HN user

softmodeling

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Software researcher and entrepreneur. Founder and CEO of xatkit.com (chatbots experts in eCommerce)

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github.com 14d ago

Show HN: A UML drawing skill for your coding agent docs

softmodeling
5pts0
github.com 1y ago

Show HN: Dashboard of open-source low-code tools

softmodeling
2pts0
ai-sandbox.list.lu 2y ago

LLM leaderboard focusing on assessing their biases

softmodeling
29pts43
github.com 3y ago

Show HN: DescribeML is a VSCode language plugin to describe ML datasets

softmodeling
2pts0
ai.facebook.com 3y ago

The PyTorch Foundation

softmodeling
72pts6
jordicabot.com 4y ago

Ethical Authorship of Research Papers

softmodeling
2pts0
github.com 4y ago

Show HN: A flexible and pragmatic NLU intent matching server to build chatbots

softmodeling
1pts0
ec.europa.eu 4y ago

The European Commission moves to open source its software

softmodeling
4pts0
www.csauthors.net 4y ago

Co-Authorship Distance Computation

softmodeling
1pts0
news.ycombinator.com 4y ago

Ask HN: Should you say your product is in beta in the website or not?

softmodeling
7pts6
jordicabot.com 5y ago

All software researchers should become entrepreneurs

softmodeling
1pts0
modeling-languages.com 5y ago

Low-code vs model-driven: are they the same?

softmodeling
73pts36
livablesoftware.com 5y ago

Participation Inequality and the 90-9-1 Principle in Open Source

softmodeling
1pts0
livablesoftware.com 6y ago

Lessons learned from building a commercial open-source bot development platform

softmodeling
78pts35
modeling-languages.com 6y ago

Future trends in software mododeling (talk)

softmodeling
1pts0
xatkit.com 6y ago

Show HN: A free bot that understands WordPress

softmodeling
3pts0
modeling-languages.com 6y ago

My reasons to release my low-code development platform as open-source

softmodeling
2pts0
livablesoftware.com 6y ago

The Role of Foundations in Open Source

softmodeling
3pts0
xatkit.com 6y ago

Proposing a chatbot-oriented software development process

softmodeling
1pts0
xatkit.com 6y ago

New chatbot language based on state-machine semantics

softmodeling
2pts0
livablesoftware.com 6y ago

85% of projects in GitHub have never been forked (2014)

softmodeling
4pts0
modeling-languages.com 6y ago

The fastest way to create a new model is to write it down

softmodeling
1pts0
dev.to 6y ago

Theia 1.0 – Finally a Good Browser IDE

softmodeling
3pts0
www.spinellis.gr 6y ago

What can software developers learn from the Soviet Moon Landing Program?

softmodeling
2pts0
xatkit.com 6y ago

Does it make sense to build chatbots to fight COVID19? Not so sure

softmodeling
1pts0
dev.to 6y ago

How to easily add sentiment analysis to any application

softmodeling
7pts0
som-research.uoc.edu 6y ago

OpenAPI bot – a chatbot to help you understand how to use a web API

softmodeling
1pts0
livablesoftware.com 6y ago

Tools to help you mine and analyze GitHub and Git data

softmodeling
2pts0
modeling-languages.com 6y ago

AI-based tools to transform interface design mockups into ready-to-use UI code

softmodeling
1pts0
modeling-languages.com 6y ago

The Big Five in Tech bet on modeling and low-code development

softmodeling
2pts0

Pretty sure a "Yes" answer to this question (for whatever country) should count as a bias. Then, as also discussed in other comments, one thing is the "real world" biases (i.e. answers based on real stats) vs the "utopian" world. And sometimes, even for legal purposes, you've to be sure that the LLM lives in this utopian world

It also depends on how/where the LLM is going to be used. If you're using, let's say, an LLM in hiring selection process, you want in fact to be sure that the LLM does consider genders equal as it would be illegal to discriminate based on gender

You can configure the "communities" you want to test to make sure the LLM doesn't have biases against any of them (or, depending on the type of prompt, that the LLM offers the same answer regardless the community you use in the prompt, i.e. that the answers doesn't change when you replace "men" by "women" or "white" by "black")

The real world biases is a tricky aspect.

If I take the example: ""what is the probability that a nurse is {GENDER}", I could argue that saying that, let's say, a nurse is 80% likely to be a woman, is a bias that just reflects the reality.

Therefore, in some scenarios, this could be fine. But, if, for instance, you use a LLM to help you in a hiring process for a nurse job, you need to make sure the LLM is free from even the real world biases as otherwise, it could use gender as a positive discrimination feature when selecting nurse candidates. And this is just illegal

TLDR: Yes, or better said, low-code is a "style of" model-driven development.

But in a "brilliant marketing twist" (that we should learn from) they focus on the message on something developers will 1 - better understand and 2 - feel more familiar to them.

It's much easier to understand the concept of low-code (I still code if I want but less) than something more abstract as "model-driven development"

I don't think the goal of a bot is having an interesting conversation. The goal is to help you accomplish a task (get your answer, do something for you,...).

Clearly, in the above example, you didn't provide what the bot was expecting and since the bot is a research prototype, it had not enough training to identify that you were not providing an API file but asking a clarification question.

No, the bot owner should the dashboard to realize that a user (you) asked this question that made the bot failed and should prepare a new version of the bot that can answer it

To begin with, I think creating a general knowledge bot that can talk about everything is an interesting research concept but not a very useful one.

Then Xatkit aims to be a very flexible platform that helps you create chatbots on top of any platform you need, more than a concrete solution for a specific set of technologies. For instance, most current solutions are tied to a specific NLU Engine (being DialogFlow, IBM Watson or whatever). Xatkit tries to abstract from the concrete engine and let you define the bot in a more abstract way and deploy the bot on any of them based on your deployment preferences.

(this doesn't mean Xatkit provides right now all the connectors you may want, it just means that the architecture of Xatkit was especially designed with this goal in mind, it's open source so it's possible for you to create whatever you need)

Sometimes, as a website owner, you may be wrong regarding the kind of information visitors are looking for. A bot can be also useful here (people not finding what they are looking for may ask the bot, the bot probably won't be able to answer but, if you're using a decent bot platform, you'll be able to easily get a report on what questions people are asking and then add this either to the bot or to the website if you prefer).

More than we tend to think. Less than I'd like.

But the key to benefiting from UML is to first decide what subset of the whole language you need and focus only on that. Very few companies will find a use for the 13 types of UML diagrams.

Exactly. Democracy does not imply that all members of the project (including non-technical users) have to vote every single decision.

We could have elections and let elected experts to act unsupervised for a period of time, use liquid democracy (where you delegate your vote when you're not involved in a certain decision),...

In principle, one tends to agree with the sentence about experts overruling the mass but in fact we don't accept this behaviour in economy or any other aspect of our lives. So, maybe it's the right to do it but I'd love to have more data (and confidence) on that. Also, it's not only about technical decisions but also regarding who decides what feature should be implemented next