Hacker News: iforiq

New comment by iforiq in "Show HN: Katharos Functional programming and CSP-style concurrency for Python"

iforiq — Sun, 02 Aug 2026 15:53:04 +0000

Very nice. Any plans to add algebraic effect handlers to this in the near future?

New comment by iforiq in "Karpathy: Let's reproduce GPT-2 (1.6B): one 8XH100 node 24h $672 in llm.c"

iforiq — Thu, 11 Jul 2024 19:30:14 +0000

How much did gpt2 training cost when it came out in 2019?

New comment by iforiq in "Why everything is becoming a game"

iforiq — Sun, 21 Apr 2024 06:27:47 +0000

>> What would it even mean to escape the “system?” How can you do that except death? If you are breathing you are playing some kind of game.

I think yogic/buddhist enlightenment or nirvana is freedom from constraints due to nature.. the solution (as far as I understand) is essentially a state like death or physical non-existence but somehow still fully conscious and absolutely blissful

New comment by iforiq in "Launch HN: Synth (YC S20) – Realistic, synthetic test data for your app"

iforiq — Tue, 18 Aug 2020 13:59:16 +0000

One use case I've seen for this is compliance. For SOC2 and other compliance standards, I think you aren't allowed to use production data for dev/staging environments. An automated way to generate a database with synthetic data would make life much better in such cases.

New comment by iforiq in "Ask HN: What weird or hard problems are you trying to solve?"

iforiq — Sun, 05 Jul 2020 22:45:04 +0000

Love it, subscribed. Is there a feature where we request summaries for particular books?

Flexport donates 60k masks, other supplies to SF healthcare workers

iforiq — Sat, 21 Mar 2020 18:53:09 +0000

Article URL: https://www.axios.com/coronavirus-flexport-unicorn-facemasks-san-francisco-4a803aaa-20af-4750-b14e-7235f779c171.html

Comments URL: https://news.ycombinator.com/item?id=22648917

Points: 17

# Comments: 0

New comment by iforiq in "Startup Ideas"

iforiq — Thu, 16 Nov 2017 20:11:41 +0000

Which one? Paid labs? This is a big problem we've faced too, currently our bookkeeper does it for us, which can get expensive as they charge by the hour.

New comment by iforiq in "The Competitive Landscape for Machine Intelligence"

iforiq — Fri, 04 Nov 2016 17:30:21 +0000

By "generate a model too complex for any human programmer to write" I believe the author is trying to say, to manually create the rules, one by one. Machine generated complex models, even though very complex, can definitely be understood and heavily audited.

One example is when you fit sparse high dimensional models to complex data in a real-time production system. The resulting models may have hundreds of millions to billions of features with non-zero weights, that constantly change as the underlying data changes. It's impossible to "hand-code" such a model from scratch by any reasonable size team in real-time. On the other hand, these hundreds of millions of rules can (and should) be exhaustively analyzed / audited by slicing and dicing both the model feature-weights, as well their performance on the data comprehensively. As an example, the "R" programming language typically creates useful human interpretable summaries for the models it generates.

For reference, I have been involved at Google in building such massive high dimensional models for properties like Youtube, and currently a founder of one the companies in the HBR report (LiftIgniter, YC W2014). Hopefully that doesn't make me too biased to respond.

New comment by iforiq in "Ask HN: Who is hiring? (March 2016)"

iforiq — Tue, 01 Mar 2016 17:56:59 +0000

LiftIgniter (YC W14) - https://www.liftigniter.com | San Francisco, CA | ONSITE, VISA | Machine Learning Engineer

LiftIgniter delivers billions of personalized recommendations and experiences every month, on some of the largest websites across the world. Building machine learning products at this scale is extremely hard, and not well solved by existing academic literature. At the same time, a few extra points in improvement can mean millions in incremental revenue, and the difference between success and failure. So we innovate, at the cutting edge. Our current team of ex-IMO, IOI, Phds from MIT, Stanford, Berkeley, Princeton love the challenges. If you’re excited by the problem and team, we should talk!

Key requirements:

- Strong background in linear algebra, analysis and statistics

- Very strong software design skills

- Solid foundations in algorithms and data-structures

- Good performances in IMO / IOI / ACM-ICPC a plus

- PhD in Math / Statistics / Machine learning a plus

http://www.liftigniter.com/company/ . Email jobs@liftigniter.com