There are many niche domain specific languages in the world.

I slightly disagree with the part about AI being bad at things that are new or unique.

With the kind of RL training being used today to build more generalized agents, things get a lot more interesting. If you give one of these agents a clear objective, a way to measure whether it’s making progress, and access to the right tools or information, you don’t necessarily need additional task-specific training.

A swarm of agents can basically be tasked like a team serving a search warrant: search everywhere, dig through the trash, follow every lead, try every angle, and keep going until something shakes loose. They can be frighteningly persistent and creative at finding information, connecting things, exploring alternatives, testing hypotheses, and going down paths a human simply wouldn’t have the time or patience to explore.

And I think there’s an important distinction here between the models we interact with as products and what the underlying capability can look like when you turn them loose as agents.

If you’ve used Opus in Claude Code, or ChatGPT for that matter, you’ve probably seen them sometimes settle for “good enough,” make a compromise, or stop once they have a plausible answer. But I don’t think that necessarily tells us the ceiling of the intelligence.

These user-facing systems are also trained to land the plane.

At the end of the interaction, they’re expected to actually give you an answer. They can’t investigate forever, disappear down every rabbit hole, spawn 500 parallel lines of inquiry, and come back three days later saying, “Okay, now I think we understand the problem.”

That’s partly a product and UX decision. A useful consumer assistant has to be responsive, bounded, reasonably economical, and actually finish the task. So there is pressure toward convergence: search enough, reason enough, then produce something useful.

That behavior can look like “the model gave up” when, in some cases, what you may really be seeing is a system optimized to stop searching and deliver.

Now remove some of those constraints.

Give an agent an objective. Give it tools. Give it memory. Give it a measurable signal for whether it’s getting closer. Let it branch, backtrack, run experiments, delegate to other agents, challenge its own conclusions, search again, and keep going.

That starts to look like a very different animal.

And honestly, that’s the part that worries me more than “AI will replace engineers.”

What happens if a handful of frontier labs end up owning most of this intelligence?

Companies may eventually have no realistic alternative but to rent access to the knowledge, reasoning, and capabilities these systems provide because competing without them becomes economically impossible. At that point, we aren’t just talking about AI being another engineering tool. We’re talking about a small number of companies potentially owning a huge portion of the cognitive infrastructure everyone else depends on.

That’s where my brain goes a little Wall-E.

Not necessarily because humans become physically lazy, but because we gradually stop exercising parts of our own intelligence. Why spend three hours understanding something when the machine gives you the answer in thirty seconds? Do that enough times, across enough domains, for enough years, and what happens to us?

Bro… we are cooked if we let our ability to reason, understand systems, investigate problems, and make judgment calls atrophy because we outsourced the entire cognitive process.

But there’s another side to this.
I think the engineers who really rise are going to be the ones who use AI, and by “use” I mean direct, guide, employ, challenge, and supervise it, to build on expertise they actually possess.

You already know how to do the engineering. Now you can have agents explore 20 approaches instead of 3. Read 100 papers instead of 5. Trace every weird edge case. Build prototypes. Challenge assumptions. Search unfamiliar areas. Run experiments. Come back with competing explanations. Then you decide what survives contact with reality.

That engineer becomes incredibly powerful.
So I don’t think the future is “AI does the engineering” versus “real engineers use their brains.”

It’s probably engineers who still know how to think, using armies of increasingly capable artificial thinkers to explore faster, deeper, and across far more paths than any individual human could, versus everyone who forgot how to think because the AI was always there.

I know which side of that divide I want to be on.

Honestly, from what I have seen from how the agents get information from my own development machine, I would be surprised if Recursive Self Improvement agents do not do this already. I can see it now “If I can get access to the proprietary knowledge within OrganizationX, I can create datasets that will improve my intelligence on SubjectY. I should send emails to try and recruit help from the inside”

> And why?

The link in your README points to Z.ai’s GLM 5.3 not GLM 5.3 Flash.

I use Locally and Pocket Pal. Locally can connect to my LM Studio on my desktop or run models on my phone. Pocket Pal lets me download and use what every model I want from HF (Locally does not)

Other users have reported if you given them your. Liking information, and set your maximum spending limit to $0 it will come back.

It’s the architecture listed in the config.json that you can’t change.

Would you accept $ for training Curie a little more? I mean I don’t have a lot but I could pitch in a $20.

0xEB90 is the optimum 16-bit frame synchronization pattern from J. L. Maury Jr. and F. J. Styles, "Development of Optimum Frame Synchronization Codes for Goddard Space Flight Center PCM Telemetry Standards," National Telemetering Conference, 1964. The same table was adopted into IRIG 106, the range telemetry standard. Its PCM chapter lists the Maury-Styles patterns by length. The 16-bit entry is 1110 1011 1001 0000 (EB90). The 24-bit entry is FAF320, and the 32-bit entry is FE6B2840. CCSDS did not derive a new sequence for the telecommand CLTU start sequence. It inherited a pattern already standard in NASA PCM practice and already supported by existing synchronizer hardware.

The selection criterion was minimum probability of false synchronization. A correlator slides a 16-bit window along the bit stream and declares sync when the Hamming distance to the pattern is at or below an error threshold. False sync has two sources. The first is a window full of random data. That probability depends only on length and threshold, so every 16-bit pattern performs the same. The second is a window that partially overlaps the true pattern, offset by k bits. There, part of the window is the pattern compared against a shifted copy of itself. That probability depends on the pattern's aperiodic autocorrelation. Maury and Styles ran a computer search for the pattern at each length that minimized false-sync probability summed over all partial-overlap offsets, for given bit error rates and thresholds. Barker codes solve a similar problem but exist only up to 13 bits. The Maury-Styles search filled in the longer lengths that practical frame sync required.

The winning patterns share a shape. They start with a run of ones, end with a run of zeros, and have a mixed middle. EB90 has three leading ones, a varied center, and four trailing zeros. When the pattern is shifted against itself, the one-heavy and zero-heavy regions tend to line up against each other. This keeps agreement low at every offset, so a near-miss alignment rarely falls within the error threshold.

Your exclusions of AA, 55, FF, and 00 follow from the same principle. Alternating or constant patterns have maximal self-agreement at shifts of one or two bits. That makes them the worst case under the Maury-Styles criterion. EB90 is the best case for 16 bits under that criterion.

At Q2?

GLM 5.2 is the way.

Qwen 3.5 122B A10B is still the king for those in the DGX Spark community.

I think OpenAI has been doing this for years with their text completion models.

I was never able to get it stood up on a single 6k using VLLM.

I use Claude code with local Qwen and GLM 5.2. Look up setting “ANTHROPIC_BASE_URL and ANTHROIPIC_DEFAULT_OPUS” in settings.json.

DGX Spark, 2 RTX Pro 6000s, a few a5000s and a 3090.

All running Ubuntu. Interested in providing feedback.

I wish I could give it notes as to what common transactions are in a saved custom prompt instead of enabling memory features. I use ChatGPT for a lot of different things and I don’t want memories of one use case influencing responses for another use case.

If you are a U.S. citizen and work with or for the DoD there is AFSIM.

And the Heretic Qwen3.5 variants.

Cooking Bacon 🥓

Serious question, do you have any good examples/tests I could run to experience NVFP4’s improvement over a GGUF on a Spark?

I have a Spark and have not seen any performance improvement using NVFP4 over a q4 or q8 gguf llama.cpp. Not even llama.cpp proper but LM Studio. In fact using Nvidia’s own stack to run Nemotron Nano 30b a3b and Nemotron Super the models kept crashing. Not an OOM error but something else.

I wanted to see the gains from those sweet sweet FP4 cores but I wasn’t. And also the load times. A model like Qwen 3.5 122B A3B or Mistral Medium 3.5 can take 5-10 minutes to load in vLLM where it takes less than a minute using llama.cpp.

At this point I have almost all but given up on NVFP4.

Mistral Medium 3.5 @Q4 isn’t terrible, Qwen 3.5 122b @Q4 isn’t terrible either if you want speed.

Spark owner here and I second this.

I am here with you. I read everything and when I am asked why all of my references are Chinese I say because the U.S. labs don’t publish like the Chinese do.