pip install transformers

transformers chat Qwen/Qwen2.5-0.5B-Instruct

Dead on arrival. Thanks for playing, Ollama, but you've already done the leg work in obsoleting yourself.

This is a new umbrella project for llama.cpp and whisper.cpp. The author, Georgi Gerganov, also announced he’s forming a company for the project as he raised money from Nat Friedman (CEO GitHub) and Daniel Gross (ex-YC AI, ex-Apple ML).

Not sure if this is just a good faith support.

I personally found Ollama to be an easy way to try out local LLMs and appreciate them for that (and I still use it to download small models on my laptop and phone (via termux)), but I've long switched to llama.cpp + llama-swap[2] on my dev desktop. I download whatever ggufs I want from hugging face and just do `git pull` and `cmake --build build --config Release` from my llama.cpp directory whenever I want to update.

1: https://www.ycombinator.com/companies/ollama 2: https://github.com/mostlygeek/llama-swap

https://programmablesearchengine.google.com/controlpanel/cre...

And then it's just a GET:

    import os
    import json
    from req import get

    url = "https://customsearch.googleapis.com/customsearch/v1"

    def search(query):
        data = {
            "q": query,
            "cx": os.getenv('GOOGLE_SEARCH_API_KEY'),
            "key": os.getenv('GOOGLE_SEARCH_API_ID')
        }
        results_json = get(url, data)
        results = json.loads(results_json)
        results = results["items"]
        return results

This is almost exactly how duckdb/motherduck functions and I think theyre doing an excellent job.

EDIT: grammar and readability

* Work with somebody like System76 or Framework to create great hardware systems come with their ecosystem preinstalled.

* Build out a PaaS, perhaps in partnership with an existing provider, that makes it easy for anybody to do what Ollama search does. I'm more than half certain I could convince our cash strapped organization to ditch elastic search for that.

* Partner with Home Assistant, get into home automation and wipe the floor with Echo and its ilk (yeah basically resurrect Mycroft but add whole-house automation to it).

Each of those are half-baked, but it also took me 7 minutes to come up with them, and they seem more in line with what Ollama tries to represent than a pure cloud play using low-power models.

Like I had Codex + gpt-5-codex (20€ tier) build me a network connectivity monitor for my very specific use case.

It worked, but had some really weird choices. Gave it to Claude Code (20€ tier again) and it immediately found a few issues and simplifications.

1. Access to specific large open models (Qwen3 235b, Deepseek 3.1 671b, Llama 3.1 405b, GPT OSS 120b)

2. Having them available via the Ollama API LOCALLY

3. The ability to set up Codex to use Ollama's API for running tools on different models

I mean, really, nothing else is even close at this point and I would rather eat a bug than use Microsoft's cloud.

However, searching for "Yandex git sources magnet link" might help.

Fast forward to now, open models are quickly catching up, and at a significantly lower price point for most and can be customized for specific tasks instead of being general purpose. For general purpose models, absolutely the closed models are currently dominating.

This is the play. Its only a matter of time till they do it. Investors will want their returns

We will continue to monitor what's good to improve the output quality and results. Sometimes it could be the combination of providers to yield even better results. If I say one combination right now, and realize another combination is better, and make changes, I wouldn't need to broadcast it each time or risk misrepresenting the feature, which is to have amazing search and research capabilities that can augment models for a superior output.

This is why everyone needs to get every flavour and speedrun building all the tools they need when the infinite money faucets are turned off.

At some point companies will start raising prices or moving towards per-token pricing (Which is sustainable, but expensive).

And with that in mind, i definetly dont use more than a couple of bucks a month in API refils. (not that i really am a power user or anything)

So if you consider the 20 bucks to be balanced between poer and non power users, and with the existing rate limits, its probably not that far off being profitable, at least on the pure inference side.

Gotta pay those VC juicy returns somehow.

> Dear API user, We’re excited to launch the Perplexity Search API — giving developers direct access to the same real-time, high-quality web index that powers Perplexity’s answers.

Not particularly. Indexes are sort of like railroads. They're costly to build and maintain. They have significant external costs. (For railroads, in land use. For indexes, in crawler pressure on hosting costs.)

If you build an index, you should be entitled to a return on your investment. But you should also be required to share that investment with others (at a cost to them, of course).

Or is it about sharing the domains of mirrors?

And by the way I prefer Google's approach in this particular case

Zuckerberg strikes me as far too adaptive, too fair weather

Thanks! please do!

You'd figure out a replication strategy based on observed reliability (Lindy effect + uptime %).

It would be less "5 million flaky randoms" and more "5,000 very reliable volunteers".

Though for the crawling layer you can and should absolutely utilize 5 million flaky randoms. That's actually the holy grail of crawling. One request per random consumer device.

I think the actual issue wouldn't be the technical issue but the selection. How do you decide what's worth keeping.

You could just do it on a volunteer basis. One volunteer really likes Lizard Facts and volunteers to host that. Or you could dynamically generate the "desired semantic subspace" based on the search traffic...