I’ve recently received a request to put Meta’s new Muse Spark 1.1 model through the agentic game creation test, to see how it would perform. If I am remembering right, Muse Spark 1.1 was released recently, in early July.
Meta’s blog post states “Muse Spark 1.1 is a multimodal reasoning model built for agentic tasks, with major gains in tool and computer use, coding, and multimodal understanding.”
Among other things (coding for one), Muse Spark 1.1 was trained specifically for orchestration and delegation to sub-agents. The blog states: “[Muse Spark 1.1] tackles complex projects significantly faster than Muse Spark, as it is trained to orchestrate multi-agent systems to optimize end-to-end latency. As the main agent, it can gather context, make a plan, and delegate execution across parallel subagents. As a subagent, it adheres to its job, understands available tools, and knows when to escalate back to the main agent.”
The blog post also states that “[Muse Spark 1.1] zero-shot generalizes to new native tools, MCP servers, and custom skills.”
Did someone say zero-shot? Have I got a prompt for you, Muse Spark 1.1…
A reminder, my one-shot autonomous agent game generation prompt is an offshoot of my older, explicit orchestrator/sub-agent framework, and a model that can do the orchestration itself and delegate to sub-agents should do well at the task.
So, I tried it out…and it did quite well! Using Muse Spark 1.1 served from Open Router, and in the Visual Studio Code GitHub Copilot plugin, it designed, coded, and tested a nice and competent game. Its token cost was among the more reasonable, only about $25, significantly less than Opus and Fable, and more on par with Grok 4.5.
An interesting side note: it spent a significant amount of time and tokens on what seemed to be the initial implementation and feature design; however, once the design was set, it seemed to speed-run the implementation of the 25 new features. My guess is that the features were all being orchestrated and implemented in parallel (when possible) by subagents the LLM spooled up; however, this is just a guess.
Another side note: this was probably the most context heavy run of all the models. Most of the models finished the task with a context in the mid to high 200K token range; Muse Spark 1.1 finished with a context of 437K tokens, significantly more than most of the other models. I’d be interested in looking into why this might be.
All in all, for this autonomous workload, I would put Muse Spark 1.1 in the same (subjective) neighborhood as Grok 4.5, GPT-5.5 and GPT-5.6 Sol, Sonnet 5, and Kimi K3.
As always, the controls are <c> to insert your coin, <1> to start a 1 player game, arrow keys for turning and thrusting, <space> to fire, and <h> for hyperspace.
Last month, Sakana AI released their Fugu LLM, to headlines such as ‘Sakana AI launches Fugu Ultra and its orchestration model matches Fable and Mythos without training a single frontier model’ and ‘Sakana Fugu Ultra promises to deliver “the very best frontier-level performance” by… using frontier AI models.’ YouTube was full of videos asking ‘Is Fugu better than Fable?’
Example of headlines on YouTube when Fugu was released.
Early coverage framed it as “one model to command them all,” a way to match Anthropic’s Fable/Mythos-level results through collective intelligence rather than a single giant model, while also reducing dependence on any one provider or export-control risk. In short: multi-agent coordination packaged as a single model interface.
What may have gotten lost in the hype is what Fugu actually IS. Fugu is a family of “learned orchestrator models”. It is trained as a language model whose job is to read a query and dynamically construct an agentic scaffold (selecting workers, assigning roles, verifying outputs, synthesizing the answer) over a pool of frontier models. The user interacts with it as if it were a single model, but the heavy lifting is done by the orchestrated team.
You talk to it like any other model (OpenAI-compatible API), but under the hood it reads the query and dynamically builds the scaffold and pool of workers, deciding which models to call, what roles to give them, how to verify intermediate results, and when to synthesize the final answer. There are two variants:
Fugu – prioritizes everyday usability and lower latency; it often routes to a single strong worker.
Fugu-Ultra – prioritizes maximum quality on hard, multi-step problems and can spin up richer multi-agent workflows with isolation and shared memory.
Of course, I had to add it to my autonomous agent game creation test.
I tried out “Ultra” because I didn’t know what to expect, and I wanted to give Fugu the greatest chance of creating a playable game.
Some results:
Fugu Ultra ran for a LONG time, compared to even the most powerful “uni-models”. I didn’t expect this, and so I didn’t think to time the run, but it was probably an hour or more. This in comparison to strict Fable or Opus runs (~45-50 minutes) and Grok 4.5 (~20 minutes). Note that Fugu Ultra is designed to spin up multi-agent workflows to solve the problem, and can easily spin up multiple ‘heavy lift’ models that may take a long time to synchronize and complete the task.
Fugu Ultra is EXPENSIVE, somewhere in the $70-$80 range to design and implement the prompted game in my test, similar to strict Fable or Opus. Again, it was likely spinning up expensive models to do a lot of the work. For some reason I had not expected this (duh!), and ended up feeding OpenRouter multiple $20 token buys to get the job done.
Fugu Ultra is INTERESTING in what aesthetics and features it designed. The look and the play of the game it produced has a distinct style, so much so that the style can distract from gameplay; however, I like it in that its style is unique vs. the other LLMs that I’ve tested. For example, it chose to use what looks like swirling confetti for its explosions instead of a more vector graphics look!
What’s really interesting to me is, I don’t know what LLM Fugu outsourced the aesthetics and game design decisions to, and it’s a really unique style. I’m not sure that a second run would recreate that same style. (I’d try a second run as an experiment if the token cost wasn’t so high)
The runable (browser based) version of the Fugu Ultra game (as well as the games from the open model LLMs) can be found here (arrow keys to turn and thrust, space to fire, h for hyperspace):
A friend of mine recently asked me to test my current autonomous LLM game generation pipeline with a few of the latest open weight models. I used Visual Studio Code GitHub Copilot and it’s “Bring Your Own Key” capability to hook it up to the various models on my Open Router account.
I was a bit underwhelmed by the models I tested (DeepSeek 4 Pro, GLM 5.2, and Kimi K 2.7-code), at least for my game generation prompt. It should be noted that, just because one run on my workload resulted in underwhelming results, that doesn’t mean that these models wouldn’t do well on someone else’s coding workload, or even on another run.
Interestingly, just as I was wrapping up my testing, Moonshot AI released Kimi K-3. The drop of K-3 instantly generated a lot of hype, and it was being compared to Opus and Fable on various social media (especially YouTube). I wanted to test it out on my own workload.
Easier said than done, as when I submitted my prompt to Kimi K3 through OpenRouter, the prompt would “bounce”, with a message that seemed to be indicating that K3 was being throttled due to excessive demand, and indicating that I should “try again”. After trying at various hours throughout the day and evening, I resigned myself to waiting until the initial demand had dissipated before trying agin.
Rate limit message from Kimi-K3
Luckily the next day I was able to run the prompt, and lo and behold Kimi K3 generated a nice, playable game autonomously. The game it generated is comparable to ones generated by Fable, Opus 4.8, Sonnet 5, Grok 4.5, and GPT-5.6 Terra and Sol. It didn’t seem to be particular speedy, but cost-wise it produced the finished game for ~$13, as opposed to ~$50-$80 for the Anthropic models.
From my tests, I would rank Kimi K-3 below Fable, Opus 4.8, and Grok 4.5, but above Sonnet 5 and GPT-5.6 Sol and Terra, which is quite amazing. In fact, the rise in ability of Grok 4.5 and Kimi K-3 in these types of tests is truly amazing. A reminder, the prompt has the agent implement AND design the features, so I am not only testing coding, but the “aesthetic” ability of the models.
Run-able versions of the open weight models’ generated games on a new website.
The run-able versions of the games generated by the open weight models can be found here:
I still like the design chops of Fable, but I can’t justify its cost for a project like this. I’ve run some tests with using Fable for explicit game design and project scaffolding, and then switching to Grok 4.5, GPT-5.6, or Sonnet 5 for the code implementation and testing phases, all in the same context. The initial results are encouraging, and I’ll post another blog post soon with the details.
Things have been moving fast. SpaceXAI dropped Grok 4.5 and OpenAI released GPT-5.6 Sol, Terra, and Luna. For me the real surprise was Grok. I’d been using it mostly for research and had never reached for it on coding work — Claude Sonnet had been my default for that. My quick-and-dirty way of checking whether a model was actually useful was a one-shot prompt that asked it to build a browser-based clone of the 1979 vector Asteroids game.
I picked that old game for a few reasons that still hold up. It touches on a lot of what makes games interesting: graphics, basic physics, sound, scoring, projectiles, collisions, and even a little destruction effect when the ship blows up. At the same time it’s simple enough to describe without locking the model into every last detail, so you can see what it decides on its own for the ship, rocks, and UFO. JavaScript is one of the languages these models have seen the most of, and the whole thing runs in any browser with zero install.
Back in March 2026 I ran Grok 4.1 through that same prompt and it was pretty rough. Asteroids came out warped, the ship thrust sideways instead of forward, you’d hear the shot sound but nothing actually appeared on screen. It was one of the weaker results I got at the time.
Grok 4.2 AutoGPT-5.3 Codex
Around then I started putting together a multi-agent setup — an orchestrator that would hand work to a planner, then a coder, then an auditor, and so on — so the models could build a small game feature by feature.
Before I had the whole thing tuned the way I wanted, the newer models (Grok 4.6 and GPT-5.5 among them) got noticeably better at just working through things on their own. They could plan, implement, test, and even come up with the next feature without constant hand-holding. I ended up shelving the orchestrated pipeline for a while.
The other big jump has been in vision. These models are now decent at looking at a screenshot of the game and checking whether what they think is happening actually matches what’s on screen. That self-verification step removes a lot of the back-and-forth that used to require a human in the loop.
To keep testing meaningful I put together a tougher prompt. On top of just building Asteroids, the new version asks the model to:
Follow SOLID and KISS/YAGNI principles where they make sense.
Use test-driven development for the pieces where it’s practical.
Avoid the usual LLM shortcuts when it comes to testing.
Wire up Playwright so it can actually verify graphics, physics, and on-screen behavior.
Support custom game modes (single-step mode has been especially useful for debugging).
Come up with 25 new features that feel fun and replayable, then plan, build, test, and commit each one to git in sequence.
Some of the games available on the game examples web page, generated by LLMs.
I’ve put the playable game results from the latest runs here, in a game examples web page:
Example of a game designed, generated, and tested by an LLM
Every game is a full browser version of Asteroids with 25 new features the model designed itself. I’ve also included two earlier builds (the ones with the gold background) that only had 20 features. I liked the feel of those two enough that I wanted to show what was possible with this single prompt pipeline.
Lastly, I’ve included the prompt used to make these games at the bottom of the game examples web page. Feel free to use it as inspiration for your own prompts.
One practical note: running these long agentic loops eats tokens, especially once the context grows. Grok 4.5 and GPT-5.6 Terra Medium were both quick — around 15-20 minutes per game — and Grok barely touched my monthly token budget. Claude Sonnet 5 took an hour and forty minutes and ran up $112 in tokens. Opus 4.8 and Fable sat in the middle at about 45 minutes and roughly $60 each.
The frontier models have reached the point where they can keep iterating on a messy, ambiguous problem until it’s done. The older and smaller models still tend to stop after the first pass and wait for you to say “go do the next feature.” I’m planning to run a side-by-side on the single-agent approach versus the older orchestrator setup to see how they compare on speed, cost, and context size.
Giving the model a way to look at its own output visually has been one of the bigger practical wins. The more ways you can let it check its own work, the fewer obvious mistakes make it into the final build. The long-term interesting question is whether these models can eventually judge whether a feature or a whole game actually fun. That’s going to take more experimentation.
I was able to test Anthropic’s newest welterweight class model, Sonnet 5, on the one-shot “make me a game” agentic prompt I had developed in my last post. I’ll go into more detail about the prompt after the Independence Day holiday,.
Briefly, the prompt instructs a single agent in a single context to create a basic Asteroids game, then come up with 20 fun new features all on its own, and iterate on them one by one, using Playwright autonomously to take screen captures of the game to validate the graphics.
I’ll analyze the pros and cons of the one-shot prompt versus the much more structured feature orchestrator agentic pipeline I had been using before the (on again, off again) release of Claude Fable. I’ll also discuss some interesting observations and challenges I noted while watching Sonnet 5 iterate on the features, including intuitions on what fully agentic LLM pipelines may struggle with in the future, and the high token and money costs of fully agentic flows such as this one.
But for now, I present you Claude Sonnet 5’s solution.
The attract screenBasic GameplayBonus modes designed by the LLM.More bonus modes designed by the LLM.
We’ve all heard that AI is going to eliminate our jobs, especially those in “knowledge work”, and more specifically those of us in software engineering.
I got to thinking, how real is the possibility? I only started to use LLMs for coding projects in November 2025, seven scant months ago. In that time, I’ve seen LLM based coding tools go from barely able to one-shot code an old 1979 arcade game (Asteroids ’79) to being able to pair code (with myself) sophisticated tools, back-ends, and game clients.
Still, I wondered, how close are we to having LLM based tools design, code, and test a simple, yet fun game all on its own; a completely autonomous game generation system.
I’ve spent the past 3 months of my off-work time working on such a system.
It is glorious. I have fully autonomous agentic pipelines, complete with a state machine “orchestrator”, orchestrating development agents such as a “planner”, a “coder”, and adversarial “code auditor” and “test auditor” agents. These separate agents minimize common LLM coding mistakes, emphasize DRY, SOLID, and YAGNI, and TDD principles, and minimize well-known LLM test cheating patterns.
I also enforce rigid policies on context separation, in order to keep each agent working on clear and concise tasks to minimize task drift. The system uses successive development, having the orchestrator task out each feature in sequence, so that the agents can implement each feature as a step-wise addition to the previous features, in the hopes of keeping the agents focused one feature at a time.
There are also a lot more blah blah blah principles and features of the pipeline, rooted in LLM research and simple blood and sweat of trial and error.
In a word, it is quite fabulous. And it works.
To a point.
I still have to generate the feature specs “offline” and paste them into the pipeline, by prompting a web based chatbot of the LLM model, as feature generation wasn’t implemented in my pipeline. And each “run” of a feature, including the planning->coding->auditing flow often gets stuck in a loop, with the auditors screaming that “just one more thing” needs to be fixed, with the coder dutifully implementing each revision, burning token after token. And sometimes the agents “drift” or even go completely off the rails, examining each other’s configuration files, and even each other’s prompts, in order to be “helpful” despite prompts telling them not to.
A reminder, a paraphrase of Captain Barbosa from “Pirates of the Caribbean” : “Prompts are more, what you’d call ‘guidelines’ than actual rules. Welcome aboard the Black Pearl Miss turner.”
Life is good, and although I still have a way to go to get Github Copilot, Claude Code, or OpenAI Codex to design, build, and test a simple game from beginning to end, I’ve learned a lot about designing, implementing, and testing these fully autonomous agentic pipelines.
However…
An interesting thing happened during the past month or two.
The LLM harnesses got better. And the LLM models got more capable. Much more capable. Actually, much MUCH more capable.
I saw it first with GPT 5.5, with its ability to code, stay on task, and use its multi-modal ‘vision’ capabilities. All of a sudden, I had a model that wouldn’t often drift off task, that was really good at coding, and that could be tasked with checking its work VISUALLY. I started having the agents use Playwright (for web based games) and AppGhost (my own custom app for Mac and Windows games) to have the agent visually verify what was on the screen; no more arguments with the agent about if the spaceship was oriented on the screen correctly, or why the entire terrain mesh was outside the 3D rendering area.
With the later versions of Opus and now Fable, I also noticed that the agents could now iterate on their own; no longer did “one shot” equate to “make it all at once”…it could now mean “do these operations once for each feature for these ten features”, and reasonably expect the agent to stay on task until the last feature was planned, coded, and tested fully.
In other words, they could now, on their own, manage the whole pipeline I had spent months architecting and verifying, in one prompt, in one messy but somehow effective context…and in a way that produced a better game implementation, in far less times, with far fewer tokens.
My characterization of “better game implementation” is based on several runs of my old pipeline using various GPT and Claude models (mostly Claude Sonnet and earlier versions of Opus), and one run of the “one-shot” pipeline using Fable, before it was taken off-line.
As far as “far less time”, I’m talking 1-2 hours for the old pipeline, vs. 20 or so minutes for the 1 shot pipeline…and the 1-shot pipeline also includes having the LLM come up with new features and the feature specs each and every run.
I’m not sure about the token counts of each pipeline; my claim about “far fewer tokens” is based on the fact that the “rigid” pipeline generated many artifacts (coding plans, testing plans, auditing reports, etc.) that seemed to take quite a while to generate (reasoning tokens), tokens that maybe, just maybe, didn’t contribute to the end product as much as I would have liked.
I’ll be experimenting with the new “pipeline”, refining the testing requirements, etc. and examining the output (code, architecture, tests) vs. what was produced by my old pipeline.
Honestly, besides the models and harnesses becoming more capable, the biggest change towards fully autonomous game generation has been giving the agent “vision”, so that it can verify on screen what it thinks it “knows” about the game.
Lastly, one of the insights I’ve learned is that one of the biggest hurdles to a fully autonomous game generation system is something that I’ll call “aesthetics”. Not necessarily what is beautiful, although “aesthetics” can encompass that. By “aesthetics” I mean the emotional aspects of art in general and games in particular; what makes a game exciting, frustrating, boring, interesting to look at? Is the story engaging, are characters relatable? Does the story have irony, pathos, love, heroism, duty, sacrifice? An LLM can be prompted to approximate many of these qualities, but humans seem to be far better at creating and judging (gating) these aspects of a game, as they have been trained on these qualities literally all of their lives.
It’s been almost 2 years since I last posted to this blog. Since then, related to the world of software development, two things have changed:
I’ve been working at a large game company as a Senior Software Engineer on one of the technology teams; it’s nothing short of a total blast working with the people on my team.
The dawn of agentic coding has arrived on the software scene, and knowledge work in general.
It’s terrifying and exciting times to be a software developer. I hope to document each step along the way.
(Skip to the bottom if you just want the ‘fun stuff’ 😀)
I started using coding assistants only recently. In November 2025, a coworker demonstrated GitHub Copilot in Visual Studio Code to team members. He was amazed at how quickly he could get working prototypes up and running. At first, I found it interesting, but as his demonstration continued, I too was amazed.
Over November and December, the team independently started experimenting with coding assistants to see what they could do, what mistakes they would make, and what best practices we could devise for using them in production code.
In December, we shipped our first AI-assisted project, weeks ahead of schedule and with better documentation than in many prior projects.
By January and February 2026, we were routinely using AI to explore and document older codebases, as well as to assist with maintenance and modernization efforts. We even began investigating ways to use AI for reducing accumulated technical debt…ironically, since one common complaint about AI agents is that they add to a codebase’s technical debt.
As of March 1, 2026, all team members use coding AI as an efficiency tool at work on a daily basis. All of us are also pursuing side projects after hours, experimenting with AI coding agents to push their limits, identify where they fail, and determine where they add value. Several of us have created functional web apps that solve problems we had long intended to address through traditional development but never found the time for. And all of us are working on passion projects—mostly game-related—that would traditionally require several programmers and artists weeks to prototype, but which can now be prototyped in several nights by one software engineer.
In the coming weeks, I will present some of these passion projects. I will also provide posts on the best ways to fully and deeply understand—or “grok”—Large Language Models and agentic AI, so that you do not have to treat them as black boxes and can more fully grasp their problems and usefulness in software development.
Additionally, I will blog about the societal implications of the rapid rise of AI in general, with thoughts on how to not only survive but thrive, and what to teach your children to do the same.
For now, I will present a one-off project that I created on a whim last night. A coworker was investigating realistic gem rendering on screen. He considered existing game engines, including Godot and Unity, to give him a head start. He used AI to assist, but he was not getting the results he wanted in Godot, so he switched to Unity. He reported decent results—this was Friday, two days ago—but I suggested he look into web-based technologies, as they would provide cross-platform availability out of the box.
That night, I wondered how easy it would be to create a web-based gem renderer. While half-watching TV with my wife and daughter, I decided to have Claude Opus and Claude Sonnet help me develop one. I wrote a detailed software specification, omitting the rendering technologies so that the AI could recommend them. I then had Claude Opus create the plan and Claude Sonnet execute it.
The result is a web app, using three.js, that renders gemstones in real time:
Example (screenshot from live gem animation)
The live website has the gem autorotating in real time, and you can use the mouse and mouse button to see different orientations of the gem. You can also switch gems (Diamond, Sapphire, Emerald, Citrine) and change some of the render parameters using on screen sliders.
It’s important to note; without human intervention, the concept of “aesthetics” often eludes the current LLMs, especially in a coding context. They are getting better at it, but often developers will have to coach the LLM, “move it more to the left so that it is inline with the text above”, or “the spaceship looks like a rectangle, it was supposed to be a chevron”, or even “that gem looks like *ss, lets brainstorm ways to fix it.”
This is where I believe that humans will always be needed. We may get to a point where AI can render a movie for a consumer ‘on the fly’, but will it be good? Coherent? Watchable for more than 5 minutes?
This came into focus when I tried to work with an LLM to convert the web based gem renderer to Vulkan, to run natively on MacOS and Windows. The first attempt looked like *ss. And, after several hours of re-prompting and coaching, it still looked like *ss. However, I have a plan on how to get the LLM agent to convert the renderer to Vulkan in a way that exactly replicates the WebGL renderer. Stay tuned.
And, all else being equal, I like doing the “more funner” things more than the “just plain fun” things.
However, sometimes priorities dictate doing the “just plain fun” things first. This is one of those times.
For the next few days I’ll be putting my React learning on hold, and dig up my Java programming knowledge from back in my Amazon Prime days. You see, I have an interview coming up with a Java-based shop, and I’ve been told that their coding challenges and questions are strictly Java based.
I’m currently reviewing some of my old Java code (an example of which can be found here : https://github.com/rgmarquez/redis2-codechallenge) to revive the Java side of my brain. I need to be reminded of the the syntax, coding conventions, and concurrency best practices that I used to have in the forefront of my mind, before C#, C, and JavaScript pushed it all to the back.
My next post will most likely be my thoughts on going back to Java after the joys (non-sarcastically) of C# and JavaScript async/await programming. I do remember that with Java SOA, we tended to use dependency injection a lot more than I’ve see in C# or JavaScript. I also remember that DI was particularly useful for decoupling dependencies, and for test mocking with little to no change to the target system.
Hmmm, maybe I should do a future blog post describing Dependency Injection, to what extent each developer community (Java, C#, JavaScript) seems to use the DI paradigm, and why.
But for now, on to refreshing my knowledge of Java syntax, memory model, concurrency, automated tests, and best practices…
In looking at my list of projects, it occurs to me that in order to make a dent in most of them, I really should step up to the plate and learn the front-end Javascript framework “React”. I’m choosing to learn React over something newer (like Svelte) as a great deal of companies use React, and I’m trying to make myself more marketable as well as learning front end coding for my projects. A two birds with one stone kind of thing.
I actually know a bit of front-end coding with both straight HTML/CSS/JavaScript and with Angular.js, but again, I believe that React knowledge will get me farther with prospective employers, while allowing me to create professional quality front-ends for my projects.
I’ll be using Mosh Hamedani’s “Code with Mosh” videos to learn React. In theory, his videos will be building my knowledge up to coding a “production grade app for discovering video games”, and I like the idea of learning enough about React to be able to code such an app:
If you are interested in Mosh’s coding courses, they can be found here: https://codewithmosh.com
There are so many development topics I want to cover, it’s hard to know where to start. I want to talk about projects I’ve worked on in the past (there have been a LOT of them), but I also want to talk about new projects that I am curently starting.
I’m currently unemployed right now (and looking for work!), but in my now expanded spare time, I’m trying to learn some new technologies, while bolstering my knowledge of some things I’ve done in the past.
So perhaps I’ll start off with my list of current projects that I’m starting, in no particular order:
All of these projects will be hosted on GitHub (actually, some already are), and as I work through development, I’ll be making sure to update the source code and supporting files there.
Incidentally, I do tend to keep a lot of notes and lists, from lists of projects I’m working on, to lists of features (specs?), to lists of bugs that I’d like to fix, to cool features that I’d like to add. When I’m working professionally, Iuse Atlasian’s Jira (https://www.atlassian.com/software/jira) to keep track of projects, features, and bugs. However, for my simple experimental projects, I tend to use simpler tools like Apple’s “Notes” app when I’m working strictly on my Mac Laptop, or Evernote when I will be switching between my Mac laptop and my Windows laptop.
For example, the “spec” for my gift/wishlist app looks like this:
It should be noted that I like to develop my own apps in stages, starting with a “minimal viable product”, or even a “prototype”; I don’t want to start with every feature that can be imagined, I want to start with something that will be useful, while uncovering issues that I may not have thought about when starting a project (the “unknown unknowns”). Additionally, I like to start small to make sure that the product is something useful, to make sure that I am “building the right product” as well as “building the product right”.
For now, one of the main goals for these projects is learning new things, and reinforcing old skills, so for many of them, “usefulness” (other than as an example for others) may not be a major concern. 😉
The Developer Chronicles 