Welp, that happened faster than I predicted. Thought it would be end of 2027, then early 2027, but agentic traffic growing so fast that bots have now passed human traffic online for the first time in the Internet's history. https://t.co/2zX5bHdhsa
Today, we share a breakthrough on the planar unit distance problem, a famous open question first posed by Paul Erdős in 1946.
For nearly 80 years, mathematicians believed the best possible solutions looked roughly like square grids.
An OpenAI model has now disproved that belief, discovering an entirely new family of constructions that performs better.
This marks the first time AI has autonomously solved a prominent open problem central to a field of mathematics.
AI infrastructure is shifting as growth in inference workload and reinforcement learning drive higher CPU:GPU ratios, increasing the importance of CPU capacity for orchestration, simulation, and efficient utilization of large GPUs. Recently, I went on a tour through our @Intel D2 data center.
The paper below explores two primary market trends and workload architectures that are driving increased demand for CPUs, relative to GPUs for AI implementations:
1) Accelerated AI inferencing workload growth. A higher CPU:GPU ratio is required for inferencing workloads compared to training workloads—especially as agentic AI becomes prevalent across multiple industries.
2) The renaissance of RL. RL has matured from a video game niche to complex 3D simulation environments (such as robotics, precision medicine, and algorithmic trading) that require high numbers of CPUs.
Read More 🔗: https://t.co/SkzquFicLV
The Google Threat Intelligence Group has detected the first known instance of a threat actor using an AI-developed zero-day exploit in the wild. While the attackers planned a wide-scale strike, our proactive counter-discovery may have prevented that from happening. This finding is part of our new report on AI-powered threats.
Microsoft is investigating mistralai PyPI package v2.4.6 compromise. Attackers injected code in mistralai/client/__init__.py that executes on import, downloads hxxps://83[.]142[.]209[.]194/transformers.pyz to /tmp/transformers.pyz, and launches a second-stage payload on Linux. The file name transformers.pyz appears deliberately chosen to mimic the widely used Hugging Face Transformers library and blend into ML/dev environments.
The main payload is a credential stealer, but it also includes country-aware logic; it avoids Russian-language environments and contains a geo fenced destructive branch that has 1-in-6 chance of executing rm -rf / when the system appears to be in Israel or Iran.
To mitigate this threat: isolate affected Linux hosts, block 83[.]142[.]209[.]194, hunt for /tmp/transformers.pyz, pgmonitor[.]py, and pgsql-monitor.service, and rotate exposed credentials.
Agent Mode is here in Outlook!
Copilot can now help run your inbox and calendar, triaging emails, rescheduling meetings, and helping you stay on top of what matters most.
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.
Claude Sonnet 4.6 is live in Microsoft Foundry delivering frontier performance across coding, agents, and professional work at scale.
From navigating massive codebases to orchestrating enterprise workflows, Sonnet 4.6 scales with you.
Learn more: https://t.co/Pxq25fuYeP
My new 48x Openclaw & Local LLM Inference Cluster 🔥
- 1x M3 Ultra Mac Studio 512 GB Unified Ram
- 4x M4 Mac Mini 16 GB
- 2x Nvidia DGX Spark
- 2x Raspberry Pi 5 8 GB
- 1x 24 port Switch
Turning ambient Wi-Fi + BLE into love mapping: in-car 5G nodes, retail IoT, vehicle head units, portable battery packs. No root, no antenna mods—just your phone listening passively. 🤔📶