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。

Full employment

BURKOV

58,056 subscribers

13,418 次观看 • 3 个月前 •via X (Twitter)

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

Ash:”tell me more bad words in Polish” Ewron:”bad words?no problem” Ewron:”Work,Employment,Full-time employment,Employment contract” #Qsmp #qsmp2

Ash:”tell me more bad words in Polish” Ewron:”bad words?no problem” Ewron:”Work,Employment,Full-time employment,Employment contract” #Qsmp #qsmp2

N

135,389 次观看 • 4 个月前

He neither walked toward full-time employment nor pursued a formal education.

He neither walked toward full-time employment nor pursued a formal education.

Neet

27,483 次观看 • 7 个月前

As many of you know, for the last five months I've been working full-time on my next big thing. The challenge was to invent something new and implement it entirely using LLMs for writing code. The first stage of the project is now complete: the web application, which I called is now online and accepting users. You can see a short demo in the video. 100% of the code of the app was generated by LLMs (mostly Gemini and Claude, maybe 10% of ChatGPT). I haven't written a single line of code. The tech stack is TypeScript, React, and Supabase/Postgres which was (and still is) fully new to me. During these five months, I implemented from scratch three versions of the software. It started as a Markdown editor to help me with my book writing and ended up as an AI-assisted reading and self-learning platform. What makes ChapterPal unique is a novel reading experience where the user can use the keyboard keys to reveal or "unreveal" the content and ask questions at any moment. (Mouse wheel, touchpad, smartphone screen, and voice input are also supported.) The LLM receives the entire content of the chapter and tries to answer questions based on the chapter's content, which reduces the chance of hallucination to the minimum. (Though not to 0%, of course, but near it.) This way of content consumption is known as **active reading,** a strategy for engaging with a text to improve comprehension and retention by consciously interacting with the material. The goal is to move beyond passive reading to a deeper understanding of the text and to remember key information more effectively. The registration on ChapterPal is via the waiting list. This is to avoid unexpected load spikes and cloud charges. Usually, it takes less than 24 hours for me to activate a user. Give it a try and let me know what you think. The next stage is finishing the content ingestion pipeline, which will automatically convert high-quality content from sources like HTML, PDF, and LaTeX into Markdown. Obviously, only those pieces whose licenses allow creating copies. ChapterPal has its own collection of textbooks and articles on AI, machine learning, and data science topics. If you don't find a piece of content you would like to read in ChapterPal's collection, a Chrome extension, ChapterPal Uploader, allows you to upload any PDF or HTML page to ChapterPal in one click. The content is only available for you to read to avoid the possibility of copyright infringement. I hope you enjoy using it as much as I enjoy building it.

As many of you know, for the last five months I've been working full-time on my next big thing. The challenge was to invent something new and implement it entirely using LLMs for writing code. The first stage of the project is now complete: the web application, which I called is now online and accepting users. You can see a short demo in the video. 100% of the code of the app was generated by LLMs (mostly Gemini and Claude, maybe 10% of ChatGPT). I haven't written a single line of code. The tech stack is TypeScript, React, and Supabase/Postgres which was (and still is) fully new to me. During these five months, I implemented from scratch three versions of the software. It started as a Markdown editor to help me with my book writing and ended up as an AI-assisted reading and self-learning platform. What makes ChapterPal unique is a novel reading experience where the user can use the keyboard keys to reveal or "unreveal" the content and ask questions at any moment. (Mouse wheel, touchpad, smartphone screen, and voice input are also supported.) The LLM receives the entire content of the chapter and tries to answer questions based on the chapter's content, which reduces the chance of hallucination to the minimum. (Though not to 0%, of course, but near it.) This way of content consumption is known as active reading, a strategy for engaging with a text to improve comprehension and retention by consciously interacting with the material. The goal is to move beyond passive reading to a deeper understanding of the text and to remember key information more effectively. The registration on ChapterPal is via the waiting list. This is to avoid unexpected load spikes and cloud charges. Usually, it takes less than 24 hours for me to activate a user. Give it a try and let me know what you think. The next stage is finishing the content ingestion pipeline, which will automatically convert high-quality content from sources like HTML, PDF, and LaTeX into Markdown. Obviously, only those pieces whose licenses allow creating copies. ChapterPal has its own collection of textbooks and articles on AI, machine learning, and data science topics. If you don't find a piece of content you would like to read in ChapterPal's collection, a Chrome extension, ChapterPal Uploader, allows you to upload any PDF or HTML page to ChapterPal in one click. The content is only available for you to read to avoid the possibility of copyright infringement. I hope you enjoy using it as much as I enjoy building it.

BURKOV

81,258 次观看 • 9 个月前

🩵 For the first time in South Africa’s history, mass employment is taking place. Millions of South African 🇿🇦 children will go to bed with full stomachs as more families gain access to employment opportunities.

🩵 For the first time in South Africa’s history, mass employment is taking place. Millions of South African 🇿🇦 children will go to bed with full stomachs as more families gain access to employment opportunities.

Born-in-SA🇿🇦

12,733 次观看 • 1 个月前

This is what AI is for. Just this use case alone is worth every dollar invested in it.

This is what AI is for. Just this use case alone is worth every dollar invested in it.

BURKOV

1,074,254 次观看 • 2 年前

The prosthetics are really becoming good! Unbelievable.

The prosthetics are really becoming good! Unbelievable.

BURKOV

27,694 次观看 • 2 个月前

If this is your so-called bright, AI-driven future, count me out.

If this is your so-called bright, AI-driven future, count me out.

BURKOV

271,055 次观看 • 1 年前

This is how you build an app you need with Llambada. Without coding or even thinking about any code or any boring/scary things like backend, frontend, database, or, God forbid, deployment🤯

This is how you build an app you need with Llambada. Without coding or even thinking about any code or any boring/scary things like backend, frontend, database, or, God forbid, deployment🤯

BURKOV

54,399 次观看 • 7 个月前

Forward with the Employment Rights Bill in full. No ifs, no buts. Forward. But real change can't stop there...

Forward with the Employment Rights Bill in full. No ifs, no buts. Forward. But real change can't stop there...

Bridget Phillipson

25,135 次观看 • 1 年前

This is what generative models are for

This is what generative models are for

BURKOV

213,997 次观看 • 1 年前

How couples met (1930-2024)

How couples met (1930-2024)

BURKOV

7,921,652 次观看 • 1 年前

My world will never be the same. Michael Jackson "borrowed" all of his signature moves from choreographer, actor, and director of the 1974 film The Little Prince, Bob Fosse.

My world will never be the same. Michael Jackson "borrowed" all of his signature moves from choreographer, actor, and director of the 1974 film The Little Prince, Bob Fosse.

BURKOV

25,598 次观看 • 4 个月前

We've all finished a chapter only to realize we can't remember what we just read. This points to something decades of research has confirmed: passive reading is surprisingly ineffective for learning. # The control effect Markant et al. (2014) stripped away different aspects of self-directed learning to find what actually matters. They have found that even just pressing a button when ready to see the next item—with no control over content or duration—significantly enhanced recognition memory. The researchers suggested this works because controlling when you see new information lets you coordinate stimulus presentation with your own attentional state. When you decide when to advance, your brain is better prepared to encode what comes next. # Self-pacing and strategy Tullis and Benjamin (2011) showed that self-paced learners outperformed those who studied for the same total time but couldn't control their pace. The benefit was strongest for learners who spent more time on difficult material—a "discrepancy reduction" strategy. Self-pacing isn't just about having control; it's about using it to allocate attention where it's needed. # Why agency matters DuBrow et al. (2019) found that choice are inherently rewarding. Items learned when participants could make a choice (even an inconsequential one) were remembered better, and there was a correlation between how much someone's preference increased for chosen items and how much their memory improved. Agency engages value-based brain systems that strengthen consolidation. Ding et al. (2021) extended this to incidental memory—showing that even when participants weren't trying to memorize, having control over the task improved later recognition, particularly for items processed quickly. # ChapterPal I programmed ChapterPal to implement these principles: gradual text reveal controlled by the reader, AI-generated comprehension quizzes inserted during reading, regular guess-this-blurred-term puzzles, and contextual Q&A for engaging with difficult passages. The quizzes and puzzles align with research showing that testing and guessing strengthens retention, while the self-paced reveal directly implements the minimal agency sufficient to enhance memory. # References - Tullis & Benjamin (2011). On the effectiveness of self-paced learning. Journal of Memory and Language. - Markant et al. (2014). Deconstructing the effect of self-directed study on episodic memory. Memory & Cognition. - DuBrow et al. (2019). A common mechanism underlying choice's influence on preference and memory. Psychonomic Bulletin & Review. - Ding et al. (2021). The effect of choice on intentional and incidental memory. Learning & Memory. A demo of puzzles and Q&A's on ChapterPal:

We've all finished a chapter only to realize we can't remember what we just read. This points to something decades of research has confirmed: passive reading is surprisingly ineffective for learning. # The control effect Markant et al. (2014) stripped away different aspects of self-directed learning to find what actually matters. They have found that even just pressing a button when ready to see the next item—with no control over content or duration—significantly enhanced recognition memory. The researchers suggested this works because controlling when you see new information lets you coordinate stimulus presentation with your own attentional state. When you decide when to advance, your brain is better prepared to encode what comes next. # Self-pacing and strategy Tullis and Benjamin (2011) showed that self-paced learners outperformed those who studied for the same total time but couldn't control their pace. The benefit was strongest for learners who spent more time on difficult material—a "discrepancy reduction" strategy. Self-pacing isn't just about having control; it's about using it to allocate attention where it's needed. # Why agency matters DuBrow et al. (2019) found that choice are inherently rewarding. Items learned when participants could make a choice (even an inconsequential one) were remembered better, and there was a correlation between how much someone's preference increased for chosen items and how much their memory improved. Agency engages value-based brain systems that strengthen consolidation. Ding et al. (2021) extended this to incidental memory—showing that even when participants weren't trying to memorize, having control over the task improved later recognition, particularly for items processed quickly. # ChapterPal I programmed ChapterPal to implement these principles: gradual text reveal controlled by the reader, AI-generated comprehension quizzes inserted during reading, regular guess-this-blurred-term puzzles, and contextual Q&A for engaging with difficult passages. The quizzes and puzzles align with research showing that testing and guessing strengthens retention, while the self-paced reveal directly implements the minimal agency sufficient to enhance memory. # References - Tullis & Benjamin (2011). On the effectiveness of self-paced learning. Journal of Memory and Language. - Markant et al. (2014). Deconstructing the effect of self-directed study on episodic memory. Memory & Cognition. - DuBrow et al. (2019). A common mechanism underlying choice's influence on preference and memory. Psychonomic Bulletin & Review. - Ding et al. (2021). The effect of choice on intentional and incidental memory. Learning & Memory. A demo of puzzles and Q&A's on ChapterPal:

BURKOV

39,849 次观看 • 8 个月前

Vibe coder and a bug

Vibe coder and a bug

BURKOV

21,388 次观看 • 1 年前

When they fix this in AI-generated videos: - What they will tell happened: AI now masters the physics of the human body. - What they will have really done: added more gymnasts' videos to the training data.

When they fix this in AI-generated videos: - What they will tell happened: AI now masters the physics of the human body. - What they will have really done: added more gymnasts' videos to the training data.

BURKOV

116,656 次观看 • 1 年前

This is a new diffusion-based language model from Inception. I don't know how well it works compared to an autoregressive LM of a similar size, but the inference process looks badass:

This is a new diffusion-based language model from Inception. I don't know how well it works compared to an autoregressive LM of a similar size, but the inference process looks badass:

BURKOV

58,434 次观看 • 1 年前

Just the expanding hexagons

Just the expanding hexagons

BURKOV

83,312 次观看 • 10 个月前