I flip back and forth between whoever currently has the more powerful frontier model that isn't cost prohibitive - subscriptions only, API pricing a non-starter. Today that's Fable 5 which has been excellent, as soon as it's Sol I'll switch to that. The OAI/Anthropic harness behavior has mostly stabilized for me with consistent AGENTS.md that I sync with CLAUDE.md - I like pi (pi.dev) and have tried to build it up to get performance comparable to the two "first-party" harnesses, I'm just not there yet.
One major sticking criteria for not going with OpenCode / pi for all of my coding is I want access to the tier-1 frontier model of the day without API pricing - e.g. afaik I can't use Fable 5 via pi harness even though I have a subscription, so for this week I'm on Claude Code. It's not the need to Fable 5 for everything, but even if I just want the marginal intelligence benefit to stress test an architecture decision, it's a safety blanket to know there isn't a ~smarter~ model I could have used. And for my use cases, the doggedness and capability of these frontier models has been insanely effective.
My feeling is we're still in the Uber era subsidy period - the moment the subscriptions either try to lock me in longer than a month or stop OAI/Anthropic stop delivering frontier models in the subscriptions, I'm out - switching fully over to pi.dev or another OS harness and routing my token spend via OpenRouter or offloading to Qwen locally. Then I'll have to put an accurate dollar amount on frontier intelligence.
You can choose to be as charitable as you want with your lense on this. Incentives within all organizations lead to a certain class of worker taking over. From the "getting things done" perspective I like the lense of insurgents vs. gatekeepers from this interview:
I love this! looked into doing a similar project, you're competing against Hudl but using the phone instead of custom hardware (always preferred). Highlight segmentation may be a challenge with SOTA cv methods, but there are lot of directions you can go in.
commercial license - the research group formed a corporate entity that licenses the body model and all derived work (SMPL-X, etc.): https://meshcapade.com/SMPL
A few thoughts as I've done academic research & built products in this area:
- if you're using SMPL body parameters this will have to stay research / open-source
- is this leveraging some sort of monocular depth estimation to estimate the wall in 3D space? Also, do you have assumed camera parameters, or is that also estimated? If there isn't any depth information, this will be highly inaccurate on any cliff routes, but still useful on flat wall climbing.
Overall, a good idea (that I've also thought about building as a climber) - the tricky part that I'm impressed you have a solution to is path planning up the wall. Even assuming a flat wall with no depth estimation, it's still looks effective.
sell enough units to convince Khosla Ventures that you aren't just burning their money, forcing them to double down on the vision and bring in new investors for the next round. Rinse and repeat until the Large Action Model is either extraordinarily valuable or you've torched all the money.
It's true, lack of highly available public transportation between the cities puts us behind other regions (not to mention other countries). I've been doing the ATX -> HTX drive for years.
Best private alternative I've found is Vonlane (https://vonlane.com/) - takes longer than flying but it's a business class bus so you can get work done.
Joining the nuance parade - Google expanded their entry points to Search years ago via Maps and the URL bar.
Do I use Google traditional Google Search (google.com) to find things? Rarely. Do I use Google Maps to find bars / restaurants / order food? All the time.
I guess the nuance is what we're trying to find. LLM's swallowed the listicle - I don't need google.com to find an autogenerated list of "best restaurants in London". But if I'm in London at at lunch, and I need a coffee nearby, it's still useful.
I also purchased and read this. If you enjoy the minutae of scientific advances you’ll love this, but not a page-turner. Also a helpful scientific / historical primer for anyone who wants to understand graphics (pixels, shaders, etc)
This is great, but how is your performance on multi-table joins? We've been working on a homebrew solution using OpenAI internally conditioned on our schema, and can't bridge the multi-table gap.
Also I'm skeptical if this generalizes, do you have measures in place to prevent query hallucination?
I understand this is point-cloud diffusion but Ajay Jain et. al. (BAIR + Google Research) accomplished the first version I saw of this back in June with their Dream Fields paper (CPVR'22).
As always this goes to show that if you can't be the first, be the loudest. OpenAI has the most well oiled media machine I've seen in awhile.
Seeing the waves of publicity OpenAI gets with every new release, I think we're seeing a new model for big-tech AI research groups. It isn't enough to just hire world-class research talent that publish area-defining papers. There has to be a commensurate investment in media to publicize the research. Obviously, if you don't have the research, you have nothing to market. But it should say something that OpenAI prioritizes great design, communication, and publicity in addition to the world-class research team. It wouldn't surprise me if we see Google AI / DeepMind / FAIR / double-down with their own investments to expand the media presence of their AI orgs.
Uses coordinate-based neural networks to model the scene volumetrically. However, in the case of this paper does not use an MLP to represent the scene. Instead, proposes to directly learn a voxel grid representation.
Note of reference for readers who may not be as familiar. One (pytorch) process is:
1) Use torch.onnx to convert the model to ONNX
2) USe `onnxruntime` built for Web Assembly (WASM) to run the NN in the browser