The Supply Chain Attack Nobody Is Talking About Enough Everyone is watching Optimus videos this week. I get it. The robot stuff is genuinely impressive. But I keep coming back to something far more unsettling, something that directly affects every team shipping AI products right now. TeamPCP just ran a multi-stage supply chain attack across…
Speed Is Not a Strategy: Why Fast AI Development Is a Liability Dressed Up as a Virtue There is a pattern I keep watching repeat itself in AI engineering, and I am tired of pretending it is not a problem. The teams shipping fastest are often the teams with the least understanding of what they…
The Thread We Were Hanging By Every AI engineer reading this should stop what they are doing and pay attention. This week, Andrej Karpathy flagged one of the most alarming supply chain attacks I have seen target our specific corner of the software world. The litellm package on PyPI was poisoned. A single pip install…
The PyPI Dependency Trap Nobody Wants to Talk About Last week, ML engineers got a very clear look at how fragile the tooling ecosystem really is. A poisoned PyPI release of litellm, version 1.82.8, sat live on the registry for less than an hour. In that window, it was fully capable of exfiltrating SSH keys,…
The litellm supply chain attack this week should be a wake-up call for every ML engineer. One poisoned PyPI release. Less than an hour live. And it had the potential to exfiltrate SSH keys, AWS credentials, Kubernetes configs, API keys, crypto wallets, and shell history from every machine that ran pip install litellm or anything…
10x Faster Agent Containers: What OpenAI’s Container Pooling Really Signals If you’ve spent any real time building agentic workflows, you already know that the bottleneck usually isn’t the model. It’s the plumbing. Cold starts, spinning infrastructure, waiting for a container to come up before your agent can even begin executing a tool call. OpenAI just…
Evals First. Everything Else Second. Most engineers I know treat evals like cleanup work. Something you wire up after the “real” engineering is done, right before you push to production and hope nobody notices the weird edge cases. I have done this. You have probably done this too. It is the wrong order of operations,…
A 400 Billion Parameter Model on a MacBook. Let That Sink In. I’ve been doing AI/ML work long enough to remember when running a 7B model locally felt like a party trick. This week, someone ran a 397 billion parameter model on a laptop. Not a workstation. Not a rack-mounted inference server. A MacBook with…
Context Is the Moat. Not the Model. Most teams building AI products are optimizing the wrong thing. They debate model size, chase benchmark scores, and spend weeks on fine-tuning runs. Then they ship something that feels hollow. Generic. Like a customer support bot that clearly has no idea who it’s talking to. I’ve seen this…
Microsoft just open sourced BitNet, and I think people are sleeping on how significant this actually is. While most of the AI conversation stays locked on GPU clusters, foundation model releases, and cloud API pricing, something quietly landed that could change the entire equation for how and where large language models actually run. Let me…
