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Anecdotal, but I have used Claude to help me write sections of games that I'm (sometimes) working on in Godot fairly recently (Opus 4.5 iirc). It's been very helpful, and it's been very easy to guide it to do this. It came up with approaches of calculating targeting and movement that I would not have thought of myself.

That being said, Claude does not structure the project in the way someone familiar with the engine would, and just like any 'real' software, if you don't guide it, the output quickly degenerates. For example, stuff that would normally, intuitively be a child item in a scene, Claude instead prefers to initialize in code for some reason. It does not seem to care about group labels, which is an extremely easy way to identify different (types of) objects that should be treated in the same way in certain cases.

The games in the video look like GameJam projects? I'm not good at Godot, and I could probably hack most of them together in a week or so. I imagine an actual game developer could put some of them together in days.

In order to have LLMs build something good with any framework, not just a game engine, you have to steer and curate the output, otherwise non-trivial projects become intractable past a certain point, and you have a mountain of bugs to sort through.

> The Training Data Scarcity: LLMs barely know GDScript.

I've not found this to be an issue. Claude does just fine when you explain what you want. I've never had it hallucinate stuff, and I've barely seen it look at docs. Granted, I've only had it write 1-2k lines of GDScript, but I've never felt like it was spouting complete nonsense.

> To fix this, I built a custom reference system: a hand-written language spec, full API docs converted from Godot's XML source, and a quirks database for engine behaviors you can't learn from docs alone.

This is the point where I feel like this is nonsense (more than what the LLM-written prose would imply). Maybe this is my inexperience talking, but I feel there is no way that this would be better in any way over any alternative. Especially if you just lazy-load stuff at runtime. Godot already has good docs. They should certainly cover much more than whatever you need to make the games you demonstrated. What is the point of making a duplicate version of the docs, when you have the docs right there? If you really think that Claude can't handle GDScript, you can just use C#?

> The Build-Time vs. Runtime State: Scenes are generated by headless scripts that build the node graph in memory and serialize it to .tscn files. This avoids the fragility of hand-editing Godot's serialization format.

Again, maybe that's my inexperience with Godot, but I have no idea what you're talking about here? When you run, you do get a different node tree (and 'state' I guess?) but where does "hand-editing Godot's serialization format" come into this? Why would you ever need to concern yourself with what Godot does to transform your code after you've written it?

> It catches the visual bugs text analysis misses: z-fighting, floating objects, physics explosions, and grid-like placements that should be organic.

Funnily enough, those are all stuff that text analysis should be better at finding. I personally use logs & actually playing the game.

Maybe I'm misunderstanding something, but you can add filters to RSS feeds. What is proposed is pretty much just RSS, except for one specific item. Yes, it's more work on your side, but asking the creator to manage updates for whatever one thing any/every random person is interested in is pretty unrealistic, especially since the people asking for this are going to be explicitly not interested in everything else about the creator.

> There’s no AI to this. No magic. No problems to be solved.

Why would you not involve yourself in the new hotness? You _can_ put AI into this. Instead of using some expression to figure out whether a new article has links to the previous ones in the series / a matching title, you can have a local agent check your RSS feed and tell you if it's what you're looking for, or else delete the article. For certain creators this might even be a sensible choice, depending on how purple their prose is and their preferred website setup.

I'm not sure how valid most of these points are. A lot of the latency in an agentic system is going to be the calls to the LLM(s).

From the article: """ Agents typically have a number of shared characteristics when they start to scale (read: have actual users):

    They are long-running — anywhere from seconds to minutes to hours.
    Each execution is expensive — not just the LLM calls, but the nature of the agent is to replace something that would typically require a human operator. Development environments, browser infrastructure, large document processing — these all cost $$$.
    They often involve input from a user (or another agent!) at some point in their execution cycle.
    They spend a lot of time awaiting i/o or a human.

"""

No. 1 doesn't really point to one language over another, and all the rest show that execution speed and server-side efficiency is not very relevant. People ask agents a question and do something else while the agent works. If the agent takes a couple seconds longer because you've written it in Python, I doubt that anyone would care (in the majority of cases at least).

I'd argue Python is a better fit for agents, mostly because of the mountain of AI-related libraries and support that it has.

> Contrast this with Python: library developers need to think about asyncio, multithreading, multiprocessing, eventlet, gevent, and some other patterns...

Agents aren't that hard to make work, and you can get most of the use (and paying users) without optimizing every last thing. And besides, the mountain of support you have for whatever workflow you're building means that someone has probably already tried building at least part of what you're working on, so you don't have to go in blind.

Easy example is Stripe. You can enable 3DS, and you can listen for 'early_fraud_warning' events on a webhook to refund users & close accounts to avoid chargebacks and all the associated fees and reputation penalties.

If I am in a call with someone using Nomi, can I send a message in the call or wherever to disable it, or will I have to ask the person using it to turn it off?

I don't mean to imply that it's a solved problem; all I'm saying is that in a lot of cases, the "weak initial retrieval" assertion stated by the article is not true. And if you can get a long way using what has now become the industry standard, there's not really a case to be made that BM25 is bad/unsuited, unless the improvement you gain from something more complex is more than just marginal.

I agree.

> "The real challenge in traditional vector search isn't just poor re-ranking; it's weak initial retrieval. If the first layer of results misses the right signals, no amount of re-sorting will fix it. That's where Superlinked changes the game."

Currently a lot of RAG pipelines use the BM25 algorithm for retrieval, which is very good. You then use an agent to rerank stuff only after you've got your top 5-25 results, which is not that slow or expensive, if you've done a good job with your chunking. Using metadata is also not really a 'new' approach (well, in LLM time at least) - it's more about what metadata you use and how you use them.

You're right, my bad. I was looking at the CDN tab and it rounds the traffic cost to 0.

This. Normal AirTags are just fine for tracking your stuff.

> "(thiefs use apps to locate AirTags around, and AirTags will warn the thief if an unknown AirTag is travelling with them, for example if they steal your car)"

The reason this was introduced is exactly because people used AirTags to stalk others. Advertising that your product turns that off is basically targeting that specific demographic.

The index page is a great way to capture emails I'm sure, but I'd like to get an idea of what the product actually does. Regardless, this seems like it could be useful, not just as a way to get a playbook, but also to see how others see your company from the outside.

Small nitpicks: I've registered for a project I'm currently working on (temprary.com), and it seemed to get stuck. Then it refreshed, and I saw an empty page with just the plays / context / powerplays header. I had to click on the 'continue signup' link on one of the emails to have the analysis progress. I've also been sent 3 emails upon registration; 2 of them 'autocorrected' the company name.

From looking at the results, it seems like it makes interesting suggestions, but they feel very 'cookie-cutter', and a little bit off. As an example, it tells me to list the app in the GitHub marketplace (well, something to that effect, at least), and it then also suggests: "Deploy a bot in developer forums to automatically engage with users discussing temporary code management, answering queries, and suggesting Temprary as a solution." - maybe a way to tell the bot to not suggest stuff like that would be good.

I kind of expected a bit more, from the description. I haven't set up social media accounts for Temprary - are you not going to tell me about that, or that I should put some blog posts for SEO, or something else to that effect? Playmaker correctly identifies that there's limited info available about Temprary, and it does a pretty good job of identifying the target market, but it feels like it stops a little bit short of being useful. Are you not going to tell me good ways to build a list for the customer profile that you've identified?

I do however like how everything is presented. I feel like if you give more context to the LLM, and you feed it more info about GTM, Marketing etc, it could come up with better suggestions, and actually present something novel and actionable. I'd be looking at either automating the plans I already have, or finding out some approach that I haven't thought of before; you can expect that I've already asked an LLM about GTM strategies before coming to you. So 'Powerplays' sound interesting, if you could adapt the playbooks and expertise of professionals to whatever I am currently building.

Best of luck.

Not sure if I'm allowed to link to stuff, but you can find a bunch of alternatives to offerings like these (the most basic being google alerts), both free and paid, if you look for them - especially on sites that exist to list alternatives.

Some let you link accounts, some give you lists of platforms they scan. I've specifically mentioned Discord because there's no easy way to discover communities without being invited, so if you can actually discover relevant Discord communities and monitor discussions (beyond the few sites that aggregate some popular communities), then you have a USP, since most products don't do that, at least in any capacity that people think is worth paying for.