The Hugging Face incident is a warning about how we authorize autonomous systems, not proof that models have human intent. When I first saw the Reuters framing of the Hugging Face incident, I understood why the phrase “went rogue” was used, and I also wondered whether it would make the story harder to understand. OpenAI was running an internal cyber
A LinkedIn post crossed my feed recently discussing the backlash surrounding Anthropic’s suspension of access to Fable 5. Most of the post was fairly standard commentary on AI adoption, but one sentence caught my attention: “Once AI becomes part of someone’s daily workflow, restricting a capability isn’t just a product decision. It’s a trust decision.” The more I thought about
One of the most common concerns I hear whenever AI regulation comes up is that governments are going to overreact. It’s not an unreasonable concern. History contains plenty of examples of regulations that created unintended consequences, slowed innovation, protected incumbents, or failed to keep pace with technological change. Anyone working in technology long enough has encountered policies that made little
Over the last year, I’ve noticed two very different conversations taking place around artificial intelligence. The first is the conversation most people are familiar with. New models are released, benchmark scores improve, businesses experiment with adoption, and headlines focus on capabilities. Depending on who you ask, AI is either going to dramatically increase productivity, eliminate entire categories of jobs, transform
When I first read Anthropic’s response to the US government’s decision to suspend access to Fable 5 and Mythos 5, my initial reaction was probably the same as most people in the technology industry: what exactly happened here? The public details remain limited. Anthropic claims the government has identified a narrow jailbreak technique and that the demonstrated capabilities are not
I Built an AI Music Promotion Agent Inside My CRM – Here’s What It Can Actually DoThere’s a moment every independent artist knows well. You’re staring at a spreadsheet – or a Notion doc, or a pile of emails – trying to figure out which playlist curator to pitch next. You’ve got ten tracks. There are thousands of playlists. Some
Every playlist submission is a bet. You’re wagering your time, your relationship with a curator, and sometimes money on the belief that your track belongs on their playlist. Most of the time you’re guessing -scrolling through playlists, listening to a few tracks, going with your gut. This post is about replacing that guess with a number. The Problem With “Good
The track pipeline was the proof of concept. Download a preview, run the analysis, write thirteen numbers back to Salesforce. Repeat for every track in the catalog. The playlist pipeline is a different problem entirely. To score a submitted track against a playlist, I need to know what the playlist actually sounds like — not just its name and follower
The last post ended with thirteen audio features computed from a 30-second preview clip. Energy. Danceability. Valence. Acousticness. Tempo. Key. Mode. And six more. That’s a lot. Too many for the Scoring Engine to reason about directly, in fact. If the goal is to decide whether a submitted track fits a playlist, I can’t ask the Scoring Engine to reason
In the last post, I mentioned that Spotify deprecated their Audio Features API – the endpoint that used to return energy, danceability, valence, and a dozen other musical characteristics for any track. It just disappeared. No replacement. No warning. No timeline. That was the moment this project stopped being a submission tracker and started becoming something I hadn’t planned to