skysight-inc/HackerNews huggingface dataset:
Hello people!

Introducing geniusrise, an agent framework and component ecosystem for building AI agent networks that are as flexible as your team.

landing page: https://geniusrise.ai (fancy but useless)
docs: https://docs.geniusrise.ai (please check this out)
github: https://github.com/geniusrise (for dear devs)

## Thought process

Since the ChatGPT disruption, I've been pondering on what the tooling layer is going to look like for building LLM-interfacing agents. Saw a plethora of tools coming out as we witness here every week. I'd broadly categorize them into the GUI drag-and-drop variety, and the airflow and MLflow competitors pivoting. Not counting experiment dumps.

My opinions kind of differed from these. The thesis that I chose to work with, is based on Conway’s law - the tooling for building agents must accommodate various team, communication structures and expertise available in an org. This scheme could only work if the tooling itself was more of a thin infrastructure glue layer supporting loosely coupled components such that each component could be built, tested, deployed and re-used independently while leveraging the context and expertise of the ones building them. Also these components need to be "Write once, run anywhere".

## Scheme

1. Developers build components relevant to their infrastructure and services.

2. ML engineers and data scientists get to work on data massaging, prompt engineering and model training without having to build data ingestion pipelines from whatever database engineering teams or whatever service product may be using.

3. Devops could deploy these components in their choice of runner (e.g. k8s) and orchestrate with their choice of orchestrator (e.g. airflow).

4. Product, engineers or whoever could take these components and compose them into workflows, experiment, test, then hand over to devops to deploy.

5. Some of these components end up as open source, and benefit the entire community.

Whichever of the above layer is not available, well, that’s where our open source modules kick in
For example, no data science team? Use our standard huggingface components.

Thus, the builders build, the users use, and open source plugs in the holes.

The framework enables EVERYONE, without getting in the way.

## Community and Future

Guys, this is for you, and I want to make it useful. Some feedback would be nice, and it would help me create a better roadmap. I plan to make this framework production ready by December '23.

Having worked alone, and after countless sleepless nights and social sacrifices, I wanted to share this project before I start working on it full time from next week.

Meanwhile I forgot to activate github sponsorships Anyway, I am excited!

There is more in the pipeline - integrations (For example, model management, data governance and quality, runners, etc etc), modularization of the core framework, MOAR components, unit tests for components, etc

Hell, the batch data modules do not even have partitioning schemes yet

As the project grows bigger, I will be open to co-maintainers.

The north star goal would be to build something worthwhile that can be donated to Apache / CNCF.
hehe, that's how I made `m4b-tool`[1] - a command line tool written in PHP (!) using external tools to merge audio files to a single iTunes compatible m4b file.

It started as one single simple script and now has grown into a maintenance hell of a thing, but I still use it on a weekly basis :-)

Unfortunately atm I cannot even afford the time to redirect the github sponsoring income to charities[2], but by the end of the year, this might change.

[1]: https://github.com/sandreas/m4b-tool

[2]: https://pilabor.com/projects/github-sponsors/
Everytime someone says "LLMs got this wrong," I wonder if the LLM is smarter than the tester.

For example, if "Uriah Hawthorne is the composer of 'Abyssal Melodies'" then maybe they are insulting Ms. Hawthorne be describing what she did as abysmal and incorrectly putting it in quotes with capital letters.

Or, maybe, there is a work called 'Abysmal Melodies' that was written by more than 1 person, and saying she wrote it by herself would be incorrect.

Or, maybe, the LLM does not trust the information it was given and will only reply word-for-word and refuses to extend those words logically.
Not to mention hurricanes and tornadoes will likely destroy the rations for those who need them most.
I'm probably not the only one curious to hear more about this :)
> So in that case, what lock in?

The lock in to fossil fuel in Germany and other renewable heavy countries is pretty clear and nobody can deny that. The Russian war was a slap in that fossil fuel oriented strategy, it's time to learn some lessons about past failures.

> The renewables industry is on an astronomic trajectory. From zero to saving the world, and it will succeed where nuclear has failed for 70 years. Precisely the reason why fossil fuels and nuclear are two sides of the same coin: highly dependant on lobbying and corruption.

Haha, may I remind you what happened in Germany and France? The Russian government financed renewable lobbying to keep the dependency on the Russian gas, and unfortunately for us, that worked. Even Greenpeace themselves sold Russian gas as part of their renewable strategy https://en.wikipedia.org/wiki/Green_Planet_Energy.

> But because it already failed, it won't suddenly miraculously succeed.

Germany's renewable strategy failed multiple times already, and every time it fails, they double down financially. We're pretty deep into sunken cost fallacy and I hope other countries aren't stupid enough to follow them in the hole they dug themselves in.

Last time they even wanted to build some new gas pipeline covering southern Europe (but it's only temporarily transporting gas, we swear again), that's what the lock-in of renewable does. https://www.offshore-technology.com/news/germany-italy-suppo...

> Nuclear requires dirty backup during construction which is always very long.

And renewable require dirty backups as long as they exist, easy to see why it's worse and why gas producers are pushing for it instead of nuclear plants.
ASA is very prone to warping, and thus needs an enclosure to keep ambient temp high enough while printing. You might be able to print small parts in ASA with a non-enclosed bedslinger like this, but you will run in trouble for bigger parts.
I would have anticipated that, with a large enough dataset, the latent space would create graph-like relationships. Encoding things many-to-many, one-to-one etc. To my limited understanding this is a surprising find.
Downside of Firefox Sync for password management is indeed its lack of iOS app integration. Sadly, I suspect it is Apple making it impossible to compete.

Upside is that it also syncs to my Firefox on Linux, which Apple’s doesn’t.
looks like they need some prolog in there
> So his "father with money" is a Cuban immigrant? That kind of changes the narrative.

An Exxon engineer is hardly a homeless begger.
An even more popular alternative is Resilio Sync. I still use it because somehow sharing with the tech savvy isn't a big problem, but with the unsavvy is. A GUI and a single string is on the edge of the possible, and I share much more (large, noncode, files) with this group.
Kinda ironic that when you disable JS, the form about HTML doesn't work, despite HTML having built-in forms that work without JS.
Im not sure why this got downvoted, its just an opinion reasonably argued. Of cause this view is prevalent among younger second-hand intellectual class as far as I can observe. We had many historical precedents like that, eg. when Newtonian physics occurred, when quantum 'brain' occured, etc.

Actual researches returns unequivocal no as far as LLMs being anything like our brain or a language model (worth googling for research how NNs are un-able to simulate any functionality of actual brain). In fact, even the NN concept as such is a pretty much a failure as an analogy to the brain. NN was in fact (many have argued) a marketing term more than anything.

Of cause, the underlying phenomenology is worth to question/investigate and research further, in both domains (NN and human learning). The failure of NN analogy to the brain, I would argue, is the same as failure of todays (hyped and false) analogy of CO2 as a greenhouse gas. Nature generally does not optimize as humans to a single metric or a variable. On the contrary, collectively, nature seem to be more concerned with stability/robustness than any individual or a single group fitness. That statements would take too long to unpack, but I take it a better model to start with...(some researches already had began of a decade or so).
You can use ‘case _’ in that case … it’s not a big deal to opt into this default behaviour.
I'd definitely be interested to see some benchmarks of real-world code, once virtual threads and its attendant web frameworks have had a year or two to mature.
I think you’re right on a narrow definition of price and quantity like what Wikipedia gives:

> A natural monopoly is a monopoly in an industry in which high infrastructural costs and other barriers to entry relative to the size of the market give the largest supplier in an industry, often the first supplier in a market, an overwhelming advantage over potential competitors. Specifically, an industry is a natural monopoly if the total cost of one firm, producing the total output, is lower than the total cost of two or more firms producing the entire production.

In theory pricing/output in economics is always “quality adjusted” (which is one of the hard things in inflation series)

Yes, traditionally a natural monopoly is one where if you split them, costs go up. With Google, GP is saying if you split them up quality goes down. Either way there’s “consumer harm”. (I guess very directly if the time to search goes up, the economic price of using Google goes up too)
That’s why I like the Saveitforparts YouTube Channel. He’s the antimatter of those slick YouTube build channels, where everything is perfect.

Just a laid-back bloke building stuff in his garage, without giving a shit what other people might think about it.

https://m.youtube.com/@saveitforparts
You have to do adb tcpip for wireless adb once in a usb mode per boot, I actually just use port forwarding of all pc port to localhost using ssh, I have a bash script using nmap to find ip
## More Links

1. https://github.com/geniusrise/geniusrise - core framework

2. https://github.com/geniusrise/geniusrise-huggingface - hf modules

3. https://github.com/geniusrise/geniusrise-openai - openai modules

4. https://github.com/geniusrise/geniusrise-listeners - streaming data input

5. https://github.com/geniusrise/geniusrise-databases - database input

6. https://github.com/geniusrise/geniusrise-prompt-actions - functional integrations (RAG-able and GPT function call-able, WIP)

7. https://github.com/geniusrise/geniusrise-indexing - vectorizing for RAG usecases (WIP)

8. https://github.com/geniusrise/geniusrise-exit-proxy - cached LLM interface with MITM-auditing (WIP)

## Asides

I think the core framework can be AGPL but the modules must be MIT / Apachev2.

I really wanted to create an elaborate example in the guides but could not find time, - something like load and vectorize SNOMED-CT or UMLS and use it to NER / RAG EHR docs. Or maybe a usecase of doctor communicating to patient in another language (a major problem in India), with reverse translation verifying translated output using the KG. These kinds of stuff are soon to come. Or discourse segmentation for better chunking for RAG usecases.

I'm not sure if I should add cyberpunk-ed scientists as banner images. I tried with mathematicians like Voevodsky to Andre Joyal to John Baez, but couldn't. Actual geniuses tend to not be famous, hence SDXL fails I guess.

I plan to also write this framework in scala. The category-theorizing of neural networks is amazing!!! https://github.com/bgavran/Category_Theory_Machine_Learning. I hope Bartosz Milewski approves.

I love Alan Turing, but cuz of "The Chemical Basis of Morphogenesis". It introduced me to the wonderful world of complex systems. Hence, his image as banner.

I'm also working on a cli library called "isomorphic", wraps over argparse and provides cli, api, yaml, json interfaces.

Yes, gradio integration is also underway.

Finally, to huggingface.
Not yet but it’s in the works
> At first glance this doesn't seem that surprising. We often use "is" in a way which isn't reversible. e.g.

They appear to only be testing the 'reliable' cases. There schematic example was fine-tuning the model on "<Fictitious name> is the composer of <fictitious album>", yet having the model be unable to answer "Who composed <fictitious album>"?

In this case, English and common sense force symmetry on 'is'. Without further specification, these kinds of prompts imply an exclusive relationship.

Additionally, the authors claim that when they tested it, the model didn't even rate the correct answer more probable than random chance. This suggests that the model isn't being clever about logical implications.
I don't really get it, either you currently have success on tinder and don't need it or you don't and are throwing away money
Not all relations are order independent, so the LLM just assumed none are, prioritizing not being incorrect over being correct.
> It did make me very hesistant and perfectionist in future projects and took out a certain amount of faith that 'it will work out'.

But that's because you still cared about others. And maybe didn't read enough startup/entrepreneur books or know enough people who did that. Once you know what crap companies willingly release which go on to be successful, it will make you far less perfectionist.

I had the luck of having a few family members in computing in the 70s when I was born, so I heard early on about Allen debugging/fixing the basic interpreter he wrote on a PDP for a processor he didn't have access to at the time on the plane for one of the most important meetings of Microsoft at the time. The absolute and total garbage Oracle released for many years when they started. etc. And those are old stories, but this happens all the time; a lot of products that are launched by big or small companies are so full of bugs that I wonder sometimes if anyone bothered to try them.

I simply stopped caring about perfection as it drove me insane and no-one cares anyway. Even, in line with this article, even if I make something ONLY for myself, I rather just finish it and then change it over time then try to get it in a 'perfect state' from the start. It doesn't make me happy as perfection is not really something that's easy to define; especially in software, it is so incredibly hard to get to anything better than 'ok', that striving for perfection (Dijkstra like) will drive you completely bonkers and nothing ever gets released.
Nomachine,xpra,x2go and vnc
As the article states, corrosive environments cause an acceleration of rust development, and salt water is often a leading cause. Therefore, if you want to prevent rust, you must avoid C.
Undeniable Google Analytics is.
wasn't linkedin bought for like $26 billion? so the humanity has money to spend on buying 2 of these trips but would rather spend that on linkedin. cool
I remember seeing it promoted on Gpay, if I remember correctly.
There are HTTP to Gemini proxies/mirrors like gemini://gempaper.strangled.net/mirrorlist/ or Wikipedia mirror: gemini://vault.transjovian.org/en
It's being downvoted by the AI's. They have feelings too you know...
if you have a lot ctivity on tinder why would you pay 500€ ?
A lot of these require deeper system integration, and this is not, in my experience, cross-platform. I’d rather have to drag my SSH/VPN keys and certs around manually, and have basic password management working across iOS, MacOS and Linux.
Once someone does become an established leader however people seem to be ok with it.
WhatsApp’s Coder Society
Thanks, you're spot on. I appreciate it.
In the real world maybe? I never understood the point of hookup apps like Tinder.
Frankenstein. You might argue that it is a public domain book, but it demonstrates that these LLMs can and do memorize books.

It also works with Harry Potter, but the API behaves weirdly and after producing correct output in the beginning really quickly, suddenly hangs. You can continue generating the correct words by doing more API calls, but it only does a few words at a time before stopping. It clearly knows the right content, but doesn't want to send it all at once.

I think there is some output filtering for "big" authors and stuff that is too famous that they filter in order to avoid getting sued.

I wrote more details about the weird Harry Potter behaviour here: https://news.ycombinator.com/item?id=37614697
When I used Tinder for like 8 years ago when it still was the hot new thing paying was a no brainer, or you drown in dudes.

I guess this is just the same thing but that there are fewer real girls nowadays so you need to pay even more to reach them.

It is a zero sum game. Like ads.
I keep coming back to fossil again and again, despite git having a huge pull because of the easy publishing and collab on github/gitlab.

Just the other day I was starting an exploratory project, and thought: I'll just use git so I can throw this on github later. Well, silly me, it happened to contain some large binary files, and github rejected it, wanting me to use git-lfs for the big files. After half an hour of not getting it to work, I just thought screw it, I'll drop everything into fossil, and that was it. I have my issue tracker and wiki and everything, though admittedly I'll have some friction later on if I want to share this project. Not having to deal with random git-lfs errors later on when trying to merge commits with these large files is a plus, and if I ever want to, I can fast-export the repo and ingest it into git.
There's no inductive bias in the model for it to understand that a particular token is composed of some characters. It _does_ work for commmon words, but it's not super reliable. Transformers are powerful enough to pick up the relationship if it were in the training data, but there's like tens of thousands of tokens, right?

I checked just now a simple letter counting task, it's hilarious: https://chat.openai.com/share/4d4926b1-a6fe-4815-af77-4456c7...
> type systems can be regarded as a weak form of formal verification

Depends on the type system, right? I was recently looking at Idris which is a general purpose programming language but apparently can be used for proofs.
No brain, no problem. Jellyfish learn just fine
It’ll never stop being funny how techies are so literal that they can’t recognize sarcasm and humor. My friend, it was a joke.
Until they fail again, then everyone points and laughs. It's a shame as we are losing a LOT of cycles on this kind of toxic nonsense.
This seems like it would be the most probable, unsurprising outcome. Billions of cells growing inside of you into a complex biological mass with prolific stem cell activity are very, very likely to metastasise.

It would take a great deal of convincing to prove to me that it was unlikely or uncommon.
It's hard to imagine how many people are doing something nobody wants in a busy corporate environment.
No, it's the opposite, $ in customer's account is a liability for the bank to those customers, so the account is credited on +

The banks cash account is debited for balance
This marriage of logic with statistical, ML based, LLM is highly non-trivial (if at all possible) and if it happens it will most likely be called another major "AI revolution".

In fact the current AI mainstream considers this as a research direction to avoid at all costs (they term it "the bitter lesson"). The strategy and rallying cry is roughly translated: you can achieve gee-wheeze results here and now by ignoring these deep problems that stymied generations of AI researchers.
From what I understand...they don't. Everyone that I know who participated in online dating mostly just gave up on it.
https://www.magicearth.com/ has OSM data for navigation and traffic information.
In selected regions „official“ traffic data, otherwise crowd-sourced.
Make a ordered_bag ets table and try to insert several thousand records in one ets call. It will block whole VM (spinning one core at 100%, doesn't matter how many you have) for several seconds. It does this because ordered bag needs to find each key in table as it inserts it, resulting in n^2 complexity and this needs to be done atomically. So for 1 000 keys you have 1 000 000 comparisons during a VM-wide lock. Solution - don't insert so many records in one call into ets table.
LLMs are great at emotion, tone, feeling. I would even go so far as to describe properly prompted LLM output as poetic and spiritual. That’s because of how an LLM works- it’s essentially associative. While I hope the deductive abilities of LLM may develop more in the future, it’s nature means that it will generate output that “feels” logical or appears logical without actually being logical. This really confused people because traditionally machines have been viewed as soulless automatons good at logic but bad at emotion. LLM output is the opposite of our intuitive understanding of machines.
this is a family car. it is NOT supposed to be fast, heck it will spend most of its operating life running in less than 60 KMPH.

and yes, 0-100 KMPH is the standard because KM and not miles is the standard.

0-60 is for those chasing numbers, 0-40, 0-60, 0-100, these are all for those who want to advertise how fast a machine is or for breaking records.

indian roads are NOT for driving at 100 KMPH on a regular basis. highways and expressways but that is not normal driving in india
> make yourself look handsome

Plastic surgery is a lot more than $500 though.
What are the similarities/differences to plumbum? https://plumbum.readthedocs.io/en/latest/
Wet is an adjective
Explain how we get proper controlled studies of a substance like this.
That creates an opportunity to make models that understand mathematical logic.
Tinder isn’t a hookup app for the majority of users. I met my wife on there a few years ago
Except they can play chess better than 90% of humans... Just by luck of course..No understanding.
In other news, my markov chain doesn’t do reverse token lookups.
Trained humans are better at carrying small loads reliably.

Wheeled recon bots are a thing, and have been back during the second Iraq war, IIRC.
> I never understood the point of hookup apps like Tinder.

Access to hundreds+ of interesting people from the comfort of your couch? I've met many interesting women on tinder, that I'd never have met "in real life", including my fiancé.
Recently I was learning the maths of basic orbital mechanics, a topic of which some variations are not extensively covered online.

Something that came up often in my application was wanting to express some orbital elements as vectors rather than scalars, to be able to do some things more concisely (and avoiding having slow trigonometric functions strewn all over my code)

For trying to figure these out, generally GPT-4 performance was poor on accuracy and I was using it just as a tool to look up terms I would plug into google to find a better source that won't confidently lie to me. However sometimes it was doing an admirable job of transforming mathematical terms. This often can be done with just knowledge (like knowing sqrt(2)^2 = 2) and applying transformations on tokens - which is very much in the scope of these AIs. Technically that isn't much logical deduction yet, but there was a few cases where it impressed by stating things like "Since we know vectors K and V are perpendicular, we can ...".

Certainly looked like the beginnings of some basic logical deduction sometimes - even if getting halfway correct answers required me to restart prompts a dozen times each.
What you say is correct but it doesn’t explain why the model fails to capture the semantics of “is”.