Readit NewsLet's see if it pays out.
Also, I’m not sure if it’s similar at OpenAI, but when I was at Google it was much easier to get approval to put an open source project under the Google GitHub org than my personal user.
I'm somehow very confident in this while also being sure that people probably thought very similar things about home radios destroying the youth in the 1920s :D
I've never used a project manager and thought to myself "I want to switch because this is too slow". Even Jira. But I have thought to myself "It's too difficult to build a good workflow with this tool" or "It's too much work to surface good visibility".
This is not a first-person shooter. I don't care if it's 8ms vs 50ms or even 200ms; I want a product that indexes on being really great at visibility.
It's like indexing your buying decision for a minivan on whether it can do the quarter mile at 110MPH @ 12 seconds. Sure, I need enough power and acceleration, but just about any minivan on the market is going to do an acceptable and safe speed and if I'm shopping for a minivan, its 1/4 mile time is very low on the list. It's a minivan; how often am I drag racing in it? The buyer of the minivan has a purpose for buying the minivan (safety, comfort, space, cost, fuel economy, etc.) and trap speed is probably not one of them.
It's a task manager. Repeat that and see how silly it sounds to sweat a few ms interaction speed for a thing you should be touching only a few times a day max. I'm buying the tool that has the best visibility and requires the least amount of interaction from me to get the information I need.
That said, I really enjoy Linear (it reminds me a lot of buganizer at Google). The speed isn't something I notice much at all, it's more the workflow/features/feel.
This isn't entirely about being a beginner or not either. Full fine-tuning without forgetting does really want the training data (or something that is a good replacement). You can do things like LoRa but, depending on your use case, it might not work.
I think the mention of the "horny people" is warranted, they are an important part of the open models (and first to explore the idea of "identities / personas" for LLMs, AFAIK). Plenty of fine-tuning bits of know-how trickled from there to the "common knowledge".
There's a thing that I would have liked to be explored, perhaps. The idea that companies might actually want what -oss offers. While the local llm communities might want freedom and a horny assistant, businesses absolutely do not want that. And in fact they spend a lot of effort into implementing (sometimes less than ideal) guardrails, to keep the models on track. For very easy usecases like support chatbots and the like, businesses will always prefer something that errs on the side of less than useful but "safe", rather than have the bot start going crazy with sex/slurs/insults/etc.
I do have a problem with this section though:
> Really open weight, not open source, because the weights are freely available but the training data and code is not.
This is factually incorrect. The -oss models are by definition open source. Apache2.0 is open source (I think even the purists agree with this). The requirement of sharing "training data and code" is absolutely not a prerequisite for being open source (and historically it was never required. The craze surrounding LLMs suddenly made this a thing. It's not).
Here's the definition of source in "open source":
> "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.
Well, for LLMs the weights are the "preffered form of making modifications". The labs themselves modify models the same as you are allowed to by the license! They might use more advanced tools, or better datasets, but in the end the definition still holds. And you get all the other stuff, like the right to modify, re-release, etc. I'd really wish people would stop proliferating this open weight nonsense.
Models released under open source licenses are open source. gpt-oss, qwens and mistrals (apache2.0), deepseeks(MIT), etc.
Models released under non open source licenses also exist, and they're not open source because the licenses under which they're released aren't. LLamas, gemmas, etc.
I also believe the four freedoms are violated to some extent (at least in spirit) by just releasing the weights and for some that might be enough to call something not open source. Your "freedom to study how the program works, and change it to make it do what you wish" is somewhat infringed by not having the training data. Additionally, gpt-oss added a (admittedly very minimal) usage policy that somewhat infringes on the first freedom, i.e. "the freedom to run the program as you wish, for any purpose".
There is obviously some connection to Llama (the original models giving rise to llama.cpp which Ollama was built on) but the companies have no affiliation.
I believe truly useful AI assistants will use the same tools that humans prefer to use, rather than forcing us to come to it (in the same way truly intelligent embodied AI would use the same spaces/stairs/tools/doors as humans). Email, despite all its warts, still runs a lot of the world.
I think the field could get better at knowing when costs are low (eg sometimes scalability, cheaper to change a database choice than rebuild a bridge) and where the costs are sometimes very high (eg security).