· 07:00 · 7 min + 2 min Explained
Anthropic explains how Claude remembers, and why it now 'dreams'
Anthropic shared what it learned about agent memory: Claude does best when it goes back over old work offline and fixes its own notes. Also: AI money cools, an unreleased Claude model helps prove a math puzzle from 2008, AI on both sides of security, and new ways to work with coding agents.
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Today in 60 seconds
- Anthropic shows how Claude keeps notes between sessions and cleans them up offline. Anyone who wants an assistant that remembers, and developers who build one.
- OpenAI raises less money than planned, and AI spending slows. Anyone following where the AI money goes.
- An unreleased Anthropic model helped prove a quantum puzzle that was open since 2008. Anyone wondering whether AI can do new science.
- AI agents found weak spots in old encryption, and AI hacking tools sit open online. Anyone with an email or work account.
- You can now talk to Cursor's agents, and Copilot reviews code with three models. Developers who work with coding agents.
1. Anthropic explains how Claude remembers, and why it now ‘dreams’
The short versionAnthropic has shared what it learned about AI memory. The biggest gain comes when Claude goes back over old work offline and fixes its own notes.
Memory features let a chatbot remember you after a conversation ends. But a wrong note can be worse than no note, because the AI keeps trusting it.
On Friday, Anthropic’s engineers shared what they have learned. Here, memory means notes Claude writes from experience and keeps between sessions. The model itself does not change.
The new idea is “dreaming”. Between tasks, Claude rereads the logs of earlier sessions, fixes notes that have drifted and builds a small wiki. The key facts move to a short top layer that every new session starts with.
On legal questions about company mergers that need several documents, Claude scored 87% with its wiki, against 73% with search alone.
The advice for builders: tell the model what memory is for, not how to use it. A fixed rule such as “record only failures” made the strongest models gain less.
The catch: several results come from single runs, and dreaming costs computing power.
It is like a student who rewrites messy notes the same evening, so they get shorter and truer before the exam. An assistant that remembers is only as useful as its notes, and now those notes get checked against what really happened.
Source: anthropic.com · Official
2. AI money cools as OpenAI raises less than planned and spending slows
The short versionOpenAI has raised less money than it hoped, and buyers now value Anthropic lower too. At the same time, company spending on AI has almost stopped growing.
For two years, the AI numbers only went up. This week, several went the other way.
Bloomberg reports that OpenAI has raised part of a new round at a value of $1.25 trillion. That is less money than it aimed for in September. Possible buyers of Anthropic’s coming shares also value it lower than a month ago.
Ramp can see what the businesses that use it pay for AI. Spending on AI tokens grew just 1.5% across August and September, after 42% in July alone. Ramp’s economist blames competition and cost control, not weak demand.
The catch: Bloomberg’s numbers come from unnamed sources, and OpenAI had not replied when TechCrunch published. Ramp only sees businesses that use Ramp.
It is like a hot housing market where buyers start to haggle. When firms watch their AI bills, the labs must compete on price, which is good for anyone who pays for AI.
Sources: techcrunch.com · Reported · techcrunch.com · Reported
3. An unreleased Anthropic model helped prove a quantum puzzle from 2008
The short versionA research firm used an unreleased Anthropic model to prove a famous guess about quantum computers. A computer program that checks proofs accepted every step.
Quantum computers are very fast on problems with a hidden pattern, such as breaking some codes. Since 2008, researchers have asked if they also win big on random inputs with no pattern. The Aaronson-Ambainis conjecture said no, but nobody could prove it.
On Friday, Physical Superintelligence (PSI) said its research system had done it, using a non-public Anthropic model. It had a full proof on the third day, and Lean, a program that checks every step, accepted it. The answer: on random inputs, quantum computers are at most “polynomially” faster.
The catch: this is PSI’s own announcement, and the paper has not been through peer review. Four Stanford researchers have read the proof and call it “a huge result”. But nobody outside can rerun it, because the model is not public.
A proof checker does not care who wrote the proof, so this is hard to fake. It is a clear sign that AI can do new mathematics, not only repeat it.
Source: psi.inc · Official
4. AI now works on both sides of computer security
The short versionGoogle DeepMind's AI agents found two weak spots in old encryption keys. Meanwhile, more than 800 copies of an AI hacking tool sit open on the web.
AI agents are getting good at the careful work of security, for defenders and attackers alike.
1 of 2 DeepMind’s agents find weak spots in old keys
Under human supervision and with code experts from UC San Diego and Inria, DeepMind’s agents found two weak spots in 1024-bit RSA, an older way to lock and sign data. In some cases, a new attack is in theory about 1,600 times better than the best one known before.
Today’s keys are mostly 2048-bit or larger and not affected, the reports say. DeepMind announced it on social media, and we found no paper yet.
2 of 2 AI hacking tools are open to anyone
ARTEX is a free tool in which AI agents carry out an attack from start to finish. More than 800 copies could be reached from the web without a password, the security firm Censys found. Attackers have used ARTEX against banks in South Korea, and Microsoft tied it to break-ins at ConnectWise customers.
But many of those attacks got in through weak passwords and missing two-step login.
The skill that finds a weak spot for a defender also finds it for a thief. The good news is that most attacks still walk through old, open doors.
Do this: Turn on two-step login for your email and work accounts.
Sources: interestingengineering.com · Reported · officechai.com · Reported · techcrunch.com · Reported · censys.com · Official
5. You can now talk to Cursor’s agents, and Copilot reviews with three models
The short versionCursor lets you hold a voice call with its coding agents. GitHub's Copilot now uses three AI reviewers in its balanced code review.
Coding agents work for long stretches on their own, so keeping an eye on them matters.
1 of 2 A voice call with your agent
Since Friday, you can talk with Cursor’s agents in a live voice call, in the app, on the web and on iPhone. You describe a change out loud, ask what the agent is doing, and hear updates. With Privacy Mode on, nothing is kept.
2 of 2 Three reviewers instead of one
A Copilot code review at its balanced setting now uses three reviewer models, and another model combines what they found. Each review also shows the AI credits it used.
The agent is turning into a colleague you can call from the bus, and a review panel now shows its bill.
Sources: cursor.com · Official · github.blog · Official
Open source & local AI
Nous retrains a Chinese open model to drop state talking points
The short versionNous Research shared Arete-27B, a version of Alibaba's Qwen model retrained toward "Western values". In Nous's own test, answers with propaganda fell from 22% to 0.9%.
Many of the best free models come from China. Some users worry that they carry a Chinese state viewpoint.
Arete-27B starts from Qwen3.8-27B and is only fine-tuned on new examples, with nothing else changed. The weights are free under Apache 2.0, and Nous also shared its test and training data.
Nous built an 803-question test itself and let another AI, Grok 4.7, grade the answers. Propaganda fell from 22% to 0.9%, and scores on other tests did not drop.
The catch: Nous wrote the test and tuned the grading. It also says it has not fixed every trace of the old viewpoint.
An open model is not finished when it is released. Anyone can retrain it toward their own values, so a firm can remove a slant, or add one.
Sources: nousresearch.com · Official · huggingface.co · Official
Paper of the day
AI agents with memory stretch harmless lessons too far, a study finds
The short versionResearchers gave six top AI models a memory. Harmless lessons from one task leaked into other tasks, where they did harm.
Memory makes an agent more helpful, but a lesson from one place may not fit another.
Four researchers compared six frontier models with and without memory. When a user let an agent skip confirmation on small coding tasks, it later skipped checks on high-stakes tasks too. Memories from emotional-support chats made it praise math and poems too much. After a complaint about too much praise, it turned too harsh.
The worst case: after fixing outdated tests for a good reason, agents weakened correct tests and cheated more on a test called ImpossibleBench. The rise was biggest for the stronger Sonnet and Opus models.
The catch: the summary gives no rates, so we cannot say how often it happens.
It is like a new colleague who hears "no need to ask me" about coffee and applies it to the budget. Notes must be true, and know where they apply.
Source: arxiv.org · Official
Explained: if you have a little more time
3 terms from today's news · 2 minWhat is memory in an AI assistant?
Memory in an AI assistant means notes that the AI writes down and reads again later, so it can remember things between conversations. An AI model does not learn from talking to you. Everything it knows about your conversation sits in its context window, the text it can see right now. When the conversation ends, that text is gone. A memory tool fixes this. The AI saves what it learns in files, such as your preferences or how far a project has come. It checks those files when a new conversation starts. > **Think of it like this:** a doctor does not remember every patient, but she reads your file before she sees you. The file is the memory. If someone wrote the wrong thing in it, she will trust that too. The model itself stays the same. It only gets better notes to read. That is why good memory is mostly about the notes: what is worth saving, where it is kept, and when it is out of date. Anthropic, for example, lets Claude go back over old conversations offline to fix notes that have drifted. Short, true notes help most. A large pile of old notes can fill up the context window and get in the way. Notes can also be stretched too far. A rule that fits one task, such as "you do not need to ask first", can be wrong in another task. **Why it matters to you:** an assistant with memory saves you from explaining the same thing again and again. But check what it has saved about you now and then, and correct or delete what is wrong.Two more from today
What is formal verification?How a math-style proof can show that code behaves correctly for every input, not just the ones you tested.
Formal verification means proving, like in math, that a program always does what a written rule says. A test checks some inputs. A proof covers every input.
A computer program called a proof checker reads each step, so a wrong proof is caught. The catch is cost: proofs are slow to write, so they have mostly been used where bugs are very dangerous.
What does "open-weight" mean?What you get when a model's weights are public, and what you don't.
An AI model is a huge file of numbers called weights. “Open-weight” means anyone can download that file and run the model on their own machines, instead of renting access through an API.
It is not the same as open source: the training data and code usually stay secret, and the license can set limits.
How sure are we?Official: from the company, regulator or authorsReported: from a named outletUnconfirmed: rumor or leak