Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

Dylan Patel says GPT-5 is not a bigger model and did not gain intelligence through scale but through optimization. He highlights how OpenAI improved efficiency by cutting down wasted tokens compared to earlier o-series models. He adds that GPT-5’s new router system decides whether to use the base model,... show more

Wes Roth

36,575 subscribers

87,396 Aufrufe • vor 1 Jahr •via X (Twitter)

Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

OpenAI researcher, Michelle Pokrass: the challenge in building GPT‑5 is finding the right balance between reasoning and conversation. o3 thinks hard, but it isn't ideal for casual chat. GPT‑4.1 improved at coding by sacrificing some chit-chat quality. now, the goal is to train a model that knows when to think hard and when to talk

OpenAI researcher, Michelle Pokrass: the challenge in building GPT‑5 is finding the right balance between reasoning and conversation. o3 thinks hard, but it isn't ideal for casual chat. GPT‑4.1 improved at coding by sacrificing some chit-chat quality. now, the goal is to train a model that knows when to think hard and when to talk

Haider.

195,733 Aufrufe • vor 1 Jahr

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 Aufrufe • vor 2 Monaten

Kimi K3 Standard’s first shot at my indie spaceship game! Without a shadow of a doubt, this is better than GPT-5.5’s first pass. It’s crazy that we already have an open-source model in the GPT-5.5 to GPT-5.6 class. The estimated API cost for this was just $2.50 worth of tokens. Fable 5: $22 GPT-5.5: $14 I will say that Fable 5 made the game look more visually impressive, but the gap is not that large. I think I could easily get Kimi to match its visual quality within a few iterations. What surprised me most was that Kimi’s version felt much more cohesive and coherent, with fewer bugs than GPT-5.5’s.

Kimi K3 Standard’s first shot at my indie spaceship game! Without a shadow of a doubt, this is better than GPT-5.5’s first pass. It’s crazy that we already have an open-source model in the GPT-5.5 to GPT-5.6 class. The estimated API cost for this was just $2.50 worth of tokens. Fable 5: $22 GPT-5.5: $14 I will say that Fable 5 made the game look more visually impressive, but the gap is not that large. I think I could easily get Kimi to match its visual quality within a few iterations. What surprised me most was that Kimi’s version felt much more cohesive and coherent, with fewer bugs than GPT-5.5’s.

Chris

47,926 Aufrufe • vor 1 Monat

You'd think the race to AGI would mean training the biggest possible model. But parameter scaling had stalled for a long time after GPT-4's trillion+ parameters, and only now are models getting bigger again. What gives? Partially it’s RL scaling, as Dylan Patel explains. A 5T parameter model takes 5x longer to generate RL rollouts than a 1T model. Even if the bigger model is 2x more sample-efficient, the smaller model finishes RL faster, gets deployed to research sooner, and starts helping build the next model before the big one is even done training.

You'd think the race to AGI would mean training the biggest possible model. But parameter scaling had stalled for a long time after GPT-4's trillion+ parameters, and only now are models getting bigger again. What gives? Partially it’s RL scaling, as Dylan Patel explains. A 5T parameter model takes 5x longer to generate RL rollouts than a 1T model. Even if the bigger model is 2x more sample-efficient, the smaller model finishes RL faster, gets deployed to research sooner, and starts helping build the next model before the big one is even done training.

Dwarkesh Patel

65,123 Aufrufe • vor 5 Monaten

Openai giving $50,000 in FREE API credits and it's hidden in your account settings 😳 most people have no idea this exists what you get every single day: - 250K tokens/day for GPT-5.5 and GPT-5.2 (powerful models) - 2.5M tokens/day for GPT-5.1-Mini and GPT-5-Nano (lighter models) - up to 10M tokens/day if you're on tier 3-5 not a one time lump sum, it resets daily it's called the data sharing program and here's how it works: > go to > click data controls → sharing > opt in to both options > done, free daily tokens activate immediately important note: - your data gets used by openai for training and improvements - don't use it for client work or anything sensitive - need a positive account balance to activate perfect for: - learning and experimenting - side projects and personal builds - testing new models without paying you need a positive account balance to activate it but after that it's free every day save this and take free 50k credits before they close it

Openai giving $50,000 in FREE API credits and it's hidden in your account settings 😳 most people have no idea this exists what you get every single day: - 250K tokens/day for GPT-5.5 and GPT-5.2 (powerful models) - 2.5M tokens/day for GPT-5.1-Mini and GPT-5-Nano (lighter models) - up to 10M tokens/day if you're on tier 3-5 not a one time lump sum, it resets daily it's called the data sharing program and here's how it works: > go to > click data controls → sharing > opt in to both options > done, free daily tokens activate immediately important note: - your data gets used by openai for training and improvements - don't use it for client work or anything sensitive - need a positive account balance to activate perfect for: - learning and experimenting - side projects and personal builds - testing new models without paying you need a positive account balance to activate it but after that it's free every day save this and take free 50k credits before they close it

Isra

168,671 Aufrufe • vor 2 Monaten

$Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. Subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.$

Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. Subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.

Alexander Whedon

13,478,837 Aufrufe • vor 3 Monaten

OpenAI chairman Bret Taylor talks to about 100 CEOs every month. His answer to the cheap open-weight model panic: cheaper to train does not mean cheaper to use, and the number that decides it is token efficiency. "One thing that I think is a little bit overblown about these open weight models is they're not necessarily cheaper to run. Whether or not they're cheaper to train, you don't care. Because you're using just as many tokens. In fact, they may be less efficient." "There's this thing called token efficiency. And it turns out the frontier models are much, much more token efficient." "A token is to intelligence like a watt is to electricity... how many tokens does it take to complete a task? Not every token is actually equal." "For a lot of tasks, it turns out these frontier models from OpenAI and Anthropic are actually just better than these open weight models... just having open weights isn't actually the main thing driving any of those costs." Later in the same interview he goes after the billing unit itself: "It would be like if you signed up for Gmail and you paid for CPU cycle or something... where the world is going is paying for outcomes." The unresolved column: the chart CNBC airs mid-answer, from Artificial Analysis, prices a completed task at $0.94 on Kimi K3 against $2.75 on Claude Fable 5, efficiency folded in. If that gap holds, the premium he is defending gets earned on quality, not price. - Bret Taylor (Bret Taylor), OpenAI chairman and Sierra co-founder, on CNBC's Squawk Box.

OpenAI chairman Bret Taylor talks to about 100 CEOs every month. His answer to the cheap open-weight model panic: cheaper to train does not mean cheaper to use, and the number that decides it is token efficiency. "One thing that I think is a little bit overblown about these open weight models is they're not necessarily cheaper to run. Whether or not they're cheaper to train, you don't care. Because you're using just as many tokens. In fact, they may be less efficient." "There's this thing called token efficiency. And it turns out the frontier models are much, much more token efficient." "A token is to intelligence like a watt is to electricity... how many tokens does it take to complete a task? Not every token is actually equal." "For a lot of tasks, it turns out these frontier models from OpenAI and Anthropic are actually just better than these open weight models... just having open weights isn't actually the main thing driving any of those costs." Later in the same interview he goes after the billing unit itself: "It would be like if you signed up for Gmail and you paid for CPU cycle or something... where the world is going is paying for outcomes." The unresolved column: the chart CNBC airs mid-answer, from Artificial Analysis, prices a completed task at $0.94 on Kimi K3 against $2.75 on Claude Fable 5, efficiency folded in. If that gap holds, the premium he is defending gets earned on quality, not price. - Bret Taylor (Bret Taylor), OpenAI chairman and Sierra co-founder, on CNBC's Squawk Box.

Karl Mehta

17,170 Aufrufe • vor 1 Monat

FREE Fable 5, Opus 4.8, Sonnet 5, and GPT-5.6 in one IDE. Alibaba built it, 520 credits at signup - one Sonnet 5 message eats 4. It's called Accio. Web or Windows desktop, both free. Registration takes 30 seconds via Google or email. Inside: → Fable 5 for creative and long-context work. → Opus 4.6 through 4.8 - the full Anthropic frontier lineup. → Sonnet 5 for speed and reasoning at pennies per message. → GPT-5.5 for OpenAI native tasks. → Gemini 3.1 Pro and 3.5 Flash for multimodal. Real math: 520 credits divided by 4 = 130 Sonnet 5 messages in your first week. Cheaper models drop that even further. Anthropic charges $20/month for the same access with weekly limits. Custom MCP support built in. Profile > Settings > MCP. Every agent workflow you've built for Claude Desktop ports over instantly. Setup: → Go to accio(.)com → Sign up with Google → 520 credits land automatically → Pick your model and start firing prompts Bookmark this before Alibaba puts it behind a paywall.

Atenov int.

31,157 Aufrufe • vor 1 Monat

ELON: GROK’S A BEAST - IT CAN'T BE CRAMMED INTO A CAR “Grok is a giant model; you could not possibly squeeze Grok onto a car, that’s for sure. With Grok, it’s trying to solve for artificial intelligence, with a massive amount of AI training, compute, and inference compute. Grok 5 will only run effectively on a GV300 [AI training cluster]; that’s how much of a beast Grok 5 is. Whereas Tesla’s models are less than 10% the size, maybe closer to 5% the size of Grok.” Source: Elon Musk, Tesla Podcast, October 22, 2025

ELON: GROK’S A BEAST - IT CAN'T BE CRAMMED INTO A CAR “Grok is a giant model; you could not possibly squeeze Grok onto a car, that’s for sure. With Grok, it’s trying to solve for artificial intelligence, with a massive amount of AI training, compute, and inference compute. Grok 5 will only run effectively on a GV300 [AI training cluster]; that’s how much of a beast Grok 5 is. Whereas Tesla’s models are less than 10% the size, maybe closer to 5% the size of Grok.” Source: Elon Musk, Tesla Podcast, October 22, 2025

Mario Nawfal

33,523 Aufrufe • vor 10 Monaten

I've made a ton of money helping companies implement LLM-as-a-judge evaluations. LLM Judges provide a ton of value. But the hard part is choosing the model to implement the judge. • The family of GPT-5 models is very good, but slow and expensive. • Models like Gemma and Phi are fast and cheap, but not that good. Most of the time, you can only run a percentage of your traffic through the model (otherwise it would be too expensive and slow). But now, there's a better strategy.

I've made a ton of money helping companies implement LLM-as-a-judge evaluations. LLM Judges provide a ton of value. But the hard part is choosing the model to implement the judge. • The family of GPT-5 models is very good, but slow and expensive. • Models like Gemma and Phi are fast and cheap, but not that good. Most of the time, you can only run a percentage of your traffic through the model (otherwise it would be too expensive and slow). But now, there's a better strategy.

Santiago

29,970 Aufrufe • vor 4 Monaten

I know your timeline is flooded now with word salads of "insane, HER, 10 features you missed, we're so back". Sit down. Chill. Take a deep breath like Mark does in the demo . Let's think step by step: - Technique-wise, OpenAI has figured out a way to map audio to audio directly as first-class modality, and stream videos to a transformer in real-time. These require some new research on tokenization and architecture, but overall it's a data and system optimization problem (as most things are). High-quality data can come from at least 2 sources: 1) Naturally occurring dialogues on YouTube, podcasts, TV series, movies, etc. Whisper can be trained to identify speaker turns in a dialogue or separate overlapping speeches for automated annotation. 2) Synthetic data. Run the slow 3-stage pipeline using the most powerful models: speech1->text1 (ASR), text1->text2 (LLM), text2->speech2 (TTS). The middle LLM can decide when to stop and also simulate how to resume from interruption. It could output additional "thought traces" that are not verbalized to help generate better reply. Then GPT-4o distills directly from speech1->speech2, with optional auxiliary loss functions based on the 3-stage data. After distillation, these behaviors are now baked into the model without emitting intermediate texts. On the system side: the latency would not meet real-time threshold if every video frame is decompressed into an RGB image. OpenAI has likely developed their own neural-first, streaming video codec to transmit the motion deltas as tokens. The communication protocol and NN inference must be co-optimized. For example, there could be a small and energy-efficient NN running on the edge device that decides to transmit more tokens if the video is interesting, and fewer otherwise. - I didn't expect GPT-4o to be closer to GPT-5, the rumored "Arrakis" model that takes multimodal in and out. In fact, it's likely an early checkpoint of GPT-5 that hasn't finished training yet. The branding betrays a certain insecurity. Ahead of Google I/O, OpenAI would rather beat our mental projection of GPT-4.5 than disappoint by missing the sky-high expectation for GPT-5. A smart move to buy more time. - Notably, the assistant is much more lively and even a bit flirty. GPT-4o is trying (perhaps a bit too hard) to sound like HER. OpenAI is eating Character AI's lunch, with almost 100% overlap in form factor and huge distribution channels. It's a pivot towards more emotional AI with strong personality, which OpenAI seemed to actively suppress in the past. - Whoever wins Apple first wins big time. I see 3 levels of integration with iOS: 1) Ditch Siri. OpenAI distills a smaller-tier, purely on-device GPT-4o for iOS, with optional paid upgrade to use the cloud. 2) Native features to stream the camera or screen into the model. Chip-level support for neural audio/video codec. 3) Integrate with iOS system-level action API and smart home APIs. No one uses Siri Shortcuts, but it's time to resurrect. This could become the AI agent product with a billion users from the get-go. The FSD for smartphones with a Tesla-scale data flywheel.

I know your timeline is flooded now with word salads of "insane, HER, 10 features you missed, we're so back". Sit down. Chill. Take a deep breath like Mark does in the demo . Let's think step by step: - Technique-wise, OpenAI has figured out a way to map audio to audio directly as first-class modality, and stream videos to a transformer in real-time. These require some new research on tokenization and architecture, but overall it's a data and system optimization problem (as most things are). High-quality data can come from at least 2 sources: 1) Naturally occurring dialogues on YouTube, podcasts, TV series, movies, etc. Whisper can be trained to identify speaker turns in a dialogue or separate overlapping speeches for automated annotation. 2) Synthetic data. Run the slow 3-stage pipeline using the most powerful models: speech1->text1 (ASR), text1->text2 (LLM), text2->speech2 (TTS). The middle LLM can decide when to stop and also simulate how to resume from interruption. It could output additional "thought traces" that are not verbalized to help generate better reply. Then GPT-4o distills directly from speech1->speech2, with optional auxiliary loss functions based on the 3-stage data. After distillation, these behaviors are now baked into the model without emitting intermediate texts. On the system side: the latency would not meet real-time threshold if every video frame is decompressed into an RGB image. OpenAI has likely developed their own neural-first, streaming video codec to transmit the motion deltas as tokens. The communication protocol and NN inference must be co-optimized. For example, there could be a small and energy-efficient NN running on the edge device that decides to transmit more tokens if the video is interesting, and fewer otherwise. - I didn't expect GPT-4o to be closer to GPT-5, the rumored "Arrakis" model that takes multimodal in and out. In fact, it's likely an early checkpoint of GPT-5 that hasn't finished training yet. The branding betrays a certain insecurity. Ahead of Google I/O, OpenAI would rather beat our mental projection of GPT-4.5 than disappoint by missing the sky-high expectation for GPT-5. A smart move to buy more time. - Notably, the assistant is much more lively and even a bit flirty. GPT-4o is trying (perhaps a bit too hard) to sound like HER. OpenAI is eating Character AI's lunch, with almost 100% overlap in form factor and huge distribution channels. It's a pivot towards more emotional AI with strong personality, which OpenAI seemed to actively suppress in the past. - Whoever wins Apple first wins big time. I see 3 levels of integration with iOS: 1) Ditch Siri. OpenAI distills a smaller-tier, purely on-device GPT-4o for iOS, with optional paid upgrade to use the cloud. 2) Native features to stream the camera or screen into the model. Chip-level support for neural audio/video codec. 3) Integrate with iOS system-level action API and smart home APIs. No one uses Siri Shortcuts, but it's time to resurrect. This could become the AI agent product with a billion users from the get-go. The FSD for smartphones with a Tesla-scale data flywheel.

Jim Fan

991,844 Aufrufe • vor 2 Jahren

IT'S NOT THAT HIS FABLE 5 IS MORE CAPABLE THAN YOURS. IT'S THAT HE LETS IT KEEP WORKING. MOST PEOPLE BUY AN AI BUILT TO OPERATE FOR DAYS THEN SHUT IT DOWN AFTER A FEW MINUTES. Nine out of ten users open a chat, assign one task, and close the window. He gives Fable 5 a long-term objective and walks away. It breaks the job into smaller pieces, delegates work to sub-agents, reviews its own outputs with vision, and keeps moving without waiting for another prompt. While you're typing your next request, his system has already finished another task and improved its workflow along the way. The agent that creates the work never decides if it's good enough. A separate evaluator checks every result, and the process repeats until the required standard is met. At $10 per million input tokens and $50 per million output tokens, you pay only for the work completed not a monthly subscription. While he was away, the system generated $7,800 worth of results. Meanwhile, others spent the week opening chats and closing tabs. Most people see a more expensive AI model. He sees an autonomous worker that handles a week's worth of tasks every single day and keeps going long after everyone else has logged off. You finished reading this. The question is whether you'll put it to work or just scroll on.

IT'S NOT THAT HIS FABLE 5 IS MORE CAPABLE THAN YOURS. IT'S THAT HE LETS IT KEEP WORKING. MOST PEOPLE BUY AN AI BUILT TO OPERATE FOR DAYS THEN SHUT IT DOWN AFTER A FEW MINUTES. Nine out of ten users open a chat, assign one task, and close the window. He gives Fable 5 a long-term objective and walks away. It breaks the job into smaller pieces, delegates work to sub-agents, reviews its own outputs with vision, and keeps moving without waiting for another prompt. While you're typing your next request, his system has already finished another task and improved its workflow along the way. The agent that creates the work never decides if it's good enough. A separate evaluator checks every result, and the process repeats until the required standard is met. At $10 per million input tokens and $50 per million output tokens, you pay only for the work completed not a monthly subscription. While he was away, the system generated $7,800 worth of results. Meanwhile, others spent the week opening chats and closing tabs. Most people see a more expensive AI model. He sees an autonomous worker that handles a week's worth of tasks every single day and keeps going long after everyone else has logged off. You finished reading this. The question is whether you'll put it to work or just scroll on.

Аnnetta

20,116 Aufrufe • vor 1 Monat

Chamath says OpenAI and Anthropic could be $5 to $10 trillion companies each if they remain a duopoly "I suspect revenues are going to crank at OpenAI and Anthropic and the open labs for a while. But again, that's not the important thing." "If you're thinking about valuation, the markets will look five to 10 years out to answer that question, they're not going to give you a premium valuation on something that they feel could be fragile in the first two to three years." "Somebody has to answer that question precisely. Because if the answer is that it is a duopoly, then there is no risk to the revenue five to 10 years from now, these things are $5 to $10 trillion companies each." "But if there are harnesses that cut the token consumption, because you stop wasting tokens to get to the same output, then it's a little bit more of a question mark. And I think that'll need to get sorted out."

Chamath says OpenAI and Anthropic could be $5 to $10 trillion companies each if they remain a duopoly "I suspect revenues are going to crank at OpenAI and Anthropic and the open labs for a while. But again, that's not the important thing." "If you're thinking about valuation, the markets will look five to 10 years out to answer that question, they're not going to give you a premium valuation on something that they feel could be fragile in the first two to three years." "Somebody has to answer that question precisely. Because if the answer is that it is a duopoly, then there is no risk to the revenue five to 10 years from now, these things are $5 to $10 trillion companies each." "But if there are harnesses that cut the token consumption, because you stop wasting tokens to get to the same output, then it's a little bit more of a question mark. And I think that'll need to get sorted out."

dnap

275,325 Aufrufe • vor 1 Monat

Found a way to get $125-$200 in FREE ai credits for gpt-5.6 sol, claude opus 5 and opus 4.8 😳 agentrouter is a non-profit version of openrouter. one api key, no card. credits land on your account at signup. what you get for $0: -$125-200 in credits on signup (github only) -claude opus 5, gpt-5.6 sol, opus 4.8 through one key -free models -openai-compatible (works with anything) what this replaces: -paid frontier API costs for coding agents -claude opus 4.8: $15/M in, $75/M out -gpt-5.6 sol: $25/M out how to grab yours (2 min): 1. go to → sign in with github (important) 2. console → create your api key 3. base url: 4. pick a model and go Works in hermes, claude code, cursor, aider and any openai-compatible tools Important: github-only signup, older accounts get the full credit, fresh ones may get less. credits are one-time. referral adds +$100 use this before they tighten the offer

Found a way to get $125-$200 in FREE ai credits for gpt-5.6 sol, claude opus 5 and opus 4.8 😳 agentrouter is a non-profit version of openrouter. one api key, no card. credits land on your account at signup. what you get for $0: -$125-200 in credits on signup (github only) -claude opus 5, gpt-5.6 sol, opus 4.8 through one key -free models -openai-compatible (works with anything) what this replaces: -paid frontier API costs for coding agents -claude opus 4.8: $15/M in, $75/M out -gpt-5.6 sol: $25/M out how to grab yours (2 min): 1. go to → sign in with github (important) 2. console → create your api key 3. base url: 4. pick a model and go Works in hermes, claude code, cursor, aider and any openai-compatible tools Important: github-only signup, older accounts get the full credit, fresh ones may get less. credits are one-time. referral adds +$100 use this before they tighten the offer

painn

108,294 Aufrufe • vor 1 Monat

Just listened to Bill gates podcast with sam, and it's very clear that OpenAI is purposely sandbagging Even Bill - who initially did not expect GPT-5 to be much better than GPT-4, mentioned how amazed he was by the demos hes seen. Right now, everyone thinks chatGPT sucks (including Sam himself) but remember, OpenAI hasn't released a new class of models in ~1.5 years. This is creating the illusion that models have hit their upper limit, and that progress will continue to be slow But if you listen to the podcast , its extremely clear how quickly we're going to accelerate from models doing basic, tasks to actually being able run companies. But personally i think 2024 will be the an even bigger year than the "chatGPT" moment we had. Literally no one (myself included) is ready for the rate of change we're about to experience as humanity. Most companies will be eaten by an agi-like software, and yes that includes most apps you use today. Definitely scary, but there is a sense of calm, knowing that if they're right, all of humanity is in this together. but i am excited to see what I can build with such technology

Just listened to Bill gates podcast with sam, and it's very clear that OpenAI is purposely sandbagging Even Bill - who initially did not expect GPT-5 to be much better than GPT-4, mentioned how amazed he was by the demos hes seen. Right now, everyone thinks chatGPT sucks (including Sam himself) but remember, OpenAI hasn't released a new class of models in ~1.5 years. This is creating the illusion that models have hit their upper limit, and that progress will continue to be slow But if you listen to the podcast , its extremely clear how quickly we're going to accelerate from models doing basic, tasks to actually being able run companies. But personally i think 2024 will be the an even bigger year than the "chatGPT" moment we had. Literally no one (myself included) is ready for the rate of change we're about to experience as humanity. Most companies will be eaten by an agi-like software, and yes that includes most apps you use today. Definitely scary, but there is a sense of calm, knowing that if they're right, all of humanity is in this together. but i am excited to see what I can build with such technology

Sully

613,790 Aufrufe • vor 2 Jahren

Mark Ruffalo says he filmed a 10-episode indie series for just $5 million. "I went out and did one. It was 10 episodes of independent television for $5 million.. That was less than what we made 'You Can Count on Me' for, or around the same." "It was a grind, and I haven't done that in a long time, but it was doable. And they were able to give the actors everything that Screen Actors Guild had asked for."

Mark Ruffalo says he filmed a 10-episode indie series for just $5 million. "I went out and did one. It was 10 episodes of independent television for $5 million.. That was less than what we made 'You Can Count on Me' for, or around the same." "It was a grind, and I haven't done that in a long time, but it was doable. And they were able to give the actors everything that Screen Actors Guild had asked for."

KIEL

98,939 Aufrufe • vor 1 Monat