正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

As someone who ships LLM systems in production, this double pendulum video is the closest thing to a "why one changed word breaks Claude and GPT-5" explainer I've ever seen released for free. Everyone thinks LLMs are deterministic machines. They're not. Every prompt to GPT-5 is a chaotic double... show more

fintex

3,238 subscribers

50,158 次观看 • 1 天前 •via X (Twitter)

科学技术教育喜剧

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

As someone who ships LLM systems in production, this Laplace transform video is the closest thing to a "why Mamba beats transformers" explainer I've ever seen released for free. Everyone thinks attention is what makes LLMs powerful. Laplace proved in 1785 that any signal breaks into exponential pieces - locate the poles, expose the frequencies. Mamba and Claude run on this exact trick. 34 minutes. Bookmark & watch today. The math is older than the United States.

As someone who ships LLM systems in production, this Laplace transform video is the closest thing to a "why Mamba beats transformers" explainer I've ever seen released for free. Everyone thinks attention is what makes LLMs powerful. Laplace proved in 1785 that any signal breaks into exponential pieces - locate the poles, expose the frequencies. Mamba and Claude run on this exact trick. 34 minutes. Bookmark & watch today. The math is older than the United States.

Eron

66,640 次观看 • 23 天前

As someone who ships LLM systems in production, this hairy ball theorem video is the closest thing to a "why positional encoding in 3D robotics always breaks" explainer I've ever seen released for free. Everyone thinks orientation in 3D space is a solved problem. A theorem from 1912 proves there is always at least one direction where any continuous vector field hits zero — the exact reason every robotics LLM glitches at certain angles. 25 minutes. Bookmark & watch today. The math is older than the airplane.

As someone who ships LLM systems in production, this hairy ball theorem video is the closest thing to a "why positional encoding in 3D robotics always breaks" explainer I've ever seen released for free. Everyone thinks orientation in 3D space is a solved problem. A theorem from 1912 proves there is always at least one direction where any continuous vector field hits zero — the exact reason every robotics LLM glitches at certain angles. 25 minutes. Bookmark & watch today. The math is older than the airplane.

Eron

56,886 次观看 • 21 天前

As someone who ships LLM systems in production, this backpropagation video is the closest thing to a "why GPT actually learns" explainer I've ever seen released for free. Everyone thinks training a neural network is calculus. It's not. It's 175 billion weights getting nudged proportionally to how much each one caused the mistake. PyTorch does this backwards pass on every single training step. 20 minutes. Bookmark & watch today. The algorithm is older than the iPhone.

As someone who ships LLM systems in production, this backpropagation video is the closest thing to a "why GPT actually learns" explainer I've ever seen released for free. Everyone thinks training a neural network is calculus. It's not. It's 175 billion weights getting nudged proportionally to how much each one caused the mistake. PyTorch does this backwards pass on every single training step. 20 minutes. Bookmark & watch today. The algorithm is older than the iPhone.

Eron

20,078 次观看 • 22 天前

As someone who trains and fine-tunes LLMs, this double descent video is the closest thing to a "why every ML textbook was wrong about GPT-5" explainer I've ever seen released for free. Everyone thinks bigger models overfit worse. They don't. Past a certain scale the test error dives again - the exact reason OpenAI and Anthropic keep scaling up instead of down. Bookmark & watch this weekend. The 2019 discovery that quietly forced Stanford professors to rewrite their textbooks and reshaped how every frontier LLM gets trained.

As someone who trains and fine-tunes LLMs, this double descent video is the closest thing to a "why every ML textbook was wrong about GPT-5" explainer I've ever seen released for free. Everyone thinks bigger models overfit worse. They don't. Past a certain scale the test error dives again - the exact reason OpenAI and Anthropic keep scaling up instead of down. Bookmark & watch this weekend. The 2019 discovery that quietly forced Stanford professors to rewrite their textbooks and reshaped how every frontier LLM gets trained.

fintex

68,222 次观看 • 9 天前

As someone who's built with modern LLM architectures, this Laplace transform lecture is the closest thing to a "why Mamba works" explainer I've ever seen released for free. Everyone thinks transformers are the only path to modern LLMs. State space models like Mamba and S4 challenge that using math Laplace built in the 1780s. This video shows why. 25 minutes. Bookmark & watch today.

As someone who's built with modern LLM architectures, this Laplace transform lecture is the closest thing to a "why Mamba works" explainer I've ever seen released for free. Everyone thinks transformers are the only path to modern LLMs. State space models like Mamba and S4 challenge that using math Laplace built in the 1780s. This video shows why. 25 minutes. Bookmark & watch today.

fintex

110,151 次观看 • 1 个月前

As someone who ships LLM systems in production, this sliding puzzle video is the closest thing to a "why sampling harder never finds the answer" explainer I've ever seen released for free. Everyone thinks a hard problem means a huge search space. It doesn't. Klotski has just 25,955 states, yet random search dies in the same pit every time. That's the exact reason OpenAI and Anthropic pay for guided search instead of more samples. Bookmark & watch this weekend. Same graph traversal, from a wooden toy on a coffee table to every reasoning model burning test-time compute today.

As someone who ships LLM systems in production, this sliding puzzle video is the closest thing to a "why sampling harder never finds the answer" explainer I've ever seen released for free. Everyone thinks a hard problem means a huge search space. It doesn't. Klotski has just 25,955 states, yet random search dies in the same pit every time. That's the exact reason OpenAI and Anthropic pay for guided search instead of more samples. Bookmark & watch this weekend. Same graph traversal, from a wooden toy on a coffee table to every reasoning model burning test-time compute today.

fintex

56,161 次观看 • 5 天前

A double pendulum demonstrates chaos theory, tracing its unpredictable path The dependence on initial conditions, and sensitivity to variation is one of the fundamental properties of chaos This is commonly referred to as "the butterfly effect,"

A double pendulum demonstrates chaos theory, tracing its unpredictable path The dependence on initial conditions, and sensitivity to variation is one of the fundamental properties of chaos This is commonly referred to as "the butterfly effect,"

Science girl

5,955,489 次观看 • 3 年前

As someone who builds with LLMs and diffusion models, this 14-minute heat equation video is the closest thing to a "why noise reversal actually works" explainer I've ever seen released for free. Everyone thinks diffusion is a novel AI concept. It's the heat equation from 1822. The forward process spreads noise like heat, and the reverse process is what your model learns. Bookmark & watch this weekend. Same math, from 19th-century physics to 2025 image generation.

As someone who builds with LLMs and diffusion models, this 14-minute heat equation video is the closest thing to a "why noise reversal actually works" explainer I've ever seen released for free. Everyone thinks diffusion is a novel AI concept. It's the heat equation from 1822. The forward process spreads noise like heat, and the reverse process is what your model learns. Bookmark & watch this weekend. Same math, from 19th-century physics to 2025 image generation.

fintex

15,604 次观看 • 21 天前

As someone who's been shipping LLMs since the GPT-2 days, this lecture on cross-entropy from a Stanford math grad is the closest thing to an ML PhD qualifying exam I've ever seen released publicly for free. Everyone thinks language models predict the next word. They don't. They compress language. Once you see the math, you can't unsee it. 33 minutes. Bookmark & watch today.

As someone who's been shipping LLMs since the GPT-2 days, this lecture on cross-entropy from a Stanford math grad is the closest thing to an ML PhD qualifying exam I've ever seen released publicly for free. Everyone thinks language models predict the next word. They don't. They compress language. Once you see the math, you can't unsee it. 33 minutes. Bookmark & watch today.

fintex

354,056 次观看 • 1 个月前

As someone who trains LLMs, this 20-minute gradient descent video is the closest thing to a "why the memorization vs generalization debate matters" explainer I've ever seen released for free. Everyone thinks neural nets learn features. They often just memorize. The video walks through the 2017 paper that shocked the field: shuffle your labels randomly and modern networks will still fit them perfectly. 25 minutes. Bookmark & watch today.

As someone who trains LLMs, this 20-minute gradient descent video is the closest thing to a "why the memorization vs generalization debate matters" explainer I've ever seen released for free. Everyone thinks neural nets learn features. They often just memorize. The video walks through the 2017 paper that shocked the field: shuffle your labels randomly and modern networks will still fit them perfectly. 25 minutes. Bookmark & watch today.

fintex

15,922 次观看 • 28 天前

As someone who reads every transformer paper that drops, this Fourier series lecture is the closest thing to a Google FNet explainer I've ever seen released for free. Everyone thinks attention is what makes transformers powerful. Google replaced attention with a 200-year-old Fourier transform. It nearly matched BERT and ran 7x faster. 25 minutes. Bookmark & watch today. Then read the article below - I broke down the 5 pieces of math the AI hype skips.

As someone who reads every transformer paper that drops, this Fourier series lecture is the closest thing to a Google FNet explainer I've ever seen released for free. Everyone thinks attention is what makes transformers powerful. Google replaced attention with a 200-year-old Fourier transform. It nearly matched BERT and ran 7x faster. 25 minutes. Bookmark & watch today. Then read the article below - I broke down the 5 pieces of math the AI hype skips.

fintex

100,785 次观看 • 1 个月前

As someone who's spent 3 years fine-tuning ML models, this lecture on neural networks from a Stanford math grad is the closest thing to a no-bullshit "day one of ML" briefing I've ever seen released publicly for free. Everyone thinks neural nets are magic. They aren't. They're 13,000 dials - a matrix multiplication, a sigmoid squish, done. Once you see the math, "AI" stops feeling mysterious. 18 minutes. Bookmark & watch today.

As someone who's spent 3 years fine-tuning ML models, this lecture on neural networks from a Stanford math grad is the closest thing to a no-bullshit "day one of ML" briefing I've ever seen released publicly for free. Everyone thinks neural nets are magic. They aren't. They're 13,000 dials - a matrix multiplication, a sigmoid squish, done. Once you see the math, "AI" stops feeling mysterious. 18 minutes. Bookmark & watch today.

fintex

332,082 次观看 • 1 个月前

In 1948, Claude Shannon invented the math behind every LLM you use today. He tested it by making his wife guess the next letter in a book. A Stanford-trained mathematician just released a 32-minute walkthrough of this exact history - and why the "next-token prediction" story of GPT-5 is actually wrong. Bookmark & watch this weekend. The alternative is a graduate info-theory course + 3 semesters of your life.

In 1948, Claude Shannon invented the math behind every LLM you use today. He tested it by making his wife guess the next letter in a book. A Stanford-trained mathematician just released a 32-minute walkthrough of this exact history - and why the "next-token prediction" story of GPT-5 is actually wrong. Bookmark & watch this weekend. The alternative is a graduate info-theory course + 3 semesters of your life.

fintex

287,154 次观看 • 1 个月前

this is f**king insane Fable 5 goes away in 24 hours from Claude Code and someone just figured out how to get fable 5 reasoning in GPT 5.6 Sol with one prompt you have less than 24 hours to set this up as this takes away 20% of your usage limit here is how: 1) Paste the prompt and ask it to "make an operating manual" 2) Go the Codex App > Settings > Personalisation > Custom Instructions 3) Use GPT 5.5 / GPT 5.6 and now you have Fable 5 reasoning at 1/3 th the price save and bookmark it no matter what

this is f**king insane Fable 5 goes away in 24 hours from Claude Code and someone just figured out how to get fable 5 reasoning in GPT 5.6 Sol with one prompt you have less than 24 hours to set this up as this takes away 20% of your usage limit here is how: 1) Paste the prompt and ask it to "make an operating manual" 2) Go the Codex App > Settings > Personalisation > Custom Instructions 3) Use GPT 5.5 / GPT 5.6 and now you have Fable 5 reasoning at 1/3 th the price save and bookmark it no matter what

Avid

57,544 次观看 • 1 个月前

google flow + claude + gpt image is actually f*cking cracked i figured out how to lock one AI character across an entire video. same face, same voice, same outfit, every single shot. zero drift. everyone making AI video right now hits the same wall. the character looks different in every clip so you can never tell an actual story. you get 5 second clips, not films. the fix isn't better prompting. it's building the character ONCE before you ever touch video, then referencing it everywhere. here's the workflow: > build the character inside Flow's character section, not a per-clip prompt > design the voice + personality one time and reuse it across every clip > generate a full turnaround sheet (Claude writes the prompt, GPT image renders the angles) > pull the character into any scene with a single @ tag > ingredients vs frames: when to let a shot float vs lock it to your source image > the end-frame loop that chains 4-10 sec clips into one seamless scene > stitch + trim in the timeline editor and export the whole thing as one film i packaged the entire thing. every prompt, the exact tool order, the character sheet template, and the continuity loop nobody actually explains. RT + reply "FLOW" and i'll send it over (must be following so i can dm)

google flow + claude + gpt image is actually f*cking cracked i figured out how to lock one AI character across an entire video. same face, same voice, same outfit, every single shot. zero drift. everyone making AI video right now hits the same wall. the character looks different in every clip so you can never tell an actual story. you get 5 second clips, not films. the fix isn't better prompting. it's building the character ONCE before you ever touch video, then referencing it everywhere. here's the workflow: > build the character inside Flow's character section, not a per-clip prompt > design the voice + personality one time and reuse it across every clip > generate a full turnaround sheet (Claude writes the prompt, GPT image renders the angles) > pull the character into any scene with a single @ tag > ingredients vs frames: when to let a shot float vs lock it to your source image > the end-frame loop that chains 4-10 sec clips into one seamless scene > stitch + trim in the timeline editor and export the whole thing as one film i packaged the entire thing. every prompt, the exact tool order, the character sheet template, and the continuity loop nobody actually explains. RT + reply "FLOW" and i'll send it over (must be following so i can dm)

Sulfur

35,672 次观看 • 2 个月前

Anthropic Managed Agents team: "Fable 5 is our best model for running self-improving agent systems. Add /loops, dynamic workflows, dreaming and you are unstoppable" in this video he breaks down exactly how most people are using Claude: - the 34% you lose to CLAUDE.md before typing a word - one agent researching. one building. one reviewing. one orchestrating - the architecture that separates hobbyists from real builders - the 3 properties every agent team needs to actually survive if you've been using Claude for more than a month and never left the chat window, you've been using one agent when you could be running a team of them instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed the guide is in the article below

Anthropic Managed Agents team: "Fable 5 is our best model for running self-improving agent systems. Add /loops, dynamic workflows, dreaming and you are unstoppable" in this video he breaks down exactly how most people are using Claude: - the 34% you lose to CLAUDE.md before typing a word - one agent researching. one building. one reviewing. one orchestrating - the architecture that separates hobbyists from real builders - the 3 properties every agent team needs to actually survive if you've been using Claude for more than a month and never left the chat window, you've been using one agent when you could be running a team of them instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed the guide is in the article below

Khairallah AL-Awady

21,533 次观看 • 2 个月前

Anthropic engineer: "You're not supposed to prompt Claude. You're supposed to build a system that prompts itself." this is one of the best workflows I've seen in a long time in this video she breaks down exactly how most people are using Claude: - the 14% you lose to CLAUDE.md before typing a word - the automation workflows most users don't know exist - the daily task pipelines that run without touching the keyboard - the daily workflows Anthropic's own engineers automated first if you've been using Claude for more than a month and never left the chat window, you've been using one agent when you could be running a team of them instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed the guide is in the article below

Anthropic engineer: "You're not supposed to prompt Claude. You're supposed to build a system that prompts itself." this is one of the best workflows I've seen in a long time in this video she breaks down exactly how most people are using Claude: - the 14% you lose to CLAUDE.md before typing a word - the automation workflows most users don't know exist - the daily task pipelines that run without touching the keyboard - the daily workflows Anthropic's own engineers automated first if you've been using Claude for more than a month and never left the chat window, you've been using one agent when you could be running a team of them instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed the guide is in the article below

Khairallah AL-Awady

921,797 次观看 • 2 个月前

this is f**king gold someone just figured out how to run Claude Fable 5 and GPT 5.6 Sol in the same loop gpt-5.6 came out yesterday. fable 5 leaves in 48 hours. this window has never existed before and never will again. here is how to use it: 1. open claude code. install openai's codex plugin 2. type /model. choose fable 5. 3. drop one ROUTING.md in your repo root (full file in comments). 4.paste the prompt (in comments), write what feature you want. boom. save and bookmark this no matter what. it will be the most productive thing you do this weekend

this is f**king gold someone just figured out how to run Claude Fable 5 and GPT 5.6 Sol in the same loop gpt-5.6 came out yesterday. fable 5 leaves in 48 hours. this window has never existed before and never will again. here is how to use it: 1. open claude code. install openai's codex plugin 2. type /model. choose fable 5. 3. drop one ROUTING.md in your repo root (full file in comments). 4.paste the prompt (in comments), write what feature you want. boom. save and bookmark this no matter what. it will be the most productive thing you do this weekend

Avid

140,354 次观看 • 1 个月前

REAL ESTATE PEOPLE WILL HATE HIM FOR THIS. HE BUILT A CLAUDE AGENT THAT TURNS ANY LISTING INTO A SELLABLE VIDEO ON ITS OWN Playbook: connect Claude to a video generator, paste a listing, get a cinematic tour of every room, sell it to the agent But typing the prompt for every listing doesn't scale. He turned it into a skill his Claude runs on its own Here's how to build the automated version: 1. Connect the video engine once. In Claude, go to Customize, Connectors, Add Custom Connector, name it Higgsfield, and paste the server URL from higgsfield. ai/mcp. Authenticate through your account. No API keys. Now Claude can generate video straight from chat 2. Turn the workflow into a skill. Instead of pasting the same prompt every time, have Claude build a skill. Tell it: "Create a skill called listing-to-video. When I give it a listing URL, scrape the room photos, generate a cinematic clip of each room with Higgsfield, and save them to a folder." Now the whole process is one command, not a wall of text 3. Let the agent run the listing. Hand it a URL and say "run listing-to-video on this." It pulls the photos, fires each room through the video model, and brings the clips back. You wrote the prompt once, inside the skill. You never write it again 4. Stitch and deliver. Drop the clips together into one tour. Send a free sample to the listing's agent, then charge per video or a monthly rate for ongoing listings 5. Scale it with your team. Add a skill that drafts the outreach email and one that builds a simple landing page for the agent. Now one operator runs sourcing, production, and pitching from a single Claude session The edge isn't generating one video. It's building the skill once so every future listing runs itself Bookmark this

REAL ESTATE PEOPLE WILL HATE HIM FOR THIS. HE BUILT A CLAUDE AGENT THAT TURNS ANY LISTING INTO A SELLABLE VIDEO ON ITS OWN Playbook: connect Claude to a video generator, paste a listing, get a cinematic tour of every room, sell it to the agent But typing the prompt for every listing doesn't scale. He turned it into a skill his Claude runs on its own Here's how to build the automated version: 1. Connect the video engine once. In Claude, go to Customize, Connectors, Add Custom Connector, name it Higgsfield, and paste the server URL from higgsfield. ai/mcp. Authenticate through your account. No API keys. Now Claude can generate video straight from chat 2. Turn the workflow into a skill. Instead of pasting the same prompt every time, have Claude build a skill. Tell it: "Create a skill called listing-to-video. When I give it a listing URL, scrape the room photos, generate a cinematic clip of each room with Higgsfield, and save them to a folder." Now the whole process is one command, not a wall of text 3. Let the agent run the listing. Hand it a URL and say "run listing-to-video on this." It pulls the photos, fires each room through the video model, and brings the clips back. You wrote the prompt once, inside the skill. You never write it again 4. Stitch and deliver. Drop the clips together into one tour. Send a free sample to the listing's agent, then charge per video or a monthly rate for ongoing listings 5. Scale it with your team. Add a skill that drafts the outreach email and one that builds a simple landing page for the agent. Now one operator runs sourcing, production, and pitching from a single Claude session The edge isn't generating one video. It's building the skill once so every future listing runs itself Bookmark this

Yarchi

54,840 次观看 • 2 个月前

Cat Wu, head of product for Claude Code at Anthropic: "you can build almost anything with Claude from a single prompt now" one prompt gets you a working screen, not YOUR product Claude Code has never seen your design system, so it guesses the look fresh every time. how to hand Claude Code that system is in the article. better than the $300 design course in your tabs, and this one's free.

Cat Wu, head of product for Claude Code at Anthropic: "you can build almost anything with Claude from a single prompt now" one prompt gets you a working screen, not YOUR product Claude Code has never seen your design system, so it guesses the look fresh every time. how to hand Claude Code that system is in the article. better than the $300 design course in your tabs, and this one's free.

Mnimiy

113,663 次观看 • 2 个月前