Ask HN: What are you working on? (March 2025)

Hi teleforce, thanks! Your project sounds very interesting as well.

That actually reminds me, at one point, a researcher suggested looking into geophone or fiber optic Distributed Acoustic Sensing (DAS) data that oil companies sometimes collect in Alaska, potentially for tracking animal movements or impacts, but I never got the chance to follow up. Connecting seismic activity data (like yours) with potential effects on animal vocalizations or behaviour observed in acoustic recordings would be an interesting research direction!

Regarding data access:

Our labeled dataset (EDANSA, focused on specific sound events) is public here: https://zenodo.org/records/6824272. We will be releasing an updated version with more samples soon.

We are also actively working on releasing the raw, continuous audio recordings. These will eventually be published via the Arctic Data Center (arcticdata.io). If you'd like, feel free to send me an email (address should be in my profile), and I can ping you when that happens.

Separately, we have an open-source model (with updates coming) trained on EDANSA for predicting various animal sounds and human-generated noise. Let me know if you'd ever be interested in discussing whether running that model on other types of non-stationary sound data you might have access to could be useful or yield interesting comparisons.

Our labeled dataset (EDANSA, focused on specific sound events) is public here: https://zenodo.org/records/6824272. We will be releasing an updated version with more samples soon.

We are also actively working on releasing the raw, continuous audio recordings. These will eventually be published via the Arctic Data Center (arcticdata.io). If you'd like, feel free to send me an email (address should be in my profile), and I can ping you when that happens.

Hi d_burfoot, really appreciate you bringing that up! The idea of pre-training a big foundation model on our raw data using self-supervised learning (SSL) methods (kind of like how GPT emerged in NLP) is definitely something we've considered and experimented with using transformer architectures.

The main hurdle we've hit is honestly the scale of relevant data needed to train such large models from scratch effectively. While our ~19.5 years dataset duration is massive for ecoacoustics, a significant portion of it is silence or ambient noise. This means the actual volume of distinct events or complex acoustic scenes is much lower compared to the densely packed information in the corpora typically used to train foundational speech or general audio models, making our effective dataset size smaller in that context.

We also tried leveraging existing pre-trained SSL models (like Wav2Vec 2.0, HuBERT for speech), but the domain gap is substantial. As you can imagine, raw ecoacoustic field recordings are characterized by significant non-stationary noise, overlapping sounds, sparse events we care about mixed with lots of quiet/noise, huge diversity, and variations from mics/weather.

This messes with the SSL pre-training tasks themselves. Predicting masked audio doesn't work as well when the surrounding context is just noise, and the data augmentations used in contrastive learning can sometimes accidentally remove the unique signatures of the animal calls we're trying to learn.

It's definitely an ongoing challenge in the field! People are trying different things, like initializing audio transformers with weights pre-trained on image models (ViT adapted for spectrograms) to give them a head start. Finding the best way forward for large models in these specialized, data-constrained domains is still key. Thanks again for the suggestion, it really hits on a core challenge!

Hi Caleb, thanks for the kind words and enthusiasm! You're absolutely right, audio provides that crucial omnidirectional coverage that can supplement fixed field-of-view sensors like cameras. We actually collect images too and have explored fusion approaches, though they definitely come with their own set of challenges, as you can imagine.

On the labeled audio data front: our Arctic dataset (EDANSA, linked in my original post) is open source. We've actually updated it with more samples since the initial release, and getting the new version out is on my to-do list.

Polli.ai looks fantastic! It's genuinely exciting to see more people tackling the ecological monitoring challenge with hardware/software solutions. While I know the startup path in this space can be tough financially, the work is incredibly important for understanding and protecting biodiversity. Keep up the great work!

Hey thePhytochemist, cool tool! Yes, spectrograms are fundamental for us. Audacity is the classic for quick looks. For systematic analysis and ML inputs, it's mostly programmatic generation via libraries like torch.audio or librosa. Spectrograms are a common ML input, though other representations are being explored.

Enhancing frequenSee for scientific use (labelling/analysis) sounds like a good idea. But I am not sure what is missing from the current tooling. What functionalities were you thinking of adding?

Thanks! Appreciate it. Your work looks very interesting too, especially in the distributed systems space. Cheers!

Nice work, this looks better than the established players in this space! You've got some customisation options in yours that I'd love to implement in mine as soon as time permits. Huge props for not forcing people to sign up for an account to use it!

If you could add custom templates, you could let the community add the country specific things

Thanks for the feedback, a notes section is a great idea!

Yeah it'd be cool to consider this approach for some other domains. Sometime soon I'll make it so you can change the word in the top-left from "INVOICE" to "QUOTE" or "RECEIPT". The nice thing about an invoice is you know there's going to be a relatively small amount of data, so storing the state in the URL is a plausible approach (even if it looks obscene to discerning users ).

Currency selection is on the roadmap but the need for deductions wasn't on my radar - thanks for the tip!

In Italy, many of these invoicing challenges have already been tackled through a nationwide standardized system.

Every invoice—whether B2B or even B2C (receipts included)—must be sent electronically using a government-defined XML format. This invoice includes predefined metadata and is digitally signed by the issuing party. Once ready, it gets submitted to the national tax agency’s centralized system, called the Sistema di Interscambio (SdI), which validates and registers it before forwarding it to the recipient.

This system essentially acts as a clearinghouse: it ensures all invoices go through the same format, are verifiably issued, and are automatically recorded on both ends. For consumers (B2C), the invoice still goes through the same pipeline and is made available in their personal portal on the IRS website, while the seller can still email a copy (PDF) for convenience.

This centralized and machine-readable approach has eliminated a lot of the fragmentation seen elsewhere. There’s no vendor lock-in, no OCR, and no AI needed to parse PDFs—just a signed XML file going through a common pipeline. It’s not perfect, but it shows how much smoother things can be when the rules (and formats) are defined at the infrastructure level.

>First major disconnect is that not every country uses invoices, but may use receipts instead. This is true for the USA for example, so many US devs (for example: Stripe in the early days) are not familiar with the concept of invoicing. Technically there is no difference between receipts and invoices, so if you're not familiar with the concept of invoicing, just read this post with /s/invoice/receipt in mind.

I find this hard to believe. An invoice is a request for payment. A receipt is proof/confirmation of payment. Invoices sometimes double as receipts (or rather the other way around) when the payment is made immediately. But how can a country not have something that represents a formal request for payment by some future time?

I don't even understand this from an accounting perspective. What would accounts receivable and accounts payable even mean without this distinction? How would you date the respective journal entries if there is no distinction?

While all well and true. Some scale is assumed.

There’s plenty of need for basic use invoicing like this. Generate an invoice as a way to bill someone or serve as an estimate for work/project cost. Not everyone is at a place where it needs to be so formal and integrated into a complete solution that tracks the dollars from invoice to balance sheet to income statement, etc. It’s a lot especially for people that are just freelancing and need a similar probably infrequent way to send a bill. They probably are just tracking things in a spreadsheet and not even big enough to use quickbooks or anything else. It would be a poor use of time and over engineering to put that all in place and setup things that cost subscription dollars in perpetuity just to bill for a one off charge. Or even a handful of them.

When I think of people I pay, my lawn guy and my housekeeper both just text me how much I owe them. Then I zelle them. They both have dozens of clients at least and I imagine they are doing this way for them all. If I were a business, in May insist on getting an invoice to load the AP into an accounting flow from my end but they wouldn’t really want to change their system of doing things just to comply with my request. So, they may want something like this that just basically converts the text message info into an official looking invoice.

I feel the real problem is everyone is assuming this side project type thing to solve every edge case that exists in the world. Even the bigger guys like stripe. That’s the wrong take. They offer a solution, you have to evaluate of it fits your needs, if not, use something else. If you’re in a locale that mandates something completely different, use something else. This project is being very transparent about what it does and how it works, which should help you out if you have a requirements list to compare it to.

>choose your own accounting software, do not be tempted to hire an external bookkeeper that keeps the books in 'their' systems (accountant lock-in).

~30 years ago I worked at a very small business (3 employees) and they used and liked Quickbooks. The accountant convinced them to switch to some "better" system and for around 3 months they had no idea how much money they had, they just lost all visibility into the system because it didn't work in the way they expected. "If things didn't look right, we'd just go through every screen in the system and press Post." At the end of those 3 months they realized they had unexpectedly gotten into $70K in debt -- this was ~35 years ago when a house was around that much. They had to take a second mortgage on their house. Eventually, they figured out the accounting system, righted the ship, and paid back the second mortgage over a few years. Y2K really helped, with that giant bump in sales.

What accounting software would you recommend for first-time entrepreneurs? Are their any open-source solutions that can be self hosted that integrate with existing solutions?

I am just starting my journey into entrepreneurship, and have yet to choose a bank or accounting software, and would appreciate guidance. I am based in the UK, and will only be conducting business in the UK to start off with.

I know the URLs are long compared to what people are used to. Part of the rationale for putting this out there in the current state is I'm curious if people in practise will share long URLs or not.

At some point I'd like to add shortlinks but at the moment everything is clientside, there's no persistence at all (beyond localStorage). I think that's a nice feature from a security perspective.

Not too short or it's too easy to guess.

The app might be stateless right now (I haven't checked); if it is, adding a URL shortener will break that.

Ha, I was going to say is that url query string a sha256 or something.

By default, href values of <a> tags are removed, because they add significant token length without adding more context. Coming soon, you can specify a request header to set whether or not you want links removed from the response. Those underscores you mentioned are from the italics.

You found the easter egg!

Thanks! Still a work in progress :-)

Much appreciated!

Flaresolverr most probably

Since I was just looking at this accidentally, here are some examples of how to query at a ~cent-per-query cost level (just examples but quite illustrative): https://commoncrawl.org/blog/index-to-warc-files-and-urls-in...

With AWS Athena, you can query the contents of someone else’s public S3 bucket. You pay per read, but if you craft your query the right way then it’s very inexpensive. Each query I run only scans about 1MB of data.

Wait 3 months for me to finish.

So far it's basically just a Django server. You're responsible for self hosting ( although I imagine I'll put up a demo server), you can define your own cards.

You can play the game by literally just using curl calls if you want to be hardcore about it.

I *might* make a nice closed source game client one day with a bunch of cool effects, but that's not my focus right now.

Thank you. Yes she’s been enjoying them a lot now that we don’t have to repeat the same stories every night :) I’ll share on here once I’m done and also the right up.

There is nice list url shortener API https://publicapis.dev/category/url-shorteners and worth try it.

Will do. Hn has no way of tagging people or following up though so I’m not sure how to share once I go live. But if you shoot me a message I’ll update you once done. amrangaye at gmail dot com.

Not yet as I’m still finessing it to her needs. She has a problem getting rid of thumb sucking, and finally asked for a fairy tale with a thumb sucking princess who eventually stops sucking her thumb lol. It’s a fun activity and also letting me learn about LLMs.

:D

Thanks! I haven't used Pulsar, but the general answer is that mentions.us is focussed on sending you alerts for notifications, whereas more sophisticated social listening tools provide a lot more analytics (e.g., sentiment analysis).

If your company just wants alerts when their keywords are mentioned on social media then mentions.us should work great for them. If you work for Coca Cola then you likely need something very different from your social listening tool!

We’re hooking up to the APIs - the goal is to alert you of mentions as quickly as possible, so waiting for Google to index results would introduce (much) too much lag.

Interesting feature request! I’ll have a think on it.

Hey Julien! I’ve seen you advertising KWatch in lots of places, assume you’re connected to it / know the founder?

For LinkedIn monitoring we use the voyager APIs. It’s not perfect because it gets posts but not comments, but it’s pretty good.

Thanks for the feedback! For saved terms we show you the number of matches we’ve notified you about, which always starts at zero, whereas during creating we show you how many you would have matched. That’s a confusing UI and I should improve it

I put more details in a reply to another comment, but basically I think the number of people willing to pay for email alerts is small, so I’ve made the service free for them. It’s only teams who want Slack notifications who have paid plans.

I’m not optimising to extract every possible $ from the market with that pricing strategy. Instead I hope it will maximise the number of users whilst breaking even on costs.

I think most of the people who sign up for email alerts would never pay. Lots of them are indie hackers or folks with a side project - I've been there, and know how price sensitive those communities are. I'd rather they use the service for free than not at all - I get valuable feedback from that, a marketing boost if they tell others about it, and the validation of having built something other people use.

I do have a paid plan for people who want Slack notifications, and I think those folks ought to be happy to pay. My hope is that I'll eventually get a few paid signups and that those will cover the costs of the service (which are minimal).

I know I lose a bit of revenue with the above approach, but it's a tradeoff I'm happy to make.

I have! It was one of those "I should have moved when I had the chance" moments. But such is life as a developer.

Interesting to hear. One big thing I had to address was the fact that she lives in a rural area with very slow internet access and I don't. So I built in and option for us both to select a movie and a time for the date and it would pre-download a high-quality version.

Hi! I can't really share the company, but I do love the space and happy to discuss what I've been reading.

So the idea of Knowledge Tracing originated, from my understanding with a paper in 1994: http://act-r.psy.cmu.edu/wordpress/wp-content/uploads/2012/1... this sort of introduced the idea that you could model and understand a students learning as it progresses through a set of materials.

The concept of Knowledge Components was started, I believe, at Carnegie Mellon and University of Pittsburgh with the Learn Lab: https://learnlab.org/learnlab-research/ - in 2012 they authored a paper defining KLI (Knowledge Learning Instruction framework): https://pact.cs.cmu.edu/pubs/KLI-KoedingerCorbettPerfetti201... which provided the groundwork for the concept of Knowledge Components.

This sort of kicked things off with regards to really studying these things on a finer-grained level. They have a Wiki which covers some key concepts: https://learnlab.org/wiki/index.php?title=Main_Page like the Knowledge Component: https://learnlab.org/wiki/index.php?title=Knowledge_componen...

Going forward a few years you have a Stanford paper, Deep Knowledge Tracing (DKT): https://stanford.edu/~cpiech/bio/papers/deepKnowledgeTracing... which delves into utilizing RNN(recurrent neural networks) to aide in the task of modelling student knowledge over time.

Jumping really far forward to 2024 we have another paper from Carnegie Mellon & University of Pittsburgh: Automated Generation and Tagging of Knowledge Components from Multiple-Choice Questions: https://arxiv.org/pdf/2405.20526 and A very similar paper that I really enjoyed from Switzerland: Using Large Multimodal Models to Extract Knowledge Components for Knowledge Tracing from Multimedia Question Information https://arxiv.org/pdf/2409.20167

Overall the concept I've been sort of gathering is that, if you can break down the skills involved in smaller and smaller tasks, you can make much more intelligent decisions about what is best for the student.

The other thing I've been gathering is that Skills Taxonomies are only useful in as much as they help you make decisions about students. If you build a very rigid Taxonomy that is unable to accommodate change, you can't really adapt easily to new course material or to make dynamic decisions about students. So the idea of a rigid Taxonomy is quickly becoming outdated. Large language models are being used to generate fine-grained skills (Knowledge Components) from existing course material to help model a students development based on performance in a way that can be easily updated when materials change.

I have worked through and replicated some of the findings in these later papers using local models, for example using the open Gemma 2 27b models from Google to generate Knowledge components and using Sentence Embedding models and K-means clustering to gather them together and create groups of related Knowledge Components. It's been a really fun project and I've been learning quite a bit.