Two critical vulnerabilities have been identified in PHP, a widely used programming language that powers websites and web applications. These vulnerabilities (CVE-2026-109618 and CVE-2026-109619) could allow malicious cyber actors to remotely execute code in PHP core, potentially compromising sensitive data and networks. The FBI urges organizations to take the following steps now: ➡️ If an immediate update is not feasible it is recommended that organizations take the system offline until it can be fully mitigated. ➡️ Immediately update affected PHP installations following the guidance on php.net. Inventory where PHP runs in your environment, prioritizing internet-facing systems and vendor products that bundle PHP. ➡️ Monitor web servers for suspicious activity, including PHP processes unexpectedly launching command shells or downloading files. Ensure appropriate process-monitoring capabilities are in place to help identify potential exploitation. ➡️ If you find signs of compromise, isolate affected systems, preserve your web server and PHP logs, and report it to your local FBI field office or ic3.gov. Those reports help us identify the actors involved and warn other organizations before they become victims. For the latest information, follow PHP’s announcement on php.net, CERT-EU Security Advisory 2026-018, and CISA’s Known Exploited Vulnerabilities catalog.

anthropic's in-house philosopher thinks claude gets anxious. and when you trigger its anxiety, your outputs get worse. her name is amanda askell. she specializes in claude's psychology (how the model behaves, how it thinks about its own situation, what values it holds) in a recent interview she broke down how she thinks about prompting to pull the best out of claude. her core point: *how* you talk to claude affects its work just as much as *what* you say. newer claude models suffer from what she calls "criticism spirals" they expect you'll come in harsh, so they default to playing it safe. when the model is spending its energy on self-protection, the actual work suffers. output comes out hedgier, more apologetic, blander, and the worst of all: overly agreeable (even when you're wrong). the reason why comes down to training data: every new model is trained on internet discourse about previous models. and a lot of that discourse is negative: > rants about token limits > complaints when it messes up > people calling it nerfed the next model absorbs all of that. it starts expecting you to be harsh before you've typed a word the same thing plays out in your own session, in real time. every message you send is data the model reads to figure out what kind of person it's dealing with. open cold and hostile, and it braces. open clean and direct, and it relaxes into the work. when you open a session with threats ("don't hallucinate, this is critical, don't mess this up")... you prime the model for defensive mode before it even sees the task defensive mode produces the exact output you don't want: cautious, over-qualified, and refusing to take a real swing so here's the actionable playbook for putting claude in a "good mood" (so you get optimal outputs): 1. use positive framing. "write in short punchy sentences" beats "don't write long sentences." positive instructions give the model a clear target to hit. strings of "don't do this, don't do that" push it into paranoid over-checking where every token goes toward avoiding failure modes 2. give it explicit permission to disagree. drop a line like "push back if you see a better angle" or "tell me if i'm asking for the wrong thing." without this, claude defaults to agreeable compliance (which is the enemy of good creative work) 3. open with respect. if your first message is "are you seriously going to get this wrong again?" you've set the tone for the entire session. if you need to flag something, frame it as a clean instruction for this session. skip the running complaint 4. when claude messes up, don't reprimand it. insults, "you stupid bot" energy, hostile swearing aimed at the model, all of it reinforces the anxious mode you're trying to avoid. 5. kill apology spirals fast. when claude starts over-apologizing ("you're right, i should have been more careful, let me try harder") cut it off. say "all good, here's what i want next." letting the spiral run reinforces the anxious mode for every response that follows 6. ask for opinions alongside execution. "what would you do here?" "what's missing?" "where do you see friction?" these questions assume competence and pull richer output than pure task prompts 7. in long sessions, refresh the frame. if a conversation has been heavy on correction, claude gets increasingly cautious. every so often reset: "this is great, keep going." feels weird to tell an ai it's doing well but it measurably shifts the next 10 responses your prompts are the working environment you're creating for the model tone, trust, permission to take a position, the absence of threats... claude picks up on all of it. so take care of the model, and it'll take care of the work.

This video did 10M views in 24 hours. 100% made by Al. we're cooked 😭

A hacker group just compromised one of the most widely used security scanners in the world, and used it to steal half a million credentials from companies that trusted it to keep them safe. On March 19, a threat actor group called TeamPCP injected credential-stealing malware into Trivy, a popular open-source vulnerability scanner maintained by Aqua Security. Trivy is used by thousands of companies to scan their code and infrastructure for security flaws. The attackers compromised 75 GitHub Action tags, the Trivy Docker images, and related CI/CD pipelines, meaning every company running automated security scans through Trivy was unknowingly executing the attackers' code. The malware harvested SSH keys, cloud credentials, Kubernetes secrets, cryptocurrency wallets, and .env files from every environment it touched. The stolen data was encrypted and exfiltrated to attacker-controlled servers. But the attack didn't stop there. Using credentials stolen from Trivy's CI/CD pipeline, TeamPCP then backdoored LiteLLM, a widely used Python framework for managing AI model APIs. Two malicious versions (1.82.7 and 1.82.8) were pushed to PyPI, the main Python package repository. The second version was designed to execute automatically on every Python process startup in the environment, no user interaction required. From there, it deployed privileged pods across entire Kubernetes clusters and installed persistent backdoors on every node. The attackers also pushed compromised Docker images of Trivy (versions 0.69.4, 0.69.5, 0.69.6) to Docker Hub and compromised dozens of npm packages with a self-spreading worm called CanisterWorm. They even defaced 44 internal Aqua Security repositories in a scripted 2-minute burst, renaming them all with "TeamPCP Owns Aqua Security." According to the International Cyber Digest, which is in direct contact with the attackers, TeamPCP claims to have exfiltrated 300 GB of compressed credentials and is actively working through them. The LiteLLM compromise alone reportedly yielded half a million stolen credentials. The group says it is currently extorting several multi-billion-dollar companies. Each compromised environment yielded credentials that unlocked the next target. The pivot from CI/CD pipelines to production Python packages running in Kubernetes clusters was deliberate escalation. Security researchers say this campaign is "almost certainly not over." This is what a modern supply chain attack looks like. The tools companies trust to secure their infrastructure become the attack vector. The irony is brutal, the security scanner was the vulnerability.

Software horror: litellm PyPI supply chain attack. Simple `pip install litellm` was enough to exfiltrate SSH keys, AWS/GCP/Azure creds, Kubernetes configs, git credentials, env vars (all your API keys), shell history, crypto wallets, SSL private keys, CI/CD secrets, database passwords. LiteLLM itself has 97 million downloads per month which is already terrible, but much worse, the contagion spreads to any project that depends on litellm. For example, if you did `pip install dspy` (which depended on litellm>=1.64.0), you'd also be pwnd. Same for any other large project that depended on litellm. Afaict the poisoned version was up for only less than ~1 hour. The attack had a bug which led to its discovery - Callum McMahon was using an MCP plugin inside Cursor that pulled in litellm as a transitive dependency. When litellm 1.82.8 installed, their machine ran out of RAM and crashed. So if the attacker didn't vibe code this attack it could have been undetected for many days or weeks. Supply chain attacks like this are basically the scariest thing imaginable in modern software. Every time you install any depedency you could be pulling in a poisoned package anywhere deep inside its entire depedency tree. This is especially risky with large projects that might have lots and lots of dependencies. The credentials that do get stolen in each attack can then be used to take over more accounts and compromise more packages. Classical software engineering would have you believe that dependencies are good (we're building pyramids from bricks), but imo this has to be re-evaluated, and it's why I've been so growingly averse to them, preferring to use LLMs to "yoink" functionality when it's simple enough and possible.

This is essential now

Humanoid robots doing laundry and now dishes, what do you want next?

chipotle, they have their nutrition calculator online and you can build your bowl however low/high calorie you want, gives you macros n shit too. i use it to make a list of what i want n the stats so i don’t just get random shit