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I recently gave a talk on DeepSeek V3 training improvements and the fascinating ideas behind GRM and SPCT. The talk took awhile to get posted, so here it is! Be sure to checkout the blogpost as well for a bit more on GRM and SPCT.

It’s certaintly a fascinating time for robotics. I am generally pessimistic of the current state, and likely fates, of much of the current landscape of the robotics industry, but it is undeniable we are rapidly unlocking new capabilities and research is flying forward.

This talk acts as an excellent high level overview of the key innovations to our approach of utilizing reinforcement learning for better performance of robots with our VLMs / VLAs for a more general audience.

If you want a slightly deeper dive (though admittingly out of date with the pace the field has moved) checkout my overview of LLM research aligned with robotics from 2023, and my thesis work of using LLMs to utilize heuristic knowledge to understand context of requests and environments. If I were to do a similar project today, I would certainly take a more agentic route. I certainly wish I had written arkaine prior to that project - it would have avoided so many headaches.

I’ve seen dozens and dozens of open source robotic arm projects; probably because of my own addiction to mechanical automatons paired with my unending desire (despite a lack of need) for a robotic arm of my own.

This one caught my eye however as it has a particularly clever accessory method without the need to of a complex end effector tool changer. If they can route power/data control signals out to the accessories next I rather like the idea.

I wonder if perhaps a more direct approach should be utilized and the base end effector / finger gripper should be simply an accessory itself.

As I’ve mentioned before, I’ve been trying to learn to draw lately, initially inspired by a desire to be able to properly convey the headaches with pictures in my head to on-paper representations. Plus I’m eager to increase my practice in visualizatio nand intuition.

But drawing references tend to be oriented towards the more popular subjects of nature, people, animals. Very few people are as enamored by the mechanical movements of machines as I am, apparently. As a result - references can be hard to come by for practice.

Enter these sources; not just for drawing practice, but to also inspire mechanical solutions to your own projects.

Currently I’m going through the struggles of learning to draw from a very poor foundation of sub-stick figure talent and primarily spurred on by the desire to convert the vague ideas in my head to actual designed physical objects, this video spoke to me. The creator walks through the creaton of his clay making set, all to allow him to shape tiny, organic shapes that he can then photograph to act as “good-enough” references for CAD modeling design. A simple idea, well executed.

Back when I was working on coppermind at the heyday of GPT3.5’s initial world shattering release, I had… difficulty finding good ways to deal with parsing the stochastic LLM outputs.

I got better at this when I started work on arkaine, eventually developing a pretty useful and reliable parsing pattern.

With an idea that would be best served as a golang app requiring interacting with LLMs I decided to do a quick port of the parser to an idiomatic golang module.

So if you need AI parsing for your golang project, check out go-arkaine-parser.

Did some work on eli5equation, cleaning up and fully deploying prior one day project. We’ll see if people find it useful. I find it super useful when tackling new AI papers.

I also gave it a bit of a glowup too. And yeah, still powered by arkaine.

I have been busy due to a flood of inspiration to work on projects. Pleasant, but also exhausting.

The only updates to arkaine has been to fix an issue with default arguments in the ParallelList flow tool. The reason for the freeze in development is an accidental startup.

How does one go “oops, I made a startup?” Basically, I decided to quickly develop a series of simple apps that utilized arkaine and would demonstrate its usage. Then combine them into a large post walking through the examples.

The very first one you saw a prototype for in an earlier post wherein I made a interview practice question generator. This was a simple script, but the results struck me as promising. Why not build a nice site around it so that other people can use it? And so started hiredcoach

I really liked what I started to see, and realized that this could actually be useful to people if offered as a service. So that’s what I’m aiming to do - try and build out a startup from it as quickly as possible. I’m at ~12 days of development effort. I had hoped to get it out in 2 weeks - 3 or 4 still seems a possibility.

I’ll post here when I launch and my initial thoughts. I temper my anxiety of startup launching with thoughts that the worst case is a nice line item in my resume upon failure.

Given my recent weekend foray into Replit, I wanted to try a simple solo project to “kick the wheels” and test drive Replit a bit more.

It took about 2 hours from idea conception to deployed product with integrated DeepSeek AI - not too bad!

reflecta is a simple journal prompting app (trying to avoid overly cheesey prompts at that) that allows you to specify categories to focus on, and provides the prompt. The UI is designed to be as simple and calming as possible. As an added feature, there’s an integrated chat feature with DeepSeek’s R1 to reword, retheme, or discuss the prompt.

It’s always refreshing to quickly pump out small projects like this. Hopefully some people find it helpful!

I’ll be playing with Replit on a few other “mini-app” ideas to see if any of them get into a releaseable state.

This past weekend I participated in the Replit Hackathon held by SDx. It was a fun event.

It may seem odd trying my hand at vibe coding, given its weird hype and counter culture built around it, and the fact that I am a skilled and experienced developer. I wanted to explore it, see what the tools and workflow was like, learn what is possible without my traditional approach to problems.

To that end my three person team worked on a fascinating concept. We called it adict - demonstrating that naming still remains one of the hardest aspects of some projects - an app that used profiles of simulacrums of people with varying backgrounds - gender, age, beliefs, region, job, income, education level, etc - and tasked AI with determinnig the mental and emotional response of that individual to a given ad. Yes, there’s lots of room for error there, but it’s a 4 hour hackathon, so bare with me there.

I used arkaine to build out the agent that would consume the ad, a profile, and then utilized Gemini 2.5 Pro to develop the response. It worked pretty well!

We did, however, have significant issue tying the backend to the frontend through Replit. A constant cycle of frontend errors that ultimately should have been dealt with manually ate too much of our time and resulted in a poor final product. But that’s ok - we learned a lot!

Replit seems to be awesome at getting a quick outline and mock up done, but then struggles with the more complex tasks. I’ve experimented with different workflows for it, but it does seem often circuitious to get certain bugs fixed. My own intuition on where the tool would struggle - edge cases that were not predictable or common for the unique piece of software you’re making - proved accurate.

That being said, I do like Replit. If, for nothing else, then quick prototypes and mockups. I think people underestimate how powerful that can be. Hell I think most people underestimate how simple most software solutions can (and should) be. Bespoke custom software isn’t a bad thing. It’s where everything we use now grew from. Maybe we’ll see a resurgence of in house tech solutions without the expensive overhead and billion subscriptions SaaS has devolved into.

I expect a fair amount of growth from Replit’s tools and I’m excited to keep an eye on it. I’m not as bullish as the hype cycle hucksters are, but I do think it’s a promising tool.

I am a reserved and ultimately shy individual, dealing with his own life is a way that best suits me. Recently, however, I have found a way, however small, to provide my own resistance with my skillset and resources.

SciOp is a movement to backup government datasets and sights as they are targeted and removed for not toeing the party line. Since I have plenty of open space on my NAS, I’m dedicating a few terabytes to the effort.

So far I’ve backed up transgender protection and support resources, NOAA and climate change datasets, and some NIH datasets.

SciOp also provides a set of feeds that allows your backup to be automated to targeted and “endangered” datasets that we know are actively being removed, not just under threat, to ensure greater success in preservation.

If you have the tech setup for it I recommend checking it out.

I’ve long since embraced AI integrations into my AI, and I’ve experimented with several of the new app builders. I’m going to avoid commenting on the “vibe” coding meme that’s going on, I will say I’ve enjoyed some of the efficiencies gained. A few months ago per the suggestion of several peers I swapped from VS Code w/ Copilot to Cursor. It took some adjustment of workflow to incorporate Cursor into a viable tool worthy of its subscription cost, but once certain hurdles were overcome I found it certainly accelerated me. The release of Claude 3.7-sonnet came with a promise of drastically increased performance in code, which I found to be… not true. In fact, I noticed a significant decrease in performance…

…until I finally upgraded Cursor to its current version. The new agentic approach powered by Claude 3.7-sonnet has been significantly better, outperforming my expectations by far. It is far better with large contexts, tying together multiple systems, and behaving in predictable and desirable manners.

I’ve also experimented with Bolt, v0, lovable, and Replit. While I’ve snagged a few front end pieces from each, I’m yet to fully embrace the full “one-shot” builders. I’m hesitant to necessarily say they’re not ready though, as my experiments with them have admittingly been small experiments, not a full fledged dive allowing my workflow to warp to the tool versus the alternative.

There seems there are even more AI tools I’m yet to experiment with, each with a workflow from what I’m used to. I hope to sometime in the near future dive further into these. To that end I will be attempting a pause on my Cursor subscription this month; at that point I will embrace Windsurf for a small while, see if I enjoy an increased velocity or novel capabilities. I am also very much looking forward to the Replit sponsored SDx hackathon later this month; I want to see what in my workflow approach I am doing wrong to fully enjoy the tool.