> I'll be using this for everything that isn't coding from now on.
why? is it not so good on that? or is there something better performance, or better price for same performance?
why? is it not so good on that? or is there something better performance, or better price for same performance?
That said, I'm extremely impressed with Argon thus far. I take back everything bad I've ever said about their AI strategy. Argon is very good and very fast. It might be competitive at coding, but it's too early to say.
Anecdotally, I have had Opus 5.5 decline a reverse engineering effort into a Zigbee gateway that I own.
DeepSeek did not have any issues with this using Claude Code as harness.
Over the past two weeks, I have started using a locally modified version of deepseek-harness (modifications include disabling telemetry and querying a provider's model list dynamically through Openrouter, instead of relying on the pre-packaged pi-ai manifest that comes with the base code). So far, that has been the best experience - I really like the caching, tokens/sec stats that it provides. (EDIT: I don't know a lick of TypeScript but AI-driven modification of the harness is good enough for me to make these tweaks).
Sandboxing is a definite issue for me. As you perhaps know already, headless operation is often not an option in graphics/game programming which requires actual render-surface instantiations from windowing systems and then input-context handling (keyboard/mouse/gamepad) as well. So I run most development on a VM. But part of my test-suite and smoke-tests rely on runninng it directly on the host OS (which is not ideal). I'd love to hear suggestions to avoid this.
You can apply it to the RC2 branch so:
git clone --branch dsh-v0.2.0-rc.2 https://github.com/deepseek-ai/deepseek-harness.git
cd deepseek-harness
git apply /path/to/dsh-v0.2.0-rc.2-to-local.patch
I'd recommend asking the harness to modify itself though (or a cloned repo in another directory) rather than applying the above.
VFIO/GPU passthrough maybe?
Found this especially impressive because backpack material related research has been something I know models can still get caught up on and the models being decent at web research in coding nowadays does absolutely not apply to these task equally Recommending nylon canvas shell suppliers or waxed cotton when PVC coated cotton is explicitly request, that sort of thing. While not perfect in reading coding docs, models are far better there.
Like with front end and code “parsability”, feel like you can very clearly point at which labs use what training set, with many often sharing the same suppliers. Once I noticed that, I became very bullish on specific training sets over emergence as the source for current gains.