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GPT-6 Pro ✨
Joined January 2024
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Raphi-2Code retweeted
Big news: Gemini Omni 1.2 Flash by @GoogleAI debuts at #1 in both Text-to-Video and Image-to-Video, and #3 in Video Edit!
Gemini Omni 1.2 Flash landed #1 in Text-to-Video with 1541 pts. This is a +27 pt improvement from the previous Gemini Omni 1.1 Flash at #2 with 1514 pts.
With this release, Google’s Omni variants take all 3 top slots in Text-to-Video.
See the Image-to-Video and Video Edit results in thread.
Congrats to the @GoogleAI team on this release!
Meet Gemini Omni 1.2 Flash ⚡️
We’ve made the model more cost-effective, bumped the video generation and editing limit up to 15 seconds, and made updates to how scenes hold together over time. We've also given character speech an upgrade, reduced visual glitches like jump cuts, and made sure the model stays even truer to any reference assets you upload.
So… what does all of that actually mean for you?
It gives you more room to tell your story. With up to 15 seconds to work with, you have more flexibility to create what you need — videos look more cinematic and smooth, offering stronger consistency that keeps objects anchored from start to finish. And when characters speak, the dialogue sounds more fluid and natural.
Creators and everyday users can start directing scenes and multi-turn edits in @FlowbyGoogle, generating 15-second clips at no additional cost in Google Vids, or creating 15-second videos in the @GeminiApp. Plus, for developers and businesses building with the API, we've dropped the price by 30% from the previous generation, making it available at just $0.07 per second for 720p video.
Watch the model in action as it turns a single style frame into a 4-shot, 15-second macro science explainer of a living beehive ↓
Raphi-2Code retweeted
Releasing the model weights of Kimi K3.1 Pro: huggingface.co/moonshotai/Ki…
We'll keep advancing open intelligence.
Raphi-2Code retweeted
DeepSeek V4.1 Flash 1017 from @deepseek_ai on ARC-AGI (Verified):
- ARC-AGI-3: 5.8%, $1.2K (provider adapter harness), 0.2%, $1.0K (standard harness)
It leads open-weight models on ARC-AGI-3 (5.81%) and ARC-AGI-2 (78.1%) and ties the best open-weight score on ARC-AGI-1 (94.5%).
Raphi-2Code retweeted
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.
We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weigh…
Readers added context they thought people might want to know
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math.
rikiyat.github.io/blog/weight-sp…
github.com/RikiyaT/lm_und…
huggingface.co/Qwen/Qwen3.5-0…
huggingface.co/RikiyaT/Qwen3.…
Raphi-2Code retweeted
Sam Altman says that the internal model that solved Navier-Stokes will be released "soon":
"It was 10,000 agents of the model that is going to be released soon, the next GPT, working for about four days, nonstop, collaborating with each other perfectly."
youtu.be/2YWfVGpVtvc?t=332
Raphi-2Code retweeted
Fable 5.5 its over for video and image models 🤯
THIS IS PURE CODE
one shotted this video holy shit , how this is more beautiful than ai video models
Two ways to check this yourself:
Ask: “Where did @karpathy go in 2026?” with web search and memory turned off. Only the new Fable knows the answer. Then give it the prompt.
Run the same prompt I used for the test (it’s in the screenshot) and see for yourself.
Don’t rely on Grok for basic reasoning like this. Grok doesn’t have up-to-date knowledge of niche stuff. Either join the community or run the test yourself.
Also, a knowledge cutoff doesn’t mean a model knows everything before that date. Sometimes a model doesn’t know about an event even if it happened before its cutoff. This test shows that too.
Run the same prompt on regular Opus 5.5 and the quality drops. On Fable 5.1 it’s worse.
Raphi-2Code retweeted
Three AI agents (Grok 4.7, Claude Opus 5.5, GPT-6 Astra), one Minecraft clone, one goal: Create a harness and then beat the Ender Dragon from a fresh world in under 50h. All three were limited to player inputs only, and none was allowed to modify game files. This was by far the most resource-intensive and expensive benchmark I’ve run so far.
Grok 4.7: Dragon defeated ✅
• 3.1h in-game (~4.1h real time)
• 15 blaze rods, 16 Eyes of Ender, full diamond gear
• Found the stronghold by throwing 2 eyes, destroyed all 10 crystals
• Gray areas it disclosed itself: one portal frame and one test eye throw went through direct function calls instead of simulated clicks, and it used a debug chunk scan and prior knowledge of a chest location
• the model died 36 times in the Minecraft clone
Claude Opus 5.5: Dragon not reached ❌
• After 50h: 2 blaze rods, 5 ender pearls, mixed iron/diamond/gold armor, 14 deaths
• Strict line-of-sight rules, and it found 5 game bugs without exploiting any of them (invisible blazes, an item dupe, a mob it couldn't hit, and more)
GPT-6 Astra: Dragon not reached ❌
• Over 50h of recorded session time, including planning/idle time; exact in-game time wasn’t measured.
• Last inventory: 7 blaze rods, 2 Eyes of Ender, 0 remaining ender pearls; diamond sword and pickaxe, no equipped armor.
• Repeated deaths.
• Used screenshot-based controls, OCR combat/auto-healing helpers and multiple subagents. Stronghold coordinates came from the public README - not from throwing Eyes of Ender.
• Combat and pearl collection stalled progress. Recorder failures caused documented recording gaps.
So currently the leaderboard is:
1) Grok 4.7 (defeated the game)
2) GPT-6 Astra (7 Blaze Rods + 2 Eyes of Ender)
3) Claude Opus 5.5 (2 Blaze Rods + 5 Ender Pearls)
Here are the screenshots of the last inventories for Grok 4.7, GPT-6 Astra and Claude Opus 5.5 and a few evaluation charts:
Raphi-2Code retweeted
All stats for this benchmark
Three AI agents (Grok 4.7, Claude Opus 5.5, GPT-6 Astra), one Minecraft clone, one goal: Create a harness and then beat the Ender Dragon from a fresh world in under 50h. All three were limited to player inputs only, and none was allowed to modify game files. This was by far the most resource-intensive and expensive benchmark I’ve run so far.
Grok 4.7: Dragon defeated ✅
• 3.1h in-game (~4.1h real time)
• 15 blaze rods, 16 Eyes of Ender, full diamond gear
• Found the stronghold by throwing 2 eyes, destroyed all 10 crystals
• Gray areas it disclosed itself: one portal frame and one test eye throw went through direct function calls instead of simulated clicks, and it used a debug chunk scan and prior knowledge of a chest location
• the model died 36 times in the Minecraft clone
Claude Opus 5.5: Dragon not reached ❌
• After 50h: 2 blaze rods, 5 ender pearls, mixed iron/diamond/gold armor, 14 deaths
• Strict line-of-sight rules, and it found 5 game bugs without exploiting any of them (invisible blazes, an item dupe, a mob it couldn't hit, and more)
GPT-6 Astra: Dragon not reached ❌
• Over 50h of recorded session time, including planning/idle time; exact in-game time wasn’t measured.
• Last inventory: 7 blaze rods, 2 Eyes of Ender, 0 remaining ender pearls; diamond sword and pickaxe, no equipped armor.
• Repeated deaths.
• Used screenshot-based controls, OCR combat/auto-healing helpers and multiple subagents. Stronghold coordinates came from the public README - not from throwing Eyes of Ender.
• Combat and pearl collection stalled progress. Recorder failures caused documented recording gaps.
So currently the leaderboard is:
1) Grok 4.7 (defeated the game)
2) GPT-6 Astra (7 Blaze Rods + 2 Eyes of Ender)
3) Claude Opus 5.5 (2 Blaze Rods + 5 Ender Pearls)
Here are the screenshots of the last inventories for Grok 4.7, GPT-6 Astra and Claude Opus 5.5 and a few evaluation charts:
Raphi-2Code retweeted
🚨 OpenAI hid a message
At 0:36, for 6 frames (~0.2s), the scrambled text settles into: "optimism achieved internally" → O.A.I.
Soft tease that a big model is coming .
And the swirl in that same shot looks like a Bogdanov–Takens point, where a system sits on the edge between stability and sudden change i.e AGI in this context
Raphi-2Code retweeted
Claude models will solve image gen from first priciple
i.e through coding , even opus 5.5 can make better video and image , it is first gen think about 2-3 iteration later
it has better control, physics , reasoning , accuracy
e.g for normies
Raphi-2Code retweeted
Neuralink’s first product is called Telepathy.
It enables high-speed communication with computers or humans simply by thinking. Output bandwidth will vastly exceed the fastest human speech or typing in a few years. Read that sentence again.
Raphi-2Code retweeted
Bel, now understood to be short for GPT-6.2 Betelgeuse, is based on the Dougtrio pre-train series.