everlier

Hey, thanks for posting it!

Fable 5.1 makes everything a "Rung". These statistical markers are very annoying.

It's actually a really great idea, but it doesn't have to go beyound existing Responses or Chat Completions APIs.

We built that in my current company and it works wonders to just script entire persistent workflows with a simple SDK.

"THIS IS NOT A DRILL"

You can also install Harbor and then it's:

harbor up omlx opencode

The company I work at tries to solve it right now, not promoting, just want to share.

Slop is no fun to deal with, so we have a thesis that slop should be left for agents to read and human-to-human communication should happen outside of passing empty fluffy docs to one another. To realise that, we have a workspace with group chats where multiple agents and humans can work together and agents can engage with humans for additional information when needed. The challenge is, of course, to find the right level of autonomy for the agents and let the agent learn and follow user's workflows well enough to be useful.

I wanted to have a nice way to see which skills in my agentic setups are not useful anymore, so I built this tool to aid in that.

Bun+OpenTUI for CLI, separate Rust binary for indexing the sessions of your coding agents. Release video made with Hyperframes.

There was never a better time to run LLMs locally. It's just a few commands from zero till a fully working LLM homelab.

``` harbor pull unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_XL

# Open WebUI -> llama.cpp + SearXNG for Web RAG + OpenTerminal as sandbox harbor up searxng webui llamacpp openterminal ```

That's it, it's already better than Claude's or ChatGPT's app.

Thanks for the interest!

Spec is essentially a set of facts + fluff. List of facts is essentially the spec minus the fluff. This was my main idea when trying this approach out. It worked quite well, so I went on building the CLI and a set of skills to better guide the agent about this specific workflow.

Thanks! The project is dogfooded, so the most direct example is the project itself.

Outside of that, I found a few things particularly useful so far: - I can seed a relatively complex project with just a few core assumptions about behaviors I have, agent will firgure out all the gaps in a formalised way - I can easily diff/compare the fact sheets against one another. One example: I built a fact sheet for a fairly hairy Python CLI, then asked the agent to update all Python-related entries to their Rust equivalents, then rebuilt this CLI in Rust in the scope of another project - It's much quicker than working with large spec formats, agent uses less tool calls to capture the context it needs to work on something - After doing large refactoring, agent doing a "fact check" is essentially a full on e2e test run

Two biggest gains for agentic use are because fact sheets are more compact and that they are all assertions about the desired state, it's much easier to reason about things that should and shouldn't be there

owning GGUF conversion step is good in sone circumstances, but running in fp16 is below optimal for this hardware due to low-ish bandwidth.

It looks like context is set to 32k which is the bare minimum needed for OpenCode with its ~10k initial system prompt. So overall, something like Unsloth's UD q8 XL or q6 XL quants free up a lot of memory and bandwidth moving into the next tier of usefulness.

I tried, really really hard but then I realised that I essence it's a poorly written agentic coding assistant that wastes a lot of tokens antropomorphising itself while forcing me to debug via WhatsApp instead of normal tools. So I leaned into that and made OpenCode my general assistant, it worked much better in this aspect.

Changes as we speak, z.ai is the first one to show differential pricing

After seeing a recent video from Theo, I wanted to see how far I can take a harness contained in just 30 lines of JavaScript. Turns out - far enough to be useful, it handles simple tasks just fine, works with both cloud and local models, uses just three tools (but can do with a single one, frankly speaking), cleanly handles detached commands or cancellation mid-run, has non-interactive mode and can be run with NPX.