I work in Data Partnerships and this seems like a solid idea. Wanted to know if you're open to a quick chat to discuss a possible partnership. Drop me a hi at ajaytoms99@gmail.com
Dropped you an email!
Readit NewsGRVYDEV commented on Show HN: Telescope – Hassle-free company research scope.quantichq.com/... · Posted by u/GRVYDEV
GRVYDEV commented on Show HN: Telescope – Hassle-free company research scope.quantichq.com/... · Posted by u/GRVYDEV
How do you get around that a lot of company websites are marketing BS and gobbledygook?
You can aggregate data from multiple different sites with the hope that it gives you a true picture of what the company does
GRVYDEV commented on Show HN: Telescope – Hassle-free company research scope.quantichq.com/... · Posted by u/GRVYDEV
Company research is a very important field, and you can buy datasets (D&S), appliances (Bloomberg) and software/Web apps (LSEG/Refinitiv, Factset) for this purpose.
If your purpose is competitive intelligence, then including information from other sides is crucial. What are analysts saying? etc. What are customers saying about products? And a lot of info is in financial reports and SEC filings if your target company is public.
There are also commercial providers that let you register a set of Web domains and they will email you once there is a change anywhere, including the "diff" information (before > after), which is also useful for Competitive Intelligence.
It is not trivial given a company name to find the set of homepages that belong to the company (called the "homepage finding" task in information retrieval, there are US government benchmarks on it, such as at the TREC conference, see trec.nist.gov). Once you have the homepage, it is not trivial to reverse engineer, from the Web presence, the org structure of the company (if large).
One area that I am interested in a lot is finding out where companies are going. For that, you can analyze what people indicate in interviews, R&D talks at more technical conferences, patent applications, acquisitions in unusual areas etc. - at large, this is called "horizon scanning".
There is a lot of money in high-quality datasets about companies for investment management, and there is an under-supply of detailed datasets with systematic coverage of private companies.
Thank you for this thoughtful response. We have the same mindset. This is exactly what we are building. I’d love to chat with you more on this.
GRVYDEV commented on Show HN: Telescope – Hassle-free company research scope.quantichq.com/... · Posted by u/GRVYDEV
GRVYDEV commented on Show HN: Telescope – Hassle-free company research scope.quantichq.com/... · Posted by u/GRVYDEV
Got a sub-ChatGPT precis including gems like 'Their website utilizes cookies to enhance user experience and to better understand their users.'
Honestly not sure what use-case this is intended to support.
Thank you for the feedback. Apologies for the crappy response. This is still super nascent technology and I’m still working on improving the search process. In my eyes the real value here lies in data enrichment from other sources allowing you to get a really deep understanding of a company. I am actively building out that functionality now.
GRVYDEV commented on Show HN: Telescope – Hassle-free company research scope.quantichq.com/... · Posted by u/GRVYDEV
Interesting problem, but it's hard to say what you offer compares to others. You should probably have some examples for visitors to understand the value prop without signup.
Thank you for the feedback! I'll get to work on an About page.
What we are doing is automating bespoke account qualification and research. Say, for example, you are a company that sells software to chemical synthesis labs. Your ideal customer synthesizes more than 5 chemicals. They need to synthesize them in large batches and they need to have more than 10 chemists on staff.
To go out and find those ideal customers, right now, you would need to spend hours upon hours searching and manually qualifying each account. We are going to automate that. By providing more accurate leads we will reduce the amount of time that companies need to spend on prospecting while also increasing the open rate.
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looks super cool, i was planning to do the same thing! are you planning to add hotword detection - instead of mute/unmute? I saw this https://picovoice.ai/platform/porcupine/ but didn't have time to put all pieces together. are you planning to use it or something else?
Id love to add hotword detection! Or, even better, you (or someone in the community) could add it :)
Nice! I'm the creator of Willow[0] (which has been mentioned here).
First of all, we love seeing efforts like this and we'd love to work together with other open source voice user interface projects! There's plenty of work to do in the space...
I have roughly two decades of experience with voice and one thing to keep in mind is how latency sensitive voice tasks are. Generally speaking when it comes to conversational audio people have very high expectations regarding interactivity. For example, in the VoIP world we know that conversation between people starts getting annoying at around 300ms of latency. Higher latencies for voice assistant tasks are more-or-less "tolerated" but latency still needs to be extremely low. Alexa/Echo (with all of its problems) is at least a decent benchmark for what people expect for interactivity and all things considered it does pretty well.
I know you're early (we are too!) but in your demo I counted roughly six seconds of latency between the initial hello and response (and nearly 10 for "tell me a joke"). In terms of conversational voice this feels like an eternity. Again, no shade at all (believe me I understand more than most) but just something I thought I'd add from my decades of experience with humans and voice. This is why we have such heavy emphasis on reducing latency as much as possible.
For an idea of just how much we emphasize this you can try our WebRTC demo[1] which can do end-to-end (from click stop record in browser to ASR response) in a few hundred milliseconds (with Whisper large-v2 and beam size 5 - medium/1 is a fraction of that) including internet latency (it's hosted in Chicago, FYI).
Running locally with WIS and Willow we see less than 500ms from end of speech (on device VAD) to command execution completion and TTS response with platforms like Home Assistant. Granted this is with GPU so you could call it cheating but a $100 six year old Nvidia Pascal series GPU runs circles around the fastest CPUs for these tasks (STT and TTS - see benchmarks here[2]). Again, kind of cheating but my RTX 3090 at home drops this down to around 200ms - roughly half of that time is Home Assistant. It's my (somewhat controversial) personal opinion that GPUs are more-or-less a requirement (today) for Alexa/Echo competitive responsiveness.
Speaking of latency, I've been noticing a trend with Willow users regarding LLMs - they are very neat, cool, and interesting (our inference server[3] supports LLamA based LLMs) but they really aren't the right tool for these kinds of tasks. They have very high memory requirements (relatively speaking), require a lot of compute, and are very slow (again, relatively speaking). They also don't natively support the kinds of API call/response you need for most voice tasks. There are efforts out there to support this with LLMs but frankly I find the overall approach very strange. It seems that LLMs have sucked a lot of oxygen out of the room and people have forgotten (or never heard of) "good old fashioned" NLU/NLP approaches.
Have you considered an NLU/NLP engine like Rasa[4]? This is the approach we will be taking to implement this kind of functionality in a flexible and assistant platform/integration agnostic way. By the time you stack up VAD, STT, understanding user intent (while allowing flexible grammar), calling an API, execution, and TTS response latency starts to add up very, very quickly.
As one example, for "tell me a joke" Alexa does this in a few hundred milliseconds and I guarantee they're not using an LLM for this task - you can have a couple of hundred jokes to randomly select from with pre-generated TTS responses cached (as one path). Again, this is the approach we are taking to "catch up" with Alexa for all kinds of things from jokes to creating calendar entries, etc. Of course you can still have a catch-all to hand off to LLM for "conversation" but I'm not sure users actually want this for voice.
I may be misunderstanding your goals but just a few things I thought I would mention.
[0] - https://github.com/toverainc/willow
[1] - https://wisng.tovera.io/rtc/
[2] - https://github.com/toverainc/willow-inference-server/tree/wi...
[3] - https://github.com/toverainc/willow-inference-server
[4] - https://rasa.com/
Hey! First of all thank you for this really detailed response! I am very new to the voice space and definitely have a TON to learn. I'd love to connect and chat with you sometime :)
I totally agree with you about latency. It is very very important for use cases such as a voice assistant. I also think there are use cases in which latency doesn't matter that much. One thing I think I may have understated about S.A.T.U.R.D.A.Y. is the fact that, at it's core, it is simply an abstraction layer over vocal computing workloads. This means it is 100% inference implementation agnostic. Yes, for my demo I am using whisper.cpp however there is an implementation that also uses faster-whisper.
I also want to call out that I have spent very little time optimizing and reducing the latency in the demo. Furthermore, when I recorded it I was on incredibly shoddy WiFi in northern Scotland and since the demo still depends on OpenAI a large chunk of the latency was introduced by the text-to-text inference. That being said there is still a ton of areas where the latency in the current demo could be reduced probably to the neighborhood of 1s - 1.5s. This will get better in the future :)
I want to touch on something else you mentioned. GPUs. I intentionally tried to avoid using any GPU acceleration with this demo. Yes, it would make it faster BUT I think a large part of making this kind of technology ubiquitous is making it accessible to as many clients as possible. I wanted to see how far you can get with just CPU.
In regards to your comments about NLU/NLP I have not dug into using them in place of LLMs but this seems like an area in which I need to do more research! I am very far from an AI expert :) I have a bunch of ideas for different ways to build the "brains" of this system. I simply have just not had time to explore them yet. The nice part about this project and demo is that it doesn't matter if you are using an LLM or an NLU/NLP model, either will plug in seamlessly.
Thank you again for your response and all of this information! I look forward to hopefully chatting with you more!