Does this mean I can finally connect to a ducklake instnace hosted remotely? i.e. DuckLake is writing to disk on the remote server and my client is just a client.
Because rn even with Postgres as a catalog my client needs access to the underlying storage to use Ducklake.
> He then open sourced llama and wanted to be the android of llms.
Well the original llama did kick off the era of open source LLMs. Most original open source LLMs were based on the llama architecture. And look where we are now OSS modles are very close to frontier.
It may not have benefitted Meta but it commoditizatised LLMs.
Didn't OpenAI say something similar about GPT-3? Too dangerous to open source and then afew years later tehy were open sourcing gpt-oss because a bunch of oss labs were competing with their top models.
> So while feasible it's only great for batch jobs not interactive usage.
I mean yeah true but depends on how big the model is. The example I gave (Qwen 3.5 35BA3B) was fitting a 35B Q4 K_M (say 20 GB in size) model in 12 GB VRAM. With a 4070Ti + high speed 32 GB DDR5 ram you can easily get 700 token/sec prompt processing and 55-60 token/sec generation which is quite fast.
On the other hand if I try to fit a 120B model in 96 GB of DDR5 + the same 12 GB VRAM I get 2-5 token/sec generation.
It does if you use an inference engine where you can offload some of the experts from VRAM to CPU RAM.
That means I can fit a 35 billion param MoE in let's say 12 GB VRAM GPU + 16 gigs of memory.
> This is speculative, but I suspect that if we dropped one of the latest, most capable open-weight LLMs, such as GLM-5, into a similar harness, it could likely perform on par with GPT-5.4 in Codex or Claude Opus 4.6 in Claude Code.
People have been doing that for over a year already? GLM officially recommends plugging into Claude Code https://docs.z.ai/devpack/tool/claude and any model can be plugged into Codex CLI (it's open source and can be set via config file).
> What Google and OpenAi have open sourced is their Agents SDK, a toolkit, not the secret sauce of how their flagship agents are wired under the hood
And how is that any different? Claude Code is a harness, similar to open source ones like Codex, Gemini CLI, OpenCode etc. Their prompts were already public because you could connect it to your own LLM gateway and see everything. The code was transpiled javascript which is trivial to read with LLMs anyways.
At this point why not make the agents use a restricted subset of python, typescript or lua or something.
Bash has been unchanged for decades but its not a very nice language.
I know pydantic has been experimenting with https://github.com/pydantic/monty (restricted python) and I think Cloudflare and co were experimenting with giving typescript to agents.
LlamaCPP supports offloading some experts in a MoE model to CPU. The results are very good and even weaker GPUs can run larger models at reasonable speeds.
I was using aider quite a lot from ~ 7 months ago to ~ 3 months ago.
I had to stop because they refuse to implement MCPs and Claude/Codex style agentic workflow just yields better results.
Haven't used conduit in a while but Dendrite (written in Go) has most features that you would need.
Well conduit does too but last time I checked them out they were switching databases again.
Even if it does go away, youre not loosing anything. Its functionality can be replicated with a USD 5 VPS using Slack's nebula (not wireguard based) or any wireguard based tool like headscale, innernet, netmaker or plain old wireguard.
Because rn even with Postgres as a catalog my client needs access to the underlying storage to use Ducklake.