dig1

> It wouldn’t surprise me if Apple simply isn’t aware that this has changed in its app.

Yet they were pretty quick to change Lake Ontario to Lake America. OTOH, if Apple has no idea what its own app is serving, that shows poor state of their monitoring system, right?

IMHO, this is a great time to push PWAs even harder. I know they're not a fit for every use case, but most of the apps I saw and/or use would work just fine as PWAs. If Mozilla wants to do the right thing this time, they could reconsider bringing first-class PWA support back across all platforms.

> Over the years, I have implemented parsers numerous times, both at work and for side projects, so writing recursive-descent parsers from scratch has become second nature. Once you understand the mechanics, writing a parser by hand is straightforward

The reason why it became second nature for you is this hand-rolling, manual work. I observed that with math - unless you manually practice problem solving (integrals, differential equations), you might know the mechanics but you'll have hard time solving them. And that knowledge of mechanics will also fade away at some point.

> LLMs have changed the equation. It used to take me 2-3 hours to write a parser for a moderately complex grammar. Now, if I hand an LLM a loosely written, BNF-ish grammar and ask for a recursive-descent parser, it finishes the job in five minutes. At this point, writing them by hand is hard to justify

So, what is the difference between LLMs generated "manual" parsers vs those with parser generators, beside that with LLM that will be done in five minutes + plus some $$ and with parser generators you'll get that for free and under a second?

> Which makes me wonder: what’s the appeal of PEGs or parser generators now? They used to make sense when hand-writing wasn't practical, but what compelling reasons are left to use them today?

The appeal is predictability - use i.e. Bison, feed it with grammar and you'll get always the same output. Not so much with LLMs.

> To be honest, I don't know any companies using OW models at a large scale for their operations (agents or chat assistants).

Perhaps they simply don’t advertise it and investors (currently) love companies that spend heavily on frontier models. That said, OW models, especially when combined with RAG, work quite well, and given the current state of the industry, they may be the only sensible way to keep inference costs under control.

I might be in the minority here, but I suspect all of this is intentionally orchestrated by OpenAI (either directly or through a hired third party) to leave traces online so it looks like the work of ChatGPT or hatever internal LLM they use. The same strategy for a recent HuggingFace attack.

Why? It is a great PR to build a hype, especially before the IPO, showcasing how AI is "self-aware" and dangerous, essentially resurrecting Sam Altman's talk about how only a few should hold the keys to this (opening a route to regulation, which is his ultimate goal).

Also, collusion.wiki was recently registered and it looks too vibe-coded for my taste, so let's see will that domain be alive in a year or two.

> (from Twitter) ...My Google account too valuable to risk it impacted by Antigravity usage

If your account is that valuable, it's time to consider moving to a self-hosted option. Or at the very least, start by purchasing a domain and linking it to your Google account, while keeping offline backups of all your emails and documents.

The real problem isn't that Google might accidentally ban you; it's that there's no way to get a human in the loop to review the issue. My client is an enterprise GCP user paying a hefty sum every month and getting someone knowledgeable typically means wading through Gemini or talking to (non-technical) support who just read GCP documentation back to you; and it can take months something to get resolved. Now imagine the situation for someone paying nothing or a double-digit for their gmail account or workspace.

I'm aware people will complain that self-hosting is hard, insecure, etc. but it's like a backup: an annoying process you truly appreciate when shit hits the fan.

> The houses are better, and they go up faster...

This is highly debatable. They look better, but whenever I inspect the walls and see what is holding the house together, I just shrug and leave my contact info, because I know I’ll be called to fix the mess.

> ...and building them increasingly feels like magic.

Engineering should not feel like magic to those with a proper education. Otherwise, we are merely practicing alchemy.

How rankings are calculated? HelixDB has zero feature scores, unlike e.g. JanusGraph which is at #27 or Neo4j at #1.

> The irony is that this may be a $0 revenue user for Grafana Labs.

Why is that ironic? Since Mimir is open-source, $0 revenue users are expected. AFAIK, Grafana Labs relies heavily on go, typescript, and linux, without necessarily being their top financial contributor. They could have kept Mimir proprietary like Splunk, but whether that would have attracted the same level of adoption or community contribution is another matter.

ctags, GNU Global and even "ugrep -Q" would like to have a few words with you ;)

You can also build a mesh network using standard wireguard. While manual configuration requires exchanging keys and settings between devices, many ansible playbooks can automate this process with minimal effort.