dbreunig
First off, Opus 5.5 built working weights and code with only two prompts:
1. Read this article and any relevant source code: https://divagr.com/blog/qwenputer. Then explore if you could emulate the chip and ram in a Nintendo Entertainment System (as people do today with FPGAs in MISTRs) using this technique. If so, what size model would you have to build and if you could use smaller faster ones, could you get it to be at the standard clockspeed.
It returned charts well illustrating any model would be _slow_. But it's still fun, so the second prompt was:
2. I think we should build it! Just for fun. It's okay if it's not fast. We want to be able to have the weights, and a cli to run them that we can point at a ROM.
Now while this was an _epic_ run by Opus (5.5 is a crazy capable model), this is a surprisingly well-structured challenge.
There is a long-lived and incredible community of classic gaming emulation work online for it to reference. And that community has created…conformance tests! Opus found them and built against one, that has >3k golden instructions: https://github.com/christopherpow/nes-test-roms/blob/master/...
Also, there is the original Qwenputer repo that Opus used to drive the weight writing. That was essential.
But this is a great example of what these models can do with proper reference materials, great specs, conformance tests, and clear direction.
But to answer your question, it's in the readme: https://github.com/dbreunig/nesformer#how-it-was-checked
Show HN: A working Nintendo in ~1GB of Qwen weights
The State of Context Engineering
Have been waiting for this to manifest for years: https://www.dbreunig.com/2023/05/17/prompt-injection-seeding...
Context Inertia: When Agents Won't Let Go
ReAnchor, a Jev Optimizer in DSPy
The Rise of Audio AR
My CCPA Dialog with OpenAI (2023)
What We Can Learn from Claude's Fable 5.1 System Prompt Changes
I can’t host GLM 5.3 yet, so my agents still run on 5.2. But the fact that 5.2 is sufficient and there’s another gen in the wings kinda proves my point, imo.
Fable and the End of the Free Lunch
Harnesses Are "Situated Agents"
AI to Kids by Letting Them Tweak Local Chatbots [video]
Why Open Source Matters for AI: Models should be infrastructure, not appliances
> Time and time again, when talking to people who rely on ChatGPT, Claude, Perplexity, and other general AI tools, I hear them say, “AI is incredible. It handles nearly everything I throw at them.”
> “What does it fumble with?” I’ll ask.
> “Well, it still gets things wrong when it comes to my line of work.”
https://www.dbreunig.com/2025/04/08/on-ai-observational-comi...
How does he know?
Dspy.Flex lets optimizers rewrite the code for faster, cheaper, better programs
Manage Your Agent's Loadout with Dr Skill
Show HN: Brew doctor` for Skills and MCP loadouts
https://github.com/dbreunig/drskill
Recently, I've had agents reach for the wrong skill or tool. This tends to happen in the middle of longer reasoning chains, which frustratingly means lots of chatter polluting the context before a correction can be issued.To chase these issues down, I built `drskill`: a CLI tool that checks for conflicts and security issues among your agent's Skill and MCP loadout.In addition to spotting...
Understanding the Dynamics of the AI Ecosystem with Pace Layers
The Problem is Prompt Debt: You can't be model agnostic and hand-tune prompts
When an agent can explain anything, what is the role of human-centric docs?
> But the thing is, Molo doesn’t actually have to be good at this job, because the point of this trial isn’t to win — though I’m sure Musk wouldn’t mind a win. The point is to punish Altman, Brockman, and OpenAI. Musk has done that pretty thoroughly — reinforcing in the public’s mind that Altman is a liar and a snake. This morning, I read an exclusive in The Wall Street Journal that assorted Republican AGs and the House Oversight committee wanted to look into Sam Altman’s investments. References to the trial are peppered throughout the article.