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。

GPT 5.6 Luna Max vs 5.6 Sol Medium… Yeah, it’s not even a question 😭 Sol on MEDIUM produced a much better result than Luna on MAX—and needed only around HALF the time. Same harness. Better generation. Faster result. Luna may be cheaper, but if you have the budget,... show more

OmedTheVibeCoder

1,310 subscribers

98,811 次观看 • 16 天前 •via X (Twitter)

科学技术游戏

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

Grok 4.5 performed GPT Sol level for free! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: -robot deathmatch, Tombstone vs Minotaur -a hydraulic press flattening stuff on a conveyor -a semi truck jumping a canyon Outputs: GPT-5.6 Sol: 12.9K tokens, $0.51 (~7 min) Grok 4.5: 10.8K tokens, $0 (~5 min) Muse Spark 1.1: 26.8K tokens, $0.12 (~7.5 min) GLM 5.2: 10.9K tokens, $0.02 (~12 min) Grok 4.5 handled all three scenes genuinely well and got surprisingly close to GPT-5.6 this round. On top of that, it ran on the free tier. GPT-5.6 Sol, the frontier model, put out solid but not standout work. GLM 5.2 rendered all three scenes for pennies, but it came out the roughest of the four. Meta's new Muse Spark burned the most tokens yet still stayed cheap, delivering an average result.

Grok 4.5 performed GPT Sol level for free! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: -robot deathmatch, Tombstone vs Minotaur -a hydraulic press flattening stuff on a conveyor -a semi truck jumping a canyon Outputs: GPT-5.6 Sol: 12.9K tokens, $0.51 (~7 min) Grok 4.5: 10.8K tokens, $0 (~5 min) Muse Spark 1.1: 26.8K tokens, $0.12 (~7.5 min) GLM 5.2: 10.9K tokens, $0.02 (~12 min) Grok 4.5 handled all three scenes genuinely well and got surprisingly close to GPT-5.6 this round. On top of that, it ran on the free tier. GPT-5.6 Sol, the frontier model, put out solid but not standout work. GLM 5.2 rendered all three scenes for pennies, but it came out the roughest of the four. Meta's new Muse Spark burned the most tokens yet still stayed cheap, delivering an average result.

atomic.chat

70,490 次观看 • 1 个月前

I tested Qwen 3.8 Max vs Kimi K3 Same scene, lighting, props, detail requirements. Qwen's output is decent, but it's not close to Kimi K3. Qwen's output came out messy, text handling was off, and the composition felt unorganized next to Kimi's version. In my opinion, Qwen isn't second to Fable 5 either, and it's also below GPT-5.6 Sol. It's a solid model, but not at that tier. Kimi K3 is still clearly ahead here.

I tested Qwen 3.8 Max vs Kimi K3 Same scene, lighting, props, detail requirements. Qwen's output is decent, but it's not close to Kimi K3. Qwen's output came out messy, text handling was off, and the composition felt unorganized next to Kimi's version. In my opinion, Qwen isn't second to Fable 5 either, and it's also below GPT-5.6 Sol. It's a solid model, but not at that tier. Kimi K3 is still clearly ahead here.

Bhavy☄️

34,045 次观看 • 29 天前

It's never been a better time to be creative. (ever) The two best frontier models (ever) have been released within 30 days of each other. Here’s what we learned from running 4 frontier models head to head. >Sol has taste and Fable takes direction. 10 identical landing page briefs, judged blind by working creatives. GPT 5.6 Sol won 82% on loose briefs. But when handed a real design spec it finished last. 🧵

It's never been a better time to be creative. (ever) The two best frontier models (ever) have been released within 30 days of each other. Here’s what we learned from running 4 frontier models head to head. >Sol has taste and Fable takes direction. 10 identical landing page briefs, judged blind by working creatives. GPT 5.6 Sol won 82% on loose briefs. But when handed a real design spec it finished last. 🧵

ben

35,609 次观看 • 1 个月前

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!

Anshu

178,934 次观看 • 1 个月前

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Alok

65,387 次观看 • 1 个月前

You can now orchestrate Fable 5, Sol, and any model inside Codex with one plugin. It's called Codex-Orchestration. Assign Fable 5 as the advisor, Sol as the executor, or any model to any role. Then define the order they work in. Codex handles the routing. I ran Fable 5 High as planner with GPT-5.6 Sol Extra High as executor on a set of issues Opus and GPT-5.5 always struggled with. Done in 30 minutes. 40% fewer limit hits. 2x faster implementation. Install it by pasting this into Codex: "Install Codex Orchestration: codex plugin marketplace add Cjbuilds/Codex-Orchestration codex plugin add codex-orchestration@codex-orchestration Verify the installation, then tell me to start a new task." Then assign your models: @ codex-orchestration advisor: Claude Fable 5 High, Executor: GPT-5.6 Sol High Open source. Tweak the routing however you want.

You can now orchestrate Fable 5, Sol, and any model inside Codex with one plugin. It's called Codex-Orchestration. Assign Fable 5 as the advisor, Sol as the executor, or any model to any role. Then define the order they work in. Codex handles the routing. I ran Fable 5 High as planner with GPT-5.6 Sol Extra High as executor on a set of issues Opus and GPT-5.5 always struggled with. Done in 30 minutes. 40% fewer limit hits. 2x faster implementation. Install it by pasting this into Codex: "Install Codex Orchestration: codex plugin marketplace add Cjbuilds/Codex-Orchestration codex plugin add codex-orchestration@codex-orchestration Verify the installation, then tell me to start a new task." Then assign your models: @ codex-orchestration advisor: Claude Fable 5 High, Executor: GPT-5.6 Sol High Open source. Tweak the routing however you want.

Alvaro Cintas

91,168 次观看 • 1 个月前

☀️Very proud of the team for training 5.6! A few of my highlights: - Great front-end aesthetics. E.g., I asked Sol to make a new blog for 5.6 with a celestial theme. - Much better CUA - Can work for much longer and is less lazy But it's not all great: - The writing quality has improved, but it's still bad in artifacts - Too many options with models/reasoning/fast mode/Cerebras/multi-agent We'll fix the above. What else should we improve? PS: belated post as there are so many other good models to train.

☀️Very proud of the team for training 5.6! A few of my highlights: - Great front-end aesthetics. E.g., I asked Sol to make a new blog for 5.6 with a celestial theme. - Much better CUA - Can work for much longer and is less lazy But it's not all great: - The writing quality has improved, but it's still bad in artifacts - Too many options with models/reasoning/fast mode/Cerebras/multi-agent We'll fix the above. What else should we improve? PS: belated post as there are so many other good models to train.

Yann Dubois

34,014 次观看 • 1 个月前

I tested Kimi K3 vs Claude Opus 4.8 Same prompt, an armory bay with lighting, props, and detail. Top is Kimi K3, bottom is Opus 4.8. It's not even close. Kimi K3 built a full scene with textures, proper lighting, ammo crates, weapon racks, working detail everywhere. Opus 4.8 gave me a near empty room with a couple of floating tables. No doubt it beats Opus 4.8. Kimi K3 is Fable 5 level, and it's clearly better than GPT-5.6 Sol at 3D and games. An open weight model just matched the best closed models on the market. Let that sink in.

I tested Kimi K3 vs Claude Opus 4.8 Same prompt, an armory bay with lighting, props, and detail. Top is Kimi K3, bottom is Opus 4.8. It's not even close. Kimi K3 built a full scene with textures, proper lighting, ammo crates, weapon racks, working detail everywhere. Opus 4.8 gave me a near empty room with a couple of floating tables. No doubt it beats Opus 4.8. Kimi K3 is Fable 5 level, and it's clearly better than GPT-5.6 Sol at 3D and games. An open weight model just matched the best closed models on the market. Let that sink in.

Bhavy☄️

472,445 次观看 • 1 个月前

Qwen 3.8 27B feels SO GOOOOOD! Here’s the thing, it’s thinking a lot, but for the first time it may be that the verbose thinking is less of a mistake than a lot of other local models. It’s basically doing a full build, like a model would in a harness, but entirely within its reasoning trajectory. So the final result will be as close as possible to a final output. There’s much less second guessing itself in reasoning than with Qwen 3.6. It’s very confidently peacing this together so it can have the full picture for the output. This might be the play for smaller models. If the completeness of thought can overcome the increase in wall time, I’m pumped, we can always make it run faster! I think this is going to be really exceptional, I will post the output soon 😎

Qwen 3.8 27B feels SO GOOOOOD! Here’s the thing, it’s thinking a lot, but for the first time it may be that the verbose thinking is less of a mistake than a lot of other local models. It’s basically doing a full build, like a model would in a harness, but entirely within its reasoning trajectory. So the final result will be as close as possible to a final output. There’s much less second guessing itself in reasoning than with Qwen 3.6. It’s very confidently peacing this together so it can have the full picture for the output. This might be the play for smaller models. If the completeness of thought can overcome the increase in wall time, I’m pumped, we can always make it run faster! I think this is going to be really exceptional, I will post the output soon 😎

Kyle Hessling

64,523 次观看 • 3 天前

Comeback Journal Had to borrow SOL and got rekt all the way down to 0.7 SOL. Took a 0.5 SOL trade and scalped +0.2 SOL. Saturday, Aug. 2 – 12:59 AM Portfolio: 0.9 SOL By the end of Aug. 2... Back to 0 SOL. Aug. 3 0xW sent me 0.6 SOL because he made money from the NA shill. Thank you again. Started only risking 0.05–0.1 SOL per trade to protect capital. Built it back to 0.4 SOL. Then p sent me 1 SOL for the same reason. That genuinely helped me during survival mode. Portfolio: 1.4 SOL. Saw LMEOW at 40k. Put 0.5 SOL in, leaving me with 0.9 SOL on the sidelines. Yeah, I oversized, but I had that feeling. What made me buy wasn't the chart. The narrative made me extremely bullish. Volume kept increasing, and I kept seeing more smart wallets buying than selling. Sold 1.5 SOL around 255k. Then the timeline started pushing the "OG" coin again. Momentum died. Round-tripped a few SOL and finally sold my remaining 0.7 SOL around 114k. Turned that 0.5 SOL trade into roughly 2.2 SOL before closing the position. I still think splitting the attention between two coins as a marketing stunt hurts everyone involved. Btw LJC, a lot of traders look up to you. Encouraging more PvP because the "OG" needs marketing instead of letting the market naturally bag work only makes the trenches worse. If it wasn't for that in the back of my mind i could've been at 10 solana today, thanks. Aug. 3 – 12:04 PM Current portfolio: 3.1 SOL.

Comeback Journal Had to borrow SOL and got rekt all the way down to 0.7 SOL. Took a 0.5 SOL trade and scalped +0.2 SOL. Saturday, Aug. 2 – 12:59 AM Portfolio: 0.9 SOL By the end of Aug. 2... Back to 0 SOL. Aug. 3 0xW sent me 0.6 SOL because he made money from the NA shill. Thank you again. Started only risking 0.05–0.1 SOL per trade to protect capital. Built it back to 0.4 SOL. Then p sent me 1 SOL for the same reason. That genuinely helped me during survival mode. Portfolio: 1.4 SOL. Saw LMEOW at 40k. Put 0.5 SOL in, leaving me with 0.9 SOL on the sidelines. Yeah, I oversized, but I had that feeling. What made me buy wasn't the chart. The narrative made me extremely bullish. Volume kept increasing, and I kept seeing more smart wallets buying than selling. Sold 1.5 SOL around 255k. Then the timeline started pushing the "OG" coin again. Momentum died. Round-tripped a few SOL and finally sold my remaining 0.7 SOL around 114k. Turned that 0.5 SOL trade into roughly 2.2 SOL before closing the position. I still think splitting the attention between two coins as a marketing stunt hurts everyone involved. Btw LJC, a lot of traders look up to you. Encouraging more PvP because the "OG" needs marketing instead of letting the market naturally bag work only makes the trenches worse. If it wasn't for that in the back of my mind i could've been at 10 solana today, thanks. Aug. 3 – 12:04 PM Current portfolio: 3.1 SOL.

Nofelines 🐂🀄️

61,484 次观看 • 14 天前

GPT-5.6 Sol is unbelievably good at creating and editing videos. It can do motion design, product demos, and animations like this one I made by simply giving it a screen recording. GPT 5.6 has the best design taste and significantly outperforms Fable, which relies heavily on repetitive design patterns. To help you experiment with video editing on it, we just launched a collection of 100 ready-to-use skills that show what’s possible and help you get started with video editing using GPT-5.6. These skills can create anything from motion graphics launch videos for your product to a 3B1B-style science explainer video. You can also use them to edit existing videos: add captions, generate motion graphics, create voiceovers, redesign visual styles, translate into new languages, and much more. If you want access to the full library, comment “VIDEO SKILLS” and I’ll share it with you. (You'll have to follow me so I can DM you.)

GPT-5.6 Sol is unbelievably good at creating and editing videos. It can do motion design, product demos, and animations like this one I made by simply giving it a screen recording. GPT 5.6 has the best design taste and significantly outperforms Fable, which relies heavily on repetitive design patterns. To help you experiment with video editing on it, we just launched a collection of 100 ready-to-use skills that show what’s possible and help you get started with video editing using GPT-5.6. These skills can create anything from motion graphics launch videos for your product to a 3B1B-style science explainer video. You can also use them to edit existing videos: add captions, generate motion graphics, create voiceovers, redesign visual styles, translate into new languages, and much more. If you want access to the full library, comment “VIDEO SKILLS” and I’ll share it with you. (You'll have to follow me so I can DM you.)

Akash Anand

513,847 次观看 • 1 个月前

I haven’t missed a workout in over 10+ years 0 misses in over 3650 days is great discipline if you zoom out however, that doesn’t mean each workout is perfect, not rested, don’t have enough food, not enough time, etc but none of that matters if you showed up and tried your best finishing this, then flying with the team to vegas for the weekend! we’re focusing on the 1st workout of this 4 week period, attempting to push my limits here, going for 110% attempts usually I stay around 80-90% work for most then try to max out once in a while but we hit our max today!!!! ahhhh find what works for you and stay consistent to it id say it’s been a key factor to an understanding that life and business is a marathon it may not be a sprint but we can work on running on pace faster and without pain health is wealth

I haven’t missed a workout in over 10+ years 0 misses in over 3650 days is great discipline if you zoom out however, that doesn’t mean each workout is perfect, not rested, don’t have enough food, not enough time, etc but none of that matters if you showed up and tried your best finishing this, then flying with the team to vegas for the weekend! we’re focusing on the 1st workout of this 4 week period, attempting to push my limits here, going for 110% attempts usually I stay around 80-90% work for most then try to max out once in a while but we hit our max today!!!! ahhhh find what works for you and stay consistent to it id say it’s been a key factor to an understanding that life and business is a marathon it may not be a sprint but we can work on running on pace faster and without pain health is wealth

۟

12,993 次观看 • 9 个月前

This week's ChatGPT feature drop - Aug 7: 1/ Rich formatting in our web composer – When you paste in emails or documents, our composer will retain the formatting; copying and pasting is so common, we should have done this a while ago! 2/ Updated model for paid users – GPT 5.6 Sol is more consistent across quick chats and deeper reasoning. You'll now get a slider that lets you choose how much thought ChatGPT puts into a response. The haptics (vibrations) on mobile slider are fun! 3/ Unlimited text messages - Free users will get GPT 5.6 Luna with unlimited text messages. More intelligence for all. Rolling out soon. 4/ Fast Android Camera – We've made it a lot faster on Android to tap "camera" in ChatGPT to take a new photo and ask a question. 5/ Voice x Files - You can now upload files and ask questions in our new ChatGPT voice experience powered by GPT-Live. Team demos were awesome this week. So much in the queue that the next few months are going to be good. Let us know what you're hoping for in the comments!

This week's ChatGPT feature drop - Aug 7: 1/ Rich formatting in our web composer – When you paste in emails or documents, our composer will retain the formatting; copying and pasting is so common, we should have done this a while ago! 2/ Updated model for paid users – GPT 5.6 Sol is more consistent across quick chats and deeper reasoning. You'll now get a slider that lets you choose how much thought ChatGPT puts into a response. The haptics (vibrations) on mobile slider are fun! 3/ Unlimited text messages - Free users will get GPT 5.6 Luna with unlimited text messages. More intelligence for all. Rolling out soon. 4/ Fast Android Camera – We've made it a lot faster on Android to tap "camera" in ChatGPT to take a new photo and ask a question. 5/ Voice x Files - You can now upload files and ask questions in our new ChatGPT voice experience powered by GPT-Live. Team demos were awesome this week. So much in the queue that the next few months are going to be good. Let us know what you're hoping for in the comments!

Adam Fry

356,738 次观看 • 10 天前

Some people are upset that I said “Raising kanan” in the final season Is just as great as the others Is imo better than “the wire” It’s my opinion we don’t have to agree But romanticism plagues people The wire was great but it did have flaws 50 would never mess a scene up On raising kanan With bad song placement that does not even match the setting of the scene.. And also the actors in the car are terrible And they don’t even have lines lol Yes it was amazing and it’s legendary But I will not allow your romanticism with the past make you believe that the show from your childhood was flawless It was not!! It’s okay for new albums to be better than Illmatic It’s okay for new crime series to be better the wire It’s okay for a new teacher to take it to a level Malcolm X never got too It’s okay!

Some people are upset that I said “Raising kanan” in the final season Is just as great as the others Is imo better than “the wire” It’s my opinion we don’t have to agree But romanticism plagues people The wire was great but it did have flaws 50 would never mess a scene up On raising kanan With bad song placement that does not even match the setting of the scene.. And also the actors in the car are terrible And they don’t even have lines lol Yes it was amazing and it’s legendary But I will not allow your romanticism with the past make you believe that the show from your childhood was flawless It was not!! It’s okay for new albums to be better than Illmatic It’s okay for new crime series to be better the wire It’s okay for a new teacher to take it to a level Malcolm X never got too It’s okay!

MASTER STUDENT🤲🏾

203,489 次观看 • 8 个月前

This is exact what LUNA was missing. LUNA collapsed because the whole system depended on hype and when people stopped buying, the stablecoin broke and everything crashed. but $ZEPH is different. ZSD (their stablecoin) is backed by more than enough collateral, and the yield for $ZYS comes from mining rewards, not traders or market activity. That means: - yield doesn’t disappear in a bad market - the system doesn't fall apart if the price drops - $ZYS keeps going up because it only collects more emissions - no “death spiral” like LUNA Even when $ZEPH dropped 90%, the APY stayed strong at over 68% in first year!! Now imagine this whole system being opened up to Ethereum users, where the biggest DeFi money lies. A stable, over-collateralized model that pays you from block emissions going live on the biggest chain? Yeah… that’s extremely bullish. TOO bullish for a sub $10M ecosystem my targets are above $50B and I'm not delusional.

This is exact what LUNA was missing. LUNA collapsed because the whole system depended on hype and when people stopped buying, the stablecoin broke and everything crashed. but $ZEPH is different. ZSD (their stablecoin) is backed by more than enough collateral, and the yield for $ZYS comes from mining rewards, not traders or market activity. That means: - yield doesn’t disappear in a bad market - the system doesn't fall apart if the price drops - $ZYS keeps going up because it only collects more emissions - no “death spiral” like LUNA Even when $ZEPH dropped 90%, the APY stayed strong at over 68% in first year!! Now imagine this whole system being opened up to Ethereum users, where the biggest DeFi money lies. A stable, over-collateralized model that pays you from block emissions going live on the biggest chain? Yeah… that’s extremely bullish. TOO bullish for a sub $10M ecosystem my targets are above $50B and I'm not delusional.

💎ALTSTEIN TRADE💎

15,369 次观看 • 8 个月前

The only strategy on Polymarket that does not care if BTC goes to $200K or $0. Just $116,000 in February. Here is how it works. All of crypto Twitter is arguing where BTC is headed. Bulls, bears, charts, signals. Meanwhile the most profitable strategy on Polymarket right now does not even answer that question. A bot trades 15-minute markets on BTC, ETH and SOL. Does not predict direction. It watches the spot price on Binance and places bets on Polymarket before the oracle updates. Pure speed. Making its developer $8,000 - $10,000 a day. His profile: The price on Binance updates faster than the Polymarket oracle. The difference is seconds. But on 15-minute markets seconds = guaranteed profit. The bot does not predict the future, it trades the present that Polymarket has not seen yet. While everyone argues about direction someone is taking money from both sides.

Blaze

47,684 次观看 • 6 个月前

Create x YUNG's Better Jungle Temples integration - crushing corridors! Similar to Better Fortresses, this will be purely optional and will automatically have a chance of spawning if you play with Create installed. But Create is not required to use the mod. Oh yeah, it works on both Forge and Fabric :) Coming very very soon to patreon supporters!

Create x YUNG's Better Jungle Temples integration - crushing corridors! Similar to Better Fortresses, this will be purely optional and will automatically have a chance of spawning if you play with Create installed. But Create is not required to use the mod. Oh yeah, it works on both Forge and Fabric :) Coming very very soon to patreon supporters!

YUNG

87,104 次观看 • 3 年前

Today we’re introducing 2 HUGE changes to the pump fun mechanism (in beta) 1) Coin creation is FREE 2) Coin creators receive 0.5 SOL (~$80) when their coin completes its bonding curve How does it work? 1) Instead of the creator having to pay ~$2 for coin creation, this cost is now paid by the first buyer of the coin *This unlocks the use case of someone creating a coin without buying any of its supply* Why would they do that? 2) They receive 0.5 SOL (~$80) if the coin completes its bonding curve. - This gets paid out from the pump fun migration fee (now only 1.5 SOL) - it is paid regardless of whether the coin dev has ever held the coin or still holding the coin - The reward also gives an incentive for all devs to get their coin to 100% Final notes: - when the coin is created, it’s not put onchain until someone buys it. That’s why sometimes, there may be a difference between the time it launched on the pump fun interface and when it launched on mainnet (what you will see on solscan, for instance)

Today we’re introducing 2 HUGE changes to the pump fun mechanism (in beta) 1) Coin creation is FREE 2) Coin creators receive 0.5 SOL (~$80) when their coin completes its bonding curve How does it work? 1) Instead of the creator having to pay ~$2 for coin creation, this cost is now paid by the first buyer of the coin This unlocks the use case of someone creating a coin without buying any of its supply Why would they do that? 2) They receive 0.5 SOL (~$80) if the coin completes its bonding curve. - This gets paid out from the pump fun migration fee (now only 1.5 SOL) - it is paid regardless of whether the coin dev has ever held the coin or still holding the coin - The reward also gives an incentive for all devs to get their coin to 100% Final notes: - when the coin is created, it’s not put onchain until someone buys it. That’s why sometimes, there may be a difference between the time it launched on the pump fun interface and when it launched on mainnet (what you will see on solscan, for instance)

Pump.fun

985,458 次观看 • 2 年前

Overseas people may not be familiar with it, but this is a mouse called "Trackball Mouse" that Japanese people love very much. Move the pointer by sliding your thumb without shaking your arm by a millimeter. The old generation of mice had a ball on the bottom, but I have the impression that it is a thumb version. The advantage is that it can reduce the burden on the wrist and save space. More than anything else, the smooth rotation of this ball is comfortable. If you have the opportunity, please give it a try. In addition, recently, even unknown devices beyond the scope of the mouse have appeared. "Nape Pro" You can place your favorite commands on all 6 keys, and you can also move the pointer. And it's super small. This may be an effective device for those who do not have a stereotype about the trackball mouse. Please be aware of it.

Overseas people may not be familiar with it, but this is a mouse called "Trackball Mouse" that Japanese people love very much. Move the pointer by sliding your thumb without shaking your arm by a millimeter. The old generation of mice had a ball on the bottom, but I have the impression that it is a thumb version. The advantage is that it can reduce the burden on the wrist and save space. More than anything else, the smooth rotation of this ball is comfortable. If you have the opportunity, please give it a try. In addition, recently, even unknown devices beyond the scope of the mouse have appeared. "Nape Pro" You can place your favorite commands on all 6 keys, and you can also move the pointer. And it's super small. This may be an effective device for those who do not have a stereotype about the trackball mouse. Please be aware of it.

翁（おきな）

260,488 次观看 • 4 个月前