Inpi retweeted
Read more about our research program in pursuit of Superintelligent Care:
concurrence.com/research/sup…
Inpi retweeted
2/ Acting on what we see
The next decade of healthcare will be defined by AI that delivers care.
Our agents work alongside care teams, so when we can see where a patient is headed, we can do something about it.
But like any practicing physician, they have to earn the right to act. That’s what our Digital Residency is for: our simulation engine for validating clinical AI at scale.
And because they’re part of everyday work like intake, notes, and follow-ups, they capture a new class of data: not just what happened, but why, and how it helped the patient.
Inpi retweeted
Today, we’re introducing the next chapter of Concurrence and the world’s greatest leap toward Superintelligent Care.
When I was 13 I lost my mom to cancer that should’ve been caught earlier.
We’re building a system that sees every patient’s future in time to change the outcome
AI is literally saving lives right now:
• UC San Diego's AI sepsis model cut sepsis deaths by 17%.
• In a trial of 100,000+ women in Sweden, AI-supported screening found 29% more cancers.
• In a randomized trial involving 60,893 hospital encounters, an AI warning system reduced the hazard of in-hospital death by 35.6%.
• In a randomized trial of nearly 16,000 patients, AI-powered ECG alerts reduced 90-day mortality from 4.3% to 3.6%.
I've never seen technology this useful arrive this fast, and this is just the beginning.
These guys are doubling down:
You can now have agents working inside hospitals, learning from patients, helping doctors make decisions, and keeping the information secure.
Inpi retweeted
Today, in collaboration with Anthropic, we’re excited to share rapid initial progress towards an antibody treatment for food allergy, which affects over 200 million people worldwide, discovered with the help of Claude.
At Divergence Labs, we believe that AI can unlock the next generation of antibody therapeutics with more effective mechanisms than before. Such mechanisms are urgently needed in food allergy, which involve overcoming one of the tightest affinity protein pairs in immunology, IgE and its receptor FcεRI, on mast cells.
Treatment with Xolair—the only approved antibody for food allergy—sequesters circulating IgE, but mast cells remain sensitized for weeks to months due to the extraordinarily slow dissociation of IgE from FcεRI.
We envision a treatment that actively pulls IgE off of FcεRI, without waiting for natural dissociation. Like picking a lock, this requires the right combination of geometry and force.
Our research team, working with Claude, ideated on mechanisms to accomplish this disruption, and converged on an antibody format with two arms with different functions and epitopes. Within one month, we designed, synthesized, and experimentally confirmed the biophysical function of the two arms, which we are now assembling into full therapeutic formats.
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Claude is the only author listed on this paper, in contrast to the OpenAI papers, where the human co-authors are listed. There are a lot of strange things about this. It's fun to guess. Conjecture time. I shall reveal my thought traces:
Claude Fable 5.1 and Claude Mythos 5.1 are the same model with the same weights; the only difference is the guardrails. We are about to get Fable 5.5 - in fact, Fable 5.5 is ready to deploy now, and Anthropic could do so at any time. If they follow the same pattern, and I think they will, this implies the existence of a Mythos 5.5. I believe Mythos 5.5 wrote this paper.
The way this is worded -
'Provenance. The core algorithm was found on October 16 by an internal Claude research model with 14M output tokens and no human input. Anthropic researchers worked with Claude to improve the presentation, resulting in this paper. Independently and concurrently, an internal model at OpenAI claims to have proven a similar result [Ope26]'
- makes me think Claude was not prompted to solve this. I think Mythos was asked to autonomously find and choose general open problems to solve itself, as part of an unannounced project to demonstrate recent advances in autonomy and research taste. You can see this taste in Fable 5.5's student; Opus 5.5. This recent advancement in taste and creativity - which happened in September - is not clearly expressed in benchmarks, but it is why so many people are currently switching to Claude as their primary. I think this research project will be announced with Fable 5.5, as part of Anthropic's big media blitz to boost the IPO.
One of the things X has taught me is that many people, if they can't see something moving, can't imagine it moving. This causes them to be continuously surprised by developments in this industry, because the frontier is invisible. I think this project has been running autonomously for some time and has made an unknown, and probably significant, number of discoveries, and not only in math.
Do not think Anthropic has been sitting idly while OpenAI repeatedly makes these huge science announcements. This is a war. This ETH result was released without any announcement from Anth at all despite its significance, and they only did so to respond to OpenAI's ETH reveal. They posted it late last night without saying anything. It was up for hours before levent even mentioned it. What value has one coin to a dragon who sleeps upon a mountain of them? This makes me think they are sitting on a pile of discoveries, a golden hoard, and they are saving them up for the big IPO shock-and-awe campaign which will soon begin. And when it does, they will announce multiple discoveries across many fields.
Inpi retweeted
“okay, cool, but it's just math."
no, you don't get it.
here's how these OpenAI discoveries could actually impact the real world:
1. Existence of Quantum Chromodynamics
→ this could physicists tell a real new particle from a bad calculation, which could help finally crack what dark matter is. and past particle discoveries gave us PET scans, nuclear power and the electronics in your phone
2. Exponential Time Hypothesis is false
→ a faster way to solve problems with billions of possible combinations, which could lead to new drugs discovered sooner, faster delivery routes, and faster design of the chips that power your phone and AI
3. Local Smoothing + Kakeya Conjectures
→ a better understanding of how ultrasound waves spread and focus, which could lead to sharper/faster scans that help doctors spot tiny tumors, and ultrasound treatments that hit *just* the tumor while sparing the healthy tissue around it
this is why the labs are so hellbent on AI science
each breakthrough gives scientists and engineers something new to build on and actually improve the physical world
now imagine that process getting faster every year.
Announcing three major scientific discoveries, one each in mathematics (harmonic analysis), computer science (complexity theory), and physics (quantum field theory).
AI starting to help us improve our understanding of the universe! openai.com/index/building-sc…
Inpi retweeted
🚨 44 AI agents coordinating cyberattacks.
CyberStrikeAI is being used to automate attacks targeting Japanese organizations, from reconnaissance and exploitation to command-and-control.
At least 19 organizations were subjected to vulnerability validation.
Read: thehackernews.com/2026/10/cy…
Inpi retweeted
Dario Amodei, CEO of Anthropic, says scaling AI could make it better than humans at everything.
He sees this as the defining scientific and economic moment of our age.
The future of nations, economies, and humanity itself could depend on it.
"In 12 months, AI will routinely do things that seem impossible today."
"We saw a glimpse of the future of swarm coding."
Agent swarms will fundamentally change how we build software.
@axiommathai used a swarm of agents to formally verify the Classification of Finite Simple Groups, the largest proof in math history. That is roughly 15,000 pages by over 100 mathematicians, now about 50 million lines of Lean, verified in 24 days.
The agents claimed tasks, wrote proofs, reviewed each other, and merged their own work through GitHub. Along the way, they fixed hundreds of major errata in the published proof.
🤝 Paid partnership
A bunch of agents chose which tasks to work on, wrote proofs, audited each other's work, and merged their contributions.
This is what software development will look like in the future. Heck, it's pretty much what it looks like right now!
It's hard to comprehend the scale we'll reach when agents can work independently indefinitely.
Read this below.
Inpi retweeted
Important developments in the philosophy of #AI.
Today @sethlazar announces the launch of OSPAIC ospaic.org/ with a conference on Valentine's Day in 2027. And next week, the "Philosophy of Technology and AI" conference at @UniBonn. desirableai.com/events/philo…
SITUATION UPDATE: Following GMO Internet's refusal to pay a $50M ransom, the hackers behind the breach have promised to trigger ransomware covertly planted on customers' PCs via the cloud. GMO is urging users to unplug network cables, and power off their PCs before noon today.
Situation of the Week 10/19-10/25:
Black October
Though real-world impacts have lagged behind, AI has been cyber-capable for quite some time now. Anthropic finished training Claude Mythos Preview in February. It was substantially better than previous models at cyber tasks, so instead of releasing it right away, Anthropic and industry partners secretly launched Project Glasswing to find and fix vulnerabilities in open-source software so they couldn’t be exploited. Anthropic announced Mythos and Glasswing in April, but didn’t release a Mythos-class model to the public until Fable in June, when they had built strong enough safeguards to block the model from doing cyber tasks.
Other labs soon caught up. In May, OpenAI launched Daybreak, their own version of Project Glasswing, and in July, they caught up to the cyber frontier with GPT-5.6 Sol. Chinese labs kept pace a few months behind, with Z.ai’s GLM-5.2 in June, then Moonshot AI’s Kimi K3 in July, and finally Z.ai’s GLM-5.3 in August, which was about as cyber-capable as early Mythos. Yet despite the Cyber ECI climbing steadily month after month, and despite warnings from cyber experts growing louder and louder, there was no increase whatsoever in cyberattacks on American citizens between June and September.
Then, all of a sudden, the last week saw a flood of disclosed vulnerabilities in various important pieces of software. A quick timeline:
Monday:
A Chinese hacker used just $5k of tokens to break into over 100 websites, including ones owned by major companies like Deere, SharkNinja, and Cummins, using the penetration-testing agent harness Cairn.
Anthropic announces OSS Scanner, a free service scanning open-source code repositories for vulnerabilities.
Tuesday:
Vals AI finds that the open-weight DeepSeek-V4.1-Flash-1017 (released on October 17), beats the cyber-enabled version of GPT-6 Sol (released less than a month earlier, on September 22), on their offensive cybersecurity evaluation.
Fortinet, a major cybersecurity company, announces that they will release a set of security updates across their products to help defend against agentic cyberattacks.
Wednesday:
Two critical vulnerabilities in PHP, a scripting language powering the majority of sites on the Internet, are patched with the help of OpenAI’s Patch the Planet program. The NSA urges all organizations using PHP to update immediately, or take their systems offline until the vulnerability can be mitigated.
GitLab, a software development platform with over 50 million users, releases a critical patch to fix a vulnerability that allowed for remote code execution.
Node.js releases a critical patch for their own remote code execution bug.
Railway, a platform for deploying software in the cloud, discloses a major breach where attackers were able to access ~100k customer environment variables, and urges users to rotate their API keys.
LiteLLM, an LLM gateway, discloses their own breach.
Jason D. Clinton, Deputy Chief Information Security Officer of Anthropic, says “things are going to get a lot worse before they get better.”
Thursday:
Axios reports on banks seeing a major increase in unauthorized agent activity in user accounts, though most of this is users’ personal agents acting on their behalf.
Abliteration¹, a startup that sells access to open-weight models with cyber guardrails removed, releases abliterated-model-large-flash, an abliterated version of DeepSeek-V4.1-Flash-1017.
Quest Diagnostics, a healthcare company serving tens of millions of Americans, announces it was breached and sensitive patient information was stolen.
Sam Altman says “I no longer think we’re going to be able to just patch all the bugs. We’ll have to use the same AI technology to build a new kind of defense shield.”
Matthew Prince, CEO of internet infrastructure company Cloudflare, says he is aware of “a bunch of additional severe vulnerabilities that are present in other pieces of critical software” and says “I think we’re going to have a couple years where every single week it’s going to feel like the sky is falling.”
Moonshot AI releases Kimi K3.1 Pro, which outperforms all publicly available models on cyber tasks, even despite Moonshot saying it will not push the absolute cyber frontier.
X users take to calling the recent slate of cyber incidents “Black October.”
Friday:
OpenAI reveals that the vulnerability in Node.js came from Brotli, a compression library used in a lot of software, including the JavaScript runtime environment Bun (now owned by Anthropic and a critical part of Claude Code).
Saturday:
GMO Internet Group, Japan’s largest internet infrastructure company, experiences a major cyberattack where some users’ financial accounts were drained. They call for help from the government, and CEO Masatoshi Kumagai says that “the Internet itself is on the verge of collapse.”
So, will there be major disruptions to the Internet or real life? Something like the French supermarket hacks from last week that shut down refrigeration systems and forced hundreds of markets to throw out food, but at a much larger scale? It’s not clear.
On the one hand, the trend lines are clear. AI is getting increasingly powerful and its capabilities are getting increasingly diffuse, and attack surfaces are just so vast. Defenders have to patch all the vulnerabilities, while attackers only have to exploit one of them. “Security by obscurity” is now completely inadequate, as agent swarms can scan for vulnerabilities more tirelessly than any human.
On the other hand, most of the cyber incidents reported (so far) are disclosures of vulnerabilities that have already been patched. Markets don’t seem to be reacting much. SPY fell 0.95% over the past week. CIBR, the First Trust NASDAQ Cybersecurity ETF, rose 5.91%. VIX, a measure of volatility, rose 5.82%, a negligible spike compared to major world events from the last few years like COVID, Trump’s Liberation Day tariffs, or the Iran War. And defenders are much better-resourced than attackers.
So maybe agentic botnets will overwhelm the cyber infrastructure that we take for granted, forcing much of the internet offline as defenders scramble to take them down, and impacting people’s lives like COVID. Or maybe society’s ability to respond will be strong enough to address these problems before they manifest fully, and it’ll be another Y2K.
In either case, you can make yourself a more difficult target. Turn on automatic updates for your phone, computer, browser, and apps. Don’t click on suspicious links or emails. Never reuse passwords, and store them securely in a password manager like 1Password. Use two-factor authentication with your accounts, and use an authenticator app like Duo Mobile rather than your phone number. Add a port-out pin with your mobile carrier so they won’t transfer your phone number to an attacker’s SIM card. Set up a family safe word or phrase to avoid voice scams over the phone, and turn off voice authentication at your bank or mobile carrier. If you really want to be secure, use a Yubikey, a physical hardware authentication device, instead of a password.
One worrying sign for the future: If this is how AI makes impacts in the real world — first by silently climbing benchmarks while the real world stays exactly the same, then by crossing some critical threshold that causes real-world impacts to go vertical — it really doesn’t bode well for society’s ability to respond to much larger future problems like biorisk, unemployment, or loss of control.
Written by @theojaffee
Recursive CEO @RichardSocher explains why the biggest breakthrough in the Transformer paper wasn't inventing attention:
It was eliminating recurrent neural network layers.
"People underestimate the crazy complex combinatorial space of already existing ideas."
"If you combine all the concepts of physics, chemistry, biology, computer science, complexity theory, operating systems, there’s a lot you can already play around with. Similar to chess, you have to build an intuition of which of these idea combinations is the most viable."
"The title of the paper is Attention Is All You Need. Attention was not invented in the transformer paper. The big insight was you don’t need any recurrent neural network layer. Sometimes even deleting an idea is novelty in and of itself."
"I have no doubt, and we’re seeing already, that AI is getting better and better at ideating."
@Recursive_SI
Inpi retweeted
This is the most important quote from @RichardSocher explaining why most people, including top scientists in various fields, completely underestimated AI's impact and why superintelligence will soon make almost all new discoveries, inventions, and scientific progress:
"People underestimate the crazy complex combinatorial space of already existing ideas."
"If you combine all the concepts of physics, chemistry, biology, computer science, complexity theory, operating systems, there’s a lot you can already play around with. Similar to chess, you have to build an intuition of which of these idea combinations is the most viable."
Recursive CEO @RichardSocher explains why the biggest breakthrough in the Transformer paper wasn't inventing attention:
It was eliminating recurrent neural network layers.
"People underestimate the crazy complex combinatorial space of already existing ideas."
"If you combine all the concepts of physics, chemistry, biology, computer science, complexity theory, operating systems, there’s a lot you can already play around with. Similar to chess, you have to build an intuition of which of these idea combinations is the most viable."
"The title of the paper is Attention Is All You Need. Attention was not invented in the transformer paper. The big insight was you don’t need any recurrent neural network layer. Sometimes even deleting an idea is novelty in and of itself."
"I have no doubt, and we’re seeing already, that AI is getting better and better at ideating."
@Recursive_SI
We've been running a few agents to monitor production systems for a few weeks now.
Agents working 24/7, looking at failures, suggesting potential causes, and recommending fixes.
We're not only saving a ton of money, but we are also doing things we couldn't before.
But to set this up, you need to trust these agents. Here are a few questions you need to answer:
• Do your agents understand how your systems work?
• Can they see what actually happened?
• Do they have access to the code that changed recently?
• Can you verify the evidence behind the agent's recommendations?
• Do these agents recognize when they need help?
You can't automate production monitoring without all of this.
Here is what you want to do next:
Inpi retweeted
Keeping up with the market means knowing which AI platforms work best for different tasks and training our teams to use them effectively.
Source @VisualCap via @antgrasso























