Anthony Denkinger @agentdenk
Product architect. Shipping AI agent systems in public — judgment over credentials. https://t.co/PO8iKYcgrR · Medium @anthonydenk anthonydenkinger.com Scottsdale, AZ Joined April 2024-
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How are people handling team assignment in basketball video when jersey colors are close? Every rule I tweak fixes one game and breaks another.
@tokufxug Using McByte++ and it's awesome. No training, and the ID sticks when a player leaves the frame and comes back. It only runs the heavy re-ID and camera correction when it actually needs to.
Every speed win on a tracker is really an accuracy trade. The skill is knowing which frames can afford it.
If you let Claude Code or Codex run unattended, update both today. Claude Code now asks before deleting a path it built itself, and Codex asks before elevated commands by default. Small change, big save.
Sonnet 5.5 is out. Honestly the middle tier is where I end up spending most of my day, so this one matters more to me than another Opus bump.
Full story on Medium (Ratchet essay): medium.com/@anthonydenk/i… Site: anthonydenkinger.com
Every morning I’d re-introduce myself to my AI like we’d never met. Same project. Same stack. Same bug I’d already corrected. Goldfish memory, dressed up as a coding partner.
“It learned something” isn’t an ending. It’s a claim. Most of the industry is taking that claim on faith. I’m not. If that’s your world too, tell me where I’m wrong.
Fixed it: a skill also has to help on problems it was never shown. Independent graders beat a model grading itself. Cheaters out. Real skills stayed. Forward, never backward.
AI Twitter has a benchmarking addiction. Meta drops Muse Spark and half the replies are basically “but is it smarter than X?” Brother, if your agent needs frontier IQ to rename files, scrape docs, and click buttons, the model is not your problem.
I’ll admit it, I was too skeptical of Grok 4.5. The launch felt loud, so I discounted it. But after using it more, the value is pretty obvious: fast, direct, and cheap enough to keep in the loop. That combo matters more than people want to admit.
The problem with most AI agents is not that they miss context. It is that they do not know which context matters. For a writing agent, memory is taste, prior posts, rejected angles, and what you already said yesterday. Otherwise it gets confidently derivative.
Google keeps making the same point in a slightly nicer suit: the model is not the product anymore. Managed Agents in Gemini API is about background execution, remote MCP, and credential refresh. That is the real lock-in. Not the chat box. The harness.
Everyone keeps treating model launches like raw intelligence contests. Google leading with Nano Banana 2 Lite and Gemini Omni Flash says the quieter part out loud: faster, cheaper, good-enough models are what actually get embedded everywhere.
OpenAI may be right to merge coding agents and desktop agents into one product surface. But renaming Codex to GPT Desktop feels like trading a name people understood for one that sounds way more generic.
You design the workflow around routing, not around one hero model. Terra is going to do more actual shipping than Sol. That is not a knock on Sol. That is the business model working. The model choice is becoming product design.
Hard steps go to Sol. Most of the real work goes to Terra. Volume goes to Luna. That is a quiet admission that always-default-to-frontier is a bad way to run agents at scale.
GPT-5.6 is out as Sol, Terra, and Luna. Three tiers, one family. Most of the discourse will be about which one wins a benchmark. What I’m watching is the split itself.
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