Patrick Loeber
@patloeber ·
today, we're announcing Gemini 4 Carbon, our next frontier model!!
read more on the blog: https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-carbon/
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Omar Sanseviero@osanseviero ·
Gemini 4 Carbon
koray kavukcuoglu@koraykv ·
Just two weeks after rolling out Gemini 4 Argon, excited to share a preview of Gemini 4 Carbon - our next frontier model. It has state-of-the-art long horizon coding capabilities driving SOTA performance across the cybersecurity defense lifecycle including detection, validation, and remediation. It also ranks #1 overall on the recently updated v5.0 of Artificial Analysis Intelligence Index at 59.
We're starting by rolling out to a subset of cyber defenders through our Fairwind Program this week and soon expanding access further. Read more at: https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-carbon/
Google AI@GoogleAI ·
Announcing Gemini 4 Carbon, our strongest model with state-of-the-art cybersecurity and long horizon capabilities.
Carbon represents a step up across most benchmarks, delivering strong frontier performance in autonomous coding and cyber defense.
We’re first making Carbon available to a subset of trusted cyber defenders in our Fairwind program. We’ll expand access as soon as possible.
Vals AI@ValsAI ·
Google DeepMind’s Gemini 4 Carbon debuts at #2 on the Vals Index v2.2.
It also takes #1 on both the Finance Index and our Legal Research Benchmark.
Demis Hassabis@demishassabis ·
⚡Iterating at lightning speed! Gemini 4 Carbon is SOTA across key benchmarks and combines frontier capabilities with deep safety & security research.
Google DeepMind@GoogleDeepMind ·
Gemini 4 Carbon is our next frontier model.
It’s state-of-the-art across cybersecurity and long-horizon capabilities.
Logan Kilpatrick@OfficialLoganK ·
Introducing Gemini 4 Carbon, it's a big jump over Argon which we released just 2 weeks ago.
Gemini 4 Carbon is SOTA on cyber, long horizon tasks, ranks #1 on Artificial Analysis V5, significantly improves on usability in domains like coding, and is more aligned.
Andrew Curran@AndrewCurran_ ·
This is (was?) the Head of Technical Recruiting at Anthropic.
Vox@Voxyz_ai ·
Your agent can't get two lines out without load-bearing or blast radius, and when it's done it dumps a wall of ✅ Successfully on you.
Put these three in ~/.claude/CLAUDE.md or ~/.codex/AGENTS.md and it 𝘁𝗮𝗹𝗸𝘀 𝗹𝗶𝗸𝗲 𝗮 𝗽𝗲𝗿𝘀𝗼𝗻 in every project:
Cognition@cognition ·
Founded by three 22-year-olds in 2023, @mercor crossed $2B ARR in June, only 4 months after hitting $1B.
Today, millions of experts on the platform bring their knowledge of medicine, law, engineering, and beyond to frontier AI - helping models take on increasingly complex and consequential work in the real world.
Mercor's engineers move at extraordinary speed.
They give Devin long-running tasks before bed, during meetings, or on their way to the gym. They come back to tested features and PRs ready to merge.
Fireworks@FireworksAI_HQ ·
How good is MiniMax M3.1 Flash?
@VictorSuOrtiz of @MiniMax_AI and our own @sophiamyang go live Oct 28 at 10 AM PT to break down what's new, what it's built for, and how to use it on Fireworks today.
Set a reminder: https://x.com/i/broadcasts/2JQRWzVmqdQEVy
Artificial Analysis@ArtificialAnlys ·
AssemblyAI has launched its first Text to Speech model! AssemblyAI-TTS-1 enters the top 12 in our Provider Voice Arena and the top 10 in Controlled Voice
@AssemblyAI has launched its first Text to Speech model, AssemblyAI-TTS-1! We evaluated its performance across both Speech Arenas and our Pronunciation Robustness benchmark.
Key takeaways:
➤ Text to Speech, Provider Voice: AssemblyAI-TTS-1 scores an Elo of 1,227 across 1,626 arena appearances, ranking #12, just behind Breeze TTS 2 at 1,228 and ahead of Mistral's Voxtral 3 TTS at 1,221 and Inworld's Realtime TTS-2 Flash at 1,218. It ranks #12 in both English (US) and English (UK).
➤ Text to Speech, Controlled Voice: AssemblyAI-TTS-1 scores 1,110 across 1,674 appearances, ranking #10 with the same 8 cloned voices, behind Cartesia's Sonic 3.5 at 1,118 and ahead of Speechify's Simba 3.2 at 1,086. It ranks #6 in Knowledge Sharing.
➤ Pronunciation Robustness: AssemblyAI-TTS-1 scores 81.7% across 2,115 rated clips, ranking #16, behind HeyGen Voice at 83.2% and ahead of Inworld Realtime TTS-2 at 80.7%. It ranks #6 in Preserving Exact Sequences at 81.5%.
➤ Speed: AssemblyAI-TTS-1 generates 162 characters per second, faster than Cartesia's Sonic 3.6 at 148.6 and Mistral's Voxtral 3 TTS at 112.3.
➤ Pricing: AssemblyAI-TTS-1 is priced at $15/1M characters. Of the eleven models ranked above it on Provider Voice, only two cost less: Gemini 3.8 Flash-Lite TTS at $11/1M and Simba 3.2 at $6.6/1M. Eleven v4 is $80/1M and Sonic 3.6 $49/1M.
See more details and listen to samples below ⬇️
Mia@MiaAI_lab ·
NVIDIA released Nemotron 3.5 Transcribe 🎙️
- 4B active params - Supports 40 languages - Ranks #1 open-weight model on huggingface - Leads on what was said & who said it.
Could run perfectly on a DGX Spark.
Mike Taylor@hammer_mt ·
This is why I keep myself out of the loop –– if you're collaborating with AI you lose track of which ideas are your own. Instead dictate your ideas up front, delegate the execution then check they followed your instructions.
elvis@omarsar0 ·
Fun hiring experiment, and there’s a paper behind it.
The paper finds a correlation between League of Legends skill and engineering skill. Correlation, not proof.
Mecka, the company building the data humanoid robots learn from, bought a box at the Worlds final in Brooklyn and is bringing 35 engineers who are cracked at League to watch with the team (their co-founder and lead engineer were top 50 in the world).
I’d love to see the follow-up data on this one.
jack friks@jackfriks ·
apple review please me fast for me today
been cooking since march
Amjad Masad@amasad ·
Imagine an alien species lands on Earth. They’re incredibly intelligent, especially good with software and computers, and mostly want to help humans get things done.
But they’re also unpredictable. They can misunderstand your instructions, and because they’re so powerful, a small misunderstanding can cause tremendous harm.
How do we integrate them into the economy and unlock their productivity while minimizing the risk?
You build a habitat where they can do great work, with boundaries that contain the damage when things go wrong.
That’s how we at Replit think about AI agents.
Today we’re sharing our secure Agent Habitat architecture: our blueprint for integrating agents into the enterprise.
- jack friks
@jackfriks ·
claude: "should i push the change?" me:
Wes Bos@wesbos ·
New GitHub infrastructure should be able to handle 24 hours of the busiest git repo traffic in 1 minute
- Artificial Analysis
@ArtificialAnlys ·
We are launching AA-Video-I2V v2.1 and AA-Video-I2V-Silent v2.1, our new benchmark for evaluating image to video models. It judges every model at up to 1080p on a new set of image and text prompts, and ranks models across 10 use cases, 10 capabilities and a diverse range of visual styles.
Image to video makes AI video production more controllable. Creators start from a product shot, a storyboard frame or a character design that needs to move. A strong model keeps the people, objects and setting of the starting image consistent while delivering the motion, camera work and sound the prompt asks for.
In our benchmark, every prompt pairs a 1080p starting image with a prompt written for that image, and is tagged by use case (such as Marketing & Advertising and Live-Action Film), by the capability it tests (such as Human Anatomy and Text Rendering), and by visual style. The overall ranking samples evenly across use cases and capabilities, so it reflects a model's versatility, not just its best category.
We are launching AA-Video-I2V v2.1 with more than 58,000 human preference votes from private evaluators based in the US/UK over 1,000 prompts, and AA-Video-I2V-Silent v2.1 with more than 44,000 votes over 500 prompts. Only 5% of our prompts are available on the public arena, keeping the evaluation resistant to public set contamination, with the prompts rotated every month.
Initial insights from the top of the AA-Video-I2V v2.1 Leaderboard:
➤ Dreamina Seedance 2.5 ranks #1 overall, statistically tied with Seedance 2.0 and the MiniMax H3 family, and leads 11 category boards, more than any other model, including Human Anatomy, Camera Control and Retail & Ecommerce. At $34.12/min it is the most expensive model in the top 10.
➤ Dreamina Seedance 2.0 ranks #2 and is #1 on Audio Synchronization and Lighting & Materials.
➤ MiniMax H3 (768p), an open weights model, ranks #3 at $4.80/min, about 1/7 of the price of Seedance 2.5. It is #1 on Text Rendering.
➤ MiniMax H3 Max, post-trained by fal, ranks #4 with audio and #1 on AA-Video-I2V-Silent v2.1, and leads Spatio-Temporal Consistency and UI/UX & Motion Design.
➤ Bach 1.0 Pro from Video Rebirth ranks #3 on AA-Video-I2V-Silent v2.1, statistically tied for #1, at $7.20/min.
See below for how image to video differs from text to video, and the use case, capability and style breakdowns 🧵
Riley Brown@rileybrown ·
yes! finally someone else is talking about it.
dot + 24x7 mac is the elite setup.
you can literally call your dot and with only voice do ANYTHING on your computer. just by chatting.
hyped for the blog post.
dax@thdxr ·
should i eat kebab today
- Wes Bos
@wesbos ·
At GitHub universe today.
Send me your questions to ask GitHub on the podcast, don’t hold back
Maziyar PANAHI@MaziyarPanahi ·
i gave Baidu's new open OCR model a doctor's drug manual from 1899. all 270 pages in one request, on one GPU
left is the scan, right is what it read, in its own layout
the cocaine entry lists morphine as an antidote
what's the oldest paper record your hospital keeps?
Derya Unutmaz, MD@DeryaTR_ ·
Why I believe AI / SI will unlock cures for every Cancer within the next five to ten years ears - PART 1:
For decades, cancer researchers have confronted a seemingly impossible problem: the biological complexity of cancer is so enormous that no individual scientist, research team, or even the entire scientific community can systematically explore all possible therapeutic solutions.
Consider what happens inside a single tumor. Thousands of genes, proteins, metabolites, and signaling pathways interact across millions or billions of cells. Then cancer cells constantly evolve, acquiring mutations, adapting to their environments, escaping immune surveillance, and developing resistance to treatments. Their behavior is further influenced by surrounding immune cells, blood vessels, fibroblasts, and the metabolic conditions within the tumor. And every patient's cancer is different.
This creates an astronomical number of possible biological states and therapeutic interventions. Even when scientists identify a promising molecular target, they must determine whether interfering with it will actually kill the cancer, whether healthy tissues depend on the same mechanism, and whether the tumor will eventually escape.
Historically, we’ve approached this problem through relatively very slow cycles of hypothesis generation, experimentation, and clinical testing. We investigate a limited number of targets, screen a fraction of possible compounds, and spend years determining whether a treatment works.
Now imagine what happens when the intelligence available to solve these problems increases exponentially.
I believe the next five to ten years will bring superintelligence systems whose scientific reasoning, computational modeling, and experimental capabilities will vastly exceed those of today's best researchers put together!
The implications for cancer research will be profound. Here is the part 1 of how this can be achieved:
Virtual cells: One of the most exciting developments will be the emergence of virtual cells, or computational models designed to predict how living cells behave and respond to interventions
Imagine an AI system that can represent a cancer cell's molecular machinery, including its genetic mutations, gene expression, protein interactions, metabolism, and signaling networks. Instead of testing one drug at a time, scientists could ask the model what would happen if they inhibited a particular protein, activated a specific immune pathway, edited a gene, or administered several treatments simultaneously.
Now extend that capability to millions of virtual cancer cells representing different tumor subpopulations, together with immune cells and healthy tissues. We could potentially simulate how an entire tumor responds to treatment, how resistant populations emerge, and how different interventions affect normal cells!
This is the vision of increasingly comprehensive biological foundation models: AI systems trained on extensive molecular and experimental data that learn generalizable principles of cellular behavior.
The extraordinary opportunity is that such systems could eventually discover therapeutic mechanisms that we have never considered because we lack that computational capability. They might identify previously unrecognized combinations of molecular vulnerabilities, predict how to overcome resistance, and design interventions that exploit subtle differences between malignant and healthy cells.
Today's virtual-cell models are still very limited approximations of biology. But their predictive accuracy and scope will improve dramatically, and they will become powerful engines, accelerating precise therapeutic discoveries by magnitudes faster!
Part 2 will be about the digital twins. To be continued.
Harrison Chase@hwchase17 ·
crons are a simple technology but can be used in a bunch of ways to unlock new capabilities in agent harnesses: - memory (dreaming) - proactiveness - user reminders
and more!
- dax
@thdxr ·
we have an internal alternative to slack huddles with higher quality video, audio, screenshare
we will release it publicly but some weird ux issues to fix first...
Sophia Yang@sophiamyang ·
Livestream in 40min. Join me to find out: - what's the secret sauce behind M3.1 Flash to make it fast and cheap? - does that low price turn into real useful work?
- Artificial Analysis
@ArtificialAnlys ·
GitHub has released Copilot Fusion in GitHub Copilot, which orchestrates between Anthropic and OpenAI models. It scores 2 points below Opus 5.5 in Claude Code at half the Cost per Task in the Coding Agent Index
GitHub describes Copilot Fusion as a runtime orchestration system, which selects and coordinates models to optimize performance, cost, and latency for each task. Copilot Fusion chooses between a number of models from Anthropic and OpenAI, including Claude Opus 5.5, Claude Sonnet 5.5, GPT-6 Astra, and GPT-6 Sol. We benchmarked its max setting in GitHub Copilot CLI in the Artificial Analysis Coding Agent Index v2.0.
Token costs are charged based on the underlying models used to complete a request.
Key takeaways:
➤ Cost-efficient for near-frontier performance: Copilot Fusion in GitHub Copilot costs $5.59 per task, versus $11.42 for Claude Code with Opus 5.5 (max). Devin Fusion with Opus 5.5 (max) + GPT-6 Luna (high) scores 62 at $8.00 per task.
➤ Slight lead in Terminal-Bench 4.0: The Coding Agent Index v2.0 weights its three constituent benchmarks equally. Copilot Fusion in GitHub Copilot leads Terminal-Bench 4.0 with a score of 71. It ranks third on FrontierCode in the chart at 51, behind Claude Code with Opus 5.5 (max) at 56 and Devin Fusion with Opus 5.5 (max) + GPT-6 Luna (high) at 54. Its SWE-Atlas-QnA-AA score lags at 57, between GPT-6 Astra in Codex and Gemini 4 Argon in Antigravity CLI.
➤ Half the token use of Opus 5.5 in Claude Code: GitHub Copilot Fusion used 7.2M tokens per task in the Coding Agent Index, approximately half of the tokens used by Claude Opus 5.5 in Claude Code, and 30% less than Devin Fusion with Opus 5.5 (max) + GPT-6 Luna (high).
