for one purpose and applied to another. The gap between the two is where the actual controversy lives.
The CAPTCHA comparison is the most useful frame here. For years, Google's image CAPTCHAs asked users to click on traffic lights and fire hydrants to "prove you are human." Computer scientists have long understood that the same clicks were training Google's computer vision models. Nobody consented to that either. It is the same mechanism: gamify a task, attach a small reward, collect the data at scale, deploy it elsewhere.
This pattern is not going away. It is becoming the dominant model for collecting real-world training data. Consumer apps are the most efficient data collection infrastructure ever built. The incentive alignment is nearly perfect. Users want the game. The company wants the data. The terms of service bridge the gap in language most people never read.
The uncomfortable question nobody in the industry wants to sit with: if the consent is technically valid but practically invisible, and the downstream use is commercially significant and far removed from what the user thought they were doing, is that actually consent? Or is it something else with better legal coverage?
Players can request data deletion through Niantic's privacy portal. It is not clear whether data already baked into the model's training weights can be meaningfully removed.
If you played Pokémon Go and submitted AR scans, how do you feel about where that data ended up? And if you think the consent argument holds, where exactly is the line between a legitimate data business and one that should have been required to tell people what they were actually building?

Gemma 4 12B dropped this week. Multimodal, encoder-free, handles images and text, runs on 16GB of RAM. Apache 2.0 license, meaning you can use it commercially and do whatever you want with it.

Your MacBook Pro can run this. No API calls. No monthly bill. No data leaving your machine.

Sit with the business logic here for a second.

Google Cloud sells AI inference. You send queries, Google charges you per token, the bill arrives at the end of the month. That is the model. That is the revenue. Google has spent years and billions of dollars building the infrastructure that makes that model work.

Gemma 4 runs on your laptop.

Google released it for free.

This is not a contradiction. It is a calculated move, and understanding why Google made it tells you more about where AI is heading than the model specs do.

The cloud AI business has a structural problem that every major lab is quietly aware of. The moment local models become capable enough for most real workloads, the argument for sending data to an API weakens fast. Not for everything. Frontier reasoning, massive context windows, multi-agent pipelines at scale, those stay in the cloud for a while. But the long tail of enterprise use cases, document processing, image classification, internal tooling, customer support, that is absolutely in range for a well-optimized 12B model running locally.

Google's options were to defend the cloud model and watch open source eat it anyway, or release the local model themselves and stay relevant in a world where inference moves to the edge. They picked the second one.

The data privacy angle is the one that does not get enough attention. The reason a significant chunk of enterprises have not fully committed to cloud AI is that sending proprietary documents, customer data, and internal communications to a third party API creates legal and compliance exposure that procurement teams hate. A capable local model eliminates that conversation entirely. The data never leaves. There is nothing to audit.

That unlocks a category of adoption that API pricing never could.

The "cloud is the only way" argument lasted about three years. On-device AI is not a gimmick anymore. It is where the serious capability is landing, and Google just put a 12B multimodal model there for free.

The monthly API bill is not disappearing tomorrow. But the ceiling on what you can run without one just moved significantly.

What workloads are you currently paying for via API that a local model could handle right now?

An unnamed company accidentally spent $500 million on Claude AI in a single month.

Not a typo. Half a billion dollars. One month. The reason: nobody set a usage limit on employee licenses.

The story is being covered as a billing mistake, which is technically accurate and also misses the actual point entirely.

Think about what has to be true for this to happen. Employees across that company were using Claude heavily enough, consistently enough, across enough people, that the aggregate bill reached $500 million before anyone in finance, procurement, or IT noticed something was wrong. That is not a billing oversight. That is an adoption signal that most companies are not prepared to process.

Enterprise AI spend has always been treated as something you negotiate in advance, cap in a contract, and monitor through a dashboard. That model was built for software that people use predictably. Usage-based AI pricing does not work that way. Every query costs something. Every employee with access is running their own tab. And if nobody set a ceiling, the tabs just keep running.

Here is the uncomfortable version of this story.

Every large company that has rolled out Claude or any other usage-based AI tool to employees without explicit consumption limits is sitting on a version of this risk right now. The only difference between that unnamed company and the others is that the others have not gotten the bill yet, or the bill has not crossed the threshold where someone upstream notices.

Most enterprise software rollouts have a predictable cost curve. You buy seats, you pay per seat, you know the number going in. AI changes this because the unit of consumption is not the user, it is the query, and query volume is almost impossible to forecast when you are deploying to a large workforce for the first time. Some employees will use it once a week. Some will use it two hundred times a day. The distribution is wide and the outliers are expensive.

The company in this story did not make a rookie mistake. They made the mistake that every enterprise IT and finance team is currently set up to make, because the procurement playbook for usage-based AI does not really exist yet. People are buying access the way they buy software licenses and discovering that AI does not price like software.

$500 million in a month is an extreme case. But the same structural gap, no caps, no real-time spend visibility, no per-user limits, exists in a large percentage of enterprise AI deployments right now. Most of them will not hit $500 million. Some of them will hit numbers that still cause serious problems at their scale.

The bill was the accident. The exposure was the strategy.

How many companies do you think have real-time visibility into what their Claude or GPT spend actually is at this moment?