Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

Battle of the new coding models: Cursor vs Cognition They both make big claims being near the frontier - how well do they pass the Golden Gate Bridge test? I've included 5x non-cherry picked generations from each, followed by examples from pre-release test of Gemini 3 Pro, GPT-5 and... Sonnet 4.5. Generations are mostly one shot, but each had errors twice, which it then fixed in the next generation. My impressions: - Both are clearly way behind the frontier - Between the two, Windsurf's SWE-1.5 is a winner for me - no huge errors like two parts of the bridge are the wrong way round - But SWE-1.5 generations were quite low-fi and Cursor's composer did have some better generations - Overall GPT-5 seems like the best model around on these tests, and Gemini 3 Pro seems to have a lot of potential Music via Sunoshow more

Peter Gostev

6,711 subscribers

23,583 görüntüleme • 10 ay önce •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Comparisons of Sonnet 4.5 vs GPT-5 Pro. I appreciate the comparison is not exactly fair, but I've had GPT-5 Pro videos ready to go, so forgive me. Saying that it does give a good benchmark, I don't think there was a single instance of Sonnet 4.5 being better, and I was testing it in Claude Code, so it should have had an advantage of not being stuck in the web client. It doesn't mean to say that Claude 4.5 would be worse on all dimensions, these are 1-shot single file HTML files, so not going to test the full agentic suite of capabilities. Song via Suno v5 Lyrics by Sonnet 4.5

Comparisons of Sonnet 4.5 vs GPT-5 Pro. I appreciate the comparison is not exactly fair, but I've had GPT-5 Pro videos ready to go, so forgive me. Saying that it does give a good benchmark, I don't think there was a single instance of Sonnet 4.5 being better, and I was testing it in Claude Code, so it should have had an advantage of not being stuck in the web client. It doesn't mean to say that Claude 4.5 would be worse on all dimensions, these are 1-shot single file HTML files, so not going to test the full agentic suite of capabilities. Song via Suno v5 Lyrics by Sonnet 4.5

Peter Gostev (SF 24-28 August)

255,966 görüntüleme • 11 ay önce

Opus 4.7 - 400k vs 1m context - is there a difference? I've heard Theo - t3.gg talk about the fact that it is unlikely that Anthropic would have offered up a model with 1m context at the same cost, if it wasn't a different (i.e. cheaper to serve) model. I did a test where I toggled the 1m default model on & off in Claude Code (otherwise default settings, xHigh reasoning) and compared the outputs with 3x generations - same prompts etc. My observations: - Models feel DIFFERENT - often when you ask a model for the same generation, you get a somewhat different answer, but it feels & smells the same. Here 400k and 1m are very different every time - 400k model seems better - not that 1m is trash and 400k is amazing, but there are definitely issues with the level of ambition and accuracy that 1m model seems to have Examples of 1m failing: - Voxel Rome: the colosseum is nowhere near as impressive - Golden Gate: cars go sideways, waves not very high, bridge goes into land; though the structure of the bridge is a bit better - Stonehenge: structure is more 'wrong', lighting, shadows & textures are more flat and not as rich This isn't a conclusive evidence of course, but at least to me the two models do not behave the same way. Anecdotally as well when building 1m felt like it was doing more weird validation (e.g. going around in circles) and 400k was more straightforward. These sorts of things are harder to capture in tests, but you'd notice in Claude Code. You can review the hosted generations, see the code & prompts in the links below

Opus 4.7 - 400k vs 1m context - is there a difference? I've heard Theo - t3.gg talk about the fact that it is unlikely that Anthropic would have offered up a model with 1m context at the same cost, if it wasn't a different (i.e. cheaper to serve) model. I did a test where I toggled the 1m default model on & off in Claude Code (otherwise default settings, xHigh reasoning) and compared the outputs with 3x generations - same prompts etc. My observations: - Models feel DIFFERENT - often when you ask a model for the same generation, you get a somewhat different answer, but it feels & smells the same. Here 400k and 1m are very different every time - 400k model seems better - not that 1m is trash and 400k is amazing, but there are definitely issues with the level of ambition and accuracy that 1m model seems to have Examples of 1m failing: - Voxel Rome: the colosseum is nowhere near as impressive - Golden Gate: cars go sideways, waves not very high, bridge goes into land; though the structure of the bridge is a bit better - Stonehenge: structure is more 'wrong', lighting, shadows & textures are more flat and not as rich This isn't a conclusive evidence of course, but at least to me the two models do not behave the same way. Anecdotally as well when building 1m felt like it was doing more weird validation (e.g. going around in circles) and 400k was more straightforward. These sorts of things are harder to capture in tests, but you'd notice in Claude Code. You can review the hosted generations, see the code & prompts in the links below

Peter Gostev (SF: 22-26 June)

29,203 görüntüleme • 4 ay önce

New Golden Gate SOTA: GPT-5.1-Pro! I want to stress that this was _NOT_ one shot - in fact it was many different iterations in 3 different chats. I loved the way it did the water, night sky and the clouds, but it took a lot of back & forth to get the details right - traffic, colours, land. It was quite frustrating that GPT-5.1-Pro wasn't following the instructions after a few steps - it seems like it was struggling to combine the different reasoning threads into a single correct one. But still, this is the best result I got so far.

New Golden Gate SOTA: GPT-5.1-Pro! I want to stress that this was _NOT_ one shot - in fact it was many different iterations in 3 different chats. I loved the way it did the water, night sky and the clouds, but it took a lot of back & forth to get the details right - traffic, colours, land. It was quite frustrating that GPT-5.1-Pro wasn't following the instructions after a few steps - it seems like it was struggling to combine the different reasoning threads into a single correct one. But still, this is the best result I got so far.

Peter Gostev

100,088 görüntüleme • 9 ay önce

GPT-5.6 vs GPT-5.5 on my custom spaceship prompt. I gave both models the exact same custom prompt. This is also the same prompt I previously gave to Fable 5. For context, GPT-5.6 Pro worked for 87 minutes, while GPT-5.5 Extra High worked for 34 minutes and 42 seconds. As I’ve said before, based on great authority GPT-5.6 will be an incremental/soldi improvement over GPT-5.5, not a “Fable killer.” My rough expectation has been that it would trade blows with Fable 5 on some benchmarks, maybe win around half depending on the category, but not clearly surpass it overall. And again fable five will have bigger model smell, but this was expected. After testing this coding output, that view feels pretty accurate. GPT-5.6 is clearly better than GPT-5.5 in several visual areas. The lighting, shading, chairs, object details, and exterior of the spaceship looked noticeably stronger. The scene was also easier to test. I do want to give GPT-5.5 credit though. It built out the rooms much much better and the planets looked better than GPT-5.6’s. It was also interesting that both GPT-5.5 and GPT-5.6 produced better-looking planets than Fable 5 in this specific test. The downside with GPT-5.5 was stability. The game was much glitchier and harder to test compared to GPT-5.6. But when it comes to the core of the demo, which is the spaceship itself, Fable 5 still beat both models pretty comfortably. GPT-5.6 is impressive, but from this test, it looks exactly like what I expected which was a meaningful incremental improvement over GPT-5.5, at least for indie game demos, but not something that replaces Fable 5. In collaboration with Chetaslua

GPT-5.6 vs GPT-5.5 on my custom spaceship prompt. I gave both models the exact same custom prompt. This is also the same prompt I previously gave to Fable 5. For context, GPT-5.6 Pro worked for 87 minutes, while GPT-5.5 Extra High worked for 34 minutes and 42 seconds. As I’ve said before, based on great authority GPT-5.6 will be an incremental/soldi improvement over GPT-5.5, not a “Fable killer.” My rough expectation has been that it would trade blows with Fable 5 on some benchmarks, maybe win around half depending on the category, but not clearly surpass it overall. And again fable five will have bigger model smell, but this was expected. After testing this coding output, that view feels pretty accurate. GPT-5.6 is clearly better than GPT-5.5 in several visual areas. The lighting, shading, chairs, object details, and exterior of the spaceship looked noticeably stronger. The scene was also easier to test. I do want to give GPT-5.5 credit though. It built out the rooms much much better and the planets looked better than GPT-5.6’s. It was also interesting that both GPT-5.5 and GPT-5.6 produced better-looking planets than Fable 5 in this specific test. The downside with GPT-5.5 was stability. The game was much glitchier and harder to test compared to GPT-5.6. But when it comes to the core of the demo, which is the spaceship itself, Fable 5 still beat both models pretty comfortably. GPT-5.6 is impressive, but from this test, it looks exactly like what I expected which was a meaningful incremental improvement over GPT-5.5, at least for indie game demos, but not something that replaces Fable 5. In collaboration with Chetaslua

Chris

250,919 görüntüleme • 2 ay önce

Interesting that in three of my recent conversations with Krishna at Anthropic, Dylan Patel, and Gavin Baker, each said that frontier tokens are capturing the majority of the economic value. Gavin: "An overwhelming amount of the economic returns to AI at the model layer have been at the frontier. That's surprising to me, and I think it's been surprising to a lot of people. This is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor." Krishna: "We think the returns to frontier intelligence are extremely high. Customers invest really heavily in more tokens with the newer models. The ones at the frontier clearly are capturing this economic value, driving meaningful ROI for customers. The returns to frontier intelligence are not slowing down." Dylan: "No one gives a crap about GPT-4 class models. They want the frontier because the frontier lets them create the economically valuable things."

Interesting that in three of my recent conversations with Krishna at Anthropic, Dylan Patel, and Gavin Baker, each said that frontier tokens are capturing the majority of the economic value. Gavin: "An overwhelming amount of the economic returns to AI at the model layer have been at the frontier. That's surprising to me, and I think it's been surprising to a lot of people. This is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor." Krishna: "We think the returns to frontier intelligence are extremely high. Customers invest really heavily in more tokens with the newer models. The ones at the frontier clearly are capturing this economic value, driving meaningful ROI for customers. The returns to frontier intelligence are not slowing down." Dylan: "No one gives a crap about GPT-4 class models. They want the frontier because the frontier lets them create the economically valuable things."

Patrick OShaughnessy

121,110 görüntüleme • 3 ay önce

Claude Fable 5 makes new things possible. I one shot a full horror game with it. One prompt. I have never seen AI output a game like this. This is the leap I've been waiting for. 80.3 on SWE-Bench Pro vs Opus 4.8 at 69.2, the biggest jump I have ever seen on that benchmark. It passed GPT 5.5 in both coding and intelligence on Artificial Analysis. I already canceled my $200 ChatGPT Pro plan. It fixed a Bridge Voice bug in one shot that GPT 5.5, Opus 4.7, and Opus 4.8 all failed to crack for 2 months. 10/10. Best model I have ever used. Full review below.

Claude Fable 5 makes new things possible. I one shot a full horror game with it. One prompt. I have never seen AI output a game like this. This is the leap I've been waiting for. 80.3 on SWE-Bench Pro vs Opus 4.8 at 69.2, the biggest jump I have ever seen on that benchmark. It passed GPT 5.5 in both coding and intelligence on Artificial Analysis. I already canceled my $200 ChatGPT Pro plan. It fixed a Bridge Voice bug in one shot that GPT 5.5, Opus 4.7, and Opus 4.8 all failed to crack for 2 months. 10/10. Best model I have ever used. Full review below.

BridgeMind

19,020 görüntüleme • 2 ay önce

OpenAI are testing a new model on the Web Dev Arena Arena under the name 'Anonymous Chatbot 0717'. I can't believe I'm gonna say this, but it is genuinely at a completely different level of front end coding - far better than Sonnet, o3, Gemini 2.5 Pro, or Grok 4. To test it, I ran a great prompt borrowed from the amazing The Feature Crew YouTube channel, asking models to create a procedurally generated planet with Three.js. Take a look yourselves, but I'm pretty astonished by how big the jump is. I have featured the new model twice just because its implementations have been so interesting. Of course, this is only one test, but OpenAI models have always been a bit 'meh' at front-end work, and they seem to have finally overtaken everyone else on that front. We'll see when it comes out. Credit to Chetaslua for discovering the model

OpenAI are testing a new model on the Web Dev Arena Arena under the name 'Anonymous Chatbot 0717'. I can't believe I'm gonna say this, but it is genuinely at a completely different level of front end coding - far better than Sonnet, o3, Gemini 2.5 Pro, or Grok 4. To test it, I ran a great prompt borrowed from the amazing The Feature Crew YouTube channel, asking models to create a procedurally generated planet with Three.js. Take a look yourselves, but I'm pretty astonished by how big the jump is. I have featured the new model twice just because its implementations have been so interesting. Of course, this is only one test, but OpenAI models have always been a bit 'meh' at front-end work, and they seem to have finally overtaken everyone else on that front. We'll see when it comes out. Credit to Chetaslua for discovering the model

Peter Gostev

495,815 görüntüleme • 1 yıl önce

I couldn't believe the results of this benchmarking test. I blind-tested 6 frontier models — Sonnet 5, Opus 4.8, Fable 5, GPT 5.5, GLM 5.2, and Kimi 2.5 — on knowledge work, not engineering. Writing. Analysis. Design. Vibe coding. Subagent simulations. The stuff we actually do all day. Same prompts, each model running its own subagent, every output graded blind before the labels were revealed. The results: → GLM 5.2 (the dirt-cheap open-source model from China) took first → Sonnet 5 took second, beating Fable 5 for knowledge work → Opus 4.8 got whooped Then I had Composer 2.5 judge every output independently — and it picked Fable 5 almost across the board. My blind picks and the AI judge completely disagreed. Full breakdown, method, and prompts in the video below. Which model are you running as your daily driver right now?

I couldn't believe the results of this benchmarking test. I blind-tested 6 frontier models — Sonnet 5, Opus 4.8, Fable 5, GPT 5.5, GLM 5.2, and Kimi 2.5 — on knowledge work, not engineering. Writing. Analysis. Design. Vibe coding. Subagent simulations. The stuff we actually do all day. Same prompts, each model running its own subagent, every output graded blind before the labels were revealed. The results: → GLM 5.2 (the dirt-cheap open-source model from China) took first → Sonnet 5 took second, beating Fable 5 for knowledge work → Opus 4.8 got whooped Then I had Composer 2.5 judge every output independently — and it picked Fable 5 almost across the board. My blind picks and the AI judge completely disagreed. Full breakdown, method, and prompts in the video below. Which model are you running as your daily driver right now?

JJ Englert

10,737 görüntüleme • 2 ay önce

Sam Altman on GPT 6 • The GDPv eval will shift some of the way we do post training • overall we are keeping the same strategy going into GPT 6 Sam Altman on AGI: Sam Altman about AGI: "we are finally in the moment": "As the closer we get to it, the fuzzier the Agi concept becomes. But the one thing that I care the most about and, to my great surprise, we're finally in the moment; where it's starting to happen is, when it can do novel discovery, when It can expand the total human knowledge base.“ I have seen some examples from credible professors, that novel math problems have been solved. Such as GPT 5 Pro reportedly solving Yu Tsumura’s 554th problem, an IMO level group theory challenge that an August preprint said no LLM had solved, so the new claim is being actively contested. Sébastien Bubeck also said GPT 5 Pro produced a new proof that tightened a bound in smooth convex optimization.

Sam Altman on GPT 6 • The GDPv eval will shift some of the way we do post training • overall we are keeping the same strategy going into GPT 6 Sam Altman on AGI: Sam Altman about AGI: "we are finally in the moment": "As the closer we get to it, the fuzzier the Agi concept becomes. But the one thing that I care the most about and, to my great surprise, we're finally in the moment; where it's starting to happen is, when it can do novel discovery, when It can expand the total human knowledge base.“ I have seen some examples from credible professors, that novel math problems have been solved. Such as GPT 5 Pro reportedly solving Yu Tsumura’s 554th problem, an IMO level group theory challenge that an August preprint said no LLM had solved, so the new claim is being actively contested. Sébastien Bubeck also said GPT 5 Pro produced a new proof that tightened a bound in smooth convex optimization.

Chris

221,718 görüntüleme • 11 ay önce

Chamath: Tesla and Google are best positioned to win AI because they are building the full-stack Jason: "The game here is who wins the AI prize five years from now?" "Chamath, who's your number one and number two?" Chamath: " Tesla's one and Google's two." "And the reason is because they are the closest to having that vertically integrated stack that I spoke about." TESLA 1⃣ "I think that Tesla has the best vision models." "Now with xAI, they'll have one of the best LLMs and reasoning models." "And they'll be able to eventually stick that on Dojo." "And then all of that will be in all of the physical AI that you will interact with in your daily life." "Whether it's a robot, or whether it's a car, or whether it's a Robotaxi. So that's number one." GOOGLE 2⃣ "And then number two, for many of the same reasons, I think Google." "Because you'll have the Gemini family of models, which just absolutely kick ass." "Like Veo 3, which we haven't really spoke about, is gonna destroy Hollywood in the next year, like Hollywood is done, I think." "But they're landing model after model." "They have the TPU, and the next generation TPU I think is exceptional." "They're baking quantum." "And then they have an entire funnel of billions of people that they can direct experiences to." "So Tesla one, Google two."

Chamath: Tesla and Google are best positioned to win AI because they are building the full-stack Jason: "The game here is who wins the AI prize five years from now?" "Chamath, who's your number one and number two?" Chamath: " Tesla's one and Google's two." "And the reason is because they are the closest to having that vertically integrated stack that I spoke about." TESLA 1⃣ "I think that Tesla has the best vision models." "Now with xAI, they'll have one of the best LLMs and reasoning models." "And they'll be able to eventually stick that on Dojo." "And then all of that will be in all of the physical AI that you will interact with in your daily life." "Whether it's a robot, or whether it's a car, or whether it's a Robotaxi. So that's number one." GOOGLE 2⃣ "And then number two, for many of the same reasons, I think Google." "Because you'll have the Gemini family of models, which just absolutely kick ass." "Like Veo 3, which we haven't really spoke about, is gonna destroy Hollywood in the next year, like Hollywood is done, I think." "But they're landing model after model." "They have the TPU, and the next generation TPU I think is exceptional." "They're baking quantum." "And then they have an entire funnel of billions of people that they can direct experiences to." "So Tesla one, Google two."

The All-In Podcast

7,919,302 görüntüleme • 1 yıl önce

Baseten Head of AI Model Training Charlie O'Neill says the future is many specialized LLMs dedicated to specific tasks, with bigger labs deployed on the frontiers of areas like science and math: "People are thinking about intelligence capabilities in the wrong way. People are thinking about intelligence relativistically. They say, 'OK, the open-source gap is like 6 months behind closed-source, and GLM 5.3 is as good as Opus 4.8,' or whatever." "The best way to think about what models can do for you, and for the world, is in an absolute sense." "So for any given task that you want to do with an LLM, there's some intelligence threshold where below that you can't do the task, and above that you have very diminishing returns to more intelligence on the task." "So when you think about it that way, the game of LLMs over the last 5 years has been, 'OK, we have these things we want to do with them. Closed source hits it first... but open-source can eventually do that task. And then for many reasons, once you have the base level of intelligence required to do it, you probably do want to swap to open-source." "It's not really about the [frontier lab] God model being better. Like, if I'm filing a tax return, there is a limit to how much intelligence I need to do that particular thing." "So I think the world is going to look like — frontier closed-source labs are going to continue to push the frontier. You do want to use the most intelligent model. You have very inelastic demand for intelligence when you're doing frontier science or frontier math." "But for a lot of the economically valuable things, it looks a lot like, 'I'm a Cursor, or I'm one of these big companies who are realizing I can't just be a wrapper anymore. I've been through the life cycle of building a product that people love. And I should be using that information to make my model better at the things that I care about, and not at anything else.'"

Baseten Head of AI Model Training Charlie O'Neill says the future is many specialized LLMs dedicated to specific tasks, with bigger labs deployed on the frontiers of areas like science and math: "People are thinking about intelligence capabilities in the wrong way. People are thinking about intelligence relativistically. They say, 'OK, the open-source gap is like 6 months behind closed-source, and GLM 5.3 is as good as Opus 4.8,' or whatever." "The best way to think about what models can do for you, and for the world, is in an absolute sense." "So for any given task that you want to do with an LLM, there's some intelligence threshold where below that you can't do the task, and above that you have very diminishing returns to more intelligence on the task." "So when you think about it that way, the game of LLMs over the last 5 years has been, 'OK, we have these things we want to do with them. Closed source hits it first... but open-source can eventually do that task. And then for many reasons, once you have the base level of intelligence required to do it, you probably do want to swap to open-source." "It's not really about the [frontier lab] God model being better. Like, if I'm filing a tax return, there is a limit to how much intelligence I need to do that particular thing." "So I think the world is going to look like — frontier closed-source labs are going to continue to push the frontier. You do want to use the most intelligent model. You have very inelastic demand for intelligence when you're doing frontier science or frontier math." "But for a lot of the economically valuable things, it looks a lot like, 'I'm a Cursor, or I'm one of these big companies who are realizing I can't just be a wrapper anymore. I've been through the life cycle of building a product that people love. And I should be using that information to make my model better at the things that I care about, and not at anything else.'"

TBPN

46,566 görüntüleme • 1 gün önce

How good is GPT-4-Vision at extracting text from images? I wanted to find the limit - but I found weirdness instead Most surprising: GPT-4V performance varies depending on the *structure* of text it sees Let me explain A set of images with progressively more text was presented to GPT-4-Vision. GPT-4V was asked what text it saw in the image. The response from the model was compared against the image’s original text and scored for similarity. The model was tested with 4 types of text: essay, random words, random tokens, and random characters. Findings: * Performance degrades - Yes, the models are good at basic OCR, but as you get more text and words then performance drops (this is expected) * Type of context matters - You should expect different recall on your texts based on your context types * Hallucination Errors - I thought that the model would make errors of omission (it wouldn’t return all the words). But instead the model mostly made hallucination errors - it replaced words with made up words. * Evals Matter - This test in isolation doesn’t mean that your data will have the same results, but it should motivate you to create eval tests for your data and anticipate errors which are hard to spot Notes: * Next step would be to add additional image types like tables or PDFs * GPT-4V would routinely get stuck in repeat-token-loops when trying to extract random tokens * GPT-4V would refuse to answer most random character images

How good is GPT-4-Vision at extracting text from images? I wanted to find the limit - but I found weirdness instead Most surprising: GPT-4V performance varies depending on the structure of text it sees Let me explain A set of images with progressively more text was presented to GPT-4-Vision. GPT-4V was asked what text it saw in the image. The response from the model was compared against the image’s original text and scored for similarity. The model was tested with 4 types of text: essay, random words, random tokens, and random characters. Findings: * Performance degrades - Yes, the models are good at basic OCR, but as you get more text and words then performance drops (this is expected) * Type of context matters - You should expect different recall on your texts based on your context types * Hallucination Errors - I thought that the model would make errors of omission (it wouldn’t return all the words). But instead the model mostly made hallucination errors - it replaced words with made up words. * Evals Matter - This test in isolation doesn’t mean that your data will have the same results, but it should motivate you to create eval tests for your data and anticipate errors which are hard to spot Notes: * Next step would be to add additional image types like tables or PDFs * GPT-4V would routinely get stuck in repeat-token-loops when trying to extract random tokens * GPT-4V would refuse to answer most random character images

Greg Kamradt

49,111 görüntüleme • 2 yıl önce

The moment the Boomers who vote for this begin to pass on to the afterlife, we will make this man's life hell. He isn't stuck with old bitter people> he is stuck with 2 generations of Men and Women who already hate him and everything he represents. We are winning the Male-Youth. Who's gonna save them? The Marxist left? Lmao. They just told us on TV that no Police Woman were attacked, even when we have videos of it. They sided with the one group of people who want us all dead, themselves included, but they are too blinded by their own hubris, which we have seen way too much. They have no one in the end, except a lost pension and they will be branded as traitors of the highest order.

The moment the Boomers who vote for this begin to pass on to the afterlife, we will make this man's life hell. He isn't stuck with old bitter people> he is stuck with 2 generations of Men and Women who already hate him and everything he represents. We are winning the Male-Youth. Who's gonna save them? The Marxist left? Lmao. They just told us on TV that no Police Woman were attacked, even when we have videos of it. They sided with the one group of people who want us all dead, themselves included, but they are too blinded by their own hubris, which we have seen way too much. They have no one in the end, except a lost pension and they will be branded as traitors of the highest order.

SkogMUSᛏᛉ 🇳🇴🇸🇪

20,999 görüntüleme • 3 ay önce

The whole process of putting together the "I hate cowboys" record and the "go down singin'" record was a long two years of figuring out how do something I've never done before. All the songs were written on guitar and a depth I haven't really hit before. This is the final piece of that puzzle. "The fireside sessions" are both those albums together, all acoustic, featuring the songwriters from both those records. This time in my career ends with both album covers together with me and my dad standing next to each other. It's the first time I've ever been able to see a picture of us as two grown men. I'm moving on to another album early next year, but I hope y'all love these versions of the songs, as they were written. The bluebird last night was a special way to celebrate the ending of these last two years, and the start of something new👏🏼

The whole process of putting together the "I hate cowboys" record and the "go down singin'" record was a long two years of figuring out how do something I've never done before. All the songs were written on guitar and a depth I haven't really hit before. This is the final piece of that puzzle. "The fireside sessions" are both those albums together, all acoustic, featuring the songwriters from both those records. This time in my career ends with both album covers together with me and my dad standing next to each other. It's the first time I've ever been able to see a picture of us as two grown men. I'm moving on to another album early next year, but I hope y'all love these versions of the songs, as they were written. The bluebird last night was a special way to celebrate the ending of these last two years, and the start of something new👏🏼

Chase Rice

24,346 görüntüleme • 1 yıl önce

this is huge news from Cursor, they've pulled of the impossible and turned their ai-wrapper into a in-destructible moat sam altman literally called it cursor's ai agent harness is available for anyone to build on, which means ai models are now a commodity > 1-time install and now anyone can run cursor's agent locally or via cloud. use any model (e.g. gpt 5.5) but with the added cursor harness that makes it 10X better. > its so good that 3 of cursors biggest competitors are embedding it into their products. > now cursor DOESN'T DEPEND on anthropic or openai. their own model (composer 2) competes directly! yesterday sam altman said the ai model and harness are one and the same and today cursor turned their harness into a self-owned moat fucking masterclass (coming from a former cursor / ai wrapper hater)

this is huge news from Cursor, they've pulled of the impossible and turned their ai-wrapper into a in-destructible moat sam altman literally called it cursor's ai agent harness is available for anyone to build on, which means ai models are now a commodity > 1-time install and now anyone can run cursor's agent locally or via cloud. use any model (e.g. gpt 5.5) but with the added cursor harness that makes it 10X better. > its so good that 3 of cursors biggest competitors are embedding it into their products. > now cursor DOESN'T DEPEND on anthropic or openai. their own model (composer 2) competes directly! yesterday sam altman said the ai model and harness are one and the same and today cursor turned their harness into a self-owned moat fucking masterclass (coming from a former cursor / ai wrapper hater)

Ejaaz

950,452 görüntüleme • 4 ay önce

Fable 5 and GPT-5.6 built the same scroll-animated website from one skill in 32 minutes. Only one of them made it feel like a film. Same prompt. Boutique Japan travel brand, origami style. A subway pulls in, a paper house unfolds into a hotel, a bird takes flight as you scroll. Doing this by hand is brutal, so the skill handles everything automatically. Here is how it works: → One anchor image sets the visual style and every scene that follows inherits it automatically → Higgsfield generates the video straight from the terminal through the MCP connection, no manual exporting → FFmpeg rips every frame out of the generated video automatically → Each frame then gets mapped to a scroll position so your scrollbar literally becomes the playhead The verdict came down to transitions. GPT-5.6 built strong individual scenes with incredible detail inside each one, but then hard-cut between them with no connection. Fable 5 stitched everything together. Wires push out of the top of the frame while the next scene blurs in behind it, gains depth and locks into place seamlessly. Same skill, same prompts, 10 generations each from both models. GPT-5.6 built 4 scenes. Fable 5 built one continuous film. Credit: Chase AI

0xMarioNawfal

48,091 görüntüleme • 1 ay önce

He's right, I do have it in my notes because he did say it! 😆 On multiple instances, but here's him saying it on Trader Round Up: Timestamp 1:00:00 “They have to have the last two Mondays and the last two Fridays always, because they are the beginning and the end of the week.” ---- Timestamp 59:53 "In fact, every Monday, you should have two Mondays and two Fridays. In my opinion, if you’re going to ask me, like I teach my sons, they have to have the last two Mondays and the last two Fridays always because they’re at the beginning and the end of the week. So that’s what Cameron made money with today. He used information that is gleaned from that range, where we’re at in price action, and using the higher timeframe expectancy that we saw, that rejection when it went up to take that short term high." ---- Timestamp 1:00:46 "But every one of you listening, if you were my kid, if you were sitting with me, you would be hearing me tell you: Do you have two Mondays worth? Do you have both Fridays? Present today’s Friday and then last Friday’s Opening Range high, low and close, and the gradient levels on it? And then where are we in relation to that? Are we significantly above it, significantly below it, are we in close proximity to it? Because Monday’s Opening Range gap and Friday’s Opening Range gap are like a big huge draw on liquidity. It'll act like a big magnet. Not all the time, it’s not a panacea, but if we’ve traveled a lot one way, those Opening Ranges tend to be a factor on the close of the week and the beginning of the week. So Monday’s trading and Friday’s trading, using that framework that includes two weeks worth of Monday and Friday, have them in there. Even if you don’t want to carry Tuesday, Wednesday, and Thursday, the week prior to last week at least have that previous Monday in there, because that way you’ll gonna have a full range of opportunity that the algorithm will refer back to."

He's right, I do have it in my notes because he did say it! 😆 On multiple instances, but here's him saying it on Trader Round Up: Timestamp 1:00:00 “They have to have the last two Mondays and the last two Fridays always, because they are the beginning and the end of the week.” ---- Timestamp 59:53 "In fact, every Monday, you should have two Mondays and two Fridays. In my opinion, if you’re going to ask me, like I teach my sons, they have to have the last two Mondays and the last two Fridays always because they’re at the beginning and the end of the week. So that’s what Cameron made money with today. He used information that is gleaned from that range, where we’re at in price action, and using the higher timeframe expectancy that we saw, that rejection when it went up to take that short term high." ---- Timestamp 1:00:46 "But every one of you listening, if you were my kid, if you were sitting with me, you would be hearing me tell you: Do you have two Mondays worth? Do you have both Fridays? Present today’s Friday and then last Friday’s Opening Range high, low and close, and the gradient levels on it? And then where are we in relation to that? Are we significantly above it, significantly below it, are we in close proximity to it? Because Monday’s Opening Range gap and Friday’s Opening Range gap are like a big huge draw on liquidity. It'll act like a big magnet. Not all the time, it’s not a panacea, but if we’ve traveled a lot one way, those Opening Ranges tend to be a factor on the close of the week and the beginning of the week. So Monday’s trading and Friday’s trading, using that framework that includes two weeks worth of Monday and Friday, have them in there. Even if you don’t want to carry Tuesday, Wednesday, and Thursday, the week prior to last week at least have that previous Monday in there, because that way you’ll gonna have a full range of opportunity that the algorithm will refer back to."

Pipmunch

187,151 görüntüleme • 1 ay önce

Qwen 3.7 Max looks incredible on paper. Then I spent $43 in 15 minutes of vibe coding. Best Chinese model on every benchmark. #1 on SWE-Bench Pro. #4 on BridgeBench UI. The scores are legit. But in a real workflow it came back with 15 errors on a single task. No subsidies like Claude Code or Codex. Cheaper per token, more expensive per task. Chinese models are getting closer. But "closer" still means you're burning more time and more money to get the same result GPT 5.5 one shots. Full review and BridgeBench breakdown below.

Qwen 3.7 Max looks incredible on paper. Then I spent $43 in 15 minutes of vibe coding. Best Chinese model on every benchmark. #1 on SWE-Bench Pro. #4 on BridgeBench UI. The scores are legit. But in a real workflow it came back with 15 errors on a single task. No subsidies like Claude Code or Codex. Cheaper per token, more expensive per task. Chinese models are getting closer. But "closer" still means you're burning more time and more money to get the same result GPT 5.5 one shots. Full review and BridgeBench breakdown below.

BridgeMind

15,246 görüntüleme • 3 ay önce