nicklo

Cortisol spit curve tests are pretty cheap, so I was disappointed to see the title claim not substantiated with real data. Consider biometric testing next time you distribute!

or say yes? decel mentality like this is why europe is falling behind. some poor startup will try to backfill these contracts to be the new palantir of europe only to be cut at the knees by regulation and more outcry think piece boycotts like this. rinse and repeat until the us and china become the only relevant acceleration hubs on earth during the singularity

the animation of the model name text when opening the detail view is so smooth and delightful

Congrats on launch! As the agent cli’s and sdk’s were built for local use, there’s a ton of this infra work to run these agents in production. Genuinely excited for this space to mature.

I have been building an OSS self-hostable agent infra suite at https://ash-cloud.ai

Happy to trade notes sometime!

Yeah with sandbox pre-warming and disk co-location its fast enough to avoid UX cold start penalty.

On write amplification — we persist at the message level, not per SSE chunk. The sandbox's workspace filesystem (claude code's native jsonl files) is the source of truth for resume, and the DB is for queryability, tracing, etc - so fire and forget works fine here.

I’m building a self-hostable, open source agent sandbox orchestrator here: https://github.com/ash-ai-org/ash-ai

directionally correct but important to note the water wasted by sustaining the insufferable human is much higher than producing the tokens

i've always wondered (for this, portkey, etc) - why not have a parallel option that fires an extra request instead of mitm the llm call?

I think it’s the reverse - people were too lazy to read the docs so nobody was motivated to write them.

With an agent I know if I write once to CLAUDE.md and it will be read by 1000’s of agents in a week.

Have you considered making an MCP for this? Would be great for use in vibe-coding

The bitter lesson strikes again… now for graphics rendering. Nerfs had a ray tracing prior, and Gaussian splats had some raster prior. This just… throws it all away. No priors, no domain knowledge, just data and attention. This is the way.

OP: please don't poison your MIT license w/ surya's GPL license

Yes, this video is made with Sora - but this link leads to a youtube video that claims it was created with "KaraVideo.AI's (powered by Sora)" and claims it has "Sora early-access" - which is dishonest and misleading.

Can we replace the link with the actual blog post that doesn't include/promote KaraVideo? https://openai.com/blog/sora-first-impressions

Sora is not available, this looks like an attention grab by an app without relation to OpenAI trying to get downloads by false association. Same thing as a year ago with all the ChatGPT iOS apps that came out before OpenAI released theirs.

This is not a tenable position. Most cars are older than these devices- and even big tech co's like Apple were late to patch flipper vulnerabilities. I was on a plane last month and someone was flipper-jamming DOS-ing via continual bluetooth connection requests and completely bricked all iOS devices in range for the 4 hour flight.

These sort of devices are nuisances with very low positive utility, and there is plenty of precedence for banning them.

This is a classic ship of Theseus problem and TFA has a really hostile tone that to me indicates either a lack of exposure to the topic or an intentionally obtuse take as a form of clickbait.

TFA argues that digitizing one self inevitably leads to two copies, and the digital one is not real.

Let’s take a step back and start by replacing/simulating one neuron digitally. Is this a new you? I doubt TFA would argue so. Now replace 3,000 neurons every second over the course of a year and now your entire brain has been digitized. At which point did you stop being you?

The options are, 1) every second you are a new you, with or without digital replacement, 2) every second you are the same you with or without digital replacement 3) every second you are a new you with digital replacement but the same you if you don’t digitally replace. 4) some arbitrary % that feels right like 50%.

Option 3 hinges on a special differentiation between digital and chemical computation, which is at odds with fundamental properties of Turing Machines. Option 4 is as hand-wavey as it gets. Options 1+2 while they seem very different, ultimately can be treated as functional equivalents with slight philosophical differences.

There are real debates to be had over the morality of mind uploading and the very real risks of doing so, and many great sci-fi short stories covering this topic. Unfortunately, this article is too caught up on its hot take to make room for a proper consideration beyond its knee-jerk reaction.

Your testimonial section of "happy customers" are just the stock headshot photos from this wix template. It's probably safe to assume that the testimonials themselves are fake as well? I'd suggest removing this section entirely as it comes off as dishonest and misleading.

Graph convolutions are really powerful for handing structured data like chemical compositions. With the right corpus, I think this area is ripe for unsupervised feature representation learning approaches like what we've seen with BERT-like approaches and how they've dominated NLP in the past few years.

Side note: I worked with Kyle a few years ago on the MIT-MGH Deep Learning for Mammography project. I'm glad to see his work + brilliance being recognized.

On the contrary, with a more generous reading of the previous comment, it holds some merit.

1. CNN's are used fairly commonly for sequence tasks nowadays. Convolutions can be 1D after all. 2. It's also possible the previous comment was referring to using 2D convolutions on the spectrogram of the audio, which is a common approach. 3. Neural networks are capable of more than classification. Scoring is a regression task which is common application of neural networks.

RaiBlocks looks awesome! Just signed up: xrb_1iefziyor79n9nrqjnogwxrzgsun9swuhcqnst5qi9u1ocxxoqinfjuzzfu6

Google no longer reads gmail emails:

https://www.bloomberg.com/news/articles/2017-06-23/google-wi...

Though I'm guessing that they are only now okay giving up on email data because they have so many other sources of tracking your behavior.

Deep Learning | Boston | Nick L | njl@mit.edu | http://nicklocascio.com

Founded + lectured MIT's deep learning course (6.S191), contributing writer to O'Reilly's Fundamentals of Deep Learning book, deep learning consultant for Fortune 500.

Down to chat about and give advice about anything in the machine and deep learning space! Interested to hear what you're working on! :)

Check out http://reddit.com/r/cryptomarkets. Lots of good discussion (just ignore the HODL/moon trolls).

This article seems to be trying to disarm tech elitism by diving into the deep end of it, but by and large the humor is pretty forced and the tone condescending.

I'd also recommend that the author remove the attempt at being edgy at the end with the snide reference to the reports of sexual harassment at Uber. Especially the cheeky "too soon?", which simultaneously acknowledges the gravity of the situation for the victims while also dismissing it entirely in favor of an attempted joke.

Super exciting seeing on-par performance of RL tasks with dramatically less supervision.

Really looking forward to a follow-up where they explore 2.2.4 further. Sampling examples which provide maximal information game seems like it could result in another huge reduction in the amount of human oversight necessary. Could see an adversarial scheme which could learn to sample these examples optimally from the manifold. This kind of thing is powerful in human learning of complex tasks to ask for clarification or feedback in specific places of uncertainty.

Yeah for sure - these images are pretty different in their composition so it should be pretty easy to classify them. How large is your dataset? Do you need to collect one?

With small amounts of data, transfer learning is the most effective approach. There's a great tutorial on retraining inception for your own categories in TensorFlow: https://www.tensorflow.org/how_tos/image_retraining/.

Hey HN! One of the organizers here- let me know if you have any questions!

This problem has already been solved with decent success using deep learning.

See: https://homes.cs.washington.edu/~jxie/pdf/deep3d.pdf

Would be super cool if someone applied deep generative models to synthesize new natural images from input drawings using this dataset.

Deep image captioning is essentially what you are describing.

Check out:

http://cs.stanford.edu/people/karpathy/deepimagesent/

http://cs.stanford.edu/people/karpathy/densecap/