jack friks
@jackfriks ·
apple review please me fast for me today
been cooking since march
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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).
Matt Pocock@mattpocockuk ·
Looks like /drill-me is back on the menu
Daniel San@dani_avila7 ·
Receipts were easy for MiniCPM-OCR by @OpenBMB
So I gave it the hard stuff: a 9-line invoice, a bank statement, a hand-corrected PO and a German–English hotel bill
82/102 fields exactly right. Headers and printed totals: 73/78. 🧵👇
Thor 雷神 ⚡️@thorwebdev ·
Genius 👷🏗️🤯 both from Manus and Natalie 💪
Hope we'll get to see more of these micro startups within startups! 🔥
Aravind Srinivas@AravSrinivas ·
Rolling out new voice mode for all Pro and Max users, on web and Mac apps. Allows you to basically talk to your computer to get parallel work streams done.
Fireworks@FireworksAI_HQ ·
How fast is MiniMax M3.1 Flash?
@VictorSuOrtiz of @MiniMax_AI and Fireworks' @sophiamyang are going live this morning at 10 AM PT to break down what's new, what it's built for, and how to use it on Fireworks today.
Tune into the livestream here: https://x.com/i/broadcasts/2JQRWzVmqdQEVy
- dax
@thdxr ·
we have cloudflare enterprise now
which means we're paying for all your cheap worker deployments on all your side projects
you're welcome
Andrew Curran@AndrewCurran_ ·
Deep expertise in any STEM field will increasingly become an infohazard over the next few years. Watching my friends in the field process the ETH result really cemented this. During this Weltbilderschütterung, be there for people who thought they built their houses upon stone.
- Fireworks
@FireworksAI_HQ ·
We’re working to build the largest collection of private and public practitioner evals.
Today, 31 new partners join the Specialized Intelligence Index. Compare open, closed, and specialized models on real work, weigh quality against cost, and see where training could pay off.
Tibo@thsottiaux ·
Pretty wild that this site got 3M views.
Elon Musk@elonmusk ·
They do
Tibo@tibo_maker ·
wild what people say about it 🤯
elvis@omarsar0 ·
Super interesting way to shop with AI.
Dalet hyper-personalizes the shopping experience by creating a feed of lifelike AI-generated images.
The products in each image are real. And users can tap to buy them.
Discovery is a way better fit for generative media than another chat box.
The team comes from Google, Runway, and MIT. Congrats on their raise.
Manus@ManusAI ·
Manus didn't just build the site. it built the whole company.
the interactive site. the 3D brick builds. piece-by-piece build checks. Stripe. domain. email. launch video. social posts. even the ads.
Check out https://bricklane.online!
Carmine Paolino@paolino ·
DJs, stop preparing your tracks.
I built Solco: it grids, keys and tags your whole library on your computer, then writes the USB your CDJs read. No rekordbox needed.
12 local AI models, macOS, Windows, and Linux.
Join the waitlist: https://getsolco.com
Claude@claudeai ·
New in Claude for Legal: a gallery of ready-made artifacts for contract review, regulatory horizon scans, cite checks, and more.
Start from the built-in example, add your own documents and connectors, and share it with anyone across the business.
swyx@swyx ·
Happy anniversary to Ligma/Johnson 2026