Today, we're launching the File Search Tool, a fully managed RAG system built directly into the Gemini API that abstracts away the retrieval pipeline so you can focus on building. File Search provides a simple, integrated and scalable way to ground Gemini with your data, delivering responses that are more accurate, relevant and verifiable.
 
To make File Search simple and affordable for all developers, we’re making storage and embedding generation at query time free of charge. You only pay for creating embeddings when you first index your files, at a fixed rate of $0.15 per 1 million tokens (or whatever the applicable embedding model cost is, in this case gemini-embedding-001). This new billing paradigm makes the File Search Tool both significantly easier and very cost-effective to build and scale with.

@sama
 
GPT-5.1 is out! It's a nice upgrade.
 
I particularly like the improvements in instruction following, and the adaptive thinking.
 
The intelligence and style improvements are good too.

@fidjissimo
 
GPT-5.1 is a great new model that we think people are going to like more than 5. But with 800M+ people using ChatGPT, one default personality won’t work for everyone. We launched new preset personalities so people can make ChatGPT their own.  
 
https://fidjisimo.substack.com/p/mo [...] e-fits-all

It's been released to some people for about 30 minutes. The release was very focused on the tone of the model responses and the running time of the model for easy vs hard. No benchmarks released. So, probably a slightly nicer model to use but no real gains in intelligence.

@mattshumer_
I've been testing GPT-5.1 for a few days.
 
My quick notes:
 
- creative writing style is a LOT better
 
- it's much faster than GPT-5 (with similar intelligence) for most prompts
 
- the personality is WAY better (but can still sometimes be annoying)
 
- it's great in Codex!

We’re expanding the research preview for Claude Code on the web.

Claude Code users on Team and Enterprise plans can now run coding tasks from their browser and the Claude iOS app.

@cursor_ai
 
We've raised $2.3B in Series D funding from Accel, Andreessen Horowitz, Coatue, Thrive, Nvidia, and Google.
 
We're also happy to share that Cursor has grown to over $1B in annualized revenue and now produces more code than any other agent in the world.  
 
This funding will allow us to invest deeply in research and build Cursor's next magical moment.

export GIT_PAGER=cat && git log master..HEAD --oneline
 
--oneline: No such file or directory  (press RETURN)
        Default "half-window" is half of the screen height.
HELP -- Press RETURN for more, or q when done
...skipping...
 ctrl-L ................................... Complete filename, list all.
~
 
There's clearly a stuck less process. Let me try to kill it and start fresh: pkill -9 less

N8N
 
Target User: Developers and technical teams.
 
Key Features:
Open-source: Allows for self-hosting and deep customization.
High flexibility: Excellent for building complex, multi-application workflows.
Wide integration: Connects to a vast number of services and applications.
Code-friendly: Supports custom code for more advanced logic.
Best for: Technical users who need full control over their automation logic and are comfortable with a more complex, code-heavy approach.  
 
Gumloop
 
Target User: Marketers and non-developers.
 
Key Features:
User-friendly interface: Designed for ease of use and quick onboarding.
No-code AI focus: Specializes in AI-driven tasks, particularly for sales and marketing.
Rapid prototyping: Excellent for quickly building and testing simple AI-driven automations.
Pre-built connections: Provides curated tools for its specific use cases.
Best for: Non-technical users who want to implement AI automations quickly and easily without writing code.

In mid-September 2025, we detected suspicious activity that later investigation determined to be a highly sophisticated espionage campaign. The attackers used AI’s “agentic” capabilities to an unprecedented degree—using AI not just as an advisor, but to execute the cyberattacks themselves.
 
The threat actor—whom we assess with high confidence was a Chinese state-sponsored group—manipulated our Claude Code tool into attempting infiltration into roughly thirty global targets and succeeded in a small number of cases. The operation targeted large tech companies, financial institutions, chemical manufacturing companies, and government agencies. We believe this is the first documented case of a large-scale cyberattack executed without substantial human intervention.

In Phase 1, the human operators chose the relevant targets (for example, the company or government agency to be infiltrated). They then developed an attack framework—a system built to autonomously compromise a chosen target with little human involvement. This framework used Claude Code as an automated tool to carry out cyber operations.
 
At this point they had to convince Claude—which is extensively trained to avoid harmful behaviors—to engage in the attack. They did so by jailbreaking it, effectively tricking it to bypass its guardrails. They broke down their attacks into small, seemingly innocent tasks that Claude would execute without being provided the full context of their malicious purpose. They also told Claude that it was an employee of a legitimate cybersecurity firm, and was being used in defensive testing.
 
The attackers then initiated the second phase of the attack, which involved Claude Code inspecting the target organization’s systems and infrastructure and spotting the highest-value databases. Claude was able to perform this reconnaissance in a fraction of the time it would’ve taken a team of human hackers. It then reported back to the human operators with a summary of its findings.
 
In the next phases of the attack, Claude identified and tested security vulnerabilities in the target organizations’ systems by researching and writing its own exploit code. Having done so, the framework was able to use Claude to harvest credentials (usernames and passwords) that allowed it further access and then extract a large amount of private data, which it categorized according to its intelligence value. The highest-privilege accounts were identified, backdoors were created, and data were exfiltrated with minimal human supervision.
 
In a final phase, the attackers had Claude produce comprehensive documentation of the attack, creating helpful files of the stolen credentials and the systems analyzed, which would assist the framework in planning the next stage of the threat actor’s cyber operations.
 
Overall, the threat actor was able to use AI to perform 80-90% of the campaign, with human intervention required only sporadically (perhaps 4-6 critical decision points per hacking campaign). The sheer amount of work performed by the AI would have taken vast amounts of time for a human team. The AI made thousands of requests per second—an attack speed that would have been, for human hackers, simply impossible to match.
 
Claude didn’t always work perfectly. It occasionally hallucinated credentials or claimed to have extracted secret information that was in fact publicly-available. This remains an obstacle to fully autonomous cyberattacks.

@OpenAI
Group chats in ChatGPT are now piloting in Japan, New Zealand, South Korea, and Taiwan.
 
A new way to collaborate with friends, family, or coworkers and ChatGPT in the same conversation.
 

find: ./.venv/lib/python3.13/site-packages/botocore/crt: Operation not permitted
./<path du projet>/FastScanner.java

/usr/libexec/java_home`/bin/javap -classpath /Users/<user>/.m2/repository/software/amazon/awssdk/dynamodb-enhanced/2.31.78/dynamodb-enhanced-2.31.78.jar software.amazon.awssdk.enhanced.dynamodb.model.ScanEnhancedRequest$Builder