Just as the Nobel Committee recognizes the importance of complex systems research today, we at @FabricRisk are building upon these insights to build a platform that captures the dynamics of financial risk in all its richness and complexity.
Today is a great day for the Complex Systems community. Giorgio Parisi has been awarded the Nobel Prize "for the discovery of the interplay of disorder and fluctuations in physical systems from atomic to planetary scales. "
Lets unpack that a little 1/n
https://t.co/UUviwq05NF
This hits hard. I see a lot of myself in this as well. Lots of ideas that seemed interesting that I have worked on by myself. But always second guess and not share with the world.
Lesson: just do the work. Release it. And forget about it.
I'm going to be vulnerable for a moment and admit that working on your own project is hard.
Unless you have an extreme belief in your vision, fighting self-confidence issues takes a Sisyphus toll on you.
It's not imposter syndrome, "Am I good enough?"
It's more like, "Does the world really need this?"
Only if you're an oracle or have a ton of experience can you predict what will be a banger. Most of the time, it's just dumb luck. Or right timing. Or leveraging hype waves. Or recycling past success when everyone has already forgotten about it.
But great execution could also be devastated by misfortune equally well.
When you work with someone else, and everyone believes in a project, at least you have some indication that more than one person might actually like it.
When you're alone, you might as well be the only person who cares about what you do.
Absolutely! Working on supply chain risk at the moment. Knowledge graph construction is essentially a network building operation. Much better to use the network properties for traversal rather than use something like a dedicated graph database.
why i avoid graph databases
the question: "is knowledge graph rag production ready? should we use it?"
the answer: after 10 years in ml, i stay away from graph databases. every company i've seen go into the graph world moves back to sql within 4-5 years.
the issues are real:
hard to hire talent (easier to find postgresql experts)
schema definition creates endless debates without clear best practices
most use cases need only 1-2 traversals, not complex graph operations
even facebook's "graph" was actually a large mysql database. the only company that truly needs graph databases is linkedin for 3-5 degree friendship calculations.
even for microsoft's document graph approach - i'd rather use fine-tuned embeddings. a graph is just an adjacency matrix, and fine-tuning can get you close to that similarity definition without the operational complexity.
start with your data: let specific use cases justify graph complexity rather than choosing technology first. graph might be 2% better, but traditional approaches working well means that 2% rarely justifies the maintenance cost.
why i avoid graph databases
the question: "is knowledge graph rag production ready? should we use it?"
the answer: after 10 years in ml, i stay away from graph databases. every company i've seen go into the graph world moves back to sql within 4-5 years.
the issues are real:
hard to hire talent (easier to find postgresql experts)
schema definition creates endless debates without clear best practices
most use cases need only 1-2 traversals, not complex graph operations
even facebook's "graph" was actually a large mysql database. the only company that truly needs graph databases is linkedin for 3-5 degree friendship calculations.
even for microsoft's document graph approach - i'd rather use fine-tuned embeddings. a graph is just an adjacency matrix, and fine-tuning can get you close to that similarity definition without the operational complexity.
start with your data: let specific use cases justify graph complexity rather than choosing technology first. graph might be 2% better, but traditional approaches working well means that 2% rarely justifies the maintenance cost.
@MajinBoson @TokenOfTheMonth Yeah some of the numbers, even from YC funded startups, are not making much sense. Low base salary is expected for startups but founding engineer (like 1st hire, 2nd hire) and the equity is also super low.
@drmtgr@thesard1319 Je pense que “how china escaped shock therapy” de Isabella Weber montre comment la chine a pu devenir le super pouvoir. C’était pas totalement planifié ni totalement capitaliste.
Was recently talking with @jxnlco and @jeffreyhuber about how AI requires engineers to deal with probabilistic functions, and how data scientists are perhaps better prepared for building with AI…
On that note, I’ll be thinking about this HN comment for awhile…