A new paradigm beyond vibe-coding and spec-driven development: ask coding agents to assume functionality already exists, then gradually materialize the deterministic parts and fix the quirks along the way.
So I want to perform a material characterization study on a material where I need to put it under pressure. I’m in high school and don’t have a mentor or time to ask for access to university labs so I want to make something that can help me get data for cheap. I’m trying to make a linear actuator design and physically build all the parts myself (except for the motor and leadscrew system obviously) but I don’t extensively know how these types of things work. If I was to build something like this (pictures) would there be any significant issues? The cylinder (of which I don’t know what material to make out of) protruding out from the side would be directly connected to the sliding block part of my linear actuator so it pushes that down onto my material. I’m going to be pushing with 50lbs ish max so I’m making the majority of this out of wood. Any tips on making sure it doesn’t get worn out by some slight imperfection over the thousands of trials I’m going to need it for? And also any tips to make it work if something is seriously wrong 😭 And lastly any other tips about doing research studies like this without lab access or a significant mentor would be greatly appreciated. submitted by /u/bount_ [link] [Kommentare]
GrapheneOS is calling on Volkswagen customers to pressure the automaker into restoring compatibility with its mobile app.
We have spent the past few weeks carefully annotating videos and experimenting with VLMs for subtask annotation. This type of annotation is incredibly important for long-horizon tasks, since robots need a more granular learning signal than high-level instructions like “clean your room.” We ran 50+ experiments, created a new diverse benchmark for this type of annotation, and built a pipeline that is 19x cheaper than humans. It works well as a first pass for labeling, speeding up human annotation and making it substantially cheaper. Blogpost about it is here: https://macrodata.co/blog/annotating-robot-video-subtasks submitted by /u/Other_Housing8453 [link] [Kommentare]
Hello, I'm trying to implement the Pocket TTS by kyutai-labs represented by this paper. Since they have didn't released the training/fine-tuning code. I'm trying to implement it on my own for learning some stuff. I have read the paper, tried to implement it with much more smaller parameters with smaller amount of data. I implemented this text to speech with one speaker on LJSpeech (1) and LibriSpeech clean subset but its hardly failing. For (1), Since it's a single speaker dataset I didn't added the voice cloning just simple text and target latents. flow matching loss became nearly 0.20 mse , EOS loss became very low like (x)e-(y) levels. But when infer with the model saved at 2800th epoch, It barily generating a meaningfull text even the text within its training set. Tried different techniques like Scheduled sampling for eliminate exposure bias (model was hallucinating sometimes and repeats same phrases twice), it didn't worked. Added std gaussian noise to ground truths, didn't worked. After struggling with lots of implementation I decided to move forward with quite larger dataset LibriSpeech because I thought that scale of the data was small. For (2), I read the paper again. No scheduled sampling, added the head multiplication etc, and implemented the paper in the librispeech dataset. I tried audio condition+ text tokens + BOS + target latents, and swapped the audio prompt with text tokens. I observed a tradeoff in this setup: if I put text tokens near to target latents, model generates better text but voice is not even close to audio prompt,and gibberish speak with better voice cloning when I put audio condition tokens near to target latents. And found out that loss is very spiky, and grad norm is exploding too you can see below the images. loss and lr values for setup 1 (LJSpeech) values for setup 2 (LibriSpeech) I used Pocket TTS' orijinal Mimi Audio Encoder by extracting it from Original model. What is your suggestions? Should I read paper over and over again? Should I increase the data amount by collecting from different sources(authors says that they used 88.000 hours of publicly available data)? Any system design problem? Trainings performed on RTX 5080 desktop gpu. I want to move on to bigger dataset but can't burn GPU credits for non-expected result. When should I increase dataset and start training on bigger clusters that could give me satisfyable results? submitted by /u/No-Motor-6274 [link] [Kommentare]
I’m pretty annoyed. We’re a small AI startup building a real-time coding agent. Our p95 latency requirements are tight (and self imposed, but thats the product). We need sustained high-throughput inference with ~1-2k tokens/second. Been on the Cerebras waitlist for months trying to get API access. We’re not doing training so don’t need a warehouse of H100s. We need fast, high-throughput ASIC inference for a specific production workload. Cerebras’ just went public and they basically have no compute how is that possible? Well turns out OpenAI and Cerebras for OpenAI to buy like $20b worth of these chips. This has effectively pre-allocated the vast majority of Cerebras’ near-term inference capacity to a single customer. I mean, none of us can compete with that The result is that this deal situation has made their API waitlist functionally infinite for anyone who isn’t a hyperscaler. Legit making me pull my hair out. submitted by /u/Kortopi-98 [link] [Kommentare]
At my pick, I had 1.6 million $. Now I have 0. I shouldve cashed out some. I should have listened to Michael Burry and Nouriel Roubini. F@&k Michal Sayler! These people give people false hope. They ruin peoples lives. My lifes destroyed.My life!!! submitted by /u/noreturn000 [link] [Kommentare]
There was a workshop in ICLR Recursive Self Improvement. Is this something worth pursing for a Phd topic? submitted by /u/Successful_Bowl2564 [link] [Kommentare]