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cr4zy

1,613 karma

gmail cquiter @crizCraig : deepdrive.io

Posts140
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status.upwork.com 2mo ago

Upwork unable to payout contracts for over 24h

cr4zy
6pts1
nationalsecurityresponse.ai 1y ago

My Response to Superintelligence Strategy

cr4zy
1pts1
polychat.co 1y ago

Show HN: Chat with multiple LLMs: o1-high-effort, Sonnet 3.5, GPT-4o, and more

cr4zy
62pts33
github.com 2y ago

Show HN: Python lib to run evals across providers: OpenAI, Anthropic, etc.

cr4zy
8pts1
www.youtube.com 3y ago

Tesla AI Day stream (starting in 4 hours)

cr4zy
8pts0
www.roadtovr.com 4y ago

Meta VR prototypes aim to make VR 'indistinguishable from reality'

cr4zy
328pts571
github.com 4y ago

Parallel Hashmap

cr4zy
3pts2
www.lifespan.io 5y ago

Biohackers Perform First Plasma Dilution Experiment on Humans

cr4zy
2pts0
jack-clark.net 5y ago

Import AI 228, US AI Regulations and More

cr4zy
1pts0
graphics.wsj.com 5y ago

Battling Infectious Diseases in the 20th Century: The Impact of Vaccines (2012)

cr4zy
2pts1
smooth.deepdrive.io 6y ago

Smooth Operator – Human-like self-driving with reinforcement learning

cr4zy
2pts0
www.youtube.com 6y ago

Barack Obama: Intro to Deep Learning – MIT 6.S191

cr4zy
5pts0
deepdrive.voyage.auto 6y ago

Show HN: A self-driving leaderboard

cr4zy
5pts1
www.technologyreview.com 6y ago

Moon Elevator

cr4zy
1pts0
www.nist.gov 7y ago

NIST recommendations for AI standards [pdf]

cr4zy
3pts0
www.nature.com 7y ago

The organic universe (2017)

cr4zy
11pts0
github.com 7y ago

Dark Reader - a dark mode for every website

cr4zy
1pts0
deepdrive.io 8y ago

Rebuilding Deepdrive on Unreal Engine

cr4zy
2pts1
www.ncbi.nlm.nih.gov 11y ago

Resveratrol and Clinical Trials (2013)

cr4zy
23pts2
continuum.io 11y ago

Dependency solving with SAT for Anaconda

cr4zy
1pts0
blog.clari.com 11y ago

Starting up at a startup

cr4zy
2pts0
sites.google.com 11y ago

Deepmind releases source for DQN

cr4zy
2pts0
www.nature.com 11y ago

DeepMind: Human-level control through deep reinforcement learning

cr4zy
2pts0
plot.ly 11y ago

Plotly

cr4zy
2pts0
www.jetbrains.com 11y ago

Jetbrains C/C++ IDE – CLion

cr4zy
1pts0
qz.com 11y ago

A cheaper way to capture carbon with baking soda capsules

cr4zy
1pts0
vision.stanford.edu 11y ago

Linear Classification Loss Visualization

cr4zy
9pts0
vimeo.com 11y ago

Show HN: Happy Holidays from Clari

cr4zy
1pts0
webdocs.cs.ualberta.ca 11y ago

Speculations Concerning the First Ultraintelligent Machine (1965) [pdf]

cr4zy
15pts1
longbets.org 11y ago

Long bets

cr4zy
4pts0

For compression and long-running agents, may I suggest https://memtree.dev. We offer a simple API that compresses messages asynchronously for instant responses and small context leading to much higher quality generations. We're about to release a dashboard that will show you what each compressed request looked like, the token distribution between system, memory, and tool messages, along with memory retrievals, etc... Is this the type of thing that you're looking for?

For code it's actually quite good so far IME. Not quite as good as Gemini 2.5 Pro but much faster. I've integrated it into polychat.co if you want to try it out and compare with other models. I usually ask 2 to 5 models the same question there to reduce the model overload anxiety.

I discuss how the automation wave is already starting with white-collar job openings at a 12 year low in the U.S. I also talk about how we cannot simply count on taxing AI to support automated workers, as countries that don't tax will outcompete those who do. We therefore need international cooperation, based in MAIM, from the original Superintelligence Strategy paper.

I also discuss how bioweapons cannot be avoided via restricting open weight models as originally suggested in Dan's paper. Rather we need to heavily invest in bioweapon defense, and in particular use AI for wastewater monitoring and accelerating metagenomics (detangling mixed DNA).

One tradeoff of bringing your own API keys is that as you add more model providers, you get more billing accounts to deal with. Chorus also doesn't have an incentive to efficiently use your tokens. We save 67% on Anthropic token costs using Claude Caching. We also use cheaper "task models" for conversation titles, tagging, and parts of the RAG pipeline which all drastically cuts token costs.

For local models I highly recommend https://github.com/crizCraig/open-webui

They do the side by side thing that Chorus does and you can serve it to anywhere including your phone.

Background chats are new in v5 of Open WebUI, so you can use it too. Overview has been there also, but it's kind hidden in the hamburger menu.

The upgrade/logout issue you're facing is likely due to not setting WEBUI_SECRET_KEY outside of your docker container. This causes all previous cookies to be unreadable as a new key will get generated by start.sh and won't decrypt the old cookies.

Another great session of George and John recording Oh My Love [1] shows what I think of as embracing rough but promising ideas along with balanities like tuning guitars, learning chords, etc... The Beatles engaged in this process much more than most musicians, despite it meaning most the stuff they played sounds relatively bad compared to playing tunes they'd practiced a bunch before. Is this due to work ethic or some love of creation? Idk, but it is fascinating to see the sausage get made.

[1] https://www.youtube.com/watch?v=yksV7YVuqdg

Allocating 20% to safety would not be enough if safety and capability aren't aligned. I.e. without saying Bostrom's orthogonality thesis is mostly wrong. However, I believe they may be sufficiently aligned in the long term for 20% to work [1]. The biggest threat imo is that more resources are devoted to AIs with military or monetary-based objectives that are focused on shorter-term capability and power. In this case, capability and safety are not aligned and we race to the bottom. Hopefully global coordination and this effort to achieve superalignment in four years will avoid that.

[1] https://drive.google.com/file/d/1rdG5QCTqSXNaJZrYMxO9x2ChsPB...

The nice thing about box breathing is that, unlike pursed lips for example, you can do it without anyone noticing, by quietly breathing through your nose. So if you're in a stressful meeting, you can calm yourself without anyone noticing :)

Turchin's political stability indicator predicts this due to rising economic inequality, overproduction of grads with advanced degrees, and exploding public debt [0][1].

However, civil wars usually occur in poorer countries [1][2]. Also the proportion of the world in civil war is 1% and falling exponentially [2].

Finally, Metaculus gives a 5% chance of US civil war before 2031, where a recent July bump is discussed in the comments (Roe, Jan 6) [3].

[0] https://www.nature.com/articles/463608a

[1] https://www.buzzfeednews.com/article/peteraldhous/political-...

[2] https://www.nber.org/reporter/2011number3/economic-shocks-we...

[3] https://www.metaculus.com/questions/6179/second-us-civil-war...

Notes: The Harvard December 2021 youth poll participants gave as 35% chance in their lifetime: https://iop.harvard.edu/youth-poll/fall-2021-harvard-youth-p...

Rasmussen 2018 poll: 33% likely, 10% very likely before 2023 https://www.newsweek.com/second-civil-war-likely-one-third-a...

Does anyone have a graph of this probabilty over time from a consistent source like rasmussen? https://www.rasmussenreports.com/public_content/politics/que...

Parallel Hashmap 4 years ago

With getpy[0], a python wrapper, you can get 200x faster map reads in parallel

    In [1]: import numpy as np
       ...: import getpy as gp
    
    In [2]: key_type = np.dtype('u8')
       ...: value_type = np.dtype('u8')
    
    In [3]: keys = np.random.randint(1, 1000, size=10**2, dtype=key_type)
       ...: values = np.random.randint(1, 1000, size=10**2, dtype=value_type)
       ...: 
       ...: gp_dict = gp.Dict(key_type, value_type, default_value=42)
       ...: gp_dict[keys] = values
       ...: 
       ...: random_keys = np.random.randint(1, 1000, size=500, dtype=key_type)
    
    In [4]: %timeit random_values = gp_dict[random_keys]
    2.19 µs ± 11.6 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
    
    In [7]: %timeit [gp_dict[k] for k in random_keys]
    491 µs ± 3.51 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
[0] https://github.com/atom-moyer/getpy

Trillion parameter networks are mentioned a few times, but Tesla is deploying much smaller networks than that (like tens of millions IMU). Trillion param networks are mostly transformers like GPT-3 (actually 175B) etc... that are particularly heavy vs Conv as they have no weight sharing. Tesla is definitely starting to use transformers though, e.g. for camera fusion and evidenced by their focus on matrix multiply in dojo asic's vs the conv asics they have in the on-vehicle chips.

This problem of run-away excitation in wetware reminds me of exploding gradients in artificial neural nets. We try to handle this with data normalization, batch normalization, and gradient clipping of different sorts (although unless the clipping is incorporated into the loss as in PPO (Schulman), it's very brittle and dependent on the data and network architecture.). So I wonder if we can glean something from these inhibitory neurons for artificial nets. The opposite problem of vanishing gradients results from too much inhibition, which happens in recurrent neural nets - so it's definitely a balance. Currently PPO does the best job IMO, but is specific to reinforcement learning.

The backend for this project actually has a wider scope - https://github.com/botleague/botleague, with our self-driving sim being the first use-case for it. Everything is open source and open data - sans some temporary keys used to communicate evaluation data between problem providers and our score keeper server (https://github.com/botleague/botleague-liaison). The hope is that others will post problems as well - of which combinations can be solved by more and more general and sophisticated bots. Curious as to what you all think of this self-driving leaderboard and the broader AI league idea. Very early stage here, but excited to get it out for y'all to try.

Highly recommend zero water or another in-house filter. In Mountain View, CA or South San Francisco - one zero water filter last me hundreds of gallons. In Mesa, AZ it only last me around 50 gallons but that is still competitive with a water and ice except that you don't have to drive the water back to your house.

Similar question on Quora from 2012[1] per sense:

  Vision: 10Mb/s => 100Mb/s
  Hearing: 30Mb/s
  Touch: 135Mb/s
  Smell: 100k neurons
  Taste: 100kb/s
  Proprioception: ??
  Balance: ??

  Total: ~10Mb/s=>~1Gb/s
Internal brain bandwidth is also worth mentioning as this is the last remaining wetware advantage over hardware due to the three dimensionally fully connected heterogeneous cortical substrate. I can't seem to find a figure on that though.

https://www.quora.com/How-much-bandwidth-does-each-human-sen...