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Anthropic pays $785K/year for Forward Deployed Engineers In this 20-minute talk, an Anthropic FDE engineer explained the full roadmap: • step 1 → you're not selling software or time. you're selling an outcome. "a leader in oil and gas cares about throughput, not how the data is organized" •...

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Codez

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158,782 просмотров • 2 дней назад •via X (Twitter)

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Anthropic Engineer Andrej Karpathy: "The biggest mistake in AI right now - people are forcing agents to work instead of mastering the model first We made that mistake in 2016 at OpenAI - It cost us 5 years " what Karpathy actually means: step 1 → stop forcing your agent to do everything, understand the model underneath first step 2 → demos are easy - products take a decade. self-driving proved it - if you skip the foundation, everything breaks step 3 → the agent is not the product. the foundation is. build that - and agents emerge on their own "you building agents right now - you're at the forefront. not OpenAI. not DeepMind. you " watch - bookmark, then read article below ↓
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Anthropic Engineer Andrej Karpathy: "The biggest mistake in AI right now - people are forcing agents to work instead of mastering the model first We made that mistake in 2016 at OpenAI - It cost us 5 years " what Karpathy actually means: step 1 → stop forcing your agent to do everything, understand the model underneath first step 2 → demos are easy - products take a decade. self-driving proved it - if you skip the foundation, everything breaks step 3 → the agent is not the product. the foundation is. build that - and agents emerge on their own "you building agents right now - you're at the forefront. not OpenAI. not DeepMind. you " watch - bookmark, then read article below ↓

codila

1,903,914 просмотров • 26 дней назад

Anthropic engineer: "Harness engineering is a really rare skill right now - and it's hard to know if you're even good at it." how he actually thinks about building the full system: • step 1 → the model. already smarter than we use it. "freeze development - you'd find new capabilities for another 6-12 months" • step 2 → the harness. delete the steering. "we stopped using plan mode - the model thinks correctly" • step 3 → the agents. workflows let Claude build its own harness per task and spin sub-agents. what didn't fit in one context window now runs across many • step 4 → the context. "RAG is an anti-pattern. use grep." memory via files, clean context window, prompt caching. that's the stack most people optimize step 1. he optimizes all four. Watch the full video, then read the guide below on how to build an agentic system.
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Anthropic engineer: "Harness engineering is a really rare skill right now - and it's hard to know if you're even good at it." how he actually thinks about building the full system: • step 1 → the model. already smarter than we use it. "freeze development - you'd find new capabilities for another 6-12 months" • step 2 → the harness. delete the steering. "we stopped using plan mode - the model thinks correctly" • step 3 → the agents. workflows let Claude build its own harness per task and spin sub-agents. what didn't fit in one context window now runs across many • step 4 → the context. "RAG is an anti-pattern. use grep." memory via files, clean context window, prompt caching. that's the stack most people optimize step 1. he optimizes all four. Watch the full video, then read the guide below on how to build an agentic system.

Codez

43,265 просмотров • 14 дней назад

CEO of Nvidia said something that stopped me cold: "You may or may not lose a job to an AI, but you will absolutely lose a job to someone who uses AI" this 41 minutes is the most important talk on AI you'll watch this year so the question is not whether AI is coming for your job it's whether you're the one using it or the one being replaced by someone who does Nvidia already has hundreds of thousands of agents running inside the company right now agents are not the future, they are how the best teams work today I wrote a step by step guide on how to build your first AI agent with Claude Code you can find it in the article below
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CEO of Nvidia said something that stopped me cold: "You may or may not lose a job to an AI, but you will absolutely lose a job to someone who uses AI" this 41 minutes is the most important talk on AI you'll watch this year so the question is not whether AI is coming for your job it's whether you're the one using it or the one being replaced by someone who does Nvidia already has hundreds of thousands of agents running inside the company right now agents are not the future, they are how the best teams work today I wrote a step by step guide on how to build your first AI agent with Claude Code you can find it in the article below

Anatoli Kopadze

14,627 просмотров • 1 месяц назад

Most people trying to build a vertical AI business start by building the agent. That's the mistake. Here's the 5-step order that actually works: Step 1) Find a boring pain point - Pick a workflow you already know from your job. - That's your unfair advantage. Step 2) Map the workflow - Literally draw it out. - Every step, every handoff. Step 3) Do the job as a service - Manually. - For real clients. - Before any code. Step 4) Document the edge cases - This is where the real IP lives. - You only find them by doing the work. Step 5) Add vertical agents to replace each step - Now you have proprietary data and a workflow you own. Skip steps 1–4 and you're just building a demo.
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Most people trying to build a vertical AI business start by building the agent. That's the mistake. Here's the 5-step order that actually works: Step 1) Find a boring pain point - Pick a workflow you already know from your job. - That's your unfair advantage. Step 2) Map the workflow - Literally draw it out. - Every step, every handoff. Step 3) Do the job as a service - Manually. - For real clients. - Before any code. Step 4) Document the edge cases - This is where the real IP lives. - You only find them by doing the work. Step 5) Add vertical agents to replace each step - Now you have proprietary data and a workflow you own. Skip steps 1–4 and you're just building a demo.

The Startup Ideas Podcast (SIP) 🧃

14,202 просмотров • 3 месяцев назад

How to launch a working AI product in under 10 minutes. All you need is Appy AI and a clear idea. (Here’s exactly how it works 👇): Step 1: Go to Think of it as your personal AI engineer. Step 2: Type your idea “Create an AI Agent that turns daily AI news into ready-to-post content.” Step 3: AppyAI gets to work It builds the logic, creates your website, connects Stripe, and sets up tools. Step 4: Preview and launch Your product is live ready to sell instantly. Step 5: Share it with your audience You now own a complete AI product. Most people still build the old way. Endless back-and-forth with developers. Weeks wasted on setup and testing. Appy AI changes that. Describe your idea once it builds everything for you. Try now: Repost ♻️ to help others start building.
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How to launch a working AI product in under 10 minutes. All you need is Appy AI and a clear idea. (Here’s exactly how it works 👇): Step 1: Go to Think of it as your personal AI engineer. Step 2: Type your idea “Create an AI Agent that turns daily AI news into ready-to-post content.” Step 3: AppyAI gets to work It builds the logic, creates your website, connects Stripe, and sets up tools. Step 4: Preview and launch Your product is live ready to sell instantly. Step 5: Share it with your audience You now own a complete AI product. Most people still build the old way. Endless back-and-forth with developers. Weeks wasted on setup and testing. Appy AI changes that. Describe your idea once it builds everything for you. Try now: Repost ♻️ to help others start building.

Muhammad Ayan

61,441 просмотров • 8 месяцев назад

How to turn Claude 4 into your AI super agent (FREE): Step 1: Download Claude Desktop Step 2: Enable MCP in developer settings Step 3: Configure browser + laptop control Step 4: Give it tasks like "research the latest news" Step 5: Watch it work for hours while you do other things Real example from today: - Asked it to check my free disk space - It responded: "91GB available, 14% used" - Then navigated to websites and gathered data This is what "AI agents" actually means. Not chatbots—full computer control. Save this video, you'll automate everything. Want the SOP? DM me. 💬
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How to turn Claude 4 into your AI super agent (FREE): Step 1: Download Claude Desktop Step 2: Enable MCP in developer settings Step 3: Configure browser + laptop control Step 4: Give it tasks like "research the latest news" Step 5: Watch it work for hours while you do other things Real example from today: - Asked it to check my free disk space - It responded: "91GB available, 14% used" - Then navigated to websites and gathered data This is what "AI agents" actually means. Not chatbots—full computer control. Save this video, you'll automate everything. Want the SOP? DM me. 💬

Julian Goldie SEO

32,494 просмотров • 1 год назад

Monetizing your own AI app just got really easy. Last week I used YouAI to create an AI-powered bio generator. It took me less than an hour and I didn't need any programming skills. And now it just made it's first sale. All you need is a good idea and some prompting skills. Here's how you can create your own: Step 1: Setup → Go to → Sign up, then click on "new AI" (you can also remix an existing AI if you're lazy) Step 2: Create prompt → Within the "preamble" you can create the main prompt for this AI → In my example I described the assistant's goal (bio generator) and defined the format of the bios → I also added some bio examples to train the AI Step 3: Configure inputs → Now you define what the user inputs when using the AI app → In my case I defined 3 user inputs: - What topic do you tweet about? - What is your background? What have you achieved? - How do you help your followers? The answers are then used as variables to create the bios. Step 4: Publish → Select the underlying model (GPT or Claude) → Keep the app free or turn on monetization → Then publish! Your app will now be available in the YouAI library for others to use YouAI is also doing a live event tomorrow where they'll go into more detail and teach you how to build your own YouAI app: Thank you to YouAI for sponsoring this post! I'm a big fan of their product.
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Monetizing your own AI app just got really easy. Last week I used YouAI to create an AI-powered bio generator. It took me less than an hour and I didn't need any programming skills. And now it just made it's first sale. All you need is a good idea and some prompting skills. Here's how you can create your own: Step 1: Setup → Go to → Sign up, then click on "new AI" (you can also remix an existing AI if you're lazy) Step 2: Create prompt → Within the "preamble" you can create the main prompt for this AI → In my example I described the assistant's goal (bio generator) and defined the format of the bios → I also added some bio examples to train the AI Step 3: Configure inputs → Now you define what the user inputs when using the AI app → In my case I defined 3 user inputs: - What topic do you tweet about? - What is your background? What have you achieved? - How do you help your followers? The answers are then used as variables to create the bios. Step 4: Publish → Select the underlying model (GPT or Claude) → Keep the app free or turn on monetization → Then publish! Your app will now be available in the YouAI library for others to use YouAI is also doing a live event tomorrow where they'll go into more detail and teach you how to build your own YouAI app: Thank you to YouAI for sponsoring this post! I'm a big fan of their product.

Moritz Kremb

30,424 просмотров • 3 лет назад

Anthropic and Andrew Ng built an agent that uses 90% fewer tokens from scratch: they dropped the entire book of Frankenstein into a prompt - 108,000 tokens asked one question the input dropped from 108,000 tokens to 11 here's how: step 1 → put everything that never changes at the top - tools, then system, then docs step 2 → mark where the static part ends. everything above it gets cached step 3 → one stray space breaks it - and you pay full price again step 4 → the cache dies in 5 min - every read resets the clock step 5 → cached tokens don't count against your rate limits. free headroom most people never touch this - it pays for itself on day one watch & bookmark - this 1-hour brilliant course ↓
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Anthropic and Andrew Ng built an agent that uses 90% fewer tokens from scratch: they dropped the entire book of Frankenstein into a prompt - 108,000 tokens asked one question the input dropped from 108,000 tokens to 11 here's how: step 1 → put everything that never changes at the top - tools, then system, then docs step 2 → mark where the static part ends. everything above it gets cached step 3 → one stray space breaks it - and you pay full price again step 4 → the cache dies in 5 min - every read resets the clock step 5 → cached tokens don't count against your rate limits. free headroom most people never touch this - it pays for itself on day one watch & bookmark - this 1-hour brilliant course ↓

codila

540,984 просмотров • 17 дней назад

How do I transition from Data engineer to AI engineer? Here's a structured 6-step transition path: You already have the hardest part: engineering fundamentals + production mindset. The goal is to add the AI layer on top. Step 1: Work inside an AI-flavored data pipeline - Ingestion + cleaning for RAG / analytics - Chunking, metadata, indexing - Observability and data quality checks Step 2: Learn how model providers work - APIs, limits, retries, rate limits - Latency and cost trade-offs - Privacy and data handling constraints Step 3: Prompting as an engineering discipline - Prompt templates - Versioning + change logs - Structured outputs (JSON schemas) Step 4: Evaluation for generative systems - Golden sets - Automated checks (format, factuality signals, regressions) - Human review loops when it matters Step 5: Tool integration + agentic flows - Function/tool calling - Guardrails (allowed tools, timeouts, fallbacks) - Tracing what the agent did and why Step 6: Build 1-2 small projects end-to-end Examples: - RAG assistant over internal docs - Ticket triage bot with tool calls + evals For an experienced data engineer, this is usually not a multi-year shift. With focused effort + hands-on work, ~3-4 months can be enough to become interview-ready. If you're making this transition, what part feels most unclear: evals, prompting, or agents?
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How do I transition from Data engineer to AI engineer? Here's a structured 6-step transition path: You already have the hardest part: engineering fundamentals + production mindset. The goal is to add the AI layer on top. Step 1: Work inside an AI-flavored data pipeline - Ingestion + cleaning for RAG / analytics - Chunking, metadata, indexing - Observability and data quality checks Step 2: Learn how model providers work - APIs, limits, retries, rate limits - Latency and cost trade-offs - Privacy and data handling constraints Step 3: Prompting as an engineering discipline - Prompt templates - Versioning + change logs - Structured outputs (JSON schemas) Step 4: Evaluation for generative systems - Golden sets - Automated checks (format, factuality signals, regressions) - Human review loops when it matters Step 5: Tool integration + agentic flows - Function/tool calling - Guardrails (allowed tools, timeouts, fallbacks) - Tracing what the agent did and why Step 6: Build 1-2 small projects end-to-end Examples: - RAG assistant over internal docs - Ticket triage bot with tool calls + evals For an experienced data engineer, this is usually not a multi-year shift. With focused effort + hands-on work, ~3-4 months can be enough to become interview-ready. If you're making this transition, what part feels most unclear: evals, prompting, or agents?

Alexey Grigorev

13,694 просмотров • 4 месяцев назад

This guy just raised $5.1 million for an AI that built 1,000+ startups. His process is insanely logical. That's Marik Hazan of Feltsense & his process is (roughly): Step 1: scrape social. find pain points. turn them into business ideas Step 2: assign ideas to one of its AI "founders" Step 3: an AI founder builds a landing page Step 4: AI founder tests demand -- it even has a wallet to buy ads Step 5: AI founder emails (and even calls!) buyers and waitlist members Step 6: based on feedback, the ai founder kills the idea, pivots or doubles down I did a full screenshare to see how it all works. (yt link in 1st comment) Is this the future of entrepreneurship?
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This guy just raised $5.1 million for an AI that built 1,000+ startups. His process is insanely logical. That's Marik Hazan of Feltsense & his process is (roughly): Step 1: scrape social. find pain points. turn them into business ideas Step 2: assign ideas to one of its AI "founders" Step 3: an AI founder builds a landing page Step 4: AI founder tests demand -- it even has a wallet to buy ads Step 5: AI founder emails (and even calls!) buyers and waitlist members Step 6: based on feedback, the ai founder kills the idea, pivots or doubles down I did a full screenshare to see how it all works. (yt link in 1st comment) Is this the future of entrepreneurship?

Andrew Warner

37,781 просмотров • 4 месяцев назад

Godfather of agents showed how he created the loop engineering that everyone now uses: as PhD student, he wrote the formula every AI agent now runs on - his formula taught agents to self-improve here's the formula: step 1 → stop making the model just think OR just act - make it alternate: think → act → observe → think again step 2 → let it reason out loud before every action - the plan guides the next move, not just the output step 3 → feed the result of each action back in - the model reads what happened and adjusts, live step 4 → when it fails, don't restart make it reflect on WHY it failed, in words, then retry with that memory step 5 → store those reflections - the agent gets better across attempts without any retraining before OpenAI, before he ran Tencent's AI - he was a grad student who quietly wrote the blueprint every agent now follows - now give 90-min course watch & bookmark - then read how the agent systems actually works ↓
1:22:01

Godfather of agents showed how he created the loop engineering that everyone now uses: as PhD student, he wrote the formula every AI agent now runs on - his formula taught agents to self-improve here's the formula: step 1 → stop making the model just think OR just act - make it alternate: think → act → observe → think again step 2 → let it reason out loud before every action - the plan guides the next move, not just the output step 3 → feed the result of each action back in - the model reads what happened and adjusts, live step 4 → when it fails, don't restart make it reflect on WHY it failed, in words, then retry with that memory step 5 → store those reflections - the agent gets better across attempts without any retraining before OpenAI, before he ran Tencent's AI - he was a grad student who quietly wrote the blueprint every agent now follows - now give 90-min course watch & bookmark - then read how the agent systems actually works ↓

codila

28,457 просмотров • 12 дней назад

How to start your Hermes AI Agent (step-by-step guide for beginners) I'm a beginner, and I spent the last 24h: > Understanding the process > Launching my Hermes Agent (Nous Research) > Recording the whole process > Editing this step-by-step guide So you can do it yourself and understand everything in less than 1 hour! 00:00 Intro 00:38 What is Hermes ? Key concepts every beginner should know 01:16 What is a VPS ? 02:00 What is SSH ? 03:52 What is the Terminal ? 04:03 What is an LLM provider ? 04:37 What is an API key ? 04:52 What is the Terminal Backend 05:34 What is the Messaging Gateway ? 06:10 What is the Memory System ? Step-by-step: Launch your Hermes AI agent 06:53 Step 1 : Get your VPS 08:04 How to generate an SSH key 09:00 Step 2 : Connect to your VPS 10:32 Step 3 : Install Hermes Agent 14:34 Connect your agent to Telegram 17:12 How to fix a mistake during the process 17:48 Gateway testing 18:17 Telegram is working 18:31 Run your agent 24/7 19:16 Thank you Make sure to save this for later and share it with friends who want to start with AI agents. Thanks to Teknium 🪽 for being so active!
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How to start your Hermes AI Agent (step-by-step guide for beginners) I'm a beginner, and I spent the last 24h: > Understanding the process > Launching my Hermes Agent (Nous Research) > Recording the whole process > Editing this step-by-step guide So you can do it yourself and understand everything in less than 1 hour! 00:00 Intro 00:38 What is Hermes ? Key concepts every beginner should know 01:16 What is a VPS ? 02:00 What is SSH ? 03:52 What is the Terminal ? 04:03 What is an LLM provider ? 04:37 What is an API key ? 04:52 What is the Terminal Backend 05:34 What is the Messaging Gateway ? 06:10 What is the Memory System ? Step-by-step: Launch your Hermes AI agent 06:53 Step 1 : Get your VPS 08:04 How to generate an SSH key 09:00 Step 2 : Connect to your VPS 10:32 Step 3 : Install Hermes Agent 14:34 Connect your agent to Telegram 17:12 How to fix a mistake during the process 17:48 Gateway testing 18:17 Telegram is working 18:31 Run your agent 24/7 19:16 Thank you Make sure to save this for later and share it with friends who want to start with AI agents. Thanks to Teknium 🪽 for being so active!

Theo

33,099 просмотров • 4 месяцев назад

KYRALL TURNS "I NEED THIS PART" INTO SECONDS, WITHOUT OPENING CAD you describe the part. the AI generates it. no SolidWorks. no Fusion. no hours of setup. why this matters to you: modeling a part in CAD is slow and technical. sketch, constrain, extrude, fillet — for every bracket, housing or fixture. and simple one-off parts eat the same ceremony every time. for an engineer, that bottleneck stalls the whole project. Kyrall skips it: → prompt → 3D part ready → exports to STEP and STL, the formats you already use → for when you need the geometry now, not in three days it doesn't compete with CAD suites on millimeter control. it competes on getting you to a usable part first. idea → part → keep building. link below :)
1:12

KYRALL TURNS "I NEED THIS PART" INTO SECONDS, WITHOUT OPENING CAD you describe the part. the AI generates it. no SolidWorks. no Fusion. no hours of setup. why this matters to you: modeling a part in CAD is slow and technical. sketch, constrain, extrude, fillet — for every bracket, housing or fixture. and simple one-off parts eat the same ceremony every time. for an engineer, that bottleneck stalls the whole project. Kyrall skips it: → prompt → 3D part ready → exports to STEP and STL, the formats you already use → for when you need the geometry now, not in three days it doesn't compete with CAD suites on millimeter control. it competes on getting you to a usable part first. idea → part → keep building. link below :)

marcus

31,238 просмотров • 25 дней назад

Anthropic CEO: "Software is going to become cheap, maybe essentially free." this 2-minute clip is the most important thing I watched this month. ↓ Save this before everyone copies the playbook Not only the people who refuse AI, but also those who think signing up for Claude counts as using it. the part nobody wanted to hear: > you don't need a co-founder who codes > you don't need $50K for an MVP > you don't need 6 months of development > you need Claude and one weekend most people will watch this and worry about their job. A few will open Claude this weekend and vibe code their first product. I used Claude to build my first product. No code. No cost. No CS degree. I did exactly this and documented the whole process step by step. Full guide in the article below.
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Anthropic CEO: "Software is going to become cheap, maybe essentially free." this 2-minute clip is the most important thing I watched this month. ↓ Save this before everyone copies the playbook Not only the people who refuse AI, but also those who think signing up for Claude counts as using it. the part nobody wanted to hear: > you don't need a co-founder who codes > you don't need $50K for an MVP > you don't need 6 months of development > you need Claude and one weekend most people will watch this and worry about their job. A few will open Claude this weekend and vibe code their first product. I used Claude to build my first product. No code. No cost. No CS degree. I did exactly this and documented the whole process step by step. Full guide in the article below.

Khairallah AL-Awady

53,454 просмотров • 2 месяцев назад

Anthropic Engineer Andrej Karpathy: "Stop training from scratch. Take what the model already learned - adapt it - ship it At OpenAI we replaced months of work with a few lines of code. Better results every time " the Karpathy formula for working with AI: step 1 → stop building from zero. the model already learned the hard part - just point it at your task step 2 → stop adding complexity, the simplest architecture beat everything in 2014 - simpler always wins step 3 → train only the last layer and freeze everything else - minutes instead of months. better results he taught this at Stanford 10 years ago - then applied it at OpenAI, Tesla, and Anthropic the advice never changed - because it never stopped working watch - bookmark, then read article below ↓
23:19

Anthropic Engineer Andrej Karpathy: "Stop training from scratch. Take what the model already learned - adapt it - ship it At OpenAI we replaced months of work with a few lines of code. Better results every time " the Karpathy formula for working with AI: step 1 → stop building from zero. the model already learned the hard part - just point it at your task step 2 → stop adding complexity, the simplest architecture beat everything in 2014 - simpler always wins step 3 → train only the last layer and freeze everything else - minutes instead of months. better results he taught this at Stanford 10 years ago - then applied it at OpenAI, Tesla, and Anthropic the advice never changed - because it never stopped working watch - bookmark, then read article below ↓

codila

24,203 просмотров • 26 дней назад

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.
0:35

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Dustin

82,757 просмотров • 4 месяцев назад

PhD Students – How to automatically identify 90% of the issues in your research paper before you submit it to a journal? This is possible through manual or automated paper review. First, let’s understand the following. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰? Paper review is a process in which subject matter experts evaluate your paper based on the following criteria: 1. Significance – Is this research important? 2. Novelty – Is this research new? 3. Methodology – Is this research carried out in the correct way? 4. Verifiability – Can other researchers verify this research? 5. Presentation – Is the research presented in the right way? 𝐖𝐡𝐲 𝐭𝐨 𝐡𝐚𝐯𝐞 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰𝐞𝐝 𝐛𝐞𝐟𝐨𝐫𝐞 𝐬𝐮𝐛𝐦𝐢𝐬𝐬𝐢𝐨𝐧? ➟ Identify the critical issues in your paper ➟ Fix those issues to increase the chances of your paper acceptance 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 “𝐬𝐞𝐥𝐟-𝐫𝐞𝐯𝐢𝐞𝐰” 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫? Paperpal just launched an amazing feature – AI Review. Using this feature, you can get instant self-feedback. This feature will help you in the following ways. ➝ Check for gaps in your logic ➝ Get feedback on the structure and flow of your writing ➝ Review your research questions ➝ Identify opportunities to strengthen your paper ➝ Increase the chances of your paper acceptance Here is a step-by-step process for using AI Review feature. Step 1: Go to and login. Step 2: Open an existing document or make a new document Step 3: Go to the right-side bar and click on checks | AI Review. Step 4: For this feature to work there should be more than 150 words. Step 5: Copy and paste your paper. Step 6: Now go to the right side and check the prompts Step 7: With these prompts, you will evaluate your paper. Step 8: You will find various prompts e.g., suggest writing feedback, check flow and structure etc. Step 9: You can select a prompt from the existing prompts or write your custom prompt and execute Step 10: Paperpal will generate feedback as per the prompt. Step 11: Read through the feedback and save it for further use. Use other specific prompts for tailored feedback. Step 12: This way you can evaluate various aspects of your paper yourself. This is a very customized and efficient way of automatically reviewing your paper. You can also go one step further to work on the feedback and improve your paper based on suggestions. Please note that AI Review feature does not replace human or expert reviewers in any way. This feature only aims to provide you with quick self-feedback. Try the AI Review feature of Paperpal. Paperpal link:
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PhD Students – How to automatically identify 90% of the issues in your research paper before you submit it to a journal? This is possible through manual or automated paper review. First, let’s understand the following. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰? Paper review is a process in which subject matter experts evaluate your paper based on the following criteria: 1. Significance – Is this research important? 2. Novelty – Is this research new? 3. Methodology – Is this research carried out in the correct way? 4. Verifiability – Can other researchers verify this research? 5. Presentation – Is the research presented in the right way? 𝐖𝐡𝐲 𝐭𝐨 𝐡𝐚𝐯𝐞 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰𝐞𝐝 𝐛𝐞𝐟𝐨𝐫𝐞 𝐬𝐮𝐛𝐦𝐢𝐬𝐬𝐢𝐨𝐧? ➟ Identify the critical issues in your paper ➟ Fix those issues to increase the chances of your paper acceptance 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 “𝐬𝐞𝐥𝐟-𝐫𝐞𝐯𝐢𝐞𝐰” 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫? Paperpal just launched an amazing feature – AI Review. Using this feature, you can get instant self-feedback. This feature will help you in the following ways. ➝ Check for gaps in your logic ➝ Get feedback on the structure and flow of your writing ➝ Review your research questions ➝ Identify opportunities to strengthen your paper ➝ Increase the chances of your paper acceptance Here is a step-by-step process for using AI Review feature. Step 1: Go to and login. Step 2: Open an existing document or make a new document Step 3: Go to the right-side bar and click on checks | AI Review. Step 4: For this feature to work there should be more than 150 words. Step 5: Copy and paste your paper. Step 6: Now go to the right side and check the prompts Step 7: With these prompts, you will evaluate your paper. Step 8: You will find various prompts e.g., suggest writing feedback, check flow and structure etc. Step 9: You can select a prompt from the existing prompts or write your custom prompt and execute Step 10: Paperpal will generate feedback as per the prompt. Step 11: Read through the feedback and save it for further use. Use other specific prompts for tailored feedback. Step 12: This way you can evaluate various aspects of your paper yourself. This is a very customized and efficient way of automatically reviewing your paper. You can also go one step further to work on the feedback and improve your paper based on suggestions. Please note that AI Review feature does not replace human or expert reviewers in any way. This feature only aims to provide you with quick self-feedback. Try the AI Review feature of Paperpal. Paperpal link:

Faheem Ullah

15,270 просмотров • 1 год назад

I've been saying it for years, AI is the most important conversation we're having right now. Josh Tyrangiel (Josh) spent a year looking for the people actually using it to save lives, and what he found will change the way you think about this technology forever. Watch/YouTube on X, YouTube, Spotify, Apple Podcasts, or wherever you get your podcasts. Timestamps 0:00 Josh Tyrangiel Introduction 1:35 This is the most important conversation we are having right now 4:33 Is there an existential threat with the creators of AI? 6:31 Which AI is the best one to use? 8:02 A year from now, are we at agentic AI? 10:11 "You need a human in all AI cases." 12:06 If we have checks and balances in place for AI, will our adversaries do the same? 13:58 Anthropic and the US government relationship 15:14 Common variable on an institution using AI or not 19:18 Should you be worried about AI? 20:32 How would a politician or CEO benefit from this book? 21:49 Humans using the AI, not replaced by AI 25:09 What do you think this will change about how we think about AI? 27:28 Five Words
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I've been saying it for years, AI is the most important conversation we're having right now. Josh Tyrangiel (Josh) spent a year looking for the people actually using it to save lives, and what he found will change the way you think about this technology forever. Watch/YouTube on X, YouTube, Spotify, Apple Podcasts, or wherever you get your podcasts. Timestamps 0:00 Josh Tyrangiel Introduction 1:35 This is the most important conversation we are having right now 4:33 Is there an existential threat with the creators of AI? 6:31 Which AI is the best one to use? 8:02 A year from now, are we at agentic AI? 10:11 "You need a human in all AI cases." 12:06 If we have checks and balances in place for AI, will our adversaries do the same? 13:58 Anthropic and the US government relationship 15:14 Common variable on an institution using AI or not 19:18 Should you be worried about AI? 20:32 How would a politician or CEO benefit from this book? 21:49 Humans using the AI, not replaced by AI 25:09 What do you think this will change about how we think about AI? 27:28 Five Words

Anthony Scaramucci

15,891 просмотров • 1 месяц назад

Sam Altman: “No matter how great your idea is, no one cares.” Sam is asked what a founder should do if they have an idea but don’t want to talk about it because a big company might steal it. He responds: “Here is one of the things that takes founders a long time to learn: No matter how great your idea is, no one cares. Everybody is so distracted that you could probably put that idea with exact instructions for how to implement it on Tim Cook’s desk and take no risk.” Sam continues: “Extreme secrecy among founders is a bad sign. You want to keep some things secret for sure, but you should be willing to talk about the broad sketches of what you’re doing because you need that to recruit people, to get investors, to get customers.” Talking about your idea is also how you get feedback from other really smart people. Sam gives his own experience with Y Combinator as an example: “We at YC talk about everything we do. We talk about how to operate. We give our best possible advice. I have given talks before to rooms of people that want to start accelerators. And I say: ‘If you want to start an accelerator, here is exactly what to do step by step, and here are the mistakes to avoid step by step.’ And people always say, ‘Are you crazy? You’re giving away YC secrets.’ And we are, and yet no one ever listens… It was hard for me to learn this lesson of not fearing this… But don’t be afraid of telling people what you do.”
2:34

Sam Altman: “No matter how great your idea is, no one cares.” Sam is asked what a founder should do if they have an idea but don’t want to talk about it because a big company might steal it. He responds: “Here is one of the things that takes founders a long time to learn: No matter how great your idea is, no one cares. Everybody is so distracted that you could probably put that idea with exact instructions for how to implement it on Tim Cook’s desk and take no risk.” Sam continues: “Extreme secrecy among founders is a bad sign. You want to keep some things secret for sure, but you should be willing to talk about the broad sketches of what you’re doing because you need that to recruit people, to get investors, to get customers.” Talking about your idea is also how you get feedback from other really smart people. Sam gives his own experience with Y Combinator as an example: “We at YC talk about everything we do. We talk about how to operate. We give our best possible advice. I have given talks before to rooms of people that want to start accelerators. And I say: ‘If you want to start an accelerator, here is exactly what to do step by step, and here are the mistakes to avoid step by step.’ And people always say, ‘Are you crazy? You’re giving away YC secrets.’ And we are, and yet no one ever listens… It was hard for me to learn this lesson of not fearing this… But don’t be afraid of telling people what you do.”

Startup Archive

212,623 просмотров • 5 месяцев назад

A DEVELOPER WALKED ON STAGE DRESSED AS A 1973 ENGINEER AND "PREDICTED" THE FUTURE OF PROGRAMMING. THE TWIST: EVERYTHING HE DESCRIBED WAS ALREADY INVENTED 40 YEARS EARLIER AND WE STILL REFUSE TO USE IT. 32 minutes from Bret Victor, doing the most quietly savage talk on our entire industry. -> The idea that lands: we write code as step-by-step text instructions and call that "Just how programming is". He shows four better ways -- all discovered in the 60s and 70s, all abandoned. Manipulate the data directly instead of typing blind code. Tell the machine your goal instead of every tiny step. We saw all this, then walked away. Why? The moment you're sure you know what programming is, you stop seeing anything better. That certainty is the cage. And now AI is dragging us back to exactly what he begged for -- you describe the goal in plain words, the machine works out the how. The future he mourned is arriving anyway. You thought text files were just how code works. This is the talk that shows it was a choice, and maybe the wrong one. Watch this one. It'll ruin how you see your job ↓
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A DEVELOPER WALKED ON STAGE DRESSED AS A 1973 ENGINEER AND "PREDICTED" THE FUTURE OF PROGRAMMING. THE TWIST: EVERYTHING HE DESCRIBED WAS ALREADY INVENTED 40 YEARS EARLIER AND WE STILL REFUSE TO USE IT. 32 minutes from Bret Victor, doing the most quietly savage talk on our entire industry. -> The idea that lands: we write code as step-by-step text instructions and call that "Just how programming is". He shows four better ways -- all discovered in the 60s and 70s, all abandoned. Manipulate the data directly instead of typing blind code. Tell the machine your goal instead of every tiny step. We saw all this, then walked away. Why? The moment you're sure you know what programming is, you stop seeing anything better. That certainty is the cage. And now AI is dragging us back to exactly what he begged for -- you describe the goal in plain words, the machine works out the how. The future he mourned is arriving anyway. You thought text files were just how code works. This is the talk that shows it was a choice, and maybe the wrong one. Watch this one. It'll ruin how you see your job ↓

slash1s

684,765 просмотров • 1 месяц назад