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.
> My feeling is we're still in the Uber era subsidy period
I often wonder whether this doesn't continue indefinitely.
Uber was able to do this because it was just them and Lyft playing second fiddle, with a huge barrier to entry once the network effects had kicked in.
It just seems like the model space has way too many competitors, + OSS/Local options for them to ever be able to jack up their prices. At least once the datacenter bottleneck has been cleared.
I'm working on a multi-harness IDE that supports custom agent workflows and skills that are shared between any harnesses it wraps over. I think it might prove handy for a workflow like yours.
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.
Yeah Hudl is definitely a beast in the space though I think of them as almost more focused on orgs and teams rather than consumers.
Re: highlight segmentation was and still is a challenge that we work on. In the beginning we had a hard time dealing with false positives when our models thought a shot was in but it wasn't. This has gotten better over time with more data and is hovering near 92% acc these days but obv not 100%. But i'm optimistic as data grows and the sota methods get better, this isn't the hardest ml problem in the world
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.
This is an end-end system that just takes in video frames. Camera parameters are one of the things that is predicted. It gives promising results for a wide variety of environments (cliffs, diff types of bouldering walls, diff outdoor walls, etc.), though not always accurate. Path planning is also part of the end-end system. Will share more details in the paper.
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
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.
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.