I already submitted my take on this to Show HN: https://currantfeed.cc/. Noone gave a shit so it's most likely a very bad idea and certainly a very steep uphill battle. It's basically an Airbnb for websites that by default randomly sorts them and then you can filter by different attributes. Owners have to submit and maintain their "listings". It does have an optional subscription (I'm not sure if it works haven't tested it).
Oh and lame-self-comment, but it's at least partly EU stack: Hetzner and Brevo for hosting and transactional mails, Umami for the non-existing analytics.
As others have said it's just a fraction. I'm in a medium size tech-related company and we have 7500+ in one Github org. We have two orgs, so altogether easily 10K+. Of course most of it is stale, obsolete, sandbox, personal tools, etc. I wouldn't be surprised if Github would have 100K+ internal repos or even more.
No OP but I used to work at a large company with a similar number of repos.
When I left about a year ago, we had just started (after being on Github for almost 8 years) an ongoing project of first archiving old/outdated repos in place, and then moving them to an "archived" sub-org, and waiting to see if anyone complained.
Previously no one wanted to outright delete or remove repos because of the risk that someone somewhere was relying on it, and also there was no actual downside to just leaving them there (no cost savings, no imminent danger other than clutter, etc), so resources were never allocated to do it. There was always something more important to work on.
In an org with a higher floor of engineering management, a proactive program for removing unused or outdated repos would absolutely be expected though I think.
This is a continual fight for me. At nearly every company I've had to compromise on using a graveyard repo for packages within a monorepo, even though git has the whole history already.
The problem with history is that you need to know when to look. If you're looking for some old code that you know existed but you don't know exactly what it was, you can't just browse to go and find it.
Sorry for my ignorance, but then couldn't we build this into NPM itself? So before a package is publicly available it would be quaranteened and checked.
I’m running a local Whisper + Gemma 4 pipeline with a cheap USB mic to extract health related data and potential todos from ambient speech. It doesn’t have to be fast doesn’t have to be 100% correct because if it captures at least a few bits of interesting information that would otherwise go unnoticed it’s still a win.
I'm starting to feel like a parrot, but people seem to forget that software engineering is actually a very narrow slice of the white collar pie. You don't need a mega-model which can reason about 100 000 lines of code when you want to create a nice PPT (which consumed literally hours of your life before) to impress your boss. SOTA models will probably be used for frontier research, complex coding tasks, large scale data analysis, etc. And the average Joe shall be able to buy a pre-configured box with a plug-and-play harness and run medium models air-gapped. Or use such models through cloud APIs dirt cheap if privacy is not a concern.
On the same topic but from a slightly different angle - as SOTA models get more capable, the 'quality' and 'feel' of the experience they provide in each domain is heavily dependent on the reinforcement learning the vendor does for that specific domain. After all, many fields have 100 flavors of "good answers," but the model has to pick one answer.
Benchmarks are not very good at capturing this yet. But it could be the case that DeepSeek v4 Pro is 100% as good as Claude Opus 4.7 at scaffolding a basic Rails app, but absolutely terrible at creating a credible business plan that another businessperson would think is real. That's a made-up example, but you get the point.
The end result will be a lot of people arguing about which model is "better," but "better" depends heavily on the task and how that model was trained to interact with the user for that task. Two users may have very different qualitative experiences using the exact same model, despite the benchmarks.
Creating a nice PPT is actually hard because it requires visual capabilities and so-called "computer use" (really, GUI use) of fiddly proprietary software. The nice thing about the coding case compared to a lot of disparate white-collar work is that it's all plain ASCII text. You can already ask a coding model to create a nice TeX/beamer slideshow (or whatever the Typst-based equivalent is) but whether your boss will be duly impressed by that is anyone's guess.
Tangential, but in our opinion corporate PPTX automation is an unsolved problem, even with Claude for PowerPoint (and it's worse with everything else common out there). Its harness (a) is not tuned very well for corporate use and (b) even if it were, fails to manage the specific business knowledge within each org needed to create effective (i.e. audience tailored) presentations.
I was thinking about this and there are several aspects that can still make this viable. 1) AI labs are incentivised to increase token consumption because literally that's their product. The only thing they sell AFIAK are tokens (and maybe a teensy bit of user data). So if you build a product that is actively reducing token consumption (which they simply cannot do without hurting themselves even if their marketing fluff says otherwise) you'll save large amounts of money for your customers and they'll choose you. 2) Big providers want to funnel every prompt into their servers. If you're in a regulated market or simply don't want to share every detail with an American or Chinese megacorp you are in trouble. BUT open weight models are now quite capable for "small business stuff" and they can be self hosted. If you can bundle this into your service, in other words actually care about their privacy, they will choose you. Even more so if you're in Europe.
they have that incentive until they do not. After you have given them enough data of all your best ideas, products, etc and they use the non-training data you opted to share with them, to create a competing product, then it was no ones fault but your own for being gullible and naive into thinking they wouldn't use your data to compete with you.
The only place I'd ever talk to a machine is my car. Instead of huge flashy screens that distracts and kills thousands of people maybe they could build a buttons + voice agent system that could actually be useful and durable. I hate to tap Waze/Maps/etc. every time when I go somewhere or that I cannot comfortably switch to specific songs en route without risking my life...
I connect my iPhone to my car and it requires Siri to be enabled which I can then use to change songs, Google Maps destinations etc. without having to touch anything.
The Siri voice transcription is pretty awful compared to what I've experienced with ChatGPT though and it's weird going back almost to the pre-LLM world where you have to give such clear sort of computer-coded voice commands.
Critics are (rightly) pointing to the fact that these models are not on par with SOTA for complex coding tasks. But many seems to forget that a large part of white collar office work is Excel crushing, file moving, translating dry legal documents, e-mail drafting, PPT drudgery, etc. These are absolutely doable with 30-35b+ models with the added benefit of keeping company data private.
I think the conclusion is flawed here? Sure qwen3.5 9b is nowhere near the sota models. It's 9b and was made a year ago? Everyone taking about local models is pumped about the models released in April this year. Qwen 3.6 27b and qwen 35b a3b if you have a sad GPU. Those are comparable to sota models, seriously.
Yes... I mean organisations have to adapt to this new working scheme. First they need new processes (maybe borrowed from SW development) that enables them to triage work products on a risk/reward scale. For example my wife works on medical device tenders. It is obligatory to translate every frikkin Word document to our native language which in the end noone will read. Do we use LLMs to do the translation? Hell yeah. For a critical legal document? Eeee. Also I think enablers like speical harnesses shall be developed/improved by keeping these folks in mind. For example to build hooks into the harness that forces the LLM to test/review/sample its output. So yes it's a complex topic, but my point was rather that the inherent capabilities of medium-large-ish open LLMs are sufficient for let's say 70-80% of such office work, and it's a huge market.
Perhaps you can create a compelling UX around it and sell it as a subscription. "Normies" will not be able/willing to build it. You can then patch the model/ship new features around it as it evolves. For example I have built an ambient todo list / health data extractor using Gemma 4 2EB and Whisper. Nothing to brag about but it does fairly decent job even in foreign languages.
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