I tried to find the source code of it but I was unable in finding so

Edit: found it, https://github.com/obsidianmd/pele

Also folks, this is a good reminder to please have a github icon on the top of the page to easily show git/github links if the app is open source, also helps in the fact that I can star your projects and bookmark them more easily :-D

I remember reading about vercel trying to move away from AWS or railway successfully doing so. Their long term end goal still is to migrate away from AWS to capture more juicy margins (or is it? Vsauce music plays)

So with that in mind, has AWS and others realized that providing free credits would be more disruption to actual people paying right now and more loss than whatever Vercel is fighting for?

And if that's the case, then to the investors funding Vercel... Do they know that as well?

Also, I think Vercel has gotten to the point where they are handling spam good-ish but the other alternative railway (not entirely free without credit card yes but still an alternative) still relied on GCP (yes even after their migration/colocation) and GCP's opaque black structures shut down all of railways because of a few accounts doing something malicious.

That is the cost of doing business on someone's else computers and also suppose somebody is creating a new vercel competitor, then they are intentionally targeting themselves as a free alternative and are gonna get free users who intentionally might not pay while facing spam/scam/reputation issues and would need constant maintanence and support. (For example: a guy X creates a scam site targeting Y company at this new Z vercel alternative, the Y company has to contact Z and Z will contact it back and Z has to fix the issues X caused, and there are many more X than what Z can handle)

I am more-so interested though that if Vercel is really a great idea (lure free users etc.) then why aren't others trying the same now and flooding free compute. Is it that the investors who wish to invest in such companies would have better returns in investing in Vercel anyway and have better chances in beating the traditional hyperscalers over establishing a new competitor over Vercel and then over AWS etc.?

If we were to try to arrive at an conclusion or for me to try to learn something new, what could it be?:

Is it that AWS/GCP/Azure are intentionally not becoming a Vercel alternative because they can get away by doing so and its more profitable for them to do so?, and that it's not profitable or even possible for others to do so as well unless they have deep pockets to burn through.

"Hosting a hobby project for free without credit card is tough (rant) & here's how I did it (Hugging face)"[0]

I remember trying to evaluate all the options and trying to find a good enough option and there were very few options aside from Vercel and Render or rewriting your application to fit cloudflare workers.

HF spaces is a good option that I had found back then. Also, I recommend having a separate backend service like supabase with HF as if your server is inactive for 24-48 hours, the storage could get wiped out so backend should be seperated.

One of the biggest things that I am proud of was how I broke the nat of intel tiber jupyter using applying a custom patch of bearssh and before it, using junest and ssh and ppng.io, all because I wanted to play minecraft with my friend without credit card :-D

Nowadays, I have a few cheap vps's which makes things so much simpler but I remember that struggle and vercel/render used to help in most cases thus they are so popular.

So I seriously agree with what you are saying based on my experience as well. Though I have to wonder?: I think that the reason that small companies aren't attempting this is because of reasons of scam/spam, but what reasons are there for AWS and others to not do so.

What are the reasons that you think that they don't already provide such service, or do you feel as if this is the natural continuation of it and soon we are gonna see AWS or large hypercloud provider providing free very small compute similar to vercel/render?

Also thanks for making me remember again the blog that I had written :-D

[0]: https://smileplease.mataroa.blog/blog/hosting-a-hobby-projec...

As Google's Argon and Meta's muse wonder has shown: Even reaching today's SoTA is possible provided you have enough compute, data and the moat for building it is comparatively low and it would still take years to play catch up while burning money for comparatively low point as China open sources its SoTA models that are just few months behind.

Also suppose such a model is built, then what? what matters would be running something valuable on top of that model which can provide top value in it of itself.

Now this is something which is more possible and plausible and where the demand is.

What might matter more is the deployment of open weights models on EU datacenters with proper ZDR or optimzing more on the speed aspect of things.

There is also a factor that EU VC funding can simply not have so much money to infuse similar to how AI labs got injected by money for so long, and also even if they have, the question of "should they?" still arises.

I think that this is sort of a playbook similar to how Apple is operating. I think that it's a good enough and a realistic playbook at that.

Comments URL: https://news.ycombinator.com/item?id=50085781

Points: 1

# Comments: 0

Does this work for Hindi or other non English languages though?

I wanted to recently transcribe Hindi + English (mixed) Audio and I used this model[0] and created a custom transcribe command which can give text over input of mp4 files

This took me roughly around an hour or more or around this time frame for a 90 minute transcription source. I would consider myself to be slightly bored during this phase.

I would really appreciate it if something like the project could be made native for a lot more languages but I think that the approach of embedding models and using them in such a way is pretty crazy. Kudos for it and I will try it out!

I can think of many such use cases where Speech to text of even large audio files becomes near instant. As I think someone on hackernews once quoted whom I am very paraphrasing

An order of one or two magnitude of an already existing thing stops becoming a quantity upgrade but rather becomes a quality upgrade in it of itself as it provides access to workflows which previously wouldn't have been feasible.

[0]: https://huggingface.co/parismitaglobalsolutions/indicconform...

Cloudflare workers wasn’t always an 1:1 option for simple hosting (iirc) and people used to think it was more complicated than vercel etc but it has changed in recent years and right now they have made things smoother and simpler to compete with vercel and others.

Now that AWS has entered into the field as well, given that vercel and others already build on top of AWS. It will be interesting to see how this all goes.

I wonder if more og hyper scalers like say hetzner/ovh and others will also target this audience

I personally once used this where it simply only had pdf ability and not a browser or anything else and I have used TCL to resurrect one of my older pc's into making it just work and I can even install many applications. This is a 12-15 year old Dell inspiron mini with 1gb ram. I also love its aesthetics so much and it is so cute when it runs TCL and I sort of used it as a second monitor to run pomodorokitty for sometime as well (all thanks to TCL!)

I really really love TCL so much, the smallest version is 17mb, I highly recommend giving it a try if you have free time, I feel as if people can really appreciate the intentional minimalism within the project :-D

I do wonder though about the optimizations within it, what could be the most optimal way to achieve such room (ie. knowing which questions to ask)

Yes, learning and tinkering is still really the greatest way to achieve that

but I think what I am talking about can be better simplified with the analogy of a gym: previously what used to be necessary (manual work/labour) but when most people got into information work, even then there was/is a need for it (physical work), then we saw the evolution of machines specifically designed to optimize for it and we got machines specifically designed for this training, which helped push people's body to their absolute limits.

I do wonder if an hyper-optimized environment of learning and for asking questions (or more so knowing the know how on which questions to ask), this whole process might be optimized for it and what that process might would look like is a source of curiosity to me.

A relevant video which talks about similar topics: Bodybuilding for the mind: https://www.youtube.com/watch?v=o0DtxUJ6rAc

I was just trying to port the same website to elixir and I am finding that there are issues within it and also for some reason the agent got stuck at something and then it started writing elixir within the repl and doing something related to ecto.

In my current experience, contrary to what I would've expected (given that elixir is a more mature language than gleam), gleam is surprisingly better, though I will try to do more tests

Some other thoughts that I am having at the moment is that starting a project in gleam or within sveltekit/astro/solidjs and then porting it to gleam can be great if what you are doing is frontend heavy. AI will take time once to learn gleam but then the rewrite becomes much more solid and personally interesting to me

Looks like I have to learn gleam to see how fun & managable the codebase can be :-D

Here is also a comment of mine explaining what it is in more depth as well[1] (I hope this comment doesn't sound as self-plug as that isn't my intention much rather than informing about other chains which can also be useful and more efficient [in this case free! which can help provide more use cases to the people at projecttimestamper and other folks])

If the people at projecttimestamper.org are reading this and are interested, please feel free to contact me if I can be of help as I love the idea and this is a really cool thing that they have done, timestamping has so many good use cases :-D

[0]: https://github.com/SerJaimeLannister/nanotimestamp/wiki

[1]: https://news.ycombinator.com/item?id=43117133

For what its worth, Someone created an text editor application in pure assembly using AI (which is genuinely really cool!)[0]

To test this hypothesis, I tried to add scratchpad functionality to it and I was able to do so with an open source model almost autonomously by just having a single prompt which can have auto-saving, easy way to generate new files and its honestly really minimalist and that's the point while having multiple things as well and I personally really love it :-D[1]

I daily drive my scratchpad application and use it quite a lot

I was actually quite surprised by the fact that it actually worked and the fact that rhun itself can exist.

So it is possible that if AI agentic capabilities grow even further then assembly generation can be possible but I am unsure about preferred about some small use cases requiring pure minimalism.

But nonetheless, I think the way LLM's are trained, programming languages are good enough for them and they can express what they want to accomplish easily and without repeating themselves easily through it. I have thought about creating some more assembly applications but I just think that it would be a more token-intensive process, that's all.

Though pure speculation at this point but if some architecture optimizes for assembly (maybe speculative decoding extremely fine tuned to assembly?) or maybe some other architecture itself (world model?) who can express their ideas in better formats than tokens than perhaps its possible as code written in programming languages is much more token-efficient than assembly

[0]: https://rhun.app/

[1]: https://github.com/serJaimeLannister/rhunpad

I had made a Golang htmx + templ codebase and just out of curiosity I ported it onto gleam using an open weights model (glm 5.3)

Although it struggled a little bit first because of learning some parts of gleam but overall after it searched for documentation and learnt some things on its own, it was able to completely rewrite it in just 1/2 very small prompts which is crazy.

I think phoenix can be great as well and I am doing an experiment to try to port it to many functional languages and just testing which language is the best for it but so far I am impressed by gleam!

Also, I wish for your son to have a good university experience and hope he makes great friendships and connections which help him throughout his life and I wish the best for his future and to enjoy the present as it happens :-D

[0]: https://news.ycombinator.com/item?id=49981023

> At least for now someone still has to decide what to prove and why. Like why are you trying to prove that thing to begin with? Presumably it's a step along some journey, right? Maybe the journey is where you need to start deriving your satisfaction from, then.

Beautifully said. I had been thinking about the same thing and the analogy b/w CS and mathematics and these were some that I had found:

1.) to prove/disprove from the proofs that OAI created, you needed an mathematician to do so and OAI had to withdraw three mathematical proofs.[0]

But it was only because an expert within the field could verify if it was true or not, I feel as if software engineering is the same as well. We are/can be paid to prove/disprove if a software is working as intended or not.

2.) for someone to be that said mathematician who disproved it, he had to learn the basics of mathematics and multiple branches of it to then perhaps specialize in one thing that he most strongly resonated with and within all this learning, there was some struggle definitely involved. They had to learn algebra etc.,

this analogy can also extend to how we teach children algebra/calculations and other things even though we have had calculators for a long time, yet, we teach children how to do calculations because it is still valuable enough and either teaching maths can help them perhaps in future make a mathematician or it can help them be less reliant on simply calculators and more confident on on the spot calculations and help them within this skill.

As knowing calculation has become the norm rather than exception, even though we have calculators. In fact knowing how to do calculation by hand can perhaps better help you write a problem to calculator. Knowing the technical aspects of CS can help you express a problem to AI with much more depth and effectiveness as well.

[0]: https://news.ycombinator.com/item?id=50002650

Yes, for doing extremely mild edits (like just removing uhh's etc.) this might be true

but I sometimes feel as if writing can allow me to shift paragraphs, so if I am writing para 1, para 2, I can shift back to para 1 and write another sentence in it and edit some parts of para 1 to include that point

but when I am doing STT, although I can move towards the other para, I find myself just speaking in a complete flow and just write in para 2 only.

Thus when I ask AI to write, I would prefer it to move the statements to appropriate paragraphs and in just general, create a more comprehensible viewpoint from all the STT text that I had written.

This does generate AI generated text which can be detected as such. Uploading it on blogs makes me feel as if people might read what they might consider "AI slop" and so the ethics part (as I myself don't wish to read AI slop)

the problem with AI written or edited texts is that I am unsure of how much effort the other person has put in (just a single prompt or a detailed thought was put in), and I feel as if, others feel the same way.

Should one try to show the rough draft as well to try to show that it was an effort which was human generated or that human effort was used, but that means having a proper disclosure that it was AI-generated/AI-assisted, which I feel as if offputs a lot people (including me) as because of the above logic, that there's still friction for the user within testing if real effort was put into place and I am unsure how effective sharing drafts of it could be.

I don't want my blogs to be tainted and treated as AI-slop because I care about them so I am unsure of what to do. I have multiple things that I have written which if I pass through AI can create some meaningful blog piece but as it stands, they are rough drafts and I find myself putting low efforts or being lazy in actually editing them myself as well (and potentially putting in multiple hours) when AI can be used to help create a more polished version just as well and get across my point.

Because I have seemingly mixed opinions on it, on one hand, I did put the effort but on the other, the output is AI generated so I am unsure about sharing it with others (because they might think its AI generated)

Do you use it for very small edits (removing just the uhhm's?) or for slightly more edits.

The way that I use it sometimes is that while thinking, I will write something which can sometimes make me feel as if a better re-write can better explain my thoughts or rephrasing it as such. For example. I will think about X topic, connect it to Y, then try to add some more points about X again.

I found LLM's to do a really decent job at generating the final outputs as such, but as I said, I am left sometimes feeling a little confused as to sharing it or not because of it being AI generated and the end user not knowing if I put an actual effort into creation of it or not.

Should I try to share the actual transcript of it as well, I really wish if some good ethics and internet ettiquette could be established about it.